GitLab’s internal playbook to foster AI

Published on: September 2, 2026 7 min read See how GitLab brought our Talent Development and Enterprise Technology teams together to foster AI fluency across engineering. 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. 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: "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. 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. To measure success, we watch three core areas for signal: 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. 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: 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. Enjoyed reading this blog post or have questions or feedback? Share your thoughts by creating a new topic in the GitLab community forum. Share your feedbackStart building faster today See what your team can do with the intelligent orchestration platform for DevSecOps.