AI Appreciation Day 2026: As Agentic AI Redefines the Enterprise Playbook

Artificial Intelligence has completed its transformation from mission-critical infrastructure to autonomous workforce participant in the span of a single year. As we mark AI Appreciation Day 2026, the industry conversation has moved past “how do we implement AI” toward a thornier and more consequential question: “how much autonomy are we willing to hand over, and to what end?” The rise of agentic AI—systems that don’t just recommend actions but execute multi-step tasks with minimal human oversight—has forced enterprises to rethink everything from workflow design to risk governance in real time.

The data from the last twelve months tells a story of bifurcation rather than uniform progress. Organizations that built strong data foundations and governance frameworks during their 2024-2025 pilots are now scaling agentic systems across finance, customer service, and software development with measurable efficiency gains, while others remain stalled in pilot purgatory, wary of the compliance, security, and accountability questions that autonomous AI raises. Meanwhile, the conversation around AI has broadened beyond productivity to encompass workforce transformation, model transparency, and the geopolitical dimensions of AI infrastructure and export policy—signaling that AI has become as much a boardroom and policy issue as a technical one.

This year’s VMblog expert roundup gathers perspectives from technology leaders on the front lines, from those architecting agentic workflows at scale to those wrestling with the guardrails needed to deploy them responsibly. Their insights capture a pivotal moment: the industry is no longer debating whether AI belongs in the enterprise, but how to govern its growing independence, and what that means for the workforce, the customer, and the bottom line in 2026 and beyond.

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Geoff Burke, Senior Technology Advisor, Object First

Last year on AI Appreciation Day, I cautioned my peers on the hidden cybersecurity dangers associated with AI. Since then, many of my concerns have materialized, from highly sophisticated AI-generated attacks to accelerated vulnerability exploitation. That’s not to say I am against AI – I’m a user of it myself – but the efficiency and technological advances we’ve seen from AI haven’t come without cost.

An AI agent with too much autonomy and inadequate guardrails can cause major vulnerabilities, blind spots, and challenges that may outweigh the positives. However, as long as companies are aware of and realistic about these risks, they can take action to mitigate the consequences should an AI agent malfunction and delete important data, for example. Part of this preparation should include building recovery and resilience into the foundation of IT infrastructure with Absolute Immutability, ensuring backup data cannot be modified by anyone, not even the most privileged admin, attacker, or agent.

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John “Coz” Colgrove, Founder and Chief Visionary Officer, Everpure

AI is changing what it means to be an engineer. When I was in school, people would submit their code and go grab a snack from the vending machine, because the mainframe wasn’t getting back to you anytime soon. That made engineers careful. You’d go over your code line by line before submitting it because wasting a day on some missing semicolon was expensive. That’s where I learned to write tersely.

The exacting part of coding is going away, and I don’t think anyone’s going to miss it. People assume engineers can stop thinking hard about what they’re building. But it’s the opposite; creative problem solving matters even more now. Get the spec right, AI does the grunt work. Get it wrong, and it does a useless thing very fast.

My advice for engineers today is to be curious and experiment. The ones who can think holistically enough to describe the problem the right way to their team of agents will have an edge.

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Jim McGann, Chief Marketing Officer at Index Engines

Adversaries are using AI to accelerate attacks across the entire cyber lifecycle. Traditional backup and recovery strategies are not enough. Fighting AI-accelerated attacks takes AI-powered defense, and that is what cyber resilience now demands, using AI to continuously validate backup and production data to pinpoint the last known clean copy before attackers can strike again. Security leaders need immutable protected data and automated recovery that restores trusted operations with confidence, and they need to build that resilience into every workflow from the start.

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Andy Fernandez, GM, AI & Cyber, HYCU, Inc.

I’ve never experienced a technology shift at this rate of change. It’s unlocking productivity gains while forcing us to rearchitect how we use and build products from the ground up. Every organization now faces two imperatives in parallel: how do you become AI-native, and how do you stay resilient as you do. The gains and the threats run at the same machine speed, and that’s giving rise to a new class of capabilities for stopping, protecting against, and recovering from attacks in an agent-first world.

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Simon Townsend, SVP of Marketing & Office of the CTO, ControlUp

Humans Lead. Agents Act.

As we recognize AI Appreciation Day, it’s worth stepping back from the headlines and focusing on what AI should actually achieve. Too much of the conversation has centered on whether AI will replace people. I believe the more important question is how AI can eliminate repetitive operational work so people can focus on creating greater value.

For years, enterprise IT has operated in a reactive model. Teams monitor dashboards, investigate alerts, respond to tickets, and repeat the cycle. Digital Employee Experience (DEX) platforms gave us unprecedented visibility into what was happening across the digital workplace, but visibility alone doesn’t solve problems. It simply tells us where to look.

The next evolution is moving from insight to action. Agentic AI is making that possible by continuously analyzing real-time telemetry, resolving common endpoint issues, applying missing patches, and remediating performance problems before users are impacted. Instead of asking IT teams to sift through ever-growing volumes of alerts, intelligent agents can take care of routine operational work automatically.

This is what we mean by Humans Lead. Agents Act.

Humans remain responsible for strategy, governance, policy, and business outcomes. AI handles the repetitive, time-consuming tasks that have kept IT teams trapped in a cycle of firefighting. Rather than replacing expertise, it enables IT professionals to apply that expertise where it has the greatest impact: designing resilient environments, improving employee experience, and driving innovation across the business.

As autonomous operations become more common, I believe we’ll stop measuring IT by how quickly it responds to problems and start measuring it by how rarely employees experience disruption in the first place. That represents a fundamental shift in how enterprise IT operates and where IT professionals create value.

To me, that’s the real promise of AI. Humans Lead. Agents Act. AI doesn’t replace people; it empowers them to focus on the work that requires human judgment, creativity, and leadership.

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Ali Tehrani, SVP, AI Strategy & Enterprise Architecture, Presidio

AI has made inspiration the easy part. The hard part—and the part worth celebrating—is the discipline it takes to move from a promising idea to a foundation you can build on, to a solution that’s validated, to something that actually scales across the business. On AI Appreciation Day, the milestone worth celebrating isn’t launching AI—it’s making it stick.

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Sundeep Goel, CEO and co-founder, Mavvrik

This AI Appreciation Day I’m finding myself looking back at how fast incredible ideas are turning into shipped products. Teams are standing up agents, handing entire workflows to models running with no one watching, and shipping in days or weeks, not quarters. Speed has long been a key benefit of technology, and it’s never been more exciting to watch.

But with that accelerated pace, we’re seeing important steps get skipped. Especially when it comes to cost. The AI bill shock is real and it doesn’t have to be. We’ve seen companies go from tokenmaxxing to significant pain. We have some interesting data coming out soon in our 2026 State of AI Cost Governance report: 49% have been forced to reprice products, 40% have seen board-level escalations, and 33% have put emergency spending freezes in place, all due to unexpected AI costs.

Over the next 12 months, it’s time to close the gap. My focus is helping companies put proactive guardrails in place so they can manage and optimize their spend, and keep reaping the benefits of this technology.

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Jeffrey Gregor, General Manager, OVHcloud US

AI Appreciation Day is a good opportunity to recognize how quickly the conversation has evolved. Just a year ago, much of the focus was on foundation models, GPU availability and who could deploy AI the fastest. Today, many organizations are discovering that building AI was the easy part. Operating it efficiently, reliably and economically is becoming the real challenge.

As AI moves from experimentation into production, success is increasingly determined by infrastructure fundamentals. Power, cooling, networking, storage, data movement and cost predictability have become just as important as model selection. Inference is emerging as the workload organizations will run continuously, making operational efficiency a long-term consideration rather than a technical detail.

In many ways, AI reminds me of the early days of cloud adoption. The initial excitement centered on what was possible. The next phase was learning how to operate those environments efficiently, control costs and avoid architectural decisions that created problems later. AI is reaching that same inflection point. The conversations I’m having with customers today are less about which model they should use and more about how they build an infrastructure strategy that remains flexible as workloads evolve, costs fluctuate and the pace of innovation continues to accelerate. To me, that’s a sign the market is moving in the right direction. AI is becoming less about chasing the next breakthrough and more about building an operational foundation that can support innovation over the long term.

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Malte Kosub, CEO of Parloa

The tech is ready and capable, but is dependent on a strong design and deployment strategy. That means moving away from support and service as a deflection hub, where automation is meant to reduce cost, and turning it into a revenue and loyalty engine.

Companies getting AI in CX right are seeing NPS go up as agentic AI use cases increase. Why? The caller is getting their issue resolved without waiting on hold or repeating themselves one time too many (which, according to our survey, is twice). That’s what this industry should be measuring itself against, and that’s something truly worthy of being appreciated.

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Shai Gabay CEO and Co Founder of Trustmi

For years, companies have focused on securing systems while attackers targeted those systems. AI is changing that dynamic. Today’s fraudsters don’t always need to break into a network. They can generate flawless invoices, convincing documentation, and highly personalized payment requests that fit perfectly into legitimate business processes. The real shift is that attackers are increasingly targeting business decisions rather than technology itself. That’s why AI Appreciation Day shouldn’t just be about celebrating what AI can create. It should also be about recognizing how AI is changing the way organizations defend trust.

As AI-generated fraud becomes more convincing, the question is no longer, “Does this look legitimate?” The question is, “Has this been independently verified?” Organizations that succeed in the AI era will move beyond trusting what they see and start validating the behavior, context, and risk behind every payment before money moves.

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Bill Bruno, CEO, Celebrus

The promise of AI is high, but the data quality for most brands is too low. For years, brands have been warned about the deteriorating digital data ecosystem due to the rise of privacy, compliance, and governance across web and mobile. The brands best suited now to take advantage of AI, without bias or issues, are those that heeded those warnings and invested the time, money, and effort into building a better data foundation.

The challenge is that most customer data platforms—and much of the broader marketing technology stack—were designed to ingest data, not verify its accuracy. Data is collected, stitched together, and passed downstream with the assumption that it’s complete and correct. AI simply exposes the weakness of that assumption. When inaccurate identities, missing signals, or poor-quality behavioral data become the inputs to AI, the outputs become less reliable, less explainable, and ultimately less valuable.

Organizations that prioritize data quality before rushing into AI find they can move much faster once they begin deploying real-world use cases. Rather than pausing projects to fix broken data pipelines or reconcile inconsistent customer records, they can focus on applying AI to improve customer experiences and business outcomes. The companies seeing the greatest impact from AI are the ones that built a foundation capable of supporting it.

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Sam Peters, chief product officer, IO (formerly ISMS.online)

AI Appreciation Day is an opportunity to recognize that AI is becoming a permanent part of how organizations operate, make decisions and serve customers. The conversation has shifted from experimentation to operational reality, and with that comes a responsibility to ensure AI is being deployed in a way that is secure, transparent and aligned with business objectives.

We’re seeing many organizations acknowledge that AI adoption has outpaced AI governance. In our latest research, 54% said they adopted AI too quickly and are now facing challenges implementing it more responsibly. That’s why governance frameworks such as ISO 42001 are gaining traction. They give organizations a structured way to manage AI risks, establish clear policies and demonstrate accountability, rather than trying to retrofit governance after the fact.

Perhaps the clearest sign of this shift is that AI governance is no longer just an internal concern. Customers, partners and regulators increasingly expect organizations to demonstrate that AI is being developed and used responsibly, and businesses are beginning to hold their suppliers to the same standard. As AI becomes embedded in everyday operations, responsible governance will be the linchpin that allows organizations to scale AI with confidence and build lasting trust.

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Baha Zeidan, Chief Executive Officer of Azalea Health

AI is at its best when it does more than store information — it should act on it. Every minute a provider spends chasing down data or piecing together fragmented systems is a minute taken away from a patient. The goal isn’t to build a smarter filing cabinet. It’s to build software that works alongside providers: surfacing the right information at the right moment, automating the tasks that pull them away from care, and lifting administrative work off their plate before it becomes a problem.

As AI continues to evolve, its success won’t be defined by how much data it holds. It will be defined by how much work it takes off a provider’s hands, whether it saves time, fits naturally into how practices already work, and earns the trust of the people using it every day.

On AI Appreciation Day, it’s worth recognizing technology that does the work, not just stores it. AI should reduce friction, support busy healthcare teams, and help practices focus less on process and more on delivering great care. That’s where lasting innovation begins.

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Sanjay Gidwani, CEO and founder of Kosmos

Every AI Appreciation Day, you hear the same thing: AI is transforming business, and it is. Here is what nobody says: most companies aren’t stuck because the AI isn’t smart enough. They’re stuck because their own information is a mess.

Think about how a company actually runs. Customer complaints live in one system. Engineering changes live in another. Alerts from the infrastructure live in a third. Each system does its job very well. However, none of them talk to each other. So when something goes wrong, a person has to manually connect the dots between three places that were never built to share anything.

That’s the real problem AI keeps running into. You can’t hand a smart tool a mess and expect a clean answer back. The gap everyone’s calling an “AI adoption problem” is actually an old problem: the pieces of the puzzle live in different rooms, and nobody’s connected them. AI didn’t create that problem. It just made it too expensive to ignore anymore.

The companies pulling ahead this year aren’t the ones with the flashiest AI demo. They’re the ones who did the boring work first: getting their own information talking to itself. That’s not exciting. It’s also the only way AI ends up helping instead of adding to the noise.

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Don Boxley, CEO and Co-Founder of DH2i

I really like the idea of AI Appreciation Day. Not because AI needs a birthday, but because it provides a moment to take a step back and appreciate the holistic picture of all the components that go into making these applications work and bring value to our everyday lives.

Generally, when people talk about AI, they almost always jump straight to the models. They want to talk about GPUs, NVIDIA, training, inference – all the ‘sexy’ stuff. That’s fine and good. But AI doesn’t know anything by itself. All that information that makes it so useful needs to come from somewhere. And for many organizations today, that’s databases like SQL Server. The reality is, if the database goes down, AI doesn’t suddenly become intelligent enough to work around it. It just stops being useful. So, this year on AI Appreciation Day, let’s remember that while it is critical to spend time thinking about how to make AI smarter, easier to use, and faster… we need to also remember that none of that matters if the data can’t answer when AI calls.

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Anoop Dawar, Chief Strategy Officer (CSO) of Deepgram

We’ve seen Salesforce acquire Fin, SpaceX pay $60 billion for Cursor, and OpenAI stand up a $10 billion deployment company – three very different bets on the same scarce thing: teams that can make AI agents work reliably in the real world, not just in a demo. That capability has quietly become the most valuable asset in software, because these are probabilistic systems that drift and have to be measured and monitored continuously to stay accurate. And it gets hardest in voice – real-time, unforgiving, no second take – which is exactly where the next phase of this race will be won.

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Donna Wilzcek, Chief Product & Tech Officer at Basware

This year’s AI Appreciation Day comes at a time when enterprises are realizing that AI cannot be treated as fixed infrastructure. Models and regulations can change quickly, which means companies need AI strategies that are flexible.

Enterprise leaders should avoid building around a single-model mindset. Instead, they need architectures that allow them to select the right model for the right task and shift workloads when policies, performance, or availability change. That flexibility only works if AI decisions are traceable, including what data informed the action, which model was used, and what level of oversight was required.

The future of enterprise AI is not just autonomous AI. It is governed autonomy: AI that can move work forward, but within clear boundaries that preserve control and accountability.

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Richard Boudria Jr., Chairman and CEO of BCN

Artificial Intelligence Appreciation Day shouldn’t just be about appreciating AI. It should be about appreciating the digital infrastructure that makes AI possible. Around the world, organizations are discovering that AI is only as intelligent as the networks, edge environments, and data ecosystems supporting it. The next wave of AI innovation won’t be won by whoever builds the biggest model. It will be won by whoever can move data faster, process intelligence closer to where it’s created, and deliver secure, real-time insights at global scale.

That is why network technology and edge computing have become strategic enablers of AI rather than background infrastructure. As AI moves from centralized cloud environments to factories, hospitals, retail locations, financial institutions, and smart cities, intelligence must move closer to the edge, where milliseconds can determine outcomes and resilience becomes a competitive advantage.

AI Appreciation Day is also a reminder that innovation carries responsibility. Building trustworthy AI requires secure networks, transparent governance, resilient infrastructure, and human oversight at every stage. The future of AI won’t be defined by algorithms alone. It will be defined by the quality of the digital foundation beneath them and by our ability to combine human expertise with intelligent systems to solve global challenges responsibly.

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Oded Nagel, CEO, CTERA

AI Appreciation Day reminds us that we are living through one of the most notable technological shifts in a generation. But appreciation without action is just observation.

For enterprise organizations, the real opportunity in AI is not just automation. It is unlocking the value buried inside decades of unstructured file data: documents, records, collaborative files, engineering drawings, and medical images. This is where institutional knowledge lives, and until now, it has largely been invisible to AI systems.

The businesses that will win in this AI era are not necessarily the ones with the best models. They are the ones that have the strongest data foundations. Clean, accessible, secure, and governed file data is what makes AI work at scale.

At CTERA, we have spent years building the infrastructure layer that enterprise AI now depends on: global file services, edge-to-cloud architecture, and data security baked in from the start. That was not accidental. Through experience, we came to understand that data infrastructure would become a major asset.

What excites me most is not where AI is today. It is what becomes possible when every organization can finally activate the file data it has always had but never fully used.

AI Appreciation Day should prompt every business leader to ask one honest question: Is your data infrastructure ready to support your AI ambitions? If not, start preparing it now.

The window to take action is now. Make the investment in your data foundation today.

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Shanthi Rajan, CEO, Linarc

AI Appreciation Day is a good moment to be honest about what AI can and cannot do for construction. AI is only as useful as the data behind it. If project information lives in separate systems, spreadsheets, email threads, and someone’s truck, no model can turn that into reliable insight. The contractors who will actually benefit from AI are the ones who fix data capture first.

That is why connected platforms are winning. When a foreman enters information once in the field and it flows through scheduling, cost, and reporting workflows automatically, teams stop chasing updates and start seeing problems while there is still time to act. Connected project data is not just an efficiency play. It is the training ground that AI needs to deliver earlier risk identification, more dependable schedules, and tighter cost control.

This is where my team has placed its bet, and where the whole industry is heading. We are building AI natively into connected construction workflows, not bolting it onto scattered data. The goal is not to replace the judgment of a superintendent or a project executive. It is to put current, trustworthy information in front of them faster, so decisions get made based on facts instead of last week’s report. AI that respects how contractors actually work will change this industry.

Everything else is just another dashboard nobody trusts.

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Jay Bavisi, Founder and Group President, EC-Council

AI Appreciation Day is a useful moment to recognize how deeply AI has entered everyday work. But appreciation should not be confused with uncritical adoption. The more mature conversation is about what it takes to adopt AI responsibly, defend the systems it touches and govern its use with discipline.

AI is no longer only a productivity tool that helps people write, search or analyze faster. It is moving into workflows where it can recommend actions, trigger processes, access data and influence decisions. That changes the responsibility for every organization using it. The question is not simply how much AI can improve productivity, but whether leaders understand where it is being used, who is accountable for it, how it is secured and when human judgment must intervene.

For AI to be sustainable, organizations have to look beyond immediate efficiency gains. The real measure is whether AI can be scaled without weakening trust, increasing unmanaged risk or leaving people unprepared for the decisions these systems now influence. A system that is powerful but poorly understood, widely used but weakly governed or difficult to secure will eventually create more pressure than progress.

The real test for enterprises is not whether they can deploy more AI. Most already can, and many already are. The test is whether their people are prepared to work with AI, question its outputs, defend against its misuse and govern autonomous action before it creates business risk.

That is what AI Appreciation Day should remind us of. The future of AI will not be shaped by enthusiasm alone. It will be shaped by the discipline organizations build around AI: Adopt. Govern. Defend.

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Johnny Halife, Chief Technology Officer, Southworks

The biggest surprise for companies moving AI into production is the speed. When an agentic workflow actually works, there is a near-magical quality to it. Once AI becomes part of the underlying infrastructure and starts contributing to team outcomes without being driven on a daily basis, the upside is tremendous. That is the real shift: moving from copilot to autopilot.

On the flip side, cost is the thing that catches teams off guard. Once AI is infrastructure, the token bill scales with everything else. Recent pricing and quota changes have pushed some companies to press pause on initiatives until they get that under control.

On this AI Appreciation Day, my advice to any CTO in that position is to know your models. Not every task needs frontier intelligence. Model literacy is a genuine developer skill in 2026, and understanding which model to use for which job is what flattens the AI cost curve without stalling the initiative.

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Unnikrshnan Kurup, Director of Client Consulting & Strategy, Theorem

On this AI Appreciation Day, it’s important to remember that AI’s value is clearest when it helps people discover things, get educated and make better decisions. In commerce, that is becoming more important as the path to purchase becomes more hybrid across people, content, media and AI agents. Retail media is playing the performance role, video commerce is helping build trust and education, and AI agents are becoming a new way to help consumers make decisions.  

When we think about the future of AI in commerce, it should sit where it makes everyday decisions easier and more useful. People will keep using AI when the value exchange is clear: it saves me time, it saves me money, it helps me make better decisions and it makes my life easier.  

A large share of commerce will become agent-assisted, but there will still be a separation between tasks and desires. AI agents can take over routine work like replenishment, comparison, deal hunting, delivery optimization, product filtering and subscription management. Humans will still drive taste, identity, values, gifting, discovery and emotional choices.  

Rather than replacing people altogether, agents will become a new layer between intent and decision-making. The biggest opportunity is using AI to create convenience without taking away control. AI should help people make decisions they understand and feel confident in, not decisions they cannot explain or reverse.

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Ryan Heidorn, Chief Technology Officer, C3

On AI Appreciation Day, I think less about the technology in the abstract and more about what it can make possible for teams operating under real security and compliance pressure. In the Defense Industrial Base, organizations are expected to protect sensitive government data, manage identities across increasingly cloud-native environments and prove that CMMC controls are working in practice. That creates a constant stream of alerts, evidence, access decisions and risk signals that can overwhelm even mature teams.

Where AI becomes especially valuable is in helping security teams turn that complexity into action. It can identify patterns faster, surface unusual access behavior, prioritize the risks that matter, support documentation and make continuous monitoring more achievable. For contractors preparing for a CMMC assessment, that kind of speed and visibility can help bridge the gap between security that exists on paper and security that can be demonstrated day to day.

But appreciation should not become blind trust. AI is not a substitute for governance, accountability or operational discipline. It can make strong processes faster and more scalable, but it cannot make weak processes defensible on its own. The organizations that will benefit most are the ones using AI to strengthen security processes they already own, not bypass them. For the DIB, that means applying AI responsibly to support identity management, assessment readiness and stronger protection of the data our national security ecosystem depends on.

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Shiv Agarwal, Co-Founder and CEO, Singulr AI

AI Appreciation Day is the perfect moment to move past the hype and understand how AI can actually create value inside an enterprise. That value isn’t simply created through better models and more tools or agents. AI creates value when people can use it to eliminate painstakingly repetitive tasks, make better decisions, experiment safely and securely, and move faster.

The challenge in 2026 is that most organizations are trying to govern a constantly evolving, dynamic technology with processes built for a much slower generation of software and controls that have become fragmented over time. Employees are using these new tools before IT and security teams have had a chance to review them; new AI features appear in SaaS applications without as much as a notification; and, on top of that, agents are starting to take actions across systems. The cost of using AI is skyrocketing without proper usage oversight and optimization controls. Governance can’t just apply to a policy or onboarding document anymore; it has to operate in real time where AI is actually being used. 

True AI appreciation means recognizing both the technology’s promise and the reality of how it operates within a given organization. To get the most value out of AI, your company needs visibility, clear digital and interpersonal controls, training, and confidence to keep internal policies aligned with the real world as AI continues to gain more autonomy.

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Hari Srinivasan, Vice President of Products & Strategy, Lineaje

Agentic AI is driving enterprise technology past ‘just’ assistance and directly into autonomous operational execution. As these systems independently generate code, execute workflows, and make real-time decisions, they fundamentally alter the enterprise risk profile.

The shift demands an immediate evolution in governance. Enterprises can no longer rely on periodic audits; they require continuous, real-time visibility into Agentic AI, including AI-generated software, and rigorous integrity verification throughout the entire development lifecycle. That process begins with software lineage—equipping organizations to trace exactly what AI created, verify its origin, and validate its integrity before a single line of code reaches production.

For AI Appreciation Day, the industry conversation must mature from AI adoption to strict AI governance. As Agentic AI reshapes enterprise operating models, continuous governance should become the baseline, not the exception. True value isn’t what AI can build—it’s about ensuring every AI and AI-generated outcomes can be governed, verified, and trusted.

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Freddy Kuo, Chairman of Luminys Systems Corp. and CEO of Foxlink Group

This AI Appreciation Day, the conversation should move beyond celebrating AI for its own sake. The next chapter of AI will be defined by systems that operate reliably in the real world, support better human decisions, and deliver measurable outcomes. 

That requires more than powerful models. It requires an AI Factory approach: a closed-loop ecosystem where real-world data, computing, model training, solution development, deployment, and feedback continuously reinforce one another.

As AI moves into physical environments, trust will matter as much as intelligence. The most valuable AI will not replace people. It will strengthen the teams responsible for making critical decisions every day.

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Dan Kutchel, CEO, Overtime

On AI Appreciation Day, we’re reminded that AI’s promise is not just about moving faster. It’s about building better, more flexible experiences for people. Ultimately, AI’s value should be measured not by the technology itself, but by the business outcomes it delivers.

AI’s most meaningful impact will be in how it expands choice for consumers, creating experiences that not only better meet an individual’s needs, but also help businesses drive stronger engagement, loyalty, faster payments, and growth. In some moments, consumers will want the speed, convenience, and 24/7 availability of interacting with an AI agent. In others, they’ll want the empathy, judgment, and creativity that only human connection can provide. The future we should be building is not one that forces a single way of interacting, but rather one that offers people flexibility and choice.
It’s about giving people the right experience at the right time, with trust built into every interaction.

As voice AI and agentic systems evolve, we have a responsibility to build technology that earns trust through transparency, reliability, and thoughtful design. The companies that will succeed will empower businesses to serve consumers in ways that feel more personal, natural, and human-centered.

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Jeanclaude Toma, CEO of Apricorn

The conversation around AI has spent the last two years focused on training Large Language Models (LLMs) via hyperscalers. The next phase will be determined by the quality, accessibility, and visibility of data via smaller inferencing models at the edge. Organizations cannot expect AI to deliver meaningful results if critical information remains scattered across disconnected systems and cloud environments without localized AI processing at edge locations. This elevates the need for secure data at rest and data in motion.

National AI Day is a reminder that AI readiness is about more than deploying the latest technology. It starts with understanding where your data lives, making it accessible when and where it’s needed, and building an infrastructure that can support AI at scale. When organizations improve visibility into their data, they’re putting themselves in a better position to extract real business value from AI.

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Chance Caldwell, Senior Director of PDC Threat Services at Cofense

National AI Day is a useful moment to celebrate the progress AI has enabled, but for cybersecurity leaders, it should also prompt a more practical question: how do we defend against a technology that is now accelerating both innovation and risk?

In email security, AI has become a force multiplier for defenders and attackers alike. Security teams can use AI to analyze patterns, automate workflows, and scale detection. At the same time, threat actors are using AI to create more polished, personalized, and convincing phishing campaigns. The old tells, such as awkward grammar, generic messaging, or obvious errors, are becoming less reliable as AI helps attackers make malicious emails look routine, relevant, and business-like. AI is also enabling attackers to generate and launch these campaigns at greater speed and scale, increasing the overall volume of threats organizations must now contend with.

This creates a real challenge for defenders. AI-based security tools are powerful, but attackers are constantly testing new tactics that may not match known patterns. From brand impersonation and credential phishing to QR codes and trusted-service abuse, these campaigns are designed to evade traditional gateways and reach users directly.

The organizations best positioned for this next phase will be those that combine AI-driven defense with human intelligence. Employees who are trained to recognize and report suspicious activity are not the weak link; they are a critical signal source. National AI Day should remind security leaders that the future of cyber defense is not AI replacing people, but AI strengthened by people.

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Sridhar Iyer, Sr. Director of AI/ML, Versa

AI has become one of the most transformative tools available to the enterprise today, but it is no longer just a productivity tool. It is also an impersonation engine.

A few seconds of voice can clone you. One photo can turn into a fake video. What used to require experts is now cheap, accessible, and weaponized by low-skill attackers. The result is an internet where noise is exploding and trust is collapsing.

That makes security fundamentals more important, not less. Zero trust, app-layer and network-layer protection, and unified control across silos are no longer optional enterprise ideals. They are survival requirements.

In the age of AI, the rule is simple: trust nothing, verify everything.

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Ashutosh Garg, CEO and Cofounder, Eightfold AI

“AI Appreciation Day should be a reminder that adoption matters more than admiration. According to Gallup⁠, 65% of employees in organizations that have implemented AI say it has improved their productivity and efficiency, yet only 12% strongly agree that AI has fundamentally changed how work gets done in their organization. That gap is the real story.

AI is no longer just a headline, it is becoming part of how work gets done. The next challenge is not simply deploying AI; it is redesigning work around it. As intelligence becomes more accessible, the differentiators become judgment, trust, context, and the responsibility to apply AI well.

AI Appreciation Day should remind us that AI is powerful. But more importantly, it should remind us that the real work is helping people and AI operate better together. In a world where intelligence is becoming abundant, advantage will not come from access to AI alone. It will come from knowing where to trust it, where to challenge it, and where human judgment matters most.

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Jessica Hammond, Senior Director Product Management- Gen AI, Protegrity

While AI Appreciation Day is a moment to recognize the value AI is creating for organizations, we cannot celebrate adoption without considering the risks that come with it. AI systems are moving from content generation tools to agents that retrieve knowledge and act across enterprise workflows. The more AI is embedded into daily business, the more sensitive data moves through prompts, logs, retrieval systems, tools and outputs.

Appreciating AI without considering how it handles data is how innovation quickly turns into liability. Reliable AI starts with data that is accurate, well understood and managed throughout its lifecycle. Organizations need to know where data came from, how it is classified, who can access it, when it can be used and how it is protected if something goes wrong. Those that succeed in this next phase of AI adoption will be the ones that can prove their AI is governed, protected and secure by design.

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Dan Abramson, SVP Americas at Syspro

This is the fifth AI Appreciation Day, and it feels remarkably different from the last. In just 12 months, AI has moved from being a technology the manufacturing industry was exploring to one it is broadly embedding into critical day-to-day operations. Today is a chance to reflect on not just the extraordinary pace of AI advancement, but how much manufacturers have adapted and achieved in putting it to work.

Manufacturers continue to manage supply chain disruptions, labor shortages, tighter margins, and changing customer expectations every day. AI is radically helping them cut through that complexity, making operational data easier to interpret, surfacing risks earlier, and reducing the time teams spend on manual analysis.

AI Appreciation Day also serves as a reminder that AI must serve people. The manufacturers seeing the strongest results are leveraging AI embedded into the systems their people already rely on. Whether it supports production planning, inventory management or quality processes, AI is giving teams better information and more confidence to make faster, smarter decisions.

Manufacturing has always depended on expertise, sound judgment, and accountability. Every decision has implications for production, customer commitments, quality, and safety. At its best, AI supports and strengthens those core principles, keeping teams at the center of what matters most: quality, safety, and customer commitment.

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Dror Zelber, Vice President of Product Management, Radware

AI Appreciation Day tends to focus on what AI can do. For enterprises, the more urgent question is how to secure autonomous agents once they begin accessing data, interacting with business applications and completing tasks with minimal human involvement

Security teams need to know where these AI agents are running, what they can access and whether their behavior creates risk. Without that visibility, governance becomes difficult and compliance reporting becomes much harder to support.

The next phase of AI security will be about managing the entire ecosystem of agents rather than individual applications. Organizations that want to use AI at scale will need visibility, governance and behavioral protection built into every deployment and into the way those agents operate.

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Kara Sprague, CEO, HackerOne

AI Appreciation Day tends to celebrate what AI creates. In security, the more interesting story is what it defends against.

Attackers adopted AI first. They use it to find and exploit weaknesses faster than any human team could match, and that is exactly why AI has become indispensable to the defenders. But AI has not replaced the human security researcher. It has raised the value of one. In the first half of this year, security researchers earned roughly $47 million through our platform, up 25% from the same period a year ago, even as AI made finding vulnerabilities faster than ever. AI can flag a possible vulnerability in seconds. It still takes human expertise to prove that vulnerability is real, understand how an attacker would actually use it, and decide what to fix first. Machines generate the possibilities. People confirm the truth.

The organizations getting this right are not choosing between security researchers and AI. They are pairing them. Speed from the machine, judgment from the human. That is what continuous security now demands.

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Khadim Batti, co-founder and CEO, Whatfix

AI Appreciation Day is a reminder that the biggest gains are still ahead, particularly for the organizations willing to build the trust to get there. We’ve seen this pattern before. Every major technology wave, from the internet to the cloud, has unfolded in several acts: infrastructure first, platforms second, and enterprise transformation last. This happens because business-critical workflows demand a level of reliability that takes time to earn.

Building that trust starts with how we deploy AI. Too often, AI fatigue gets blamed on the tools themselves, when it’s really an implementation failure. Employees are left to guess at boundaries, prompt their way through ambiguity, and absorb friction that good design should have removed. The organizations that will lead when AI reaches its next inflection point will be the ones that treat AI as an operating model transformation, rethinking how work gets structured so AI can execute autonomously within clear guardrails, while employees focus their energy on judgment, strategy, and creativity.

That’s the vision behind Whatfix AI. Powered by ScreenSense, our AI engine that understands context and intent, Whatfix AI embeds agentic intelligence directly into enterprise workflows. Rather than expecting employees to navigate an ever-growing collection of AI tools, AI agents operate within enterprise guardrails to deliver contextual guidance, accelerate execution, surface adoption friction, and continuously optimize how work gets done. The real promise of AI is that intelligence becomes an invisible part of the operating model, empowering people with the right support, at the right moment, while organizations retain the governance and control needed to scale AI with confidence.

When organizations get this right, AI starts to feel less like another application employees have to learn and more like a trusted collaborator woven into every workflow. It stops competing for people’s attention and starts compounding their impact, giving them back time to focus on the work that requires unique human judgment, creativity, and strategic thinking.

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Jackson Schultz, Co-Founder and CEO of ArgusEye

On National AI Day, we must recognize both the opportunities and the risks that the technology brings.

The rise of AI-driven cyberthreats is an alarming wakeup call for organizations big and small. In most situations, fighting fire with fire isn’t good advice, but when it comes to AI, that’s the exact formula organizations must follow.

While AI enables attackers to execute more sophisticated and scalable attacks, it also strengthens cybersecurity defenses, enhancing areas such as threat detection, vulnerability management, asset management, and data protection.

Beyond advancing solutions themselves, AI agents are also empowering security teams by acting as a force multiplier and alleviating the skills gap many organizations face. They can take over manual grunt work like regulatory compliance checks so team members can lend their hands where they’re more desperately needed: better equipping their organization to battle AI-driven cyberattacks.

In this new threat landscape, the organizations that remain resilient will be those who proactively leverage AI in their own cybersecurity strategies and operations. If attackers are advancing their methods, so must we.

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Rohit Gupta, CEO, Auditoria.AI

The first generation of enterprise AI proved that machines could generate answers. The next generation has to prove they can generate business outcomes. Finance is where that transition is happening first because every recommendation must be explainable, every action must be governed, and every result must stand up to scrutiny. That’s why AI Appreciation Day is no longer about celebrating possibility. It’s about recognizing that AI is becoming operational infrastructure for the modern Office of the CFO.

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Karl Bagci, Director of IT and Information Security, Exclaimer

AI Appreciation Day is a good reminder that AI’s greatest value isn’t in replacing people. It’s in removing repetitive work so people can focus on higher-value decisions. But AI is also exposing something many organizations have overlooked for years. Communication governance gaps that once affected a handful of messages can now be replicated at scale in seconds. AI hasn’t created those problems. It’s simply made them impossible to ignore. That’s why organizations need to think about governance before they think about automation.

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Paul Stokes, Co-Founder and CEO, Prevalent AI

AI deserves appreciation, but not blind admiration. It is has already changed the pace of cyber risk. Attackers can move faster, test more ideas, and find exploits at a scale that security teams were not built for. This does not make AI bad, but it makes AI-enabled visibility and governance essential. Businesses need to understand where AI is being used, which models they depend on, and where those dependencies create exposure.. Companies need to do the hard work of analyzing both their use of AI, and the data that drives it, or they run the risk of becoming its victim.

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Dan Gray, VP, Chief Technology Officer, Global Infrastructure Services at DXC Technology

One of the most exciting aspects of AI is its ability to amplify human capabilities. As AI systems take on routine analysis, information gathering, and repetitive tasks, people have more time to focus on judgment, creativity, and solving complex business challenges. We’re already seeing roles evolve from executing individual tasks to orchestrating agents that work on our engineers’ behalf, with expertise applied where it has the greatest impact. That philosophy is reflected in DXC OASIS, a platform that brings together data, automation, and human insight to enable more effective collaboration between people and AI agents. DXC OASIS redefines the way our teams run the systems that power the world’s leading enterprises, allowing them to move faster and accelerate time to value with predictive, AI-driven intelligence. DXC engineers with decades of experience in server administration and mainframe operations are now the architects of our agent development. Their knowledge and expertise make DXC OASIS smarter. The future of AI isn’t about humans versus AI—it’s about combining the strengths of both to help people make better decisions and create greater value.

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Erin Lanuti, CEO & Co-Founder, Lilypath

Most organizations are focused on using AI. Few are focused on how AI understands them.

As AI-powered search, assistants, and agents become a primary discovery layer, they’re increasingly determining which companies get surfaced, which vendors get recommended, and which experts get cited. When a buyer asks an AI assistant for the top solutions in a category, the vendors included in that response gain a place in the funnel before a prospect ever visits a website, downloads a white paper, or speaks to sales.

That’s the risk.

If AI systems misunderstand your company’s expertise, or product differentiators, your brand can become invisible at the exact moment purchase decisions begin. You’re not just losing visibility—you’re losing qualified pipeline before you even know you were considered.

For enterprise organizations, AI interpretation is quickly becoming as important as SEO was a decade ago.

The next frontier of AI isn’t generation. It’s interpretation.

Smart companies won’t just ask how they’re using AI. They’ll ask how AI is using them.

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Poonacha Kongetira, Co-founder and CEO, Classie

AI Appreciation Day shouldn’t just be about celebrating what AI can do. It should also be a reminder to use it responsibly. Across many organizations, AI is spreading faster than the policies, knowledge, and governance needed to support it. That’s creating a new form of shadow AI where employees rely on disconnected tools, inconsistent information, and prompts that no one can oversee. AI delivers its greatest value when it becomes part of a trusted knowledge ecosystem, not when it operates in isolation.

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Binny Gill, Founder and CEO, Kognitos

AI Appreciation Day feels a little like throwing a party for fire. Fire cooks your food and heats your home, and it also burns the house down. What fire deserves isn’t applause, it’s respect, and a smoke detector. AI is the same. Every prior general-purpose technology – electricity, the automobile, aviation – got safer because we treated it as dangerous first and useful second. With AI we’ve reversed the order, and that worries me. If we want a day worth celebrating, make it AI Accountability Day. Appreciate the systems that can be audited, that ask before they act, and that keep a human in the loop. Those are the ones that earn it.

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Robin Gilthorpe, CEO, Earnix

AI Appreciation Day is a good opportunity to move beyond the hype and ask a more important question: what kind of AI do insurers actually need? General-purpose AI can generate impressive answers, but insurance isn’t a general-purpose business. Every pricing decision, underwriting recommendation, and customer interaction needs to reflect regulation, business strategy, portfolio performance, and customer context. That’s why I believe the future belongs to vertical AI, purpose-built for insurance. AI becomes genuinely valuable when it understands the business it’s helping to run, not just the language people use to describe it.

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Ravi Achanta, Founder and CEO, RSA America

AI Appreciation Day should be more than a celebration of artificial intelligence; it should be about championing better business decisions. Working with independent grocers and smaller retailers, we see that AI represents a chance to achieve the kind of business visibility and insight that has long given larger chains a competitive edge. But that potential can only be realized with the right foundation. When customer, promotion, e-commerce, and loyalty data remain trapped in disconnected systems, AI simply amplifies those silos instead of delivering meaningful intelligence. Retailers that unify their commerce data and connect their operations using AI will improve margins, personalize shopper experiences, and compete with far greater confidence. That’s the AI worth appreciating.

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Sarah Hoffman, Director of AI Thought Leadership at AlphaSense

Over the past few years, we’ve moved from remarkable AI demos to the harder work of putting AI into production. Along the way, we’ve expanded access to data, but that was never the hard part. The more pressing challenge is giving LLMs the right context to connect signals, weigh evidence and generate insights that are defensible and backed by citations – not just fast. For businesses, this standard is non-negotiable.

Organizations are now scrutinizing ROI, becoming more selective about deployments, and taking a much closer look at where AI is delivering measurable operational value. That’s a healthy sign: It means the technology is mature enough to be held to a real standard. Enterprise AI expectations are rising on every front, from cost to consequences. Businesses want to evaluate performance, manage risk, and have confidence in the outputs they’re investing in. That’s driving greater emphasis on governance and domain-specific AI that can produce trustworthy results. AI will continue to evolve quickly, but the industry is getting sharper at measuring what actually works.

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Adam Field, Chief AI Officer, Tungsten Automation

There’s something very on-brand for our industry about inventing a holiday to appreciate our own technology. If we’re not careful, the agents will start expecting cards.

So much attention this year has been captured by the rapid evolution of AI models, agents, and new capabilities – flashy this and flashy that. I’ve always been a firm believer that the biggest factor determining true enterprise AI success stems directly from the work happening behind the scenes. This year, we’ve witnessed a massive wave of organizations quickly realizing that measuring AI adoption through metrics like token usage is no longer a feasible way to evaluate ROI. As a result, the industry was forced to take a healthy step back and really think about what it truly means to unlock value from AI.

At the core of it all, it’s always been about the ability to improve processes, enhance decision-making, and deliver measurable business outcomes. Achieving that comes from foundational work around data readiness, system access, security controls, compliance, and governance.

The future of enterprise AI will be built on what I call ‘boring AI.’ And what I mean by this is the critical foundational work that may not generate headlines but enables organizations to innovate responsibly and at scale. But technology is only half the equation. The other half is the people you enable to use it, and if you invest in one without the other, you will fail. Deploying AI tools is the easy part, but creating the right environment for AI to deliver value is where the real transformation happens. So, I’ll leave you with this. The next generation of AI leaders will be the ones who look beyond the latest models and usage metrics to focus on outcomes, asking investors and companies what outcomes AI is creating rather than simply what AI they are deploying.

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Mat Ryer, Senior Director of AI at Grafana Labs

AI Appreciation Day is an opportunity to recognize that AI is giving engineering teams and every individual an opportunity we’ve never really had before: the ability to iterate on software quickly while understanding it just as quickly. For years, this was gate-kept behind big teams, lots of specialized knowledge and dedicated operations tooling. AI has the potential to close that gap and allow everyone to be a builder and an operator, at least to a certain extent and scale. That’s where the real opportunity lies – empowering engineers and giving them the context they need to make better decisions.

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John Cannava, CIO at Ping Identity

Organizations are increasingly deploying AI agents across the enterprise, and the opportunities for innovation and efficiency are tremendous. These systems are doing more than just responding to prompts. They’re making decisions, taking actions, and even spawning new agents with increasing autonomy and speed. That evolution is transforming how work gets done, and it’s also reshaping the security landscape.

Now the challenge is that many organizations are adopting AI agents faster than they can establish clear identity, accountability, and governance for them. When you can’t definitively answer what an agent did, why it did it, or under whose authority it acted, you create unnecessary risk and uncertainty. This is why identity for AI must become a foundational priority. Every agent needs a verifiable identity with clear permissions and continuous oversight, just like any human user or service account. By building trust, visibility, and accountability into AI from the start, organizations can unlock the full potential of autonomous AI while managing risk and strengthening security.

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Jeff Margolies, Chief Product and Strategy Officer at Saviynt

AI Appreciation Day is a reminder that AI is becoming a powerful tool for helping organizations move faster, make smarter decisions and manage complexity at scale. As AI adoption accelerates, enterprises need the right governance and identity controls in place so teams can use it confidently and responsibly. The fundamentals have not changed. Strong visibility, effective access controls and rapid risk reduction are still essential to building trust in the AI era. What has changed is the pace of change. Organizations that act now will be better positioned to innovate securely and with confidence.

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Dr. Diana Cano, CIO at Cambium Learning Group

Over the last year, we’ve learned that successful enterprise AI adoption isn’t just about providing everyone with access to new tools. It’s about empowering employees to use AI in ways that allow them to do the work they genuinely want to do. When AI takes on routine tasks, employees have more time to make a tangible impact – whether that’s by helping a customer solve a problem or giving an educator more time to focus on student progress.

As AI continues evolving, the opportunity isn’t just to complete work quicker. It’s to spend more time on work that requires human judgment, creativity, and connection. Investing resources to achieve that outcome is worth it.

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Munu Gandhi, President of Xerox IT Solutions and Chief Technology Officer, Xerox

AI Appreciation Day is an opportunity to recognize how quickly artificial intelligence is evolving from a technology innovation into a core business capability. As organizations move beyond experimentation, the real value of AI will be measured not by the insights it generates, but by the business outcomes it enables, from improved productivity and faster decision-making to better customer experiences and more efficient operations.

At Xerox, we are helping organizations apply AI across documents, workflows, and IT environments to simplify complexity, automate routine work, and create more connected, intelligent operations. The organizations that will lead in the years ahead will be those that combine human expertise with AI-driven capabilities to accelerate execution, enhance agility, and unlock new opportunities for growth and innovation.

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Josh Bartolomie, VP, Global Head of Threat Intelligence at Doppel

AI is the most transformative technology I’ve seen in more than two decades in cybersecurity, and I believe we’re only beginning to understand its potential. Across industries, it’s accelerating discovery, solving increasingly complex problems, and enabling people to work with more information than ever. In cybersecurity, where teams are constantly overwhelmed by the volume and complexity of threats, AI is helping defenders process vast amounts of data, uncover patterns that would otherwise be missed, and turn an overwhelming volume of signals into meaningful intelligence. That’s what makes AI worth celebrating.

That said, like every major technological breakthrough, AI brings both opportunity and new challenges. Within cybersecurity, we’re seeing attackers use AI to scale phishing, impersonation, and social engineering attacks at a pace we’ve never seen before. The answer isn’t to slow AI innovation; it’s to ensure defenders can evolve just as quickly. That means building AI-native security that combines powerful models with rich, contextual threat intelligence so teams understand not just what’s happening, but why it matters and what action to take.

AI isn’t going anywhere, and its greatest impact will come from pairing its extraordinary capabilities with trusted intelligence, context, and human judgment.

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Shane Buckley, President and CEO, Gigamon

AI Appreciation Day comes at an important moment in the technology’s evolution. We’ve moved beyond asking whether AI belongs in the enterprise. The conversation is now centered on how organizations operate in the Mythos era, where increasingly capable AI models and agents are changing the economics of work, innovation, and cybersecurity. Capabilities that once required specialized expertise, significant time, or large teams are becoming dramatically more accessible. That creates tremendous opportunity, but it also changes how organizations compete, innovate, and manage risk. The growing cost of AI is also becoming a barrier to wide scale deployment across organizations. Given the massive investment in infrastructure from the leading AI companies, token costs are expected to continue to soar, putting even more pressure on IT budgets, and forcing some very important investment trade-offs.

As AI becomes embedded across business-critical systems, leaders need a clear understanding of how models, agents, and AI-powered workloads interact with their data and infrastructure. Visibility into AI activity is becoming just as important as the AI itself, providing the context organizations need to strengthen security, govern these systems responsibly, make better decisions and provide better cost/ROI controls to ensure spend is aligned to return. The organizations that create lasting value from AI will be the ones that invest as much in visibility, governance, and operational discipline as they do in the technology itself.

Laurent Landowski, Chief Product Officer, Nabla

The real shift this past year isn’t that AI got smarter, it’s that the people closest to a problem can now build the solution themselves, without waiting on engineers. A clinician can automate a workflow that used to sit in an IT backlog for 2 years. That’s where the speed comes from.

But in healthcare, speed isn’t the constraint: trust is. The moment AI touches a decision that matters, people need to see what data it used, why it landed where it did, and where a human has to sign off. Over the next year, the companies that pull ahead won’t be the ones only deploying AI fastest; they’ll be the ones who do it, but make it auditable. In healthcare, we don’t get to choose between the two, and that’s exactly the right pressure to build under.

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Chip Hughes, Chief Product Officer, Imprivata

AI Appreciation Day is an opportunity to recognize not only how quickly AI is reshaping the way we work, but also what it will take to adopt it responsibly, especially in mission-critical industries where missteps can have significant and even dangerous consequences. In healthcare, for example, AI is rapidly evolving from an assistive technology to an active participant in clinical and operational workflows, making trust just as important as innovation.

As AI agents become more capable and autonomous, healthcare organizations need confidence that every AI system is properly identified, governed, and monitored – just as they do with human users. Identity, accountability, and transparency will be essential to ensure AI operates safely in environments where decisions can directly impact patients and caregivers. Organizations that build this foundation of trust will be best positioned to realize AI’s potential to improve care, reduce administrative burden, and support clinicians, without compromising security or putting patient safety at risk.

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Mark Wojtasiak, SVP of Market Research and Strategy, Vectra AI

AI Appreciation Day shouldn’t be about celebrating faster tools. It should be about appreciating what AI can give back to people: time to think, room to be curious, and confidence to act. In cybersecurity, defenders have been buried in noise for too long. The best use of AI is not replacing them. It’s taking on the repetitive, high-speed work machines are better suited for, creating room for humans to ask better questions, make better decisions, and build security teams they’re proud to be part of.

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Aaron Fulkerson, CEO at OPAQUE

AI Appreciation Day should come with a reminder: AI’s future depends on whether people can verify its actions, not just trust promises they’re asked to accept. Public skepticism around AI is not a communications problem. It’s an architecture problem. As AI agents touch more sensitive data, make more decisions, and act across more systems, the industry can’t keep running on a ‘trust us’ model. The early web faced a similar turning point when HTTPS replaced promise with proof. AI now needs the same shift. To truly appreciate AI as a force for progress, we need collaboration across the ecosystem to make verifiable privacy and policy enforcement foundational to how AI is built, deployed, and used.

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Sundar Subramanian, CEO of Zyter

Much of today’s conversation around AI still focuses on the technology itself. We compare models, benchmark performance, and chase the latest features. Those advances matter, but they aren’t what will ultimately determine AI’s value. This is the same approach to emerging technologies that for decades rewarded the people who best knew how to build software. That deep technical and model knowledge is certainly still important. But the future of the AI economy won’t be defined by model capability alone. It will be defined by how well organizations understand what the people using AI are actually trying to accomplish.

In practice, that means looking beyond individual tasks AI can automate and asking how AI can improve an entire workflow or role. Take the healthcare industry as an example. The goal isn’t to replace clinical judgment. When AI is viewed through the lens of human expertise and experience, it can remove administrative burdens like documentation, prior authorizations, and routine coordination so a physician can spend more time actually talking to their patient and building a human-led relationship.

The real opportunity with AI isn’t about removing people from the work. It should be about creating space for people to do more of what’s meaningful, thoroughly human, and genuinely impossible to replicate.

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Bert Van Hoof, CEO of Willow

AI Appreciation Day is a good moment to ask a difficult question: has AI finally moved from describing the physical world to operating it?

Most AI still lives in bits. It shows up in documents, dashboards, and chatbots that read and summarize. Yet, the built world runs on atoms: chillers, elevators, air handlers, and refrigeration, along with the people who keep them working. Traditional building systems can tell you a chiller is underperforming but leaves a person to decide what happens next. Operational AI is what happens when AI moves from describing the physical world to operating it. It connects live operational data across a building’s systems into a single operational layer with real asset context. When a signal fires, it schedules the repair, routes the work order, and adjusts the setpoint directly.

The same layer that predicts a chiller failure in a hospital catches a jet bridge issue at an airport or a refrigeration fault in a retail portfolio. Different assets, same discipline.

Buildings shape health, safety, learning, and how communities function. AI is worth appreciating when it helps run them.

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Bojan Simic, CEO and co-founder, HYPR

As AI moves from passive assistant to active agent, the definition of identity in the enterprise has to change. We’re giving non-human actors the ability to take actions, make decisions, and touch critical data, often using legacy service accounts or blanket permissions that were never designed for autonomous execution.

If you can’t answer exactly who an agent is acting on behalf of, what its boundaries are, and how to stop it in real time, you don’t have a policy, and without policy comes real security risk and exposure.

Scaling AI safely comes down to basic identity fundamentals applied to non-human actors:

• Verifiable Ownership: Binding every agent to a human owner with an explicit, time-bounded scope of authority.
• Inline Control: Enforcing security at the point of execution through an agent gateway to ensure real-time response rather than trying to audit actions after they’ve already happened.
• Real-Time Oversight: Dynamically keeping a human in the loop who can constrain or shut down an agent instantly if it strays.

AI Appreciation Day is a good reminder that speed is only half the equation. The organizations that get the most value out of AI won’t just be the ones deploying it fastest—they’ll be the ones that solved how to govern non-human identity before things scaled out of control.

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Dhruv Majumdar, VP, Security Solutions, Fleet Device Management

AI is helping security researchers uncover vulnerabilities faster than ever before, but it’s also accelerating the speed at which those same weaknesses can be exploited, from days to hours. As AI-powered vulnerability discovery becomes more capable, the gap between finding a flaw and seeing it weaponized will continue to shrink.

We need to respond to them faster. AI-assisted, and eventually autonomous, patching will become a necessity, not a luxury, because security teams won’t be able to keep pace manually. But speed alone isn’t enough. The real challenge is deploying fixes safely, with an understanding of business context and user experience. An autonomous system can’t reboot a trader’s workstation in the middle of a billion-dollar transaction or interrupt a CEO during a board meeting just because a patch is available.

The organizations that get this balance right, combining AI-driven speed with intelligent operational guardrails, will have a significant competitive advantage over those still relying on manual processes. AI deserves appreciation because it’s pushing us towards entirely new ways of operating that simply weren’t practical before.

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Laurent Halimi, CEO and founder, Cyberr

One of the biggest misconceptions about AI is that it’s going to replace cybersecurity professionals. What’s actually happening is far more significant: it’s redefining the skills that every cybersecurity professional will need to succeed.

We’re already seeing employers look for AI knowledge alongside traditional cybersecurity expertise, and AI-focused training and certifications will soon become just as important as many of the security credentials the industry has relied on for years. Understanding AI models, prompt engineering, LLM risks and how attackers are weaponizing AI is rapidly becoming part of the baseline skillset, even for entry-level security roles.

That shift shouldn’t be viewed as a threat. Every major technological change has created new specialties, new career paths and new opportunities, and AI will be no different. Cybersecurity professionals who invest in AI skills today will likely be the ones leading security teams tomorrow. Those who combine strong security fundamentals with AI expertise will have a significant advantage over those who treat AI as someone else’s problem.

AI, whether we like it or not, is shaping the next generation of cybersecurity professionals. The challenge now is making sure education, certifications and workforce development evolve quickly enough to prepare them.

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Gunter Ollmann, CTO, Cobalt

AI is already making security teams faster and more effective, but we also need to be honest about where the industry is today. There’s a growing tendency to assume AI can replace security expertise, when the evidence suggests the opposite. Our research found that 78% of security teams have seen automated scanning tools miss critical vulnerabilities, and support for fully automated pentesting has dropped to just 9%. That’s proof that context still matters.

The challenge is even more pronounced with AI applications themselves. LLMs introduce new attack paths, business logic risks and behavioral flaws that can’t always be identified by pattern matching or automated validation. That’s why we’re seeing organizations embrace a more pragmatic approach: automate what machines do well, but rely on experienced security researchers to uncover the issues that require human judgment. As AI becomes embedded in every business, confidence won’t come from trusting automation alone. It will come from knowing you’ve validated your most critical systems with both intelligent tooling and expert adversarial testing.

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Amit Shuster, VP, Product and Engineering, Vetric

AI Appreciation Day tends to celebrate the models, but the real workhorse of the AI era is the data underneath them. An AI-powered fraud or threat detection system is only as sharp as the public data feeding it — and while defenders debate adoption, attackers have already embraced AI to impersonate executives, automate scams, and scale abuse. The best way to appreciate AI is to make sure the good guys’ AI is seeing more of the open web, faster, than the bad guys’ is.

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Steve Conner, President of EdgeCore Digital Infrastructure

AI Appreciation Day isn’t just about celebrating how far we’ve come with AI. It’s also about recognizing the physical infrastructure making these advances possible. Every new model and application depends on digital infrastructure to support unprecedented computing demands. AI adoption will only accelerate from here, and data centers will continue to be foundational to innovation across every industry.

To stay ahead, the next wave of AI infrastructure must be able to handle more power, utilize advanced cooling methods, and support denser workloads. As demand for AI capacity grows, data center developers play an important role in delivering the reliable infrastructure that organizations need with certainty and confidence. Appreciating AI also means appreciating the infrastructure underneath it all.

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Heath Mullins, Chief Evangelist at ExtraHop

AI Appreciation Day isn’t just about recognizing how fast AI is changing business operations. It’s a reality check on how it’s changing the game for everyone, ranging from large businesses scaling operations to individuals looking to perform simple tasks with the help of an LLM.

While companies may be running smarter and faster, AI advancements are also enabling cyber attackers to advance in similar ways. Recent research shows that about 85% of organizations have already been hit by an AI-powered attack, including everything from hacking attempts to AI identity theft and new tactics that slip in through third-party partners. With AI adoption inevitable for any large sized organizations, security teams face a new wave of threats that are quicker, stealthier, and tougher to stop.

On the contrary, AI is also making defenders sharper, enabling security teams to automatically detect and triage threats as they’re forming, quickly see what attackers are doing, and get a clearer picture of the whole attack surface faster. Utilizing the right context, from the network, endpoint, and identity providers, enables organizations to have success implementing a decisive AI defense.

Recognizing the rapid advancements in AI over the past few years isn’t just about the tech itself, but also about understanding how broadly it’s changing the way we operate, from daily tasks to the way we protect our digital ecosystems.

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Brian Remmington, Chief Software Architect, M-Files

On AI Appreciation Day it is tempting to celebrate what the technology can produce. What deserves more appreciation is who AI reaches and the positive impact it delivers: clearing routine work that slows organizations down and helping people get what they need faster.

We see it across our customer base. For example, at a UK charity, it automates document-heavy admin which means frontline staff can find a wheelchair grant or a local support group while sitting in someone’s home, instead of heading back to a desk to complete the paperwork. For a global pharmaceutical research company, context-enriched information helps optimize client report times by 65 percent, getting research findings into the right hands faster.

The day also provides the opportunity to consider AI ethics, where the conversation often stays fixed on the model itself. In practice, much of what makes enterprise AI ethical is decided upstream, in how information is governed. An AI assistant that respects who is allowed to see information will not overshare a stroke survivor’s personal data. One built on structured, well-described information can explain why it reached a conclusion, rather than asking people to trust a black box. Privacy, accountability and transparency are not abstractions here; they are properties of the information that AI is permitted to use.

That, for us, is what appreciating AI should mean: not celebrating cleverness but investing in the trusted foundation that lets AI serve people quickly and safely.

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Mike Toole, Director of Security and IT, Blumira

AI Appreciation Day is a good excuse to say the quiet part out loud: most of the value we’re seeing from AI has nothing to do with how sophisticated the model is and everything to do with how narrow the problem is. Teams that pick a specific bottleneck, like alert fatigue or slow investigations, and point AI directly at it are seeing real time and cost savings. Teams trying to bolt AI onto everything at once are the ones quietly burning the budget with little to show for it. For lean IT and security teams especially, the winning move isn’t “more AI,” it’s “the right AI, in the right place, solving a problem you actually have.

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Michael Adjei, Director of Systems Engineering at Illumio

AI is transforming cyber defense capabilities, but it is also dramatically expanding the attack surface. It has undoubtedly democratised knowledge and capability and with it brought speed and power both for the attackers but also defenders. Power however, is nothing without control. That is not a reason to slow adoption, but a reason to rethink how we defend.

In an AI-driven world, organisations must assume faster attacks are inevitable and likely to therefore surpass detection. So, plan for containment, because prevention alone is no longer sufficient especially due to constraints of business operational speed. National AI Day is a moment to get excited about where AI is headed, but it should also prompt a broader conversation about how organisations adapt their security strategies to keep up.

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Krishna Sai, CTO at SolarWinds

AI is only as good as the data it can see. In hybrid IT environments where networks, databases, applications, and service desks all operate in silos, that visibility gap is where most AI initiatives stall out. On AI Appreciation Day, we are focused on closing that gap: unifying observability across the full stack so AI-driven automation can surface the right issue, to the right person, before it becomes a problem for the business.

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Chris Yule, Senior Director Threat Research, Sophos Counter Threat Unit

In threat research, we’re seeing AI help accelerate everything from data collection, analysis and correlation to investigation and response. The result isn’t replacing human expertise; it’s acting as a force multiplier to allow experts to spend less time on repetitive work and more time understanding adversary behavior, identifying emerging threats, and improving protections for customers. In cybersecurity, time and expertise are our most valuable resources, and AI is helping us make better use of both.

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Michael Matta, CEO and Co-Founder, Solink

AI has evolved from a promising technology into a practical tool that’s reshaping the way businesses operate. The conversation is no longer about whether AI works — it’s about how it helps organizations achieve measurable outcomes.

For years, businesses have used AI to identify events and generate insights. Today, advances in vision language models and agentic AI are enabling organizations to move beyond detection to understanding context, recommending actions, and helping teams respond faster.

That’s where we see the greatest opportunity. Every business already has an enormous amount of operational data captured through video. When AI can understand that information in context, it helps organizations reduce loss, improve operations, optimize revenue, and make better decisions in real time.

The future isn’t about creating more alerts or more dashboards. It’s about helping people solve real problems and delivering real outcomes.

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Tony Lee, CTO, SimplePractice

In recognition of AI Appreciation Day, it’s important to celebrate how artificial intelligence is supporting clinical workflows for independent mental health clinicians. At the same time, we must recognize that AI should never substitute for the care and decision-making itself. In a clinical setting, AI’s greatest strength is supporting administrative tasks such as note-taking, billing, insurance, and scheduling, giving clinicians more time to spend on patients and direct care delivery. By reducing the burden of these non-clinical activities, AI ultimately reduces clinician burnout and allows practitioners to focus on the human-to-human connection.

In order to use AI in healthcare with confidence, we must establish a foundation of trust, safety, and security by designing purpose-built technology. As these tools become more deeply integrated into workflows, clinicians should always remain in control, with the choice on if and how they want to incorporate AI in clinical care.

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Venu Mallarapu, Chief Transformation & AI Officer, eClinical Solutions

AI is reshaping the life sciences industry, most notably in analyzing increasingly complex clinical trial data, automating repetitive processes, and surfacing insights faster without compromising quality, ultimately reducing cycle times, getting drugs to patients faster. However, real promise is in moving past efficiency gains, where we’re already seeing measurable ROI in, to the next frontier of clinical data intelligence. By combining AI agents with governed data, explainable intelligence, and human oversight within one platform, AI will transition from a standalone tool to an intelligent operating layer, driving tomorrow’s breakthroughs.

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Kate Eisenberg, MD, PhD, FAAFP at Dyna AI

We’re still early in AI development and deployment, which means those of us building and deploying these systems are making value judgments about what counts as good enough. Those judgments will have a lasting impact on how we work and interact with information. In healthcare, they determine whether AI becomes genuinely transformative or one more technology in a long list of attempts to improve care.

Being early also means we can still wrestle with the hard questions rather than discover them later. Does it work for the patients least represented in the training data? What are the failure modes at scale, and are failures obvious to users or silent? Who is responsible when the system drifts? These are governance, transparency, and data infrastructure questions. They are also workforce questions, as our investments in AI literacy today will determine whether the teams using these tools can recognize when something is wrong.

AI can expand access to trusted knowledge, support better clinical decisions, and free physicians to reconnect with their patients. But none of that happens by default. It happens because we decide, deliberately, to build it that way.

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Steve Daheb, Chief Marketing Officer at Flexera

AI Appreciation Day occurs at an important time where organizations are moving beyond isolated pilots and embedding it into every facet of business. AI has quickly become one of the most important innovations, reshaping nearly every corner of business. Yet, as AI’s footprint continues to grow, so does a new challenge: it’s quickly becoming one of the least understood cost centers.

The next phase of AI adoption must be grounded in visibility and cost management. Leaders need to know what they are consuming in order to compare that spend against outcomes. AI productivity and efficiency gains are only valuable if companies can measure whether the gains justify the cost. AI appreciation should be rooted in managing these tools responsibly in order to drive real and measurable business value.

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Yinyin Liu, VP of AI and Analytics at Seismic

On AI Appreciation Day, what we’re truly celebrating is what the technology makes possible for people. In enablement, the real promise of AI is the ability to help every seller, go-to-market leader, and customer-facing team find the right knowledge and content at the exact moment they need it. They can then turn that insight into confident, meaningful action. When AI is embedded thoughtfully into the flow of work, enablement gets supercharged, and every interaction becomes an opportunity to build trust and drive impact. AI brings the competitive advantage, giving teams a real superpower to compete and win. I believe the future of enablement is intelligent, connected, and built around people to stay deeply human. I look forward to seeing what more empowered teams can achieve with it.

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Kevin Spease, Co-Founder and President of ISSE Services

AI can bring real value to organizations, from improving efficiency to helping teams make faster, better-informed decisions. While much of the conversation focuses on innovation, organizations across the Defense Industrial Base also need to consider the security and compliance implications. Generative AI is not just a productivity tool. It introduces new risks around data governance and the protection of sensitive information. If CUI is introduced into the wrong system or handled without the right controls, companies can quickly put their CMMC Level 2 or NIST SP 800-171 compliance at risk. The organizations that get this right will be the ones that embrace AI with discipline, making sure innovation never outpaces their ability to protect, monitor, and secure sensitive information.

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Jaime Guimera Coll, Security and AI Architect at BlueVoyant

Organizations need to balance AI innovation with strong governance by first understanding the risks and putting the right controls in place. That includes defining approved AI tools, reducing Shadow AI, and ensuring leadership can make decisions quickly. This needs to happen without sacrificing data security, ethics, or accountability. The organizations that succeed will be those that move fast while maintaining clear oversight.

At BlueVoyant, AI is used to augment people, not replace them. By operating within a controlled framework, AI helps automate processes and accelerate development, allowing teams to operate at greater speed without compromising quality or security. As AI becomes embedded in day-to-day operations, employees can spend more time on higher-value decisions while AI improves consistency, scale, and efficiency.

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David Sequino, CEO & Co-Founder of OmniTrust

To truly appreciate the perks of AI, we first have to grasp the sheer scale of how it’s changing things. Once AI starts acting on a company’s behalf, old security playbooks go out the window. You can’t protect a fast-moving digital agent using the same methods designed for human employees. When AI is operating instantly behind the scenes, we have to verify its every move—ultimately, we can only enjoy the good of AI if we protect against the bad.

AI Appreciation Day is about celebrating innovation, but it is also a reminder to build a secure foundation for that progress.

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Craig Birch, Principal Technologist, Cayosoft

AI is moving incredibly fast, and every leadership team feels pressure to keep up. But in the enterprise, speed without control creates real risk. The organizations that will get AI right are the ones that understand exactly what data their AI tools can access, who has permission to use them, how changes are governed and where human accountability still sits. Security and ethics cannot be treated like paperwork at the end of the process. They need to be part of the foundation. That does not mean slowing innovation down. It means giving IT and security teams the confidence to say yes to AI in a way the business can actually trust.

Human-AI collaboration will give experienced teams more time to focus on the work that actually needs their judgment. AI can help cut through noise, summarize information, identify patterns and speed up repetitive analysis. That matters because IT and security teams are already stretched thin and need better leverage in their day-to-day work. But context still belongs to people. Understanding business impact, deciding what level of risk is acceptable and knowing when something does not look right are human responsibilities. The best use of AI is to help teams move faster while keeping people close to the decisions that matter most.

One of the biggest misconceptions is that AI is simply another software deployment. In reality, AI introduces a new layer of interaction across data, identities, permissions, workflows, and decision-making. Leaders are right to focus on productivity gains, but long-term success depends on answering a few critical questions first: What information can AI access? What actions can it influence? How are outputs validated? What safeguards exist when something goes wrong?

Another misconception is that AI can operate independently of human expertise. In enterprise environments, trust is built through visibility, governance, and human oversight. The organizations realizing the greatest value from AI are not the ones chasing every new capability. They are the ones creating the right foundation for AI to operate safely, transparently, and within clearly defined boundaries. Successful AI adoption is not just about making work faster. It is about making organizations more informed, more resilient, and better equipped to make decisions with confidence.

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Casey Marks, COO, ISC2

Cybersecurity has always depended on people learning by doing, but AI is changing what that looks like. As we recognize AI Appreciation Day, we need to remember that workforce development has to keep pace with the way cybersecurity roles are evolving.

AI may automate more routine work, but it has not reduced the need for foundational technical skills. Employers still need professionals who can challenge AI-generated outputs, validate recommendations and step in when the technology is wrong or incomplete. When AI misses context or introduces risk, human judgment still matters, and accessible, skills-based programs can help develop that oversight earlier in a cybersecurity professional’s career.

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Michael Gray, Chief Technology Officer at Thrive

AI Appreciation Day is a good opportunity to recognize how quickly artificial intelligence is evolving, but it is also a reminder that successful AI adoption is not about moving as fast as possible. The organizations seeing the greatest return are taking a “crawl, walk, run” approach by building foundational knowledge, helping employees understand and trust the technology, and introducing AI with a clear purpose. Buying the latest tools is easy. Building a workforce that knows how to use them effectively is what creates lasting value.

Getting the foundation right changes the AI conversation altogether. Instead of asking, “How can we use AI?” the better question is, “What business problem are we trying to solve?” If you don’t know what problem you’re trying to solve, AI probably isn’t the place to start. Once you understand the business need, AI can help accelerate analysis, uncover new ideas, and improve execution. However, not every AI use case is worth pursuing. Without a clear purpose, organizations can end up taking on more work than they solve. AI should strengthen what already makes your organization unique, not replace the expertise that differentiates it.

As AI becomes part of more business processes, human judgment becomes even more important. AI can dramatically increase productivity, but it’s humans that remain responsible for validating outputs, providing context, and making the final decisions. Organizations that invest in education, governance, and thoughtful adoption today will be best positioned to scale AI responsibly and realize its long-term potential.

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Abhas Ricky, Chief Business Officer and GM and Applied AI, Cloudera

The hard part of enterprise AI is no longer the pilot — it is everything that comes after. MIT’s NANDA initiative found that 95% of enterprise generative-AI programs have produced no measurable P&L impact despite $30–40 billion in spend, and that 42% of firms abandoned most of their AI initiatives in 2025, up from 17% a year earlier. The implication is blunt: access to frontier models is now commoditized, and the durable advantage belongs to whoever can sustain, govern, and defend AI once it leaves the demo.

The economics of enterprise AI are being rewritten because the bottleneck has moved from access to operation. To sustain it, measure useful work and place workloads where they pay. To govern it, enforce common standards, policy controls, traceability, and orchestration. To defend it, apply selective oversight, hard safeguards, and systems engineered for failure as carefully as for success.

The model layer is commoditizing — frontier capability is now a purchase, not a moat. What compounds is the infrastructure, the governance, and the trust wrapped around it. The 95% that stalled treated AI as something to acquire; the 5% that broke through treated it as something to operate. That gap is the entire distance between a pilot and production — and, increasingly, between the enterprises that will lead the next decade and the ones explaining to their boards why the demo never paid for itself.

Trust, not tokens, is the unit of account now. Build for it.

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Cobus Greyling, Chief Evangelist at Kore.ai

AI Appreciation Day isn’t really about celebrating magic. It’s about recognizing how far we’ve come, from stateless prompts and a few clever examples to systems we can genuinely delegate work to.

I’ve had the chance to watch this field evolve through every phase: prompt engineering, RAG, agents, orchestration and now harness engineering. What I appreciate most isn’t any single breakthrough. It’s the maturity we’ve built around the technology: better control, stronger guardrails and a shared language for designing these systems, because the concepts have become real, not theoretical.

For me, that’s what AI Appreciation Day is really about. Not the hype or the fear, but the steady engineering progress that’s turning something remarkably capable into something people can actually trust and use.

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Jerry Carter, Chief Technology Officer at Nasuni

AI Appreciation Day is a good time to pause and consider what truly will drive AI success within your organization. While enterprises are moving quickly to deploy AI, many are still struggling to achieve the outcomes they expected. In fact, recent data shows that while 97% of enterprises have deployed or are piloting AI, only 43% of AI projects are achieving their intended objectives, and nearly half say AI initiatives have exposed data quality and governance issues. The data points to a new conversation: that AI success depends on how well organizations manage and prepare their data. Too many enterprises are still relying on outdated approaches to unstructured data management, limiting their ability to unlock the full value of their proprietary operational data that powers meaningful AI.

At the same time, AI is reshaping far more than just applications and workflows. It is also redefining the infrastructure assumptions enterprise IT has relied on for years. As demand for AI infrastructure continues to place pressure on the global memory and storage supply chain, hardware procurement is becoming less predictable, forcing organizations to rethink operating models built around stable refresh cycles and long-term capital planning. The economics of enterprise infrastructure are changing, making it more difficult for IT leaders to plan, scale, and control the data environments their AI initiatives depend on.

It’s worth noting that AI is revealing these problems, not creating them. At a time of rising hardware costs, supply chain uncertainty, and growing infrastructure complexity, getting your data house in order is the ultimate no-regrets move. To achieve long-term value from AI, organizations need to reduce dependence on unpredictable hardware cycles and ensure their unstructured data is accessible, governed, and ready for both employees and the AI that supports them.

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Michael Curry, President of Data Modernization at Rocket Software

AI is creating new opportunities for organizations to extract greater value from the systems and data they already rely on every day. For enterprises, this includes the mainframe, which continues to support many of the world’s mission-critical infrastructure while housing decades of high-value operational data. As organizations expand AI initiatives, securely connecting these environments to modern AI workflows will play an important role in improving decision-making, accelerating innovation, and delivering business outcomes.

As AI Appreciation Day highlights the growing impact of AI across industries, it’s also a reminder that long-term success depends on access to trusted enterprise data. Realizing that value depends on making trusted enterprise data accessible without sacrificing governance, security, or reliability. Advancements in AI automation and real-time data integration are helping organizations extend the value of their core systems by making mainframe data available across hybrid environments. AI is giving organizations new ways to leverage their existing information, and businesses that combine trusted data with modern AI capabilities will be well-positioned to scale their initiatives with confidence.

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Husnain Bajwa (HB), SVP Product – Risk Solutions, SEON

AI’s real value in fraud prevention isn’t the model, it’s what you feed it. The teams winning are the ones grounding AI in rich, trusted signal data, device history, behavioral patterns and network context, so the model can tell a fraudster from a legitimate customer.

That’s only effective if teams aren’t locked into one vendor’s black box. AI should adapt to how a fraud team already works, not the other way around. Give analysts the freedom to choose their models, pair that with data they can actually trust, and you get detection that improves as fast as the threats do. On AI Appreciation Day, that’s worth remembering: the technology only appreciates in value when the humans running it stay in control.

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Ben Potter, OSS and Developer Relations at Coder

AI is changing the workplace, but its greatest value is not simply helping people produce more. It is expanding what individuals can contribute. We are seeing employees use AI to work beyond traditional role boundaries: product managers can build prototypes, technical teams can improve documentation, and marketers can make meaningful contributions to digital experiences.

As creating becomes easier, distinctly human qualities become even more important. Judgment, curiosity, communication and the ability to recognize what is worth building will increasingly separate meaningful work from disposable output. The goal should not be to use AI to get 90 percent of the way to more projects. It should be to help people understand unfamiliar problems, collaborate across disciplines and deliver work that is adopted, improved and sustained.

On AI Appreciation Day, we should celebrate AI not as a replacement for human expertise, but as a tool that gives thoughtful, motivated people more ways to turn their ideas into lasting impact.

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Srini Srinivasan, founder and CTO at Aerospike

Agentic AI is moving from generating content to executing transactions. A seat double-booked, a payment that clears twice or a trade at the wrong price can create an irreversible outcome. Irreversible outcomes require strong consistency.

Most of the infrastructure being pitched for agentic workloads wasn’t built for that. When one workflow fans out across hundreds of operations, a component that’s slow just 1% of the time can delay more than half of all interactions. The moment agents start executing transactions, the forgiving era is over.

The first time an agent executes a transaction on infrastructure that wasn’t built for it, the problem won’t be the model. It will be the system underneath it.

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Michael Centrella, Head of Public Policy at SecurityScorecard

Artificial Intelligence Appreciation Day is a good moment to recognize the progress AI is enabling across the enterprise. In cybersecurity, that progress is changing not only how defenders work, but also how organizations think about risk across their broader digital ecosystem.

Agentic AI is quickly becoming an extension of the vendor landscape. That creates new questions about what these tools can access, what actions they can take, and how organizations govern non-deterministic systems. But it also creates real opportunity. AI agents can help security teams take on time-consuming, repetitive work and close knowledge gaps so people can focus on higher-skill, higher-complexity problems.

That balance is especially important in a post-Mythos world. AI is compressing the time it takes to discover vulnerabilities and the time it takes to compromise systems. Defenders will need to apply AI just as thoughtfully, using it to connect signals across vendors, partners, software providers, and digital supply chains before small exposures become larger incidents.

National AI Day is a reminder that responsible AI is not only about innovation. It is about using powerful technology to build a more secure, transparent, and resilient digital ecosystem.

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John Bruggeman, vCISO at CBTS

AI Appreciation Day is a timely opportunity to recognize the meaningful progress organizations are making with AI and it’s what this day is for, just like National Ice Cream day! Across industries, teams are using AI for many amazing things, in cybersecurity AI is being used to improve network traffic (traffic analysis), pinpoint the big security incidents from the flurry of issues (accelerate security triage), identify the real threats in the log files (support better decision-making), and create stronger experiences for employees and customers.

The next phase is making this kind of AI value dependable. AI assistance is most effective when it is supported by clear governance, valid and reliable data, strong identity controls, and visibility into how tools are being used across the business. That foundation allows leaders to take appropriate risks and avoid disasters, while moving as quickly as the market demands.

For many organizations, the challenge is that AI adoption is advancing faster than the operating model around it. Smart organizations are enabling employees to test tools, build better, more efficient workflows, and create practical use cases in real time, in a safe and secure manner. That momentum can be a positive sign, but only if the organization has visibility into what is happening and can guide it responsibly. Without approved, practical pathways for AI use, this kind of employee experimentation can quickly move into the shadows in ways that expose an organization to significant risk.

For executives, the measure of AI maturity should not be how many pilots are underway. It should be whether the organization understands where AI is interacting with corporate data, business and customer workflows, user and administrative identities, and real-time decision making. Companies that can answer those questions and act on them will be best positioned to move from experimentation to sustained business value.

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Joseph Perry, Cybersecurity Researcher and Advanced Services Lead at Arcova

AI Appreciation Day is an unusual thing; a day dedicated to a commercial technology. We don’t treat any other technology in this way, because no other technology has done quite so much to capture the human imagination and incite our dreams of the future. What’s important on this day is not just recognizing the value and potential of this technology but, more importantly, the ways in which it can be of real benefit. And the ways it can’t.

The real test of AI maturity is not how many tools a company has deployed or how often employees use them. It is whether leaders can clearly explain what problem the technology solves, what it costs at scale, what data it can access and where human judgment is still required. Deploying AI is not proof that transformation has taken place. No matter what the hype might claim, no technology, not even the most sophisticated, is inherently transformational. Transformation is the result of technological potential applied the solution of human problems.

That is why implementation should not begin with a mandate to use more AI. It should begin with a specific process that is slow, repetitive or difficult to scale, followed by a clear assessment of where and whether AI can improve the outcome. From there, organizations need to determine how the technology will fit into existing workflows, who is accountable for the result and what happens when the system gets something wrong. Without that foundation, AI can create more complexity than capacity.

Leaders also need to look beyond the tools they choose to deploy. Employees may already be using public models, vendors may add AI features with little notice and threat actors are using the same technology to increase the speed and volume of their activity. An organization’s AI posture is shaped not only by its own decisions, but also by how AI enters the business through people, partners and adversaries.

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Chance Caldwell, Senior Director of PDC Threat Services at Cofense

AI Appreciation Day is a useful moment to celebrate the progress AI has enabled, but for cybersecurity leaders, it should also prompt a more practical question: how do we defend against a technology that is now accelerating both innovation and risk?

In email security, AI has become a force multiplier for defenders and attackers alike. Security teams can use AI to analyze patterns, automate workflows, and scale detection. At the same time, threat actors are using AI to create more polished, personalized, and convincing phishing campaigns. The old tells, such as awkward grammar, generic messaging, or obvious errors, are becoming less reliable as AI helps attackers make malicious emails look routine, relevant, and business-like. AI is also enabling attackers to generate and launch these campaigns at greater speed and scale, increasing the overall volume of threats organizations must now contend with.

This creates a real challenge for defenders. AI-based security tools are powerful, but attackers are constantly testing new tactics that may not match known patterns. From brand impersonation and credential phishing to QR codes and trusted-service abuse, these campaigns are designed to evade traditional gateways and reach users directly.

The organizations best positioned for this next phase will be those that combine AI-driven defense with human intelligence. Employees who are trained to recognize and report suspicious activity are not the weak link; they are a critical signal source. National AI Day should remind security leaders that the future of cyber defense is not AI replacing people, but AI strengthened by people.

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Ganesh Padmanabhan, CEO and co-founder of Autonomize AI

When people talk about appreciating AI, they often focus on what the technology can do. I think we should appreciate it for something much more important: its ability to give people their time and expertise back. In healthcare, some of our most experienced clinicians spend huge portions of their day navigating administrative processes instead of caring for patients. AI gives us an opportunity to change that. Not by replacing clinical judgment, but by making that expertise available more quickly, more consistently, and at a far greater scale. If AI allows a nurse to spend more time with patients instead of paperwork, or helps someone access treatment days or weeks sooner, that’s something worth celebrating.

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Anthony Verna, Senior Vice President and General Manager, DTECH Mission Solutions, Cubic Defense

AI is changing how defense organizations improve adaptability and generate decision advantage in complex operational environments. Modern operations require mission teams to access, process and act on mission-critical data locally without assuming constant connectivity.

The next phase of AI adoption will center on greater speed, autonomy and data operationalization at the tactical edge. We will continue to see accelerated adoption of AI-enabled edge compute, software-defined communications and integrated mission systems that support distributed operations, faster coordination, resilient communications and rapid intelligence processing.

AI is helping reshape how operators turn mission-critical data into operational advantage through faster and more informed decisions. That role will continue to grow as defense organizations prioritize technologies that improve mission effectiveness, wherever the fight goes.

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Pavel Bykov, CEO at IP Fabric

For the past few years, organizations have focused on capability: which models perform best, which providers innovate fastest, and how quickly they can deploy AI. But now, the discussion is shifting more toward AI sovereignty and control.

As AI becomes more deeply embedded in business operations, enterprise leaders are beginning to ask a new, more challenging set of questions: Where is AI actually running? What data can it access? Who controls the infrastructure?

These questions are driving the push towards local LLMs, air-gapped operations, and other approaches that give organizations greater control over how AI interacts with enterprise data.

AI’s long-term value won’t be determined solely by access to the most powerful models. Instead, it will increasingly depend on an organization’s ability to stay flexible and adopt new AI capabilities while retaining control over their data.

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Arnab Mishra, CEO of Xactly

Today, I want to applaud the people who direct and orchestrate agentic AI. When we look at what drives real change in an organization, the AI itself is a small piece of the equation. The bigger share comes from the data and systems around it, and the biggest share comes from people, process, and how well a business manages change. That’s why I see AI as an amplifier for revenue leaders and teams.

My focus is to help organizations move from simply managing performance to orchestrating it by directing a fleet of humans and AI agents toward one outcome. On a day meant to appreciate AI, the best way we know how is to keep building tools that make the humans steering it even more effective.

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Pete Luban, Field CISO at AttackIQ

AI is giving security teams faster ways to analyze risk, but faster analysis does not always lead to better decisions. Most organizations already have more findings than they can realistically address. The harder question is which exposures deserve attention first and whether current defenses can actually stop them.

By connecting threat intelligence with asset context and security control performance, AI can help narrow that field. It can surface the weaknesses most likely to create a real attack path and give teams a clearer basis for what to test next. That brings more focus to each stage of a CTEM program.

The recommendations still need proof. Teams should be able to validate whether an exposure is exploitable, take corrective action, and confirm that remediation worked. AI can accelerate the process, but evidence is what turns speed into measurable risk reduction.

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Ross Filipek, CISO at Corsica Technologies

AI adoption is moving quickly across the midmarket, often without a formal strategy behind it. Employees are already using public tools for customer support, content creation, analysis, and everyday problem solving. That creates immediate questions about sensitive data, access, and accountability.

Most midmarket businesses do not have dedicated teams for AI governance, cybersecurity, and compliance. They need practical support that connects those responsibilities. Managed service providers can help evaluate tools, establish clear usage policies, and monitor how AI is being used across the organization.

The goal should not be to block experimentation or chase every new platform. Businesses need a safe path to adoption. That starts with visibility, sensible controls, and a clear understanding of where company data is going.

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Isaac Evans, founder and CEO at Semgrep

Models like Mythos and GPT-5.6 are raising expectations for what AI can do in application security. They can reason through complex code paths, assess whether a weakness is exploitable, and surface issues that older approaches may miss.

The bigger lesson is that model capability alone is not enough. Even a strong model can overlook important parts of a repository without the right context, code selection, and analysis around it. If these models are the new intelligence layer, the harness for the model is the corporate organization. Just as an individual researcher depends on a team, AI-powered vulnerability researchers will need a supporting system that directs their work and checks their conclusions.

Security teams should focus on building workflows that give these models the right code, validate their findings, and explain why an issue matters. The exact structure is still unproven, but early results suggest these systems could become valuable members of the application security team rather than standalone replacements for it.

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Joshua Roback, Principal Security Solution Architect at Swimlane

AI is changing cybersecurity on both sides of the equation. Attackers are using it to move faster and scale their operations, while defenders are still burdened by fragmented tools, overwhelming alert volumes, and repetitive manual work. The real promise of AI in security isn’t simply producing more information. It’s turning that information into action.

Swimlane’s approach combines expert AI agents with automation to help security teams investigate threats, coordinate decisions, and execute response steps with human oversight and clear guardrails. Within Swimlane’s own SOC, this approach cut mean time to resolution by 51%, reducing it from 18 minutes to under nine minutes. This gives analysts more time to apply judgment where it matters most, while routine work happens at machine speed. As threats become more autonomous, cybersecurity needs AI that can move beyond recommendations and help defenders respond with confidence.

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Piyush Sharma, co-founder and CEO at Tuskira

Security teams do not have a shortage of information. They have vulnerability data, threat intelligence, alerts, and asset context spread across disconnected systems. The harder problem is turning those signals into one clear decision.

Agentic AI can help connect that picture. It can examine a new threat, determine whether the organization is exposed, trace a likely attack path, and assess whether existing controls would stop it. That context can then move directly into investigation and response.

This gives threat intelligence a more active role in security operations. Instead of remaining a feed that analysts must interpret on their own, it can help shape what the team does next. The value is not another AI-generated summary. It is a system that explains why a threat matters in a specific environment.

When AI connects exposure, intelligence, and response, security teams can act earlier and spend less time sorting through signals that do not require attention.

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Jan Karstens, Chief Technology Officer at Avantra

AI Appreciation Day is a good moment to talk about what’s actually impressive right now, and where it’s heading. It is the speed and scale of pattern detection: watching an entire SAP landscape in real time, across thousands of signals at once, catching anomalies no human team could track manually. That’s already happening.

Detection is the current chapter, not the ending. The direction we’re heading is AI agents that orchestrate operations end-to-end: diagnosing root cause, coordinating response, and acting, not just alerting. That also changes how operations get staffed: work that once had to be spread across large, distributed teams can sit closer to the business again, run by a smaller core team working alongside AI.

Handing an agent authority to act on production systems, not just flag issues, is a different level of trust. It has to be earned by seeing exactly what the agent did, why, and where its authority ends. That’s the honest conversation AI Appreciation Day should be having.

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David Feller, CTO, Spectra Logic

AI Appreciation Day is a good moment to recognize not only what AI can do, but what it demands from the infrastructure behind it. AI is accelerating data growth at a pace many organizations are not prepared for, creating massive volumes of training data, model outputs, video, analytics and machine-generated information that may need to be retained for years or even decades.

The challenge is that not all of this data belongs on, or due to power and supply constraints is viable to store on, always-on, high-cost flash or disk infrastructure. As AI pipelines mature, organizations need to think more strategically about where data lives, how often it needs to be accessed, and how it can be preserved securely, sustainably and cost-effectively over time without sacrificing the ability to restore and retrain models.

This is where tape and modern archival architectures become central to AI success. When integrated into object-based and hybrid storage environments, tape can provide a durable, energy-efficient and cost-predictable foundation for long-term retention, while also supporting offline protection against ransomware and other cyber threats, with restore rates that can easily keep up with demanding retrain workflows.

The organizations that will get the most value from AI are those that heavily invest in GPU farms and keep them busy 24/7 by properly balancing surrounding storage across appropriate tiers that include tape and take advantage of new technologies to optimize their AI investment. It is likely that one of the oldest storage technologies will have the biggest impact in advancing the AI revolution.

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Yoram Novick, CEO, Zadara

In recognition of AI Appreciation Day, it is important to look not only at what AI can do, but at the infrastructure choices that will determine who can use it securely, sustainably and on their own terms.

AI is quickly becoming a critical capability for enterprises, governments, service providers, and entire economies. But as adoption moves from experimentation to production, organizations are realizing that access to AI is not just a technology question. It is also a question of sovereignty, resilience, and operational control. Recent geopolitical developments have made clear that reliance on foreign-controlled AI platforms can create real business continuity risks, especially for organizations managing sensitive, regulated, or strategically important data.

The goal is not isolation or moving away from global innovation. The goal is strategic optionality. Organizations need the ability to run AI workloads locally, keep data within required jurisdictions, control who governs access to critical services and scale infrastructure in a way that is economically sustainable.

This is especially important as AI workloads become more distributed, latency-sensitive, and unpredictable. Centralized public cloud environments will remain important, but they cannot support every use case. The next phase of AI will require flexible, localized and multi-tenant infrastructure models that combine cloud-like simplicity and consumption of economics with data locality, strong tenant isolation and greater control.

What makes AI so powerful is its ability to help organizations act faster and make better decisions. To fully realize that potential, AI must be built on infrastructure that gives organizations choice, protects sovereignty and allows innovation to scale without creating new dependencies.

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Chris McHenry, Chief Product Officer, Aviatrix

As we mark National AI Day 2026, the industry is celebrating unprecedented breakthroughs in automation and productivity — but we have to confront the architectural reality: AI has fundamentally broken the traditional cybersecurity timeline. We are looking at a threat landscape where the median time-to-exploit has contracted to negative seven days, meaning adversaries are weaponizing zero-days a full week before public disclosure or patch availability. Pair that speed with the fact that 82% of intrusions now exploit valid credentials rather than software vulnerabilities, and the core structural flaw becomes obvious: our legacy defense frameworks are architecturally blind.

If an AI-driven attack utilizes authenticated credentials and generates zero anomalous telemetry data, your identity provider will happily wave it through. Identity was built exclusively to authenticate access at the front door, not to police or contain malicious behavior at the data plane. Once an automated adversary or compromised agent gains a foothold, it moves laterally at the transit layer, exploiting flat multi-cloud architectures to expand its footprint across VPCs and VNets in milliseconds.

National AI Day 2026 shouldn’t just be a celebration of technology. It needs to be a forcing function for CISOs to audit their cloud blast radiuses. If you don’t have inline, network-layer containment and micro-segmentation running between workloads, a single compromised identity can jeopardize your entire multi-cloud estate before an SOC analyst can even open a ticket. We have to stop trying to solve a network routing and containment problem with an identity tool. Security leaders who implement structural containment frameworks today will spend National AI Day 2027 scaling safely, rather than managing the fallout of an automated, cross-cloud breach.

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Arvind Parthasarathi, CEO and founder, CYGNVS

AI is now being embedded in customer service, software development, financial operations, healthcare and countless other business-critical processes, helping organizations work faster and make better decisions. But what happens when the AI goes wrong? Have organizations considered their response?

Gartner research found that sixty-one percent of senior professionals report observing AI agent automation deployed through approved enterprise software, while 59% report evidence of, or strong suspicion of, unsanctioned, employee-driven AI agents operating outside governed pathways. The OECD AI Incidents and Hazards Monitor recorded 596 AI incidents in January 2026 alone, up 200% year-over-year. AI incidents include model bias violating laws and regulations, hallucinations creating legal and customer exposure, data leakage triggering GDPR and HIPAA violations, and autonomous agents pursuing objectives in unintended or destructive ways.

When an AI agent misbehaves, organizations need to activate a cross-functional machinery spanning IT, security, legal, executives, as well as external providers like law firms. Without a playbook of what to do or a response platform to do it in, organizations reach for email and internal messaging, exactly the systems that may be influenced by or accessible to the AI under investigation.

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Corey Thuen, CEO and co-founder, Gravwell

I’m going into AI Appreciation Day with a healthy dose of skepticism, which I think we all need as we learn to coexist with and utilize it responsibly.

One of the biggest misconceptions about AI is that it’s changing how computers work…but it actually isn’t. Computers still work the way they always have. What’s changed is the interface. For the first time, users are interacting with systems that don’t always produce the same output from the same input.

We’re no longer just exploiting software. We’re social engineering computers; instead of convincing a person to ignore the rules, you’re convincing an AI model to ignore its guardrails. That’s a new attack surface for machines and one that’s evolving extremely fast.

AI deserves credit for helping security researchers uncover vulnerabilities at scale, but it also creates a dangerous misconception. Too many people assume that because AI’s writing sounds pretty good, it understands all situational context and knows what it’s doing. It doesn’t, plain and simple. It’s predicting language, not reasoning about security, policy or intent. That’s exactly why prompt injection works here: attackers manipulate AI models using language, because those models can’t distinguish between a legitimate instruction and a cleverly crafted one with malicious intent. Security teams that understand those limitations and balance AI’s weaknesses with human intelligence will build stronger defenses than those who assume it’s smarter than it really is.

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Rajan Sethuraman, CEO, LatentView Analytics

We’re officially in the next phase of AI where the conversation has shifted away from who has access to it and toward who is actively creating measurable business value with it. That shift is being driven by people: teams building responsible AI systems, leaders putting the right governance in place, and employees learning new ways of working and applying AI to real business challenges. Organizations pairing AI with trusted data, strong governance and human expertise are moving faster, making better decisions and building lasting competitive advantage. At the end of the day, AI can accelerate insight, but it’s people who provide the context, judgment and accountability that turn technology into real business outcomes.

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Jon Lucas, Co-Founder and Director at Hyve Managed Hosting

AI Appreciation Day is a timely reminder that while attention often focuses on the latest AI models and applications, long-term success depends on the infrastructure supporting them. As organizations move AI from experimentation into production, they need infrastructure that can securely and reliably support increasingly demanding workloads while maintaining performance, availability, and operational control.

As AI becomes embedded across everyday business operations, infrastructure decisions are becoming strategic business decisions. Organizations need to consider where their AI workloads are hosted and whether those environments provide the control, governance, and regulatory alignment necessary to protect the proprietary business information, intellectual property, and customer data AI systems process.

Cost is also becoming a deciding factor. For many organizations, it is the price of AI infrastructure – not the technology itself – that stands between a promising pilot and production at scale. The growing availability of right-sized, entry-level hosting options is opening AI up to businesses beyond the largest enterprises, and that accessibility will shape who benefits as adoption accelerates. Organizations that establish flexible, sovereign, and cost-efficient foundations now will be best positioned to scale AI with confidence.

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Ken Barth, CEO, Catalogic Software

The Cost of Adopting AI Without a Plan

Most companies that report disappointing results from AI did not pick bad tools. They skipped the step where someone decides what the tool is for. Licenses spread across teams, and six months later finance asks what the spend produced. Nobody can answer, because nobody defined the question.

The companies getting durable value work backward from a specific task. A support team measures how long agents spend summarizing tickets, deploys a model to draft those summaries, and tracks whether resolution time drops. The scope is narrow, the baseline is measured, and the result is checkable. This approach also surfaces AI’s real boundary quickly: models compress information well (summaries, extractions, first drafts) and handle judgment calls poorly when the context sits outside their view.

Responsible adoption comes down to operating decisions someone has to own. Who reviews output before it reaches a customer? What data leaves the building in prompts? When the model is wrong, how does anyone find out? The answers are boring: review steps, approved-tool lists, retention terms, and spot checks. Boring is the point. A company with those answers can expand AI use with confidence; a company without them accumulates risk at the speed of adoption.

Restraint does not mean waiting. It means deploying first where errors are cheap and verifiable, then expanding as verification improves. The tools are the easy part. The decisions were always the work.

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Luis Blando, Chief Product and Technology Officer at OutSystems

Despite the hype around fully autonomous systems, most enterprises today are using AI in far more practical ways, and that’s where the real value is showing up. The strongest use cases cluster around three areas: processing documents that would otherwise require human review, handling high-volume transactional work, such as mapping incoming orders, and supporting decision-making by making sense of unstructured data. In these scenarios, AI excels at summarizing complexity and offering recommendations, but not at making final calls – unless organizations are willing to accept mistakes.

Used poorly, AI behaves like a team of interns: fast and prolific, but still requiring oversight and double-checking. Used well, it becomes a force multiplier for simpler applications, especially when fueled with the right data and guardrails. Trust doesn’t come from autonomy alone. It comes from knowing when AI should assist, when humans should decide, and how the two work together.

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Kamesh Tumsi, CPO at Smarsh

AI Appreciation Day is an opportunity to recognize both the promise of the technology and the responsibility that comes with putting it to work across the enterprise. Smarsh’s recent 2026 Enterprise AI Trends Study shows that while 55% of enterprises are actively deploying AI, only 26% believe their governance frameworks are fully keeping pace. Proof of that gap should push leaders to focus on building the trusted data, visibility and accountability needed to innovate responsibly.

The organizations that lead in the AI era treat governance as a strategic capability. When AI is supported by trusted communications data, human judgment, and responsible oversight, it can help enterprises move from reactive risk management to proactive intelligence.

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Raj Mallempati, CEO, BlueFlag Security

While still a new phenomenon, National AI Day could also fit under another National Day recognized in September – National Identity Day. This is because from an IT perspective, identity is no longer a human concept. From APIs to AI, identity isn’t about humanity, it’s about access and behavior. And the reality is that AI agents have fast become non-human identities with superhuman capabilities. No where more pressing is this, than in the Software Development Lifecycle. In the broader IT ecosystem, the underpinnings and attention already exist to expand control over agents, but in the SDLC, there is a concerning lack of visibility and governance over the relationships between code, configuration and behavior. With exploitation of the software supply chain already in the news far too often, and with technologies like Mythos set to exponentially increase the risks, oversight in the SDLC is an imperative too urgent to ignore.

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Derek Slager, co-founder and co-CEO of Amperity

Before we celebrate what AI can do, enterprises should take a harder look at what they are asking it to act on. Most enterprise AI is still operating on incomplete customer information. A support ticket sits in one system, a purchase in another, and an app session somewhere else. AI does not resolve those gaps on its own. It acts on them. When a model makes decisions from a fragmented view of the customer, it can be confidently wrong at a scale and speed no human team could match.

Identity resolution can no longer be treated as a data hygiene exercise. As AI agents take on more customer-facing decisions, from recommendations and service escalation to churn prevention and next-best actions, the identity and context behind those decisions become mission-critical infrastructure. The enterprises that generate real returns from AI will not necessarily be the ones with the most models in production. They will be the ones who give those models a current, connected, and responsibly governed understanding of the customer.

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Junchen Jiang, CEO of Tensormesh

This AI Appreciation Day, while the industry celebrates flashy new models, let’s appreciate the plumbing that keeps them running.

Enterprises face a deceptively costly problem: Every inference request recomputes the same inputs, including system prompts, conversation history and tool definitions, from scratch at full price. In multi-step agentic workflows, that redundant GPU burn scales fast.

The unsung hero is KV caching. Without it, every long AI conversation would require recomputing the entire history from zero. It’s the part of the stack nobody notices until it’s missing and costs skyrocket.

People appreciate AI answers. What makes those answers affordable is the infrastructure underneath. That’s the difference between AI as an expensive novelty and AI as a utility anyone can benefit from.

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Mahdi Sharif, VP of Platform & AI Product of Conga

AI Appreciation Day is a good reminder of how quickly AI has evolved from an emerging technology into a business-critical capability. As enterprises move beyond experimentation and begin scaling AI, the biggest challenge is no longer building smarter models or even connecting AI to data – it’s turning that data into knowledge. LLMs have made access to data easier than ever, but access isn’t understanding: a doctor and a first-year student can both Google the same symptoms, yet only one actually knows what they mean. That’s the gap enterprises need to close next – building the knowledge graphs that connect intelligence across the commerce lifecycle, so sales, finance, legal, and operations aren’t just capturing data, but can use it to enhance the customer experience and finally say, ‘we know you and what you need.

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Juho Sarvikas, CEO at Inseego

On AI Appreciation Day, it’s worth asking a different question: not what AI can do, but what it’s running on. Every enterprise pushing AI into production is learning that raw speed isn’t the bottleneck anymore.

What matters is whether the network holds up: low latency, consistent performance, and the flexibility to support AI workloads that don’t tolerate downtime. Private 5G and fixed wireless are proving to be a big part of that answer, and 6G will push it further, building intelligence into the network itself rather than just running on top of it.

The enterprises that treat connectivity as core AI infrastructure, not a background utility, are the ones who’ll actually see the returns everyone’s chasing.

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Dave Russell, SVP and Head of Strategy at Veeam Software

On AI Appreciation Day, it’s worth acknowledging how far agentic AI has come. Today’s agents can move data, change configurations and make decisions at machine speed. Work that once took teams days can now happen in seconds.

But that speed also changes the risk profile. Too often, agents are granted more access than they need, and over-privileged agents are becoming a real vulnerability. Trust can’t be based on assumptions anymore—it has to be based on context & verification: knowing who or what is touching your data, what they’re doing, and why, while confirming the identity behind every action. And when something does go wrong, the difference between a bad day and a bad year is whether you can roll back to a known-clean state in minutes, not weeks.

In the AI era, resilience and verification aren’t add-ons – they are prerequisites.

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Adam Kaufman, Director of Marketing, Efficient Computer

What I appreciate most about AI this year is what it did not do. It did not replace an engineer at Efficient Computer, and we never asked it to. Engineering time is the scarcest resource a technology company has, and every hour our engineers spend building an internal tool or a support utility is an hour not spent on the processor our customers are betting on. AI let us divide the work honestly: we point our people at the problems AI cannot solve, and we use AI to build the things it can.

That division built Efficient Labs, our collection of free, browser-based tools for engineers working with the Electron E1 general-purpose processor. I am not an engineer. I described each tool to an AI, refined it in conversation, and shipped it in days: a board viewer that replaces datasheet archaeology, a pin mapper that flags conflicts as you drag, an energy profiler that reports what a region of code costs in millijoules. Our engineering team then played with every tool against real hardware to make sure it worked and the answers were right, a validation that cost them hours instead of the weeks it would have taken to build from scratch. On one customer call, someone suggested the board viewer should explain how to wire up a peripheral. The feature was live before the call ended.

The result is tools delivered at a fraction of the time and cost, to customers who need them sooner than any sprint could arrive. AI is not always right, and people still decide what ships. But we build processors on the belief that efficiency changes what is possible, and this is the year that efficiency reached our scarcest resource: our engineers’ time.

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Sergio Farache, Chief Strategy and Technology Officer, TD SYNNEX

Since ChatGPT launched an AI revolution four years ago, AI adoption has shifted from experimentation to implementation at scale. As agentic AI’s next phase takes it beyond the enterprise, the debate is no longer whether organizations should use AI – it’s how well they’re using it.

That “how well” is the next step in AI’s maturity curve. AI use skyrocketed this past year, and the smartest companies are now tying AI spend to measurable ROI to closely manage its cost. For example, FinOps — the discipline originally for managing cloud spend — is quickly becoming the mark of a company that’s figured out how to make AI pay for itself.

The opportunity lies in rethinking where AI runs, beyond just managing its costs, as rising token costs are pushing more workloads to the edge. The cloud won’t run everything. At TD SYNNEX, we see AI increasingly running on-premise, at the edge, close to where the data and the decisions already are. That shift will see businesses match the model and the location to the task, saving frontier compute for the problems that truly need it while local infrastructure handles the rest. This is the more sustainable way to scale AI for the long run.

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Michael Darwal, Chief AI & Digital Officer, ibex

AI is enabling companies to transform what “great” customer experience (CX) looks like. Leading innovators at the forefront of this transformation are utilizing AI in concert with human support to better provide seamless, personalized, and connected experiences across every touchpoint – delivering resolution faster with an improved ability to serve. The most impactful and successful organizations are using AI to automate high-volume, routine interactions while preserving context as conversations move between AI and human agents, eliminating the frustration of customers having to repeat themselves, and supporting them throughout more complex or emotionally sensitive situations, creating a customer journey that seamlessly combines AI’s efficiency with the empathy and judgment of the human experience.

As AI becomes embedded across the customer journey, businesses are further evolving how they measure success. A balanced focus on three pillars is defining success in the AI-enabled CX journey: (1) Improved CSAT through true resolution—whether customers leave with confidence, avoid repeat contacts, and feel understood, (2) Improved ability to serve –automating high-volume/low-complexity work, enabling existing human workforce to take on higher complexity interactions, faster, with improved outcomes, and (3) Positive ROI – both for organizations and customers. Tracking customer effort, sentiment, retention, and loyalty alongside the more traditional CX operational metrics provides a more accurate picture of AI’s business impact and helps ensure automation strengthens, rather than diminishes, the customer experience.

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Todd Snow, Chief Procurement Officer, Smith

As AI Appreciation Day recognizes the technology’s growing impact, it’s also worth acknowledging the hardware ecosystem that makes AI possible. While much of the conversation focuses on models and applications, AI’s influence is increasingly visible throughout the electronics supply chain. Demand for AI infrastructure is driving growth in semiconductors, storage, memory, and other critical components, while forcing manufacturers to rethink how to allocate capacity across end markets.

What makes AI unique is that its influence extend far beyond the companies building or deploying it. Investment in AI infrastructure is creating ripple effects across the broader technology ecosystem, impacting component availability, sourcing strategies and long-term supply chain planning. As AI continues to evolve, understanding the physical infrastructure behind it, and having the visibility to source it reliably across shifting market conditions, will be just as important as tracking advances in the technology itself.

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Raghuveer Subodha, EY Distinguished Technologist, EY Consulting Global Delivery Services – AI Engineering Leader

Artificial Intelligence has rapidly evolved from a mere productivity tool to becoming a strategic business capability. As firms move beyond pilots and experimentation into more production ready capabilities, the focus is also shifting from simply deploying large language models to building AI systems that understand business context, operate responsibly, and deliver measurable outcomes. The future of enterprise AI is being shaped by four foundational strategies: driving contextual outcomes using deep business knowledge through ontologies, trust in AI through codified governance and controls, and enabling scalable runtime AI agents that can reason, collaborate, and execute across complex workflows and finally value realization.

As we celebrate AI Appreciation Day, it’s worth recognizing that the next frontier of innovation isn’t just about more powerful models—it’s about making AI reliable, explainable, and deeply integrated into how organizations operate using their institutional knowledge. By combining these, we are laying the foundation for greater intelligent systems that can augment human expertise and drive accelerated transformations that create lasting business value.

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Shomit Manapure, General Manager, Agile Robots North America

AI Appreciation Day presents an opportunity for the greater tech community to pause and reflect on the astronomical transformation our industry has experienced in a year’s time – and we’ve barely scratched the surface. As the general public becomes more attune to AI via software and chatbots, we remain focused on what lies ahead: physical AI realized as robots that see, act, reason and adapt with humans from their homes to the factory floor.

The impact of physical AI stretches far beyond a simple enhancement of individual machines. It is changing the way entire production systems operate, with connected technologies that learn, adapt and improve over time. This is no longer a distant reality – it’s here – and tens of thousands of robotics solutions are already delivering measurable results worldwide. This means faster throughput, fewer errors and flexible operations that can adapt within seconds.

On this AI Appreciation Day, we celebrate the researchers, engineers and operators turning AI breakthroughs into something tangible. The most consequential AI won’t be the one you solely chat with – it will be the kind that works beside you.

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Christina Fung, Senior Vice President of Consulting Services, CGI

AI in the enterprise has matured significantly since the last AI Appreciation Day. Generative AI was just the prelude, and while today we are deploying agentic systems that autonomously execute reasoning loops, we must build organizational agility to anticipate and benefit from the inevitable future waves of AI innovation. As we navigate this evolution, we encounter the token paradox: while cost per token falls, overall spending climbs as workflows become more complex. Getting ahead of this requires more than cost controls. Strong token economics helps us optimize our AI resource consumption, making our operations more financially and environmentally more sustainable and empowering us to build ‘AI for Good’ solutions that truly benefit businesses, society and humankind.

Realizing this potential requires robust governance, centralized visibility, and intelligent model routing to ensure tokens drive clear business value. Organizations need scenario-based forecasting, intelligent model routing and centralized visibility into AI consumption so they can understand where tokens are being used, by whom, and for what business value. Equally important, they need to continuously invest in their workforce. Foundation models change so quickly that AI fluency can’t be a one-time training initiative; it must be an ongoing business capability.

On AI Appreciation Day, we recognize the engineers, finance leaders, operations teams, and business leaders helping organizations turn AI into measurable value. The organizations pulling ahead won’t necessarily be those using the most AI—they’ll be the ones using it most responsibly, combining the right governance with an agile mindset and a deep commitment to human skills and sustainable outcomes.

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Krishna Rangasayee, Founder and CEO, SiMa.ai

The next trillion-dollar AI market won’t be won in the cloud. Physical AI – AI running in robotics, drones, industrial equipment, medical devices, and vehicles — plays by different rules. It has to operate in real time, under tight power, and safety constraints.

While the AI race has historically taken place between hyperscalers competing for raw cloud compute power, scaling physical AI plays differently. The constraint hasn’t been silicon—teams have had the compute. It’s been software, turning a working model into a shipped product has meant months of integration for every new use case. That’s why scaling physical AI takes a purpose-built approach prioritizing deployment velocity and power efficiency. The companies that understand this dynamic, and build for openness and speed rather than extending a cloud-era playbook, are the ones that will define this next era of AI.

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Denise Muyco, Founder and CEO, RAVEL

The AI conversation naturally gravitates toward models, applications, and chips because that’s where innovation is most visible. But none of those advances create value without infrastructure that can actually run them. The question, however, has shifted from “How many GPUs can I buy?” to “Where can I actually turn them on? That’s a very different challenge, and it’s one that extends well beyond technology into infrastructure planning.

Every new AI cluster requires three things: space to house it, power to operate it, and cooling to sustain it. Those are finite resources that can’t be scaled overnight. Many organizations are only now discovering that moving from a proof of concept to enterprise-scale AI deployment is as much an infrastructure challenge as it is a software challenge. AI Infrastructure and Workload Orchestration can make far better use of the power and compute an organization already has, prioritizing workloads and reducing the waste that comes from treating every GPU as its own island. What it cannot do is create additional megawatts. That gap only closes through continued investment in physical capacity.

The next phase of AI won’t be defined solely by who builds the fastest model or the most powerful chip. It will be defined by who can deploy AI infrastructure most efficiently, with the least amount of adverse impacts to the community. That requires thinking beyond compute to the realities of power, facilities, operations, and orchestration. The organizations that connect those pieces together will be the ones that scale AI successfully.

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Craig Coogan, CTO & VP, BNS, Aurora Networks

The impact of Artificial Intelligence is increasingly evident across every industry. At Aurora Networks, we view AI not as a technology trend, but as a practical tool for solving real-world challenges faced by cable, fiber, and wireless service providers. Across our business, we are leveraging AI to improve efficiency and enhance the customer experience, while within our product organization we are focused on applying AI to help operators manage increasingly complex multi-gigabit networks. By correlating data from multiple systems and presenting actionable insights through intuitive interfaces, AI is helping our customers accelerate troubleshooting, improve operational visibility, and deliver better service experiences.

AI is also transforming how we build products. From requirements definition and software development to quality assurance, AI serves as a force multiplier for our engineering teams, helping uncover gaps, improve productivity, and strengthen the quality of our solutions. While we remain focused on responsible and purposeful adoption, I am incredibly encouraged by what we have achieved so far—and even more excited about what lies ahead. On this AI Appreciation Day, I am grateful for the innovation AI enables and proud of how our teams are applying it to help shape the future of broadband access networks. We have only begun to scratch the surface of what is possible.

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Saahil Kamath, Head of AI, VP Products, Eltropy

The idea that AI will replace human jobs misrepresents what’s actually happening. AI is not eliminating opportunity; it is expanding it. It will create not only more jobs, but entirely new categories of work we have yet to imagine.

In the financial industry, AI is not a substitute for people. It is an amplifier of human capability. It strengthens what people can do rather than replacing them.

History makes this clear. Every major technological breakthrough, from calculators to the internet, sparked the same fears. Each time, those technologies created more jobs and unlocked new opportunities. We will continue to see the same thing in the credit union and community banking space.

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Evan Reiss, SVP of Marketing, Innovation, Foxit

Why do I appreciate AI? I appreciate its ability to rapidly iterate and produce content. But let’s be clear, creatives and knowledge workers still matter most in this era. AI has fundamentally disrupted how work happens, and a great work inversion is taking place. It falls on strong leaders to encourage creation, ideation, and novel solutions to complex problems. That process should happen outside of AI systems first. Then, AI can handle and finish the work. Let’s appreciate AI for its completion ability, but celebrate the human creative spirit for its authenticity.

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Saurabh Gupta, President and CEO, The Modern Data Company

Back in 1956, when Dartmouth researchers coined the term “artificial intelligence,” they weren’t thinking about data quality. Today, 70 years later, on AI Appreciation Day, enterprises will abandon 60% or more of AI projects due to a lack of AI-ready data. What’s missing today is a layer to unify the data that’s already there and embed it with context, semantics, lineage, and governance. When that is present and persists by design, existing AI investments lead directly to meaningful business outcomes.

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Joe Kim, CEO, Druid AI

AI Appreciation Day is a good moment to ask what your AI is really delivering, not just whether it’s running. Customers across banking, healthcare, retail, insurance and higher education have already made their expectations clear: they want help now, in their language, on their channel. We’re seeing enterprises meet those expectations every day. Banks are delivering 24/7 service to millions of customers while reducing reliance on physical branches, healthcare organizations are expanding patient access and reducing call handling times, and universities are giving students answers when critical decisions cannot wait. Our production data shows that up to 39% of customer demand happens outside traditional business hours, making always-on AI an essential part of the modern customer experience. The organizations embracing AI today are creating an always-available extension of their business that delivers better customer experiences while empowering employees to focus on the work that matters most. That is the lasting advantage AI will bring to the enterprise.

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Malte Kosub, CEO and Co-Founder, Parloa

AI Appreciation Day is a moment to ask what we truly appreciate about the tech. Planning a trip or making a poem about your dog is one thing, but what about when the real potential of everyday AI is put to the test where it counts? Look at customer service. Our Consumer Patience Index found that 93% of consumers say legacy automated voice systems rarely or never resolve their issues, and yet 61% expect fully agentic AI to handle the entire service journey within three years. That gap between where we are and where consumers already expect us to be is the opportunity. The companies that bridge that gap will win on efficiency and, just as importantly, on loyalty. That’s an AI story worth appreciating.

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Jason Gladu, Chief Alliances Officer, Converter

On AI Appreciation Day, we must reflect on how far the technology has come, but I think it should also push companies to ask a harder question. Can you actually trust the data your AI is running on? Most businesses have spent a lot of money on models and automation over the past couple of years. Far fewer have spent that same effort making sure the data feeding those systems is accurate, permissioned, and up to date. That’s the piece that gets skipped, and it’s the piece that matters most. If the data going in hasn’t been governed, you’re asking AI to make decisions on information you can’t stand behind. Clean and governed data is what lets AI actually deliver better decisions and better outcomes for customers. So today, before we celebrate what AI can do, it’s worth taking stock of what it’s running on.

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Jason Sabin, Chief Technology Officer, DigiCert

The AI adoption story has already been told. The next chapter is about trust. Our July 7 research found that AI adoption is accelerating, but trust is still catching up: nearly eight in ten organizations have already experienced AI-related security incidents or vulnerabilities, and nearly half still lack full visibility into how AI reaches its decisions. The organizations that succeed with AI will be those that invest not only in innovation, but in the identity, governance, and accountability that make AI trustworthy.

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Andy Sen, CTO at AppDirect

Encourage people from non-technical backgrounds to experiment with AI. Whether it’s the people team, marketing or operations, enable them to build tools and vibe code apps to help them at their work. At AppDirect, we have developed a culture where that’s celebrated: when someone comes up with something cool, we give them recognition and organize events where we showcase what people have built. And with Devs.AI, we can keep track of who’s building what, and how it’s being adopted by the rest of the employees, making it easy to both maintain control and recognize the right people.

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Chao Cheng-Shorland, CEO, ShelterZoom

AI has shifted outcomes from what is aspirational to what is practical. In highly regulated industries such as healthcare, AI solutions deliver the most value by helping professionals spend less time burdened with administrative tasks, and more time focused on the work that matters. This is where AI earns trust and delivers lasting value.

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Jason Williamson, CEO, MythWorx

This AI Appreciation Day, we should all look back at the rapid pace of development an consider all the incredible achievements we’ve seen in the past several years. But we should also look forward to understand what the next era of AI will require, and ultimately what it will cost.

The next phase of AI needs finesse and innovation, not the brute force of thousands of square miles of power hungry data centers. We need to recognize that today’s infrastructure was never designed for the scale that advanced AI demands. As adoption accelerates, the industry needs to continually find ways to separate from the status quo and move towards models that reduce compute and energy requirements.

While the world races to answer the question of ROI, we also need to consider the socioeconomic and environmental costs of AI.

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Mas Ati, CEO and Founder, The XI Code

AI is one of the most exciting technologies we’ve ever created, but its greatest value isn’t that it can think faster than we can. It’s that it challenges us to think more intentionally. The future isn’t about humans versus AI—it’s about using AI to bring out the best of what makes us human.

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Kunj Pandya, Head of Product and Customer Success, AutoScheduler.AI

What companies need in this moment is a bridge between the future of AI and the realities of the warehouse floor. There’s a lot of pressure on companies to adopt AI quickly, but the opportunity is to do that in a way that respects the people, processes, and constraints already in place.

I don’t think the most interesting story is AI replacing operators. It’s AI helping operators become more effective and more influential inside the business. Some roles will change, as they always do with new technology, but there’s a huge opportunity to upskill the people who already understand the operation best.

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David Chapa, Chief AI Strategist, Hitachi Vantara

AI Appreciation Day is a good moment to separate AI readiness from AI theater. A lot of organizations think readiness means buying GPUs and launching a pilot, but true readiness starts with knowing what outcome you want, where your data lives, and whether that data is usable and trusted. The companies seeing real results aren’t the ones moving uncurated data into an object store and expecting AI to make sense of it. They’re the ones investing in curation, context and data engineering before a model ever touches production. That work may not be the most glamorous part of AI, but it is often the difference between a pilot that stalls and an AI program that scales.

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Wendy Gilbert, SVP of Product at Mark43

In public safety, AI can’t be bolted on. It has to be built into the workflow from the start, with accountability, transparency, and the human always in control. When it’s done right, AI reduces paperwork, surfaces the right information at the right time, and gives responders more time to focus on the people they serve.

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Ravi Nemalikanti, Chief Product & Technology Officer, Abrigo

AI Appreciation Day is a reminder that the real value of artificial intelligence isn’t in replacing people; it’s in empowering them. Think of it as an “Iron Man” suit rather than a robot that takes over someone’s job. Bankers stay in control, and the technology gives them the speed and insight to do more of what they already do well. The difference is context. When every relevant fact, policy, and customer context is always at your fingertips, decisioning gets faster without sacrificing judgment. That’s the real value of AI; helping skilled professionals serve their customers faster while keeping the governance and oversight our industry depends on. The institutions getting the most out of AI right now are the ones pairing new technology with that kind of context, trust, and judgment.

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Dean Drako, CEO of Brivo

AI is easiest to appreciate when it solves a real problem. In the physical security world, that problem is clear: cameras capture everything, but people almost never have the time, attention span or staff to watch everything.

That is where AI can be useful. Not as a replacement for human judgment, but as a way to help people see what might otherwise get missed. A property manager may not have time to review hours of pool footage. A restaurant manager may be too focused on customers to realize their food prep staff isn’t wearing gloves. A manufacturing plant manager may have several areas to monitor at once and not realize a worker isn’t wearing their safety gear. AI can help sift through hours of footage and flag issues in real-time so a human can step in.

This is especially important for organizations that want to improve safety without adding more burden to already busy teams. When designed, deployed, and operated with transparency and cybersecurity, AI-powered security cameras can dramatically improve business efficiency and make the world a safer place.

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Rohit Choudhary, CEO, Acceldata

AI has captured the world’s attention, but data remains the foundation of every successful deployment. Enterprise data is increasingly distributed, and organizations don’t want to choose between moving their data or using AI. The future belongs to platforms that bring intelligence to the data, wherever it lives.

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Bella Raftis, Co-Founder, NetcoreNetwork

People are putting increasing trust in systems that still run almost entirely on infrastructure they do not control themselves. As hardware improves and open-source models continue to advance, many LLM workloads can instead run where the data already resides. That’s a privacy improvement but also a fundamentally different architectural approach that gives organizations greater ownership over both their infrastructure and their intellectual property.

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Wiktor Walc, CTO, Tiugo

The first wave of AI ran on hype. Big expectations, the promise of instant breakthroughs, as well as ambition that outpaced the strategy, governance and operating models needed.

Now, teams are getting deliberate about where AI actually improves productivity, where it still falls short and what it takes to run it responsibly in production. That’s not a push toward more basic systems. Usually it’s the opposite. Agentic workflows are already spreading, backed by stronger data governance and tighter integration alongside existing processes.

AI generates content, analysis and data faster than any person can keep pace with, so the human becomes the bottleneck. Removing people from the loop is not the answer. The answer is engineering workflows that serve agents and humans equally well. Agents need secure, structured access to the data and context they run on. Humans need a full, transparent record of every action and change AI made, plus a clean way to review, accept, reject or refine it.

One of the largest limits on productivity and also one of the largest opportunities is the human-AI layer. Those who get it right will be more productive, but will also convert that output into trusted, governed work at scale.

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Sean Behr, CEO, Fountain

AI has collapsed the distance between having a question and getting an answer. In the past, a question from a leader traveled through two or three layers of people. Everyone along the way worked from a slightly different version of the truth. Now, that leader asks directly and gets an answer immediately, from the same data everyone else sees.

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Gina Nomellini, COO, Globalgig

AI is changing what’s possible in network operations. It has allowed teams to move beyond relying on reactive, alert-by-alert troubleshooting to now catching degradation before problems ever affect users. This allows teams to cut through the noise and focus on what actually matters. With the continual advancement of AI, networks that benefit most are going to be those that are built to support the shift and have the visibility and automation to act on what AI surfaces, not just wait for problems to arise.

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Roger Williams, Partnership Manager, North America, Kinsta

AI for customer service offers many appealing features for a company that wants to implement them. Speed and cost tend to be high on the list. However, from a customer’s perspective, a focus solely on speed and cost can be at odds with a company’s larger goals. Speed is important but not at the expense of accuracy. Cost isn’t a concern as customers expect service when they pay. As companies consider implementing AI customer service, it is important that they ask themselves what the goal is. If speed and cost are the primary drivers, you may end up giving bad answers and frustrating customers to the point that they cancel.

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Sriram Iyer, Senior Vice President & GM, AI Strategy and Products at OneTrust

AI Appreciation Day is a good opportunity to recognize how enterprise AI is evolving. Every organization has AI principles today. Far fewer can prove those principles are being followed. That gap is becoming increasingly important as organizations move from AI experimentation to enterprise-wide adoption.

The unit of work in the enterprise is no longer just the application or the dataset. It is the AI decision itself, including the recommendations, approvals, and actions happening across the business every day. Organizations can adopt the latest AI models, but they can’t outsource accountability. As AI becomes embedded in more business processes, governance has to evolve alongside it. That shift is already underway. In fact, recent data found that 75% of organizations say AI has exposed the limitations of their legacy governance processes.

Governance that operates alongside AI, with controls built into workflows, decisions logged as they happen, and evidence available when it’s needed, is becoming essential for enterprise adoption. Organizations that embed governance into how AI is developed, deployed, and used will be better positioned to scale AI with confidence, strengthen trust, and meet growing expectations from customers, regulators, and boards.

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Matt Jones, Executive Vice President, Strategy at Cielo

As we recognize AI Appreciation Day, the conversation shouldn’t just be about celebrating the technology. It should be about recognizing how AI is changing the way work gets done, and whether organizations are redesigning work to take advantage of it.

AI is moving far beyond isolated productivity tools. It’s becoming an embedded utility across hiring, onboarding, workforce planning and career development, changing not only how organizations attract talent but how work itself is designed. As AI takes on more coordination, administration and analysis, the value people bring increasingly lies in judgment, creativity, collaboration and the decisions technology can’t and should not make on its own. This shift is reflected in recent research, which found that 61% of HR leaders say organizations will increasingly value employees who can adapt across domains while still going deep when needed.

That’s why the biggest opportunity isn’t simply adopting AI, it’s intentionally deciding where AI creates performance and better experiences and where people create value. Organizations that thrive will move beyond automating existing processes to redesigning work around the strengths of both humans and AI. The workforce is already evolving in response. 65% of HR leaders believe AI will drive the rise of more generalist roles across organizations. At the same time, governance must evolve from a legal or IT responsibility into a shared business discipline that ensures AI is transparent, accountable and trusted by employees and candidates alike.

The organizations creating the greatest advantage won’t necessarily be those using the most AI. They’ll be the ones that thoughtfully redesign work so technology expands human capacity and capability rather than replaces it. When AI works alongside people as a digital colleague, it creates more time for the conversations, judgment, cross-functional collaboration and strategic decisions that ultimately drive stronger business performance.

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Dr. Antonis Papatsaras, Chief Technology Officer at Bluehost

There is one thought that National AI Day might retread some pretty well-worn ground. People want to move away from the exhausted discussion and argument about whether AI is going to revolutionize economic scale or destroy human creativity, with absolutely no in-between. I think National AI Day offers an opportunity to celebrate both the people using AI to improve their lives or others’ lives, and the AI innovators. The developers, indie builders, and entrepreneurs engineering new applications and new agents to solve limitless needs and problems are paving the way to level the playing field for everyone. At Bluehost, we’re focused on building AI tools and agents for SMBs who don’t have the time to cut through all the AI noise to choose what would serve them best, and are stuck using basic Swiss Army knife tools. In tandem, we’re proud to arm all the AI builders who are building anything – sites, stores, agents, apps anywhere, with the infrastructure they need to grow and run.

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Drew Shea, EVP of AI and Engineering at OneStream

Approaching National AI Day, I reflect on how quickly AI has emerged, but how little we have still yet to realize on its promise of enterprise transformation. While Agentic AI holds a lot of promise, true adoption is being held back by bad data, particularly with CFOs: Research shows that while nearly all executives (96%) view accurate, trusted data as very or extremely important to their organization’s success, nearly half (47%) admit they’ve made a material business decision based on inaccurate, incomplete or outdated financial data in the past 12 months – and those same execs are 4x more likely to be using 10 or more AI tools.

Companies don’t need more AI tools, they need an operating system where secure, contextualized data serves as the guardrails for the business. Governance, context and control are essential to making AI work for a highly regulated function like Finance. It means ensuring every number has the same meaning across every system, hierarchies are aligned and subject matter experts remain in the loop to validate outputs before they inform business decisions. With that foundation in place, AI becomes a multiplier for improved efficiency, more informed decision-making and greater business value.

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Sarkis Kalashian, VP Product Management at Smartsheet

As organizations celebrate National AI Day and the rapid pace of innovation, it’s worth stepping back to ask a more important question: Does AI have the context it needs to continue delivering meaningful business value? How can AI produce better outcomes for businesses?

Organizations are adding and embedding AI into more tools and workflows, but business outcomes rarely occur within a single application. Product launches, project management, employee onboarding, etc., require people, data, and historical context to work across teams and systems. That’s where context gets lost. If you layer AI on top of disconnected work, you’re not solving the problem. . You can’t automate your way out of a work coordination problem — you’ll just get to the wrong answer faster.

The organizations that are seeing the greatest returns from AI are moving beyond disconnected AI adoption. They’re connecting work end-to-end so AI can operate with shared context across teams.
The next phase of enterprise AI won’t be defined by who deploys the most agents. Everyone’s racing to give AI more horsepower, but the real unlock is giving it a map. That map is the organizational and business context, the shared understanding of how work flows across teams and systems. It’ll be defined by who gives AI that context to help people make better decisions and execute work better, not just produce more.

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Daniel Liechtenstein, CEO & Co-Founder, Hypercore

Financial institutions have a fundamentally different relationship with AI than other industries. Every action must be explainable, auditable, and backed by accurate data. So the question firms are asking has changed – no longer whether AI belongs in their business, but where it can be trusted to execute real work.

That question exposes the limits of the first wave of adoption. Most firms started with tools, chatbots, copilots, point solutions, and discovered that integrating AI and getting results from AI are two different things. A tool leaves the burden of accuracy, oversight, and accountability entirely on the firm, which is exactly the burden regulated institutions can’t carry alone. In private credit, where portfolios are growing more complex and investors expect more transparency, that burden lands on the operational work behind every transaction – reporting, borrower data, post-close processes that can’t run on spreadsheets and manual effort forever.

The firms preparing well are shifting their focus from which tools to adopt to how to get outcomes they can stand behind, working with partners who pair AI with institutional controls and take responsibility for the result, not just the software. Those firms won’t simply work faster. They’ll operate with greater consistency, transparency, and resilience.

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Ziv Cohen, Director of Data & AI, Cust2Mate

When e-commerce has conditioned consumers to expect personalized recommendations, relevant promotions, transparent pricing, and instant product information, the physical store must also deliver on those promises. Today, AI can bridge this gap.

In fact, the future of brick-and-mortar isn’t about competing with online shopping. It’s about combining the strengths of physical retail with the personalization consumers already experience online. Our research shows that shoppers aren’t asking retailers for more AI. They’re asking for better experiences. They want to find products faster, understand pricing more easily, discover relevant promotions, and complete purchases without unnecessary friction.

The opportunity for retailers is clear. Only 36% believe supermarkets consistently deliver the fast, hassle-free shopping experience consumers now expect. Another 68% say real-time visibility into pricing, discounts and basket totals is important, yet only 48% believe stores provide it. AI, smart carts, and connected in-store technology can provide this. Looking forward, the true potential lies in the deployment of specialized AI agents. Unlike passive systems, these autonomous agents can proactively anticipate shopper needs in real-time, optimize store operations on the fly, and act as a personalized digital assistant for every customer right on the cart.

Consequently, success won’t be defined by how much AI a retailer deploys, but by whether customers feel the experience has become more intuitive, transparent, and convenient. The future of physical retail will belong to retailers that bring digital-age experiences into the store. AI succeeds when customers don’t notice the technology itself – they simply notice that shopping feels easier.

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Ashley Raysin, U.S. Director, VoCoVo

AI can only be truly appreciated when it’s leveraged to better support the workforce using it. This is especially evident for frontline workers in retail. It’s not about replacing these teams, but ensuring they can use this technology effectively.

Yes, retailers have invested heavily in AI to advance forecasting, inventory management, and customer insights. But the next step is extending those insights beyond the back office and into the hands of frontline staff. One of the biggest misconceptions about AI is that success depends on deploying the most advanced model. In reality, retailers often see the greatest value from practical, everyday use cases that help frontline employees make faster, more informed decisions. It’s about turning AI insights into action.

To do this, retailers must focus on delivering the right information to the right employee at the right moment. This means AI should fit naturally into existing workflows so frontline employees can access information immediately, receive relevant guidance, and continue helping customers without disrupting the shopping experience. Recent research indicates 62% of consumers seek out staff when they cannot find a product, and poor or unhelpful service is the top reason why 53% would not return to a store. This emphasizes the need for technology that augments, not replaces, staff.

Ultimately, the retailers seeing the greatest AI impact aren’t treating the technology as a standalone tool. They’re embedding it into everyday workflows so frontline employees can respond faster, solve problems in real time, and spend more time serving customers.

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Yrieix Garnier, VP of Products, Datadog

Nearly 70% of organizations are already using three or more AI models, underscoring how quickly AI is changing the way companies operate. As AI becomes increasingly autonomous — detecting issues, remediating incidents, validating releases, writing and shipping code — the question is no longer what the software can do. The question now is whether an organization can see what AI is doing, govern when it acts, and trust it when it matters.

To reach that level of trust and operational control, successful AI implementation hinges on whether companies have unified visibility across the whole system — infrastructure, applications, security and AI workloads.

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Raghu Malpani, Chief Technology Officer and Chief Product Officer at UiPath

One part of AI Appreciation Day is reflecting on where AI is creating the greatest value for businesses today.

From what I’ve seen from our customers, AI use is scaling within organizations to translate into disproportionate gains for everyone. Across the value chain of complex business processes, discovering the work that should be reimagined and augmented with AI – mapping workflows, systems, exceptions that actually run the business, is step one. The judgement of what all should leverage AI and warrant a positive ROI is starting to become a bottleneck. Once that’s identified, you can then make decisions on where and how to apply AI and AI tools to remove the repetitive work that keeps employees from focusing on and solving harder problems.

One such tool are coding agents. They can generate code, accelerate development of software and new systems, and help teams move faster, but their real impact isn’t measured solely by lines of code written or software built. It’s two-fold: Changing the definition of who gets to build from ‘just’ developers to include business users; and then making sure that what gets built by this expanded set of builders stays in line with standards for governance.

The organizations that get the most from AI won’t be the ones using AI as an excuse to cut costs. They’ll be the ones that reimagine their business using that map, then build intelligent workflows that orchestrate automations, AI, systems, and people from end-to-end across the business. This is the value we see.

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Dale Hoak, CISO at RegScale

Artificial intelligence is rapidly changing the conversation around governance, risk, and compliance. Not by replacing people, but by eliminating the manual work that has slowed security and compliance teams for decades. For too long, organizations have relied on spreadsheets, screenshots, and point-in-time audits to answer questions that should be continuously validated. AI gives us the ability to move beyond documenting compliance and instead continuously measure, analyze, and verify it. That’s a fundamental shift. Security teams can spend less time chasing evidence and more time reducing risk, while executives gain confidence that compliance reflects the organization’s current security posture rather than a snapshot from months ago.

The real value of AI isn’t simply automation, it’s acceleration with context. Modern organizations are deploying new applications, cloud services, and AI systems faster than governance teams can traditionally keep pace. AI can correlate evidence across security tools, identify control failures, recommend remediation, and surface emerging risks in minutes instead of weeks. When combined with automation and continuous controls monitoring, organizations can achieve a level of visibility that was previously impossible without dramatically increasing headcount. In an environment where cyber threats and regulatory expectations evolve daily, AI helps organizations move faster while making governance stronger, not weaker.

As AI continues to mature, the organizations that succeed won’t be the ones using the most AI, they’ll be the ones governing it the best. The future of compliance isn’t about preparing for the next audit; it’s about building continuous trust. AI is becoming the engine that transforms governance from a reactive, document-driven exercise into an intelligent, real-time capability that enables innovation while keeping risk under control. That’s where the real appreciation for AI should be focused, not on the technology itself, but on its ability to help organizations confidently say “yes” to moving the business forward.

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Henry Comfort, co-founder & CEO, Geordie AI

AI Appreciation Day is a good reminder that the conversation has moved well beyond whether AI works. Through our customers, we’ve seen what AI agents can accomplish when organizations have the confidence to put them to work. They’re accelerating workflows, taking repetitive tasks off people’s plates, and helping teams move much faster than they could on their own.

That confidence doesn’t come from the models themselves. It comes from understanding what AI agents can access, how they behave, and having the right governance in place to keep them operating safely. The organizations getting the most value from AI aren’t necessarily using the most advanced models. They’re the ones getting the controls in place that let their teams adopt AI agents with confidence. That’s what I’m most excited to see as AI agents continue to mature over the next year.

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Tim Leehealey, Founder & Vice President, Strategy and Operations, Strike48

I’ve been marveling at what AI can do for years, but today, I’m most impressed by where it’s delivering measurable impact.

In security operations, AI is moving beyond copilots and chat interfaces into day-to-day execution. We’re seeing customers use AI to automatically resolve nearly 96% of their Level 1 security cases, handling tasks in under 30 seconds that previously took analysts more than 10+ minutes. In another deployment, agentic operations are projected to save a customer more than $834,000 annually.

The value extends well beyond time and cost savings. Every repetitive task an AI agent takes on gives security teams more time to investigate complex threats, strengthen resilience, and stay ahead of attackers. AI delivers its greatest value when it handles the operational work that slows people down, allowing experienced professionals to focus on where their expertise makes the biggest difference.

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Dr. Stanislav Fort, co-founder, chief scientist & CTO, AISLE

AI Appreciation Day is a reminder of how far the technology has come in a remarkably short time. Having spent years building frontier AI models, I’ve seen that progress firsthand. But one lesson has become increasingly clear: raw model capability is only part of the story.

In cybersecurity, it’s critical that we consider everything around the model. In doing so, we give it the right context, ground its reasoning, and design systems that security teams can trust when the stakes are highest. Frontier models will continue to improve, which is worth celebrating, but the next wave of innovation will come from the systems that reliably turn models’ capabilities into positive security outcomes.

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James Patrick-Evans, PhD, founder and CEO of RevEng.AI

Artificial intelligence has rapidly evolved from a productivity tool into a force multiplier for cybersecurity teams. In software development, AI is accelerating coding by helping developers generate, review, and optimize code at unprecedented speed. This allows security professionals to spend less time on repetitive tasks and more time focusing on designing resilient systems, identifying architectural risks, and embedding security throughout the development lifecycle. As AI-assisted development becomes the norm, the opportunity to build secure applications faster has never been greater.

The same transformation is happening in cybersecurity. Security Operations Centers (SOCs) are increasingly using AI to correlate vast amounts of telemetry, prioritize alerts, identify anomalous behavior, and automate investigations that would otherwise consume valuable analyst time. Rather than replacing human expertise, AI amplifies it— helping defenders detect threats earlier, respond faster, and stay ahead of increasingly sophisticated adversaries who are also leveraging AI to scale their attacks. The future of cyber defense is a positive one, and will belong to organizations that successfully combine AI-driven automation with skilled human judgment.

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