Few IT executives feel the rapid pace of artificial intelligence developments as keenly as Manu Narayan. Nine months into his role as the first chief information officer at GitLab—the software development platform that now exceeds $1 billion in annual revenue and employs more than 2,000 people—Narayan is tasked with turning the company into a proving ground for the very technologies its customers use. In a recent conversation, he described how the AI landscape is evolving so quickly that GitLab has had to constantly revisit its goals and priorities.

While product development remains with GitLab’s R&D team, Narayan’s mandate is primarily internal: modernising the business application stack, improving user support, and overhauling data and analytics. Rather than simply bolting AI onto existing workflows, his vision is to rebuild operations from the ground up. “When I was revisiting our AI strategy a few months ago, the focus was not on how we introduce AI,” he explained. “The focus was to rethink the nature of work internally, leveraging AI. It’s thinking about processes from first principles and then using agentic AI to drive them.”

Agentic AI refers to systems that can autonomously act on behalf of users, completing multi-step tasks without constant human intervention. At GitLab, this means deploying software agents that handle repetitive, time-consuming activities across the organisation. Narayan pointed to a customer success manager as an example: the core purpose of the role is to build deep client relationships, yet CSMs spend hours building quarterly business review slides, transcribing notes, and hunting for context across CRM systems, data warehouses, and chat channels. By offloading these tasks to AI agents, GitLab aims to free up employees to focus on high-level strategy and relationship building. “We want all of our team members to focus on what matters most: the core purpose of their role,” Narayan said. “We’re leveraging AI for tasks that can help them scale out in a more linear way, more than just a 10-15% increase in productivity.”

Rejecting Tokenmaxxing

As AI adoption increases across the enterprise, CIOs naturally grapple with cost control and measurement. However, Narayan is wary of strategies known as “tokenmaxxing,” where developers and employees are encouraged to maximise the number of AI tokens they use. This gamification-driven approach can lead to inflated usage metrics without genuine productivity gains. “We’ve specifically avoided and don’t want to do tokenmaxxing,” Narayan stated. “Gamification can help drive outcomes, but I think it drives the incorrect behaviour. We’re not looking for purely context-in, context-out as the measure of success. It’s really hard to know if somebody’s gaming the system. Are they just sending excessive content because they don’t actually know what they’re doing?”

Instead of tracking token burn, GitLab monitors daily active usage across the technology stack to ensure the workforce is building sustainable habits. For calculating hard return on investment, Narayan insists on anchoring AI deployments to traditional business metrics. For an AI agent assisting a sales development representative, success is measured not by the number of prompts generated, but by standard key performance indicators: outbound messages sent, meetings scheduled, and pipeline conversion rates. This approach ensures that AI investments deliver measurable business value rather than just technological novelty.

The company has built AI agents to help its 120 internal support staff across IT, people operations, and sales. These agents can instantly pull the context needed to resolve tickets or deflect routine requests entirely. The hub-and-spoke operating model centralises governance through a core AI enterprise team that handles technical building, guardrails, and policy, while dedicated “AI transformation owners” embedded in individual divisions identify time-consuming, repeatable work that is ripe for automation. This structure balances top-down control with bottom-up agility.

Build vs Buy and the Future of SaaS

As AI lowers the barrier to building internal tools, some have predicted the end of off-the-shelf software-as-a-service applications. Narayan views this as vastly overstated, particularly from a governance and compliance perspective. “We may see more custom interfaces and the disaggregation of systems of interaction from systems of record,” he said. “But the underlying governance controls in core SaaS tools aren’t going anywhere.”

He also highlighted the hidden costs of bespoke software development. “It’s easy to get to 90% of an application you develop in-house. That last 10%—the role-based access controls, auditability, immutable logging, which are things you need as a public company or as a company that deals with regulated customers—is incredibly complex to build.” This reality keeps GitLab rooted in a thoughtful build-versus-buy analysis for each use case, ensuring that custom developments do not sacrifice security or compliance for speed.

To ensure safety across custom and supplier tools, GitLab grounds its AI governance in a strict data classification standard. Public data flows through self-service platforms, while proprietary or customer data requires deeper security reviews before interacting with large language models. This tiered approach allows the company to move quickly on low-risk use cases while maintaining rigorous oversight for sensitive information.

Change Management and the Need for Speed

Despite strong executive backing and budget, change management remains a challenge. Bridging the gap between AI-forward employees and those who are slower to adapt requires a mix of departmental centres of excellence and internal AI hackathons. These initiatives help spread best practices, encourage experimentation, and build a culture of continuous learning. Narayan also noted that the greatest pressure for a CIO today is the ticking clock. “The thing that keeps me up at night is whether we’re moving fast enough,” he said. “In the AI era, our decision-making needs to happen in days and weeks, not months and quarters. But I still worry about whether we are driving the right initiatives that are going to have the right long-term ROI for us.”

GitLab’s experience offers a blueprint for other enterprises navigating the AI transformation. By rejecting vanity metrics like tokenmaxxing, focusing on workflow redesign from first principles, and maintaining a strong governance framework, the company aims to harness agentic AI not just as a productivity tool but as a fundamental enabler of strategic business outcomes. The journey is still early, but Narayan’s approach highlights the importance of aligning AI adoption with core business goals, measuring what matters, and moving with deliberate speed in a fast-changing technological landscape.

Source: ComputerWeekly.com News