What We Mean by AI Literacy

AI literacy is not about learning how AI works at a technical level or keeping up with tools.

It is the ability for leadership teams to:

• talk about AI clearly and productively, in plain English

• evaluate AI claims, vendors, and proposals without relying on hype

• distinguish near-term reality from longer-term possibility

• decide where AI belongs in the business — and where it doesn’t

• make decisions they can explain, defend, and live with later

In short: AI literacy is the capability to make good AI decisions under uncertainty.

AI decisions are no longer side experiments.
They increasingly affect customer experience, operations, data access, governance, and long-term organisational lock-in.

This framing aligns with a growing body of work from organisations such as MIT Sloan and Stanford’s Human-Centered AI group, which increasingly emphasise decision quality, human judgment, and accountability over technical fluency.

Why This Matters for Leaders

Most organisations are facing three pressures at once:

Acceleration
AI capabilities are improving rapidly, creating pressure to “move fast”.

Noise
Confident claims from vendors, consultants, and commentators — often using the same words to mean different things.

Asymmetry
Decision-makers are expected to approve AI initiatives they don’t fully understand, relying on second-hand explanations.

This creates a dangerous gap:
important decisions are being made faster than shared understanding.

Similar patterns are being observed across industry research, including recent analysis from the McKinsey Global Institute and Deloitte, which note that while AI capability is accelerating, shared organisational understanding — particularly at leadership level — often lags behind.

Done well, AI literacy doesn’t slow organisations down.
It allows them to move faster with confidence, because fewer decisions need to be revisited or unwound later.

What AI Literacy Doesn’t Try to Do

AI literacy is not:

• teaching leaders to code

• turning executives into AI engineers

• running tool demos or productivity hacks

• building AI agents by default

Those may come later — but only after the right questions have been asked.

A Common Situation: “We Should Build AI Agents”

“We Should Build AI Agents”

This conclusion often arrives:

• without clarity on what decisions the agent will influence

• without agreement on what data it can access

• without defined escalation or accountability rules

• without a clear distinction between what’s feasible now and what’s aspirational

Much of the current enthusiasm around AI agents reflects capability-focused reporting in the technology press. While these examples are often impressive, they rarely address the governance, escalation, and accountability questions that determine whether such systems should be deployed in real organisations.

An AI-literate leadership team doesn’t start with building.

Before building, examine.

AI literacy helps teams ask:

• What decision is this system meant to support or automate?

• What happens when it’s wrong?

• Who remains accountable?

• Is this a near-term capability or a longer-term ambition?

• Are we solving a real problem — or responding to pressure?

In many cases, the outcome isn’t “no”.
It’s “not yet”, “not like this”, or “not here”.

That restraint is a competitive advantage.

This work draws on current research and practitioner insight from organisations including MIT Sloan, Stanford HAI, McKinsey Global Institute, Deloitte, and the NIST AI Risk Management Framework.

How I Work with Leadership Teams

My work focuses on clarity, judgment, and decision quality — not tool evangelism.

This typically includes:

• AI literacy briefings for leadership teams

• facilitated discussions around AI decisions and trade-offs

• decision-making frameworks for AI adoption

• governance and accountability awareness

• helping leaders communicate clearly with technical teams and vendors

The aim is not certainty.
It’s better decisions under uncertainty.

If your organisation is making — or about to make — decisions about AI, start by improving the quality of those decisions.

You don’t need to move slower.
You need to move more deliberately.