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.
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Facts Only
* GitLab implemented an AI fluency initiative involving Enterprise Technology and Talent Development teams.
* The initiative launched on or before September 2, 2026.
* A hybrid governance model was adopted, combining centralized tool governance with local experimentation and functional strategy.
* The AI Literacy Ladder is a self-assessment tool used to identify fluency levels and recommend role-specific learning paths.
* Engineering pathways focus on planning, code review, pipeline repair, and security remediation.
* All-company lessons were followed by function-specific sessions and advanced workshops with practical labs.
* 87% of engineering workshop attendees reported learning immediately applicable material.
* 95% of non-technical workshop participants indicated they were likely to apply training within two weeks.
* 92% of non-technical participants reported increased confidence using the AI tool.
* Daily interactions with the primary internal AI coding tool increased by 22.3% one month after the AI Ladders initiative launched.
* GitLab offers an AI Modernization Assessment for external organizations.
Executive Summary
GitLab has integrated its Enterprise Technology and Talent Development teams to move beyond simple AI tool adoption toward "AI fluency." Recognizing that access to tools does not guarantee productive outcomes, the organization implemented a hybrid governance model. This approach centralizes the foundational infrastructure to ensure compliance and security while decentralizing experimentation to allow individual teams to develop specialized, AI-native workflows.
To personalize this transition, GitLab deployed the AI Literacy Ladder, a self-assessment tool that maps an employee's current skill level to a specific learning pathway. For engineers, these pathways are grounded in daily operational tasks such as security remediation and code reviews. Early metrics indicate a positive correlation between this structured upskilling and tool utilization, specifically a 22.3% increase in daily use of the primary AI coding tool. Success is tracked through a combination of tool adoption, employee sentiment, and output quality, acknowledging that no single metric provides a complete picture of AI transformation.
Full Take
The strongest version of this narrative is that technical proficiency in the AI era requires a shift from "tooling" to "literacy," necessitating a cross-functional partnership between IT and HR to ensure humans can critically judge AI output.
However, this is a classic vendor advertorial. The narrative arc moves from a common pain point (inconsistent AI results) to a proprietary solution (the Literacy Ladder) and concludes with a direct call to action to purchase a paid "AI Modernization Assessment." The load-bearing evidence for the success of the program—the percentages of "increased confidence" and "likelihood to apply"—relies on self-reported sentiment rather than objective productivity gains or code quality metrics. By framing the solution as a "playbook," the vendor uses borrowed credibility from internal operations to sell an external consultancy service.
Patterns detected: ARC-0043 Authority Game
The driving paradigm is the "Corporate Modernization" loop: identifying a systemic anxiety (AI displacement or inefficiency) and solving it through a proprietary framework that mandates continuous assessment and certification. This shifts agency from the individual engineer's intuition to a company-defined "Ladder" of fluency. The second-order consequence is the institutionalization of AI-native workflows, where the "correct" way to work is defined by the tool's capabilities rather than the problem's requirements.
If this were a coordinated influence campaign, the playbook would be "The Trojan Horse of Best Practices": provide a "free" internal case study that creates a perceived industry standard, making the reader feel their own organization is deficient, then offer the proprietary tool to close that gap. The content aligns precisely with this structural pattern.
Bridge Questions:
1. Would the 22.3% increase in tool interaction signify increased productivity, or simply an increase in reliance on a tool that may still produce "confidently wrong" outputs?
2. How does the "AI Literacy Ladder" account for engineers who develop highly effective, non-standard workflows that fall outside the predefined "rungs"?
3. What objective metrics (e.g., lead time for changes, change failure rate) would be required to prove "fluency" over "adoption"?
Sentinel — Human
The text reads like a detailed case study or internal leadership communication, employing specific data and personal reflections indicative of human authorship rather than generalized synthetic content.