Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Dr. Ryan Ries, Chief AI and Data Scientist for Mission, a CDW company
After a wave of experimentation, the next phase of enterprise AI will be about what actually works. The past two years brought a flood of pilots, proofs of concept, and internal demos that showcased potential but rarely reached production. Many enterprises now face the same realization: AI cannot succeed through novelty alone. In 2026, progress will come from practical design, measurable outcomes, and human context guiding machine output.
A 2025 Fortune-MIT study reported that 95% of generative AI pilots failed to have a measurable impact on P&Lt because of unclear business goals, weak governance, and a shortage of technical talent. At the same time, Gartner’s 2025 Hype Cycle found that AI agents, composite AI, and orchestration frameworks are entering a more mature phase, where early adopters are beginning to show steady results. Together, these signals point to a new focus for the enterprise: AI that delivers real value through design grounded in people, process, and purpose.
The end of the AI experiment phase
AWS data shows that the average organization ran 45 generative AI experiments in 2024, yet fewer than half ever reached an end user. Many were proof-of-concepts with no plan for scale. In 2026, enterprises will treat AI less as an experiment and more as an operational capability, and leaders will demand evidence of ROI before expanding adoption. Success will be measured by metrics such as cycle-time reduction, cost savings, and customer satisfaction rather than model complexity or novelty.
Teams that built quick demos last year are now under pressure to convert them into durable workflows that integrate with existing systems. Those that cannot demonstrate value will see projects sunset quietly as budgets tighten.
Human-AI collaboration becomes the differentiator
The current wave of AI agents has captured the imagination of both technologists and executives. The idea of digital teammates capable of independent action is compelling, but many of today’s so-called “agents” remain labeled workflows rather than systems with true reasoning. They follow scripted logic, and without human oversight, their output often drifts or fails to align with real-world conditions.
Dr. Ryan Ries, Chief AI and Data Scientist at Mission, describes this gap as one of context. “People often expect agents to just take over tasks, but that’s not how they work,” he says. “Most are probabilistic systems that need guidance to be effective. What makes AI powerful is the human input around it. In 2026, the organizations that win will be the ones that marry human judgment with machine speed, using people to shape prompts, workflows, and context so that agents actually deliver useful results.”
Human-AI collaboration will therefore become a competitive advantage. Instead of replacing expertise, AI will amplify decision-making by surfacing patterns, summarizing options, and accelerating execution. In high-stakes industries like finance, healthcare, and manufacturing, keeping people in the loop will still be important to maintain accuracy, compliance, and trust.
Designing AI workflows that scale
Enterprises moving beyond pilots will need to focus on workflow design. Three design principles will define these efforts in 2026:
- Context-first architecture: AI performs best when grounded in business data and policies. Teams that invest early in data quality, metadata, and governance will produce more reliable outcomes.
- Continuous validation: Feedback loops will remain essential. Human review detects bias, drift, and edge-case errors faster than automated monitoring alone. Enterprises that treat feedback as part of the workflow will build more trustworthy systems.
- Operational integration: AI must connect to existing tools and decision paths. In 2026, the most effective organizations will deploy AI directly into customer operations, finance, and supply-chain processes rather than keeping it in innovation labs.
Together, these practices turn AI from a prototype into a production system. They also create accountability through clear metrics, traceable performance, and transparent ownership.
From efficiency to measurable business value
The conversation about AI efficiency will mature into one about business contribution. Instead of claiming productivity gains, organizations will be expected to show how AI supports revenue growth, faster delivery, or better forecasting accuracy. CFOs will drive this shift by requiring quantifiable returns before approving new budget for AI-related projects.
Compliance and regulation will reinforce the same discipline. As new rules under the EU AI Act and similar U.S. frameworks take effect, leaders will need to document how AI decisions are made and who remains responsible for them. Transparent governance will become part of the ROI calculation instead of an afterthought.
The outcome will be a more balanced model of innovation that is measured, deliberate, and sustainable.
The human advantage
By 2026, the AI conversation will be less about capability and more about contribution. Enterprises will focus on how AI supports business goals, improves workflows, and strengthens human performance. The hype cycle is giving way to an era of accountability.
The organizations that succeed will not be those that deploy the most agents or models, but those that design with purpose and combine technology and human expertise to create measurable business value. In that sense, the next phase of AI is not artificial at all. It’s profoundly human.
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ABOUT THE AUTHOR
Dr. Ryan Ries is a renowned AI and data scientist with more than 15 years of leadership experience in data and engineering at fast-scaling technology companies. Dr. Ries holds over 20 years of experience working with AI and 7+ years helping customers build their AWS data infrastructure and AI models on AWS. After earning his Ph.D. in Biophysical Chemistry at UCLA and Caltech, Dr. Ries has helped develop cutting-edge data solutions for the U.S. Department of Defense and a myriad of Fortune 500 companies. As Chief AI and Data Scientist for Mission, a CDW company, Ryan has built out a successful team of Data Engineers, Data Architects, ML Engineers and Data Scientists to solve some of the hardest problems in the world utilizing AWS infrastructure.
