AI Literacy Is Not a Training Program

Introduction

Board AI literacy is being widely misunderstood and the misunderstanding is consequential. Across organisations, it is being treated as an awareness exercise.

• A few sessions with a technology team

• in the form of a glossary of terms

• high-level update on what large language models can and cannot do.

  
  

These interventions may build a degree of familiarity. They do not build governance capability. And in an environment where AI is increasingly embedded in decisions that affect customers, employees, risk profiles, and regulatory standing, familiarity is not enough. This post makes the case that AI literacy should be treated as governance infrastructure not a training programme, not a compliance checkbox. It is the foundation that enables boards to fulfil their core function: asking the right questions, at the right level of rigour, at the right time.

The problem with framing literacy as awareness

The awareness framing is understandable. AI is technically complex. Board members are generalists by design. And the instinct to demystify the technology to make it less intimidating and is well-intentioned.

But awareness and governance capability are different things.

Awareness means knowing that AI exists, understanding broadly what it does, and being able to follow a conversation about it. Governance capability means being able to have continuous oversight on how AI operates and to ask whether the systems being deployed are appropriate, whether the risks are understood and controlled, whether accountability is clearly assigned, and whether the evidence for responsible oversight actually exists.

Boards are not expected to become engineers. They are expected to be effective stewards of institutional accountability. That requires a different kind of literacy one oriented not toward technology fluency but toward governance judgment.

What governance-oriented AI literacy looks like

In practical terms, governance-oriented AI literacy means understanding at least five things:

1. Where AI is materially influencing decisions

Not every AI application carries the same governance weight. A chatbot handling FAQs is categorically different from a model influencing credit decisions, claims processing, or customer risk scoring. Boards need to know which systems are material — where AI is not just supporting a process but shaping an outcome. Without this, governance oversight has no meaningful anchor point.

2. What data foundations and controls sit beneath those systems

AI systems are only as reliable as the data and controls that underpin them. Boards should be in a position to ask:

what data is this system trained on?

How current is it?

What quality controls exist?

What happens when the data changes?

These are not technical questions — they are governance questions. The answers determine whether a board can reasonably assert that risk is being managed.

3. Where human accountability actually sits

As AI becomes more embedded in operational processes, accountability can become diffuse. Decisions that were previously made by identifiable individuals are increasingly mediated by systems whose outputs are difficult to trace. Boards need to be able to establish — clearly and specifically — who is accountable when an AI system produces a harmful or erroneous outcome. This includes accountability for third-party models and vendor-supplied AI, where the temptation to delegate responsibility is highest.

4. How governance intensity should scale with context and consequence

Not all AI deployments warrant the same level of board attention. Governance intensity should be proportionate to consequence — the higher the potential impact on customers, employees, or financial stability, the more rigorous the oversight. Boards that treat all AI equally will either over-govern trivial applications or under-govern consequential ones. A risk-based approach to AI governance is not optional — it is fundamental to effective stewardship.

5. What evidence demonstrates that oversight is working in practice

This is the question boards most often fail to ask. It is not sufficient to know that a governance framework exists. The board should be able to point to evidence that the framework is functioning — that models are being monitored, that bias assessments are being conducted, that audit findings are being acted upon, that exception reports are reaching the right people. Governance without evidence of its own effectiveness is governance in name only.

Why this matters now — the regulatory context

The urgency of this shift is not merely philosophical. Regulatory expectations are moving rapidly and in a consistent direction.

Under the EU AI Act, AI literacy obligations already apply to organisations operating within or selling into European markets. The Act places explicit requirements on providers and deployers of high-risk AI systems to ensure that individuals overseeing those systems have sufficient competence to do so. Literacy is not a soft aspiration — it is a compliance condition.

In India, the picture is developing with equal urgency. The RBI's FREE-AI framework, published in August 2025, establishes seven foundational principles for responsible AI in financial services and 26 actionable recommendations spanning governance, consumer protection, infrastructure, and assurance. Critically, the framework makes explicit that boards and senior management remain ultimately accountable for AI systems — including those supplied by third parties. Financial institutions are expected to validate external models with the same rigour they apply to their own.

In parallel, SEBI issued a consultation paper in June 2025 on responsible AI and ML usage in Indian securities markets. The paper calls for designated senior leadership accountability for AI oversight, mandatory model documentation and interpretability standards, periodic audit reporting, and human-in-the-loop mechanisms for consequential decisions.

Across both global and Indian regulatory frameworks, the direction is consistent: leadership teams will increasingly be expected to demonstrate not only that governance structures exist, but that responsible oversight is actively functioning. The question is shifting from "do you have a policy?" to "can you show it works?"

What boards should do?

The shift from awareness to governance capability requires deliberate effort — and it starts with the questions boards choose to ask.

Boards that are serious about AI governance should be asking management: which of our AI systems are material to our risk profile? How do we know our models are performing as intended? Where does human accountability sit when AI contributes to a harmful outcome? What evidence do we have that our oversight is working?

These questions do not require technical expertise to ask. They require governance judgment — the same judgment boards apply to financial controls, operational risk, and regulatory compliance. AI is not a special category that sits outside the board's remit. It is a governance matter like any other, and it should be treated as such.

Closing

Boards do not create trust by sounding informed about AI. They create trust by asking better questions questions that management cannot dismiss, questions that create accountability, questions that reveal whether confidence is backed by evidence.

That is what literacy should enable. Not familiarity with the technology. Capability to govern it.

If this is an issue your board or leadership team is navigating, I would be glad to continue the conversation. Visit tejasviaddagada.com, explore my books, or connect with me on LinkedIn.

These are the personal views of Dr. Tejasvi Addagada and do not represent the views of any organisation.