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.

“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.

Build vs buy and the future of SaaS

As AI lowers the barrier to building internal tools, there have been suggestions that the days of off-the-shelf software-as-a-service (SaaS) applications are numbered. 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.”

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.”

GitLab, founded in 2011 by Dmitriy Zaporozhets and Sytse Sijbrandij, has grown from a simple git repository manager into a comprehensive DevOps platform. The company went public in 2021 and now serves more than 100,000 organizations, including major enterprises in finance, healthcare, and government. Its flagship product, GitLab Ultimate, includes integrated AI capabilities like GitLab Duo, which provides code suggestions and chat interfaces for developers. The company’s recent focus on agentic AI reflects a broader industry shift toward autonomous systems that can execute multi-step tasks without human intervention.

Narayan joined GitLab in late 2025 after serving as CIO at several large technology companies, including a stint at a major cloud provider where he oversaw IT operations and digital transformation. His appointment as GitLab’s first CIO signalled the company’s maturation from a fast-growing startup to an established enterprise grappling with complex internal demands. One of his first moves was to consolidate the data engineering and analytics teams, which had been scattered across different departments, into a single unit reporting to him.

The hub-and-spoke model that GitLab uses for AI has roots in other large enterprises, but Narayan says the company has customised it based on its own product development culture. The central team, comprising about 15 people, manages infrastructure, security reviews, and model evaluation. The spokes — transformation owners in each department — are typically senior individual contributors who understand the workflows deeply. They report to both the central team and their department heads, ensuring alignment without becoming bureaucratic.

One of the biggest wins so far has been in the sales operations area. GitLab deployed an AI agent that automatically populates customer records with meeting summaries, call transcripts, and email threads from Salesforce, HubSpot, and Slack. The agent reduced manual data entry by 40%, allowing sales reps to spend more time on calls. Similarly, the people operations team now uses an AI chatbot trained on internal HR policies, which answers 30% of employee queries without human intervention.

Tokenmaxxing has become a controversial topic among CIOs. The term, coined in early 2025, describes the practice of incentivizing employees to consume as many AI tokens as possible — often by gamifying usage statistics. Proponents argue it encourages experimentation, but critics like Narayan warn it leads to wasteful consumption and poor outcomes. GitLab’s approach of focusing on daily active users (DAU) rather than token volume is gaining traction in the industry, as companies seek to measure meaningful adoption.

From a cost perspective, GitLab has found that agentic AI can actually reduce overall token spend by consolidating multiple prompts into a single orchestrated workflow. “Instead of having a developer issue 50 separate queries, an agent can reason over a problem in one long chain of thought, producing a single output,” Narayan explained. That efficiency, combined with strict data classification, helps keep the line item within budget.

The company also runs regular internal hackathons where employees pitch AI agent ideas. Winning proposals receive seed funding and support from the central team. This bottom-up innovation is balanced by top-down governance: every AI agent must pass a security review that checks for data leakage, prompt injection, and role-based access violations before it goes into production. GitLab also maintains an internal registry of all approved AI agents, similar to an app store, so employees can discover and reuse tools built by other teams.

Looking ahead, Narayan sees agentic AI fundamentally reshaping GitLab’s internal processes. He envisions a future where every department has a portfolio of AI agents working alongside humans, handling everything from expense report approvals (via natural language requests) to automated compliance audits. The key, he says, is to remain focused on business outcomes rather than technology novelty.

“We don’t need AI for the sake of AI,” he said. “We need AI that helps us deliver better products to our customers and better experiences to our employees. That’s our north star.”

Source:ComputerWeekly.com News