AI Governance
Five Questions to Answer Before Investing in Enterprise AI
A disciplined investment decision connects value, information, control, ownership and the conditions for scale.
1. Which outcome are we trying to improve?
Describe the business problem in language that a process owner recognises. Establish the current position and an intended improvement. More AI usage is not, by itself, a business outcome. Better response quality, less rework or a shorter service cycle may be.
Name a sponsor who can decide whether the investment is justified. A pilot without a decision-maker can become an interesting experiment with no path into operation.
2. Do we have the information and rights needed?
Identify the sources, their owners and the access permissions required. Inspect freshness, completeness and relevance. A sophisticated model cannot make an unreliable information estate dependable by itself.
Consider which information may leave existing systems, which service providers may process it and what restrictions apply. These decisions belong in the architecture and procurement process before sensitive information enters a trial.
3. What is the cost of being wrong?
Different tasks need different controls. A suggested internal draft and an action affecting a customer account should not share the same approval model. Define unacceptable outcomes and the controls needed to prevent or detect them.
Human review must be specific: who reviews, what they check, how much time it takes and whether they can reverse an action. A vague promise to keep a person involved does not establish effective oversight.
4. Who will own the capability after the pilot?
Agree responsibility for business performance, technical operation, access, incident response and changes. Budget for evaluation, monitoring, support, information maintenance and user enablement, as well as implementation.
Ownership should include a way to retire or replace the solution. An organization should understand what it depends on and how it can continue operating if a provider, model or commercial arrangement changes.
5. What evidence will justify scaling?
Set success criteria before implementation and test with representative work. Evaluate output quality, total process effort, operating cost and practical adoption. Include exceptions, not only the simplest cases.
Scale when the evidence supports it. A positive demonstration is a starting point; a sustainable operating model requires repeatable performance and accountable ownership. The strongest next step may be expansion, a narrower use case or a deliberate decision not to proceed.