Prerequisites and Readiness for Enterprise AI

All courses / AI Governance / Module 1 / Chapter 5

Learning objectives

After the lesson the participant can:

• Explain why a successful pilot is weak evidence of readiness to scale.
• Assess an organisation across the six readiness dimensions on the scale 0 absent to 4 measured, citing evidence for every score above zero.
• Identify the public-sector specifics that can stop a programme outright: legal mandate, procurement and transparency.
• Draft a use-case intake form whose screening answers route the proposal to the right assessments and forum.

Key concepts

Readiness assessment

A structured judgement, made before the scale decision, of whether data, platform, governance, skills and culture can carry the load. The chapter stresses that it is not a maturity ranking but a list of the things that will break.

Readiness scorecard

A version-controlled record scoring each dimension from 0 absent to 4 measured, with evidence and gaps. Its minimum to industrialise is 2, treated as a gate, and it is re-scored on a fixed cadence with the history kept.

Use-case intake form

The short form through which every proposal enters. Its screening answers are factual and not evaluative, so the provisional class, the required assessments and the review forum follow from them, and readiness dependencies stop a proposal from passing.

Legal mandate confirmation

A written note from the legal function, such as Nordvik Region's LM-2026-009, confirming that the governing legislation permits the processing. Its conditions become a prohibited purpose in the data contract and a review trigger in the system's declaration.

Generative culture

In Westrum's typology, a culture in which messengers are trained and failure leads to inquiry. It matters for AI because failures, drift and bias are discovered by people who notice a wrong output and feel safe to say so.

Interactive exercises

Do them here and get feedback at once. Choose a card and then where it belongs, or drag it.

Teaching plan

Discussion

1. The chapter says data readiness is most often overestimated and governance readiness most often underestimated. Which of the two describes your organisation better, and what is the evidence?
2. Hansa Mutual's claims assistant was blocked from full roll-out by a missing exit plan, not by a technical item. Which non-technical item would most likely block a pilot in your organisation?
3. An organisation can be ready for Mode 1 well before it is ready for Mode 4. How should a readiness report to the board show that difference without collapsing it into one number?

Written exercise

Self-test

Choose an answer to see whether it is right, or open the answer below each question.

1. What is the chapter's governance test for platform readiness?

   1. Whether the platform has enough accelerator capacity for the largest planned model
   2. Whether the organisation holds an ISO/IEC 42001 certificate for its AI management system, issued within the last year
   3. Whether the organisation can say, for any model, which version is approved, by whom and on what evidence
   4. Whether every data scientist has been given access to a shared development environment
   Show the answer

   C. Whether the organisation can say, for any model, which version is approved, by whom and on what evidence
   If the organisation cannot say which version of a model is approved, who approved it and on what evidence, the platform is not ready, however much compute it has.

2. In the chapter's example scorecard for a regional health authority, why is the outcome remediate before scaling?

   1. Because governance and the public-sector dimension score 1, below the minimum of 2
   2. Because the platform dimension scores lower than the skills dimension
   3. Because the culture dimension has not yet been scored with evidence
   4. Because the average of the six dimensions is below 3, the level at which a capability counts as operating
   Show the answer

   A. Because governance and the public-sector dimension score 1, below the minimum of 2
   The minimum to industrialise is 2 and is treated as a gate per dimension. Governance and publicSector both score 1, so they are the blocking dimensions, whatever the other scores are.

3. Why does the chapter prefer engineering practice to certifications as a skills indicator?

   1. Because certifications are not recognised under Article 4 of the AI Act
   2. Because version control, continuous integration and automated testing predict delivery performance and are the floor for AI
   3. Because engineering practice is cheaper to measure across the workforce than the Article 4 literacy programme, which covers all staff
   4. Because certified staff tend to leave the organisation sooner than others
   Show the answer

   B. Because version control, continuous integration and automated testing predict delivery performance and are the floor for AI
   Citing Forsgren, Humble and Kim, the chapter notes that those engineering capabilities predict delivery performance. They are the floor for AI, which makes practice a more honest indicator than certificates.