GitLab CIO rejects ‘tokenmaxxing’ as it rebuilds work around agentic AI
Manu Narayan tells Computer Weekly why he’s steering clear of vanity metrics such as ‘tokenmaxxing’, why reports of SaaS’s death are overblown, and why the biggest pressure is simply keeping pace
Few IT executives feel the pace of developments in artificial intelligence (AI) as acutely as Manu Narayan. Some nine months into his role as the first chief information officer (CIO) at GitLab – the software development platform with over $1bn in revenue and more than 2,000 employees – Narayan is tasked with turning the company into a proving ground for the very technologies its customers use.
“The AI space in general is changing so rapidly that we’ve constantly had to revisit our goals and things that we want to accomplish,” he said in a recent interview with Computer Weekly.
With product development sitting with GitLab’s research and development team, Narayan’s mandate is mostly internal: modernising the business application stack, user support, as well as data and analytics. But instead of bolting AI onto existing workflows, his goal 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 said. “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.”
Pointing to a customer success manager (CSM) as an example, Narayan noted that the purpose of the role is to build deep client relationships, yet CSMs spend hours on administrative tasks such as building quarterly business review slides for clients, transcribing notes and hunting for context across customer relationship management systems, data warehouses and chat channels.
By deploying AI agents to handle the grunt work, GitLab is looking to free up its workforce to focus on high-level strategy. “We want all of our team members to focus on what matters most: the core purpose of their role,” said Narayan. “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.”
To manage AI deployments, GitLab has adopted a hub-and-spoke operating model. A central AI enterprise team handles governance, technical building and guardrails, while dedicated “AI transformation owners” embedded in individual divisions identify time-consuming, repeatable work that is ripe for automation.
The approach has already been applied to GitLab’s own internal employee support network. The company has built AI agents to assist its 120 internal support staff across IT, people operations and sales, helping them instantly pull the context they need or deflect routine tickets entirely.
Rejecting ‘tokenmaxxing’
As AI adoption increases across the enterprise, CIOs will naturally grapple with cost control and measurement. However, Narayan is wary of strategies such as “tokenmaxxing”, where developers and employees are encouraged to maximise the number of AI tokens they use.
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
Manu Narayan, GitLab
“We’ve specifically avoided and don’t want to do tokenmaxxing,” said Narayan. “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 tracks daily active usage across the tech stack to ensure its workforce is building sustainable habits. For calculating hard return on investment (ROI), Narayan insists on anchoring AI deployments to traditional business metrics. For an AI agent assisting a sales development representative, success isn’t measured by the number of prompts generated, but by standard key performance indicators: outbound messages, meetings scheduled and sales pipeline conversion.
“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.”
Narayan also pointed to 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.”
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.
Despite strong executive backing and budget, change management remains a challenge for Narayan. 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.
Yet, for a CIO, the greatest pressure is the ticking clock.
“The thing that keeps me up at night is whether we’re moving fast enough,” said Narayan. “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.”
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