The CEO's Guide to AI: Questions to Ask Before You Invest

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You are sitting in a board meeting or an investor call. Someone mentions AI. Everyone nods knowingly. AI is obviously important. The question is not whether to invest. It is how much and where.

Here is the honest bit. Most executives cannot articulate exactly what problem AI is supposed to solve. They know it is important. They know competitors are doing it. They are worried about falling behind. So they approve budget without asking the right questions.

Then something lands on your desk a few months later. A pilot wrapped up. Results were "promising." Nobody really knows if it created value. The conversation dies. The investment becomes a write-off. And next time you need operational transformation, credibility is lower.

I have seen this pattern dozens of times. It is entirely preventable. Here are the seven questions every CEO should ask before approving any AI investment.

Question 1: What specific problem are we solving?

Not "improve operations." Not "be more competitive." Something specific.

A good answer sounds like: "We want to reduce motor claims intake time from 18 minutes to 10 minutes per claim." Or "process 30% of our customer enquiries without human intervention." Or "reduce renewal processing cost from £45 per renewal to £28."

A bad answer sounds like: "we want to be better at AI," "our competitors are using it," or "we need to explore our options." Push back until you get specificity. If nobody in the meeting can answer this clearly, you are not ready to invest.

Question 2: Is this task repetitive, rules-based, and resource-intensive?

These are the 3Rs. They describe the work AI actually helps with.

Repetitive means it happens again and again. Claims intake. Renewal processing. Email triage.

Rules-based means there is clear logic. If under £5,000, route here. If emergency damage, flag there. Clear rules, not judgement calls.

Resource-intensive means it ties up people you would rather have doing higher-value work. If a candidate task scores high on all three, you have a genuine opportunity.

Question 3: What does success look like in 90 days?

Not "we will have an AI system running." Specific outcomes. "In 90 days we will have processed 500 claims with 95% accuracy on initial categorisation, freeing 8 hours a week per handler." Push for the number that will tell you it worked.

Question 4: Who owns this internally?

Not the vendor. Your business. Someone needs to own the end-to-end outcome, with the authority to change processes and accountability for the 90-day goal. They should be operational, not technical: a claims manager owns claims automation, a broker operations leader owns renewal automation.

Question 5: Have we actually mapped the current process?

Before you automate a process, you need to understand it. Real example, real steps, real exceptions. A real process map takes a week or two and is tedious. It is also where most failed AI projects could have been saved.

Question 6: What happens when this goes wrong?

And it will go wrong. AI systems make mistakes, miss edge cases, and produce decisions that need review. You need escalation paths, quality oversight, and a kill switch. The best AI systems have humans built in, not bolted on.

Question 7: Are we buying a tool or building a capability?

Buying a tool means you license software and hope it works. Building a capability means investing in process knowledge, team training, and integration. Both cost money. Capability lasts. If the proposal is "buy this platform and you are done," be sceptical.

Before you say yes

Use the seven questions as your checklist. If you cannot get clear answers to all of them, you are not ready to invest. The questions are not hard. They force clarity, and they move the conversation from "should we do AI" to "should we solve this specific problem in this specific way." That is when smart investments happen.