By Dr Sivaramakrishnan R Guruvayur
Suggested edited version prepared for the author’s review. Not approved or final for publication.
Boards do not need to become technologists. They need the competence, economic discipline and governance architecture to ensure that intelligence at scale creates enterprise value without outrunning the organisation’s capacity to govern it.
What does a single AI-handled customer interaction cost? What margin does an autonomous workflow actually earn? If a director puts these questions to management, an annual AI budget line will not answer them. That gap, between spending on AI and understanding its economics, is where board governance of AI now lives or dies.
The economics are no longer hypothetical. When Klarna reported in February 2024 that its AI assistant had handled 2.3 million customer service conversations in its first month, two-thirds of its service chats, it put a figure on what boards are being asked to govern: the company said the system was doing the equivalent work of 700 full-time agents. Those are the company’s own 2024 figures, a snapshot rather than a current measure, and they say nothing by themselves about the full costs or returns of the system. Closing that gap is precisely the board’s job. Not “what are we doing with AI?” but “what does intelligence cost per outcome, and who is watching the return?”
Competence before technology
The central challenge is not to turn every director into an AI technologist. It is whether the board collectively has enough competence to challenge management: what AI can and cannot reliably do; where the organisation depends on models, data, compute and third parties; how AI creates economic value; the major safety, cyber, regulatory and ethical exposures; and where autonomous systems may act and where human accountability must remain. AI literacy belongs in the board skills matrix, director development and board-effectiveness assessment, not delegated to the one director who “does technology.”
AI strategy is also now capital-allocation strategy. Proprietary versus external models, build versus buy, data, talent, cloud and compute concentration: these are choices about the future economics of the industry, not about IT. Boards should ask which capabilities the company must own, where it should partner, and what it should never delegate to a model.
The full cost of intelligence
Generative and agentic AI change the cost structure itself. A single task can involve multiple agents and repeated model calls, multiplying token use, while retrieval and tool integrations can add further costs depending on the design. Aggregate technology budgets hide this. Boards need unit economics: cost per inference, per customer interaction, per autonomous workflow, per successful business outcome, and the margin or revenue each workload generates. The critical question is not whether AI improves productivity but whether it earns sustainable returns after the full cost of intelligence is counted. That argues for governing an AI investment portfolio, not a collection of disconnected pilots.
Who answers when the machine is wrong
Accountability is the harder test. When a customer of Air Canada was misled by the airline’s chatbot about bereavement fares, the airline argued it could not be held liable for what the chatbot said. In a 2024 decision, a Canadian tribunal rejected that argument: the chatbot “is still just a part of Air Canada’s website,” it ruled, and the company “is responsible for all the information on its website.” One tribunal ruling is not a universal legal rule, and obligations will vary by jurisdiction. But the practical lesson for boards travels: delegating a decision to AI cannot mean delegating accountability to AI. Boards should define what can be automated, what requires human review, and what must remain explicitly reserved for human decision-makers.
The surrounding risk surface is broad: hallucination, bias, model drift, privacy, copyright, data leakage, adversarial attack, third-party concentration and unintended agent behaviour. Frameworks such as the OECD AI Principles, the NIST AI Risk Management Framework, ISO/IEC 42001 and the EU AI Act provide foundations. Compliance alone, however, is not governance.
India’s financial regulators have been moving in the same direction. In November 2024 the securities regulator SEBI proposed amendments to make regulated entities responsible for their use of AI tools, and in June 2025 it consulted on guidelines for the responsible use of AI and ML in Indian securities markets. In August 2025 a Reserve Bank of India committee published its FREE-AI report, recommending a framework for the responsible and ethical enablement of AI in financial services. For this paper’s argument, the direction is the same as in the global frameworks: the questions it puts to boards, on competence, economic discipline, risk control and accountability, apply in India as much as anywhere.
An operating system for board AI governance
This paper proposes a single operating model to connect those strands to fiduciary responsibility and economics: the Board AI Governance Operating System (B-AIGOS), designed as an overlay on existing governance structures, not a parallel bureaucracy. It spans six domains: Mandate (which AI decisions belong to the board, committees and management); Competence (can the board challenge AI strategy and risk); Value (how AI contributes to ROI and enterprise value); Risk (which exposures sit within risk appetite); Accountability (who remains answerable for AI-enabled decisions); and Assurance (what independent evidence confirms that systems and controls operate as intended).
Decision rights then follow materiality against risk:
| Enterprise Materiality | AI Risk | Governance Level | Typical Decision |
| Low | Low | Management | Internal productivity tools, low-risk copilots |
| High | Low | Executive / Board Committee | Material AI investments with limited risk |
| Low | High | Risk / Technology / Audit Committee | Sensitive AI affecting privacy, compliance or customers |
| High | High | Full Board | Autonomous systems, major AI platforms, critical model dependencies |
A board AI dashboard should track outcomes rather than activity: AI-derived revenue, realised ROI, cost per business outcome, token and inference trends, model reliability, significant incidents, human overrides and third-party concentration. A five-level maturity model, from Reactive to Adaptive, lets boards assess each domain separately. Technological maturity should never be confused with governance maturity.
The first year is practical. Establish the AI inventory, board competency baseline and AI risk appetite in the first quarter. Clarify decision rights, committee ownership and AI ROI and FinOps metrics in the second. Introduce dashboards, scenario exercises and incident escalation in the third. Establish independent assurance and conduct a formal maturity review in the fourth.
Five questions for every chair
Where is AI creating enterprise value? What is its true economic cost? What could materially go wrong? Who is accountable? And how do we independently know that management’s claims are correct?
The defining boardroom challenge of the AI era will not be whether companies adopt AI. It will be whether boards develop the competence, economic discipline and governance architecture to ensure that intelligence at scale creates enterprise value without outrunning the organisation’s capacity to govern it.
