In this guide, AI readiness means the ability to introduce AI with a clear purpose, capable people and accountable decisions. A team needs to understand what the system can help with, where it may fail and who takes responsibility for the result.
A useful starting point is one real task. Choose a process people can explain, inspect and evaluate. Before introducing a tool, describe the current problem and what a better outcome would look like.
Start with a bounded use case
Consider an internal assistant that drafts summaries from approved documents. Its purpose should be specific: help staff locate and review information more efficiently. Decide which documents it can access, how readers check its claims and who handles mistakes.
This hypothetical example is easier to evaluate than a broad promise to transform knowledge work. It creates a clear boundary around the task and the evidence needed to judge it.
Build the ability to question results
AI literacy includes recognising uncertainty, checking important claims and understanding when a confident answer may still be wrong. Training should be connected to the decisions people actually make.
An OECD brief published in June 2026 identifies skills gaps as a major barrier to AI adoption. That finding supports treating workforce upskilling as part of adoption from the start.
Make accountability visible
Before a pilot begins, answer four questions:
- Who owns the process and approves changes?
- What information can the system access?
- Which outputs need review before use?
- How can someone report an error or stop the process?
The answers should be understandable to the people doing the work. A policy that stays separate from everyday practice offers little guidance when something goes wrong.
Agree what would justify expanding the pilot
Write down the baseline before the trial: how long the task takes, how errors are found and what reviewers need to check. Then decide what improvement would justify wider use. Include staff feedback, not just output volume.
A pilot should also have a stop condition. If the team cannot reliably identify consequential mistakes, the next step is to change the workflow or narrow the task.
Evaluate the whole outcome
Assess usefulness alongside mistakes, review effort and the experience of the team. A faster first draft may create extra checking work elsewhere. A useful evaluation makes those trade-offs visible.
The aim is to develop an organisation that can make informed decisions about AI and improve its approach as it learns.