Give two engineering teams the same AI tool and you can end up with two very different outcomes. One team ships faster with fewer bugs, while the other gets burned by an agent that confidently generates the wrong output.

At GitLab, our team had AI tools at their fingertips and some found real value fast, working faster and catching issues earlier. Meanwhile, others hadn't quite found an entry point yet to develop effective AI-native workflows.

We learned that building AI fluency — how our team members know what to delegate to AI, how to build the right AI-native processes, and how to judge what comes back — was just as important as AI tool adoption and access. Building that fluency across GitLab was both an operational and technical challenge requiring close partnership between our Enterprise Technology and Talent Development teams.

We’re sharing our internal playbook so other technical leaders gain another perspective on how to encourage the right kinds of AI adoption across their own organizations.

An AI strategy built for the pace of our work

Part of building the right paths for our technical teams was predicated on establishing smart foundational infrastructure. Enterprise Technology considered a few different structures to our governance. The first was a fully centralized team, but we worried that with the speed of AI technology, shipping approvals from one group could end up as a bottleneck. As a result, team members could become impatient and try to circumvent governance infrastructure to experiment with AI. The second was a fully decentralized approach, but that could fragment efforts across the company, which adds complexity and makes guardrail consistency challenging.

We landed on a hybrid model, building governance and enablement into a model that used the best aspects of centralized and decentralized strategies:

• Enterprise AI acts as a central governance and technology hub: Based out of our Enterprise Technology team, Enterprise AI operates as the platform hub, setting standards and security guardrails. They also own the central strategy for our largest AI vendors and homegrown solutions that support governance and control; any other AI tool sits on top of these guardrails.
• AI transformation owners build decentralized, function-specific AI strategy: Each function has an embedded AI Transformation Owner, a senior leader close enough to the work to spot where AI can help automate.
• AI champions evangelize the right adoption and access paths: An AI champions community in each function acts as an in-house center of excellence. This is made up of individuals inside each function that are the first to experiment, support teammates, and help drive the right AI adoption established by the Enterprise AI team and AI Transformation Owners.

"At GitLab, part of what we provide to our customers is Speed with Control. Internally, one of our operating principles is Speed with Quality. Our governance model combined those two — allowing teams to ship quickly, in a high-quality manner that aligns with corporate governance and compliance needs." — Manu Narayan, CIO

The result is a federated model where foundational AI tools are governed centrally, while experimentation and functional strategy develops locally – right where the action happens.

The partnership central to making this work

To make the roles of each group in this model effective, we also needed to understand where our team members were within their AI journey.

Talent Development helped Enterprise Technology make AI access useful and customized. A senior engineer and a new hire do not need the same type of support, so we built a self-assessment tool, the AI Literacy Ladder. This tool identifies where each team member actually stands for AI fluency and recommends a role-specific path forward, including a specific, dedicated learning pathway for engineering.

Engineering pathways center on practical knowledge and workflows: planning, code review, fixing a broken pipeline, and security remediation. Grounding the curriculum in someone’s day-to-day work helps fluency actually stick.

"Our goal wasn't to teach today's tools, it was to build the judgment and durable skills that enable our team members to adapt with confidence as AI keeps evolving." — Rob Allen, Chief People Officer

We paired the AI Literacy Ladder with hands-on upskilling. All-company lessons provided everyone a shared baseline, followed by function-specific sessions. For engineers, we held advanced workshops, including practical labs; 87% of attendees said they learned something they could apply immediately. Across a separate set of non-technical workshops, 95% of participants said they were likely to apply something from the training within two weeks, and 92% reported increased confidence using the AI tool from the workshop.

Based on training feedback, more than 87% of participating engineers confirmed they were likely to apply what they learned in the next two weeks. And a month after we launched the AI Ladders initiative, we saw a 22.3% jump in daily interactions with the primary internal AI coding tool trusted by our engineers.

We measure program impact holistically

To measure success, we watch three core areas for signal:

• Reach tells us how many team members are completing the self-assessment and the pathways.
• Depth tells us whether team members are consistently moving across the pathways.
• Applied value, which is measured through workshop feedback, clarifies whether engineers are finding practical value in training work. We also look at if there’s meaningful growth in tool usage that correlates to the program’s launch.

We evaluate all of these in conjunction with one another, since individually they don't tell the full story. Together, they help us get a clearer picture of if enablement is actually driving fluency and real value as we work through AI transformation.

Lessons to consider as you build your own playbook

You really can’t chase the perfect AI adoption strategy. AI evolves so quickly and interacts with internal culture and processes in distinct ways for every organization. Your adoption strategy needs space to flex as the market continues to shift.

If you’re earlier in this journey, here are six lessons from our own approach that helped build success:

1. Put Enterprise Technology and Talent Development in the same room from day one. Adoption is an operational and behavior change problem as much as a technical one, and neither function solves it alone.
2. Build governance that moves at the pace of AI adoption. New AI tools release weekly, so governance needs to give Enterprise Technology control without slowing down adoption, experimentation, or tool deprecation.
3. Meet people where they are instead of mandating a single curriculum. Team members have different variable rates that they’ll adopt AI at and it’s important to make the curriculum practical to effectively build fluency with late adopters.
4. Treat AI enablement as a living product. The AI tools underneath how your teams work will continue to change. Fluency needs to be continuously supported with resources from Talent Development and Enterprise Technology teams.
5. Teach durable judgment. Using AI tools and functionality is just one small part of the fluency. How and what to delegate to AI, as well as evaluating what it hands back, are just as important.
6. Don’t wait for the perfect enablement strategy. AI moves too quickly to wait for a fully comprehensive strategy. Progress requires stacking small wins and iterating from there.

AI is changing business for everyone and much faster than anyone could have anticipated. We believe in giving our team members the tools and infrastructure to help them adopt and build the fluency needed to navigate work right now. The partnership between our Enterprise Technology and Talent Development teams is at the core of how we’re building that AI-first culture, one rung at a time.

If you're curious about how to build greater AI maturity, GitLab also offers an AI Modernization Assessment to help you accelerate AI-powered development through a personalized AI maturity roadmap. Take the assessment to learn where your organization stands as you continue to build AI-enabled engineering teams.