Let AI into the boardroom

Not too long ago, at a quarterly Board meeting of a conglomerate, the group CFO spoke with conviction about the “AI agenda”. He highlighted productivity gains, tools being deployed, pilots running across functions, and the ambition of becoming an “AI-first” enterprise. The room appeared broadly in agreement. Then a director, reserved until that moment, asked a question that cut through the narrative: “How many of our Board members actually know which AI tools are being used across our companies, by which teams, and whether our enterprise data is being used by them?” What followed was a pause that felt like silence. The kind of silence that said everything.

AI has become central to strategy decks, investor conversations and operating models. Across organisations, CEOs speak of being “AI-led”, while functions increasingly position themselves as “AI-enabled”. Leadership teams now present AI systems as a core driver of enterprise value. In the Boardroom, however, the conversation is beginning to shift from ambition to application. Boards are engaging more directly with how that value is created, measured and governed.

Leaders are now discussing deployment priorities, automation opportunities, and the allocation of repetitive work to agentic AI systems. Yet across India Inc., many boards continue to be shaped by directors whose careers were built in a pre-AI era. Their judgement remains invaluable, but few have operated within the ethical, operational and governance complexity of running AI at scale. Effective boards will increasingly include technology leaders with proven operational experience in running complex tech environments. Even as regulators push for greater technology fluency, board-level engagement on AI is only beginning to take shape as a core strategic capability.

AI systems can act as a core enabler for Boards, providing a 360° view of context across the enterprise by connecting data, decisions, and outcomes. Used effectively, AI can support more forward-looking governance and sharpen decision-making. But while AI can serve as a powerful driving force, Board members must remain firmly in control of the steering wheel, by setting direction, applying judgement and ensuring accountability.

The most critical question a Board should address while shaping its AI strategy is simple: what outcome does the organisation expect from investment in a specific AI system? Is the

objective to improve efficiency? Strengthen accuracy? Reduce errors, fraud, defects or rework? The clarity of that answer defines the direction of the AI agenda. It establishes purpose, guides decision-making and ensures that deployment is aligned with measurable business value.

A structured approach to tracking implementation is equally important. Clear metrics, milestones and accountability help ensure that impact is measured against stated objectives. Cost discipline must also be built into the strategy from the outset. Boards need to define how much capital is being deployed, set clear budgets by use case and function, and ensure investment remains closely linked to measurable outcomes.

Tool selection is another critical element of an effective AI strategy. Each system serves a defined purpose, and effectiveness lies in choosing the right tool for the right outcome. The real challenge is navigating a crowded vendor market, where claims often outpace delivery. Clarity on objectives, supported by well-defined milestones, allows organisations to engage vendors with intent and deploy solutions that are tightly aligned to business value.

Data governance must sit at the centre of this conversation. Boards, in consultation with operational teams, need to understand where enterprise data resides when external AI tools are used. They must engage vendors on how data is processed, stored, and accessed. They should also assess whether any sensitive or regulated information could be exposed. Without this clarity, AI adoption can quickly create risks that are difficult to contain.

Ownership is another pillar of responsible implementation; every AI use case must have a clearly identified owner who is accountable for outcomes. That responsibility includes setting the objective, monitoring performance, and ensuring continued alignment with business intent. Clear ownership strengthens accountability, keeps a human in the loop, and reduces risks linked to model drift, hallucinations or operational errors. Across use cases, this must be supported by domain experts who can review outputs, apply judgement and ensure AI-driven workflows remain accurate and relevant.

Collaborating with AI is now essential for India Inc., but adoption must be guided by clear strategy, not momentum alone. Boards need an implementation plan that defines outcomes, ownership, data safeguards, budgets, and monitoring mechanisms. The risks, from data leakage and vendor exposure to bias, hallucinations and regulatory gaps, must be built into the strategy from the start.

The priority is not faster adoption, it is accountable adoption. Only then can AI move beyond pilots and productivity claims to become a governed enterprise capability that creates sustainable value.

(Bakhru is Partner and Family Office Leader, Grant Thornton Bharat; Gulati is Partner, Grant Thornton Bharat. Views are personal.)