No moonshots, just impact: Inside the playbook of AI-first CIOs

Artificial Intelligence has moved beyond hype to become a strategic imperative. From factory floors to customer support, supply chains to boardrooms, AI is reimagining how enterprises operate and compete.

But for CIOs, embracing AI is not just about adopting the latest tools. It’s about leading a complex, high-stakes transformation — one that demands sharp judgment, strategic patience, and relentless focus on business outcomes.

Through conversations with India’s leading CIOs, ETCIO identified three critical balancing acts that define the AI journey today. These are not just challenges to overcome — they are choices that will determine which enterprises thrive in the AI era and which falter.

• Innovation vs. risk: Building in the safe zone

Business teams are eager to unleash generative AI to automate processes, enhance decision-making, and delight customers. But compliance and security teams are rightly cautious, wary of data leaks, IP violations, algorithmic bias, and opaque decisions.

That caution is not misplaced. IBM’s Cost of a Data Breach Report 2024 found that AI-related breaches now cost organisations an average of $4.5 million, damaging both balance sheets and brand equity.

Abhinav Kumar Pandey, DGM at GAIL, sums up the tension well, “The GenAI imperative today is about balancing secured innovation with control and compliance. Security by design isn’t just a checkbox — it’s a necessity.”
At GAIL, experiments happen in rigorously defined “safe zones” — secure sandboxes with anonymised data, rollback mechanisms, and stringent access controls. Other CIOs report similar practices: dual-track environments, internal ‘red-teaming’ to test resilience, and AI steering committees that vet sensitive deployments.
The lesson: CIOs don’t stifle innovation. They channel it into structured, controlled experiments. Sandboxes, staged rollouts, and kill switches give teams the freedom to explore while ensuring the enterprise stays protected.

• Cost vs. value: Avoiding vanity projects

The allure of AI is undeniable — but so are the costs. From GPUs and cloud infrastructure to compliance tooling and niche talent, AI can strain budgets. Gartner estimates that backend infrastructure alone can swallow up to 30% of an enterprise’s AI spend.
This reality is forcing CIOs to confront a tough question: Are we investing in real value or just chasing shiny objects?

Shekhar Mishra, Divisional Head - Digital & Data Product (Fleet Mobility Solutions) at Apollo Tyres Ltd., brings this into focus. “A tyre today isn’t just a tyre — it’s a connected, intelligent asset. We use AI to solve various use cases from route optimization to predictive maintenance with proper governance,” he said.

Rather than pursue high-profile moonshots, forward-thinking CIOs are doubling down on pragmatic, high-impact use cases: Intelligent call routing, predictive maintenance, demand forecasting, defect detection.
To sustain this discipline, many organisations are establishing AI project management offices (PMOs) and product squads that prioritise initiatives by business value, manage budgets like investments, and retire underperforming pilots without sentimentality.

The message is clear: you don’t need a moonshot to deliver impact. Even modest, scalable wins can demonstrate AI’s value when measured and managed like any other business investment.

• Speed vs. governance: Scaling with guardrails

In the fast-moving AI landscape, moving slowly feels like falling behind. But haste can backfire — introducing bias, breaching regulations, and damaging trust.

Shankar Rao, CIO & CDO at Bosch Limited, frames the challenge succinctly, “Our job isn’t to say no to innovation. It’s to ensure innovation happens with guardrails — around privacy, transparency, and ethics. Governance isn’t a blocker; it’s what makes scale possible.”

Progressive CIOs are embedding governance from day one, not as an afterthought. Rao emphasized the following frameworks:

• Model documentation and explainability
• Data lineage and version control
• Fairness audits and bias detection
• Usage guidelines for generative tools
• AI risk registers and ethical impact reviews

Some organisations have even set up AI ethics councils and ‘responsible AI’ offices to institutionalise oversight. Governance isn’t about slowing down innovation - it’s about ensuring it can scale without collapsing under its own weight.

Modern CIOs are no longer just technology custodians - they are the chief balancers of risk, value, and velocity. The era of flashy pilots and vanity projects is giving way to outcome-driven initiatives tied to measurable business objectives.

Today’s CIOs are asking hard, business-focused questions:

• Can this model cut ticket resolution time by 30%?
• Can it optimize energy usage by 10%?
• Can it improve forecast accuracy enough to reduce inventory waste?

By aligning AI programmes with cross-functional OKRs and operational metrics, CIOs are showing that AI isn’t just about technology - it’s about culture, accountability, and strategic foresight.

The CIOs who get this right won’t just deploy AI - they will shape how it transforms the enterprise for years to come. And that - not a pilot, not a proof-of-concept - is the real innovation story worth telling.`