Organisations are using AI across their value chain for both internal and public/consumer facing applications. Use cases range from hiring and fraud detection to customer support and personalisation.
As a result, AI deployment has moved squarely onto the board agenda. Research shows that AI-savvy boards averaged 10.9 percentage points above industry peers in return on equity, making AI a strategic lever for efficiency, speed, and competitive advantage.
However, increased integration of AI into core and ancillary organizational functions, introduces structural risks. Vendor control is one of the many examples. Enterprise data may be reused or retained by third-party AI tools beyond agreed purposes, undermining confidentiality.
Further, reliance on third-party AI also amplifies regulatory exposure. This is because deploying companies are likely to remain accountable despite limited visibility into model design, training data, or system updates.
Additionally, AI deployment also raises privacy concerns as personal data flows through opaque systems. This constrains compliance with data protection obligations. Finally, hallucinations, bias, and accuracy failures can produce misleading or discriminatory outcomes and limited transparency around testing and remediation compounds these challenges.
Courts and regulators are increasingly scrutinizing AI-related failures. For instance, Trivago was penalised after its ranking algorithm prioritised hotel offers from advertisers paying higher commissions, even when cheaper options existed. Australia’s competition regulator held that Trivago misled consumers about best available prices.
Additionally, risks have also emerged from internal AI use. Employees have input sensitive organisational information into generative AI tools, prompting companies to recalibrate controls around data confidentiality and permissible use.
Organisations strengthening their governance defences by establishing AI oversight committees, issuing internal usage policies, and investing in workforce sensitisation. Regulatory developments are further accelerating this shift. As the EU’s AI Act moves toward enforcement, EU investors emphasised that boards must oversee how AI is designed, deployed, and monitored as a part of their governance functions.
Further US commentators argue that boards may be held accountable for AI-related failures, particularly where AI: (i) underpins the business model, (ii) is central to operations, (iii) is deployed in high-risk contexts, or (iv) produces foreseeable harm during routine usage.
India is charting a distinct, evidence-led approach to AI governance. Rather than adopting an overarching AI statute, the focus is on operationalising common principles for responsible AI use across sectors. These principles were first articulated by the Reserve Bank of India through its FREE-AI committee report for the financial sector.
The Ministry of Electronics and Information Technology subsequently endorsed them in its India AI governance guidelines. Together, these documents emphasised the need for integrating principles of trust, fairness, accountability, transparency by design, and safety to manage AI risks. These principles are now being translated into concrete governance expectations.
For instance, the RBI recommended regulated entities to adopt board-approved AI policies covering governance, ethics, accountability, and risk appetite. Similarly, the Securities and Exchange Board of India proposed that market participants designate senior management responsible for AI oversight across the lifecycle, supported by clear accountability frameworks.
These frameworks don’t prescribe a one-size-fits-all checklist. However, they signal how senior management should structure oversight around AI adoption.
To deploy AI responsibly while maintaining business agility, boards can take three steps:
First, assess AI usage and adopt a baseline governance policy. Responsible AI adoption begins with organisational visibility. Boards should commission an organisation-wide mapping of where AI is used, for what purpose, and its potential impact. Use cases should be classified into consumer-facing and internal deployments. This enables risk prioritisation and creation of an AI inventory capturing use cases, data inputs, vendors, and risk levels. This inventory can then form the foundation for the organisation’s AI governance policy.
Second, treat vendor transparency and contractual safeguards as a governance priority. Taking cues from India’s policy deliberations, deploying organisations will likely remain responsible for AI-generated outcomes even when the model is supplied by a third party. Boards should ensure clarity on what an AI system does, where it can fail, and how it uses data. Vendor contracts should define clear limits on reuse of enterprise or customer data for AI training. Similar expectations should be built around ownership of outputs, audit rights, and liability protections.
Third, institutionalise board-level reporting, continuous monitoring, and incident response. India has focused on an evidence-led approach to managing AI risks which will help in developing appropriate risk assessment and classification frameworks.
Organisations should treat AI incident reporting and continuous monitoring as part of their governance strategy. Boards should adopt internal policies that encourage early detection and good-faith reporting of AI-related incidents.
At minimum, boards should mandate periodic reporting on material AI use cases, key risks, vendor dependencies, and control effectiveness. This should be supported by a clearly defined incident response playbook covering escalation, evidence preservation, and fallback mechanisms.
With the increased AI adoption, boards should begin deliberating on how to responsibly govern AI at scale. India’s robust policy discussions give organisations valuable insight into formulating their internal governance approach. Early movers that establish frameworks for promoting visibility, ensuring accountability, and operationalising escalation mechanisms will be better positioned to harness AI’s benefits while effectively managing its risks.
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