Is your business ready for AI? Start with these five questions

A diagnostic framework for identifying whether a business is actually positioned to operationalise AI tools, not just experiment with them. The same framework the Executive Programme is built on.

Terence Kok

AI Governance & Assurance Practice. Enterprise AI Strategist and Keynote Speaker · Praxora Lab

Most organisations that fail at AI deployment are not failing because the technology is flawed. They are failing because they never checked whether the organisation itself was ready to receive it, and rushed procurement in its place creates what is best described as operational debt rather than value. McKinsey has found that seventy-eight percent of companies now use AI frequently, yet only thirty-three percent have scaled it company-wide. MIT's NANDA initiative puts the number of pilots that produce lasting profit at just five percent. In Singapore, government data shows AI adoption among small businesses has tripled to 14.5 percent, adoption that readiness has not kept pace with.

1. 78%

   of companies use AI frequently

2. 33%

   have scaled it company-wide

3. 5%

   of pilots produce lasting profit, per MIT NANDA

14.5%

SG small-business AI adoption, tripled. Readiness hasn't kept pace

The five-dimension framework starts with data readiness, because every other dimension depends on it: can the organisation access data that is clean, documented and current. Process definition follows, a test as concrete as whether a new employee could follow the process being automated on their first day, since an ambiguous process will resurface as unpredictable AI behaviour. Governance structure asks who reviews AI output before it reaches a user and what happens when it is wrong, a question that applies as much to a one-person business drafting customer emails with AI as it does to an enterprise.

The fourth dimension, team capability, asks whether staff can operate, review and correct the system without outside help, because a tool nobody on staff understands becomes a black box where nobody is sure whether its output is right. The fifth, measurement, requires the business outcome to be defined and baselined before deployment begins, since without a baseline, no later improvement can be credibly attributed to the AI system at all.

1. 01

   Data readiness

   Can the organisation access data that is clean, documented and current?

2. 02

   Process definition

   Could a new employee follow the process being automated on their first day?

3. 03

   Governance structure

   Who reviews AI output before it reaches a user, and what happens when it is wrong?

4. 04

   Team capability

   Can staff operate, review and correct the system without outside help?

5. 05

   Measurement

   Is the business outcome defined and baselined before deployment begins?

Taken together the five questions are not a checklist to complete once. They are the diagnostic a leadership team should be able to answer, specifically, for its own organisation, before the next AI budget is approved, and it is the same five-dimension model the Executive Programme scores live during its half-day session.