Thanks to increasingly capable models, some parts of your business are getting faster, more capable, and more productive every month. These teams are using artificial intelligence to compress timelines, surface insights, and automate work that has historically been time-consuming and tedious.

Meanwhile, other functions just down the hall are still waiting for a formal rollout, a governance approval, or someone to tell them what to do and how to start. The gap between the AI haves and have-nots in your organization is widening, and addressing it requires a new operating model.

Your teams need more support

When leaders notice the uneven distribution of capability across their business, the instinct is to treat it as a tooling problem. They push to get everyone access to the same platforms, provide general-use training, and hire some specialists to slot into IT.

But access is table stakes. It’s a good start, but it won’t get you to strong organizational adoption.

Harvard Business School reports that workers using these tools completed tasks 25% faster and produced results rated more than 40% higher in quality. But the same study also found that performance declined when people used the tools without understanding where they applied and where they didn’t. Fluency, not just access, drives results.

Departmental leaders need guidance on how to apply capabilities in the context of their day-to-day work. Without that knowledge, they can’t ask the right questions.

“Performance declined when people used the tools without understanding where they applied and where they didn’t. Fluency, not just access, drives results.”

Teams playing catch-up tend to focus on how to inject new tools into existing workflows, when they should be thinking about re-engineering processes entirely. They’re focused on evolution in a world undergoing revolution.

Reimagining a process also requires stepping back from it, which is easier said than done. Here’s how it plays out in practice:

An SDR team comes to IT with a specific, bounded ask: “improve our sales lead routing.” Completely reasonable. But only when someone from IT, with visibility across the broader system, dives into the problem does the real opportunity surface. The data pipeline supporting lead routing is unnecessarily complex. With the right support, the conversation shifts to overhauling the entire pipeline and opens the door to fully agentic lead follow-ups.

Departmental leaders don’t lack ambition but throwing a software license and Slack channel at them won’t build the right kind of adoption. Technical support and strategic guidance are required to reimagine work from first principles.

AI fluency must be a structural consideration

The typical pattern puts a centralized team in charge of taking requirements, interpreting them in isolation, and delivering capabilities to departments months later. This model can’t keep pace when AI capabilities launch weekly.

A more effective approach pairs a central “hub” that owns platform strategy, governance, and reusable patterns with AI engineers embedded directly inside business departments. AI engineers serve as “spokes” inside departments, helping them identify vertical use cases day-to-day and delivering the cross-functional visibility needed to make a real impact. The AI engineer who solved a problem for finance can share the pattern with someone facing the same challenge in operations.

“A more effective approach pairs a central “hub” with AI engineers embedded directly inside business departments.”

In a department just getting started, the embedded AI engineer is the primary technical capability: scouting, prototyping, building. In a more mature department, they shift toward enablement, feeding patterns back to the “hub” and helping teams navigate AI without getting buried in process. Over time, departments will organically become AI-fluent as they learn from the engineers.

Make fluency your advantage

The right operating model drives how a function actually works, and strong fluency strengthens processes and institutional knowledge, so outcomes improve over time. As the flywheel builds, each problem solved raises the ceiling of what your team can do independently. 

McKinsey finds that the right workflow redesign is the single biggest factor in whether an enterprise sees meaningful bottom-line impact. Knowing what to redesign depends on how your teams understand and work with AI.

Everyone is adopting AI capabilities. The question now is whether your operating model helps your teams see the best path forward for applying them. If it doesn’t, that’s the gap to close first.

The post Your organization prioritized AI adoption, but you actually need AI fluency. appeared first on The New Stack.

For the past two years, the conversation about AI-assisted software development has been dominated by speed. A new GitLab survey of more than 1,500 developers and technology leaders found that 60% say AI coding ROI has already exceeded expectations, and 78% report their teams are writing and committing code faster since adopting AI tools. 

But speed without control is a liability.

Most organizations have pursued agentic engineering by adding AI coding tools on top of their existing infrastructure. Coding agents are delivering speed, but that speed isn’t showing up across the full software lifecycle: Only 21% of respondents report productivity gains beyond code generation itself.

“Speed without control is a liability.”

The infrastructure problem runs deeper. Git backends, toolchains, and governance frameworks were built for human-scale concurrency. Agents operate at machine scale, and that mismatch shows up fast. Platform reliability breaks down with millions of agent sessions hitting the same backend, security exposure widens as agents touch dependencies at scale, and cost overruns mount as agents consume tokens inefficiently on infrastructure that wasn’t built for them.

Agentic adoption outpaced governance

The adoption curve for AI coding tools outpaced the development of required guardrails, with 80% of organizations saying they adopted AI tools faster than they developed policies to govern them, and 82% reporting that AI-generated code risks creating a new form of technical debt that their organizations are not prepared to manage.

In practice, that means platform reliability challenges under agent load, security and compliance exposure that widens as agents touch dependencies at volume, and agents operating with artificial confidence because they lack full context. Only 28% of organizations say their software development lifecycle tools are fully integrated with shared data and workflows, which means most teams are trying to govern agent actions across a toolchain that was never designed for them.

Agentic engineering needs agentic infrastructure

Agentic engineering requires two things: agentic coding and agentic infrastructure. Most organizations have the first but lack the second.

Agentic infrastructure spans four areas: the execution layer, the context layer, the governance layer, and the orchestration layer working together.

The first is machine-scale execution. Git backends, CI/CD pipelines, and deployment systems were designed for human-paced development. In the agentic era, they need to handle millions of agent sessions without breaking. When a production incident occurs, the path from symptom back to origin should take minutes, not days.

The second is context that travels with code. As Bastian Stahmer, Business Owner of Vehicle Software Development Platform at Mercedes-Benz, put it on a panel recently, “An agent can only be as good as the context and semantics fed to it.” A context graph connecting code, work items, pipelines, security findings, and production signals is what makes agents genuinely useful at scale and keeps artificial confidence in check.

“An agent can only be as good as the context and semantics fed to it.”

The third is governance built into the flow. Agent actions need to be tied to an identity, logged against a policy, and provable to a reviewer. Low-risk changes move fast, while higher-risk changes trigger review. For Mercedes, operating under automotive regulatory standards that require full traceability and human accountability, GitLab is the control plane where that accountability lives.

The fourth is orchestration. Execution, context, and governance are only as effective as the system coordinating them. The orchestration layer coordinates agent actions across the full software lifecycle according to the policies teams define, determining which agents run, in what order, and how failures and handoffs are managed. Without it, agentic infrastructure is a set of independent capabilities rather than a working system.

What’s next

The next phase of AI in software will focus less on generating code and more on governing it, according to 85% of respondents. That shift reflects how enterprises are maturing their thinking about AI, from a productivity tool to a foundational capability that needs to be trusted, traced, and maintained at scale.

When governance is built into the platform, speed and control are no longer in tension. Traceability becomes a competitive advantage. Context becomes institutional memory. And the codebase, rather than accumulating invisible risk, becomes an asset that grows more reliable over time.

The post GitLab just surveyed 1,500 developers. Here’s why it matters for your codebase. appeared first on The New Stack.