Congratulations! With AI in your toolbelt, developers are shipping code faster, costs are down, and you started 2026 with concrete AI wins.
But, you’re about to face a new set of problems.
The decisions you make about scaling, governance, and integration in 2026 will set the foundation for your organization’s performance for years to come. Get it right, and the wins you’ve seen in the past year will compound. Get it wrong, and your growth will plateau or worse.
Here are three ways to keep the AI momentum going.
Govern what you’ve built
At some point in the coming year, your CFO may ask why your cloud costs have skyrocketed and you’ll likely find that teams across the organization are using AI without approval or oversight from the organization. This shadow AI usage leads to multiple teams solving a similar problem using different agentic AI tools.
The spirit of experimentation is exactly what’s driving AI adoption and the discovery of helpful, quick fixes. But as teams spin up ad hoc solutions from development tools, cloud platforms and countless other sources, the lack of centralized oversight becomes an issue that you can’t afford to ignore. All of these agents will increase cloud and compute costs, but an even greater challenge is the risk to your intellectual property (IP).
When developers use unvetted AI tools to ship faster, they end up pasting in proprietary code to debug or sharing internal architecture docs for context. That information may be stored, shared, or used to train third-party models, and lead to a breach of proprietary source code or compliance risks when customer data is pulled into unvetted AI tools.
Organizations need to implement governance frameworks that track which agents are running, what data they have access to, and how they interact with each other and with critical systems. These frameworks give your teams clear guidelines on how to move fast without creating a security breach.
With governance in place, organizations will have lower cloud bills and they’ll be able to prove exactly how their AI systems are interacting with sensitive data. Developers still need the ability to experiment with new AI tools. But the most successful organizations will find the right balance between AI innovation and governance.
Build agents that go beyond automating tasks
You should already be using AI to automate all of your safely repeatable tasks. If your teams tried to implement AI, but aren’t seeing results, the problem might be the approach. Many teams believe AI doesn’t work for them because they took a buckshot approach and used AI for every possible scenario. Instead, try two AI-integrated tasks 500 times rather than 500 tasks twice. This targeted strategy allows you to continually optimize and hone your AI solutions before adding more.
Once your repeatable tasks have been automated, it’s time to use AI holistically across your pipeline. The organizations that see long-term success with AI will be those that build agentic AI systems to handle complex, multistep processes, such as code review cycles and pipeline failures.
AI agents can serve as the connective tissue between teams, handling the administrative work and streamlining review cycles that create bottlenecks. For code reviews, this looks like routing reviews, surfacing relevant context, and keeping workflows moving. For pipeline durations and reliability, agents can diagnose pipeline failures and prioritize runs, saving developers time and mental bandwidth.
By managing end-to-end processes and overseeing the full software delivery pipeline, these agents move beyond isolated use cases. Instead, they own the workflows between tasks, removing dead time, friction, and coordination overhead.
Unify your data architecture
Your AI is only as smart as the data it can access. Context is spread across different systems that operate in silos. AI might be able to write technically sound Python code, but without access to design decisions recorded in a wiki, compliance requirements buried in Jira, or a customer data model that exists only in the company’s customer relationship management (CRM) system, the code is strategically useless.
That fragmented data landscape is the main obstacle between your current AI performance and the full potential of your AI investments. To close that gap, you need to build a data architecture that actually supports the AI systems you're already running.
When you prioritize DevOps modernization with unified data and context infrastructure, you'll see faster deployments and reduced security risk. Plus, your AI systems can finally draw on institutional knowledge instead of working around it.
Are you reimagining? Or just automating?
This is the question that will separate AI leaders in 2026: Are you reimagining the way work gets done or are you just automating old processes?
Succeeding in this next chapter of AI means integrating agentic capabilities into business operations, rather than adopting point solutions and haphazardly addressing challenges across teams.
Setting your organization up for success starts with building a strong foundation: governance that enables experimentation, agentic systems that solve challenges holistically, and a data architecture that provides agents with the context they need.
Accomplish these three things, and your AI gains will turn into lasting competitive advantage.
Next steps
Application security in the digital age
Read our survey findings from more than 5,000 DevSecOps professionals worldwide for insights on how organizations are grappling with increasing attack surfaces and changing attitudes towards security and AI.
Frequently asked questions
Key takeaways
- Shadow AI creates unchecked costs and redundant solutions. Enterprises need governance frameworks that track which agents are running, the resources they consume, and the business value they deliver.
- Point solutions that speed up isolated tasks create downstream bottlenecks. Agentic systems that manage end-to-end processes serve as connective tissue between teams and eliminate review backlogs.
- AI systems are only as smart as the data they can access. A unified data architecture gives agents the institutional context they need to deliver results beyond technically sound output.