By Michelle Gill, Sr Director of Engineering, at GitLab
For most enterprises, last year’s AI efforts delivered impressive initial results. Enterprises achieved measurable gains, including faster development cycles, reduced operational costs, and widespread adoption of AI-enhanced tools.
Now comes the infrastructure challenge. The organizations positioned for success in 2026 won’t be those with the most experiments; they’ll be the ones that succeed in building a solid foundation to turn experiments into a sustainable competitive advantage.
The difference between building on your AI gains and watching them plateau will depend on three key infrastructure investments: governance frameworks, agentic systems, and unified data architectures.
Organizations that make these moves in 2026 will realize the fruits of a mature and scalable AI strategy, unlocking sustained, AI-driven growth. Those who don’t will find their early wins increasingly difficult to maintain.
Reining in shadow AI
In 2026, many organizations will face a critical inflection point as cloud costs spike. Often, these costs are traceable to redundant work: three different teams building three distinct compute-intensive agentic AI solutions to solve the same problem.
This spirit of experimentation is exactly what’s driving AI adoption and the discovery of successful solutions. But as teams spin up ad hoc solutions from development tools, cloud platforms, and countless other sources, the lack of centralized oversight becomes a liability rather than an asset. Too many ungoverned agents will increase cloud and compute costs.
This year, companies need to refine how they measure ROI to understand how their AI investments are actually performing.
The solution: Implementing governance platforms that track which agents are running, the resources they consume, the business value they deliver, and how they interact with each other and with critical systems.
Governance is always a tradeoff. Developers still need the ability to experiment with new AI tools. However, the most successful organizations will find the right balance between AI innovation and governance, reining in shadow AI and rationalizing their cost structures.
Using agents to handle complex processes
The next phase of the AI race is about moving from addressing ad hoc opportunities to building AI agents that can handle complex, multi-step processes.
For example, many companies have already found that AI-assisted coding has made their developers far more productive. But this productivity has come at a cost: Downstream developers, security, and compliance teams are likely looking at a massive backlog of code reviews. And sales and finance teams might still be waiting weeks for legal review on contracts that an agentic AI system could have flagged on day one.
In 2026, organizations will need to implement agentic systems that manage end-to-end processes, such as overseeing the B2B sales cycle or coordinating product delivery from the warehouse to the doorstep.
Crucially, these agentic systems will serve as connective tissue between teams, handling the administrative work and streamlining review cycles. Get this right, and your agentic technologies will break up bottlenecks rather than creating them.
Enhancing context for enterprise data
AI is only as smart as the data it can access. For most organizations, the data is limited because critical context is spread across different systems that don’t talk to each other. AI might be able to write flawless Python code, but if it can’t access design decisions recorded in a wiki, compliance requirements buried in a Slack thread, and a customer data model that exists only in the company’s CRM, the code will be technically correct but strategically useless.
The challenge is that business data typically resides across countless disconnected systems. That fragmented data landscape is the primary barrier preventing companies from unlocking the full potential of AI. To address it, companies will need to build data architectures that can support the AI investments they’re already making.
If you prioritize building unified data and context frameworks, you’ll benefit from faster AI and agentic system deployments, as well as reduced security risk. And that context will give you the power to leverage organizational knowledge across your entire technology stack.
Building lasting advantage
The difference between AI leaders and followers in 2026 will boil down to this question: Are they reimagining how work gets done, or are they just automating traditional workflows?
Strategic advantage in AI comes from systematically integrating agentic capabilities into core business operations. Rather than allowing disparate teams to address point solutions haphazardly, true transformation comes from deploying AI holistically.
The organizations that pull ahead in 2026 will be those that lay the proper foundations. They’ll implement governance that enables experimentation. They’ll build agents that connect teams, not just automate tasks. They’ll unify their data architectures to provide needed context. And they will fundamentally reshape how they do work, unlocking competitive advantages that could last for years.
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ABOUT THE AUTHOR
Michelle Gill combines strategic vision with hands-on expertise to build and guide high-performing engineering teams within DevOps and the rapidly evolving fields of AI and data science. Currently at GitLab, she drives innovation in AI-powered workflows that enhance efficiency and reduce cycle times across their DevSecOps platform. With direct experience in software engineering, Michelle is passionate about helping teams develop skills they need to increase impact on their company’s success, and has employed numerous methods of trial and error, servant leadership and old-fashioned hard work over the last 15 years to realize this progression at scale.
