The market for AI tools is exploding. New products launch weekly. Sales processes are aggressive and well-designed — free trials, impressive demos, case studies from companies that look like yours. It's easy to buy something that sounds perfect and gets used by no one six months later.
This doesn't happen because people are stupid. It happens because the evaluation of AI tools typically focuses on the wrong things. You evaluate the technology — is it impressive? Is the demo convincing? — instead of evaluating the fit: does this actually solve the problem we have, in a way that matches how we work?
Here are the five questions I always ask before a company invests in an AI tool. They're designed to uncover not whether the technology is good — it usually is — but whether the timing is right, the fit is good, and the organisation is ready.
Question 1: What is the precise problem this solves?
Not "we want to be more efficient" or "we want to use AI". Find the specific process, the concrete bottleneck. Can you describe it in one sentence, with a specific time cost and a concrete consequence? "Our analyst spends three days every month manually consolidating data from six platforms — and the report is outdated before it's read." That's a problem. "We want to leverage AI" is an ambition, not a problem.
If you can't formulate the precise problem in one sentence, you're not ready to buy an AI tool to solve it. Instead, spend the first two weeks mapping which processes are actually the most time-consuming and painful. That's an investment that pays off.
Question 2: Who owns the implementation — and do they actually have time?
This is the question most often overlooked — and the one that most often explains why AI tools gather dust. The best tool in the world creates zero value if no one implements it properly. And implementation requires time: time to set up the system, time to test it, time to train colleagues, time to solve the problems that inevitably arise in the first weeks.
Find the specific person who will own the rollout. Not "the IT department" generally — a named person with responsibility and time for it. Do they have space in their calendar over the next eight weeks? Do they have the technical capacity to handle the setup? Are they genuinely motivated for the project, or is it "just another management initiative"? The answers to these questions tell you more about the likelihood of success than the tool's features.
Question 3: What is your current data quality?
AI isn't a magic solution that makes bad data good. AI is an amplifier — it amplifies what's there. If your CRM is a mess of duplicates, outdated contacts and inconsistent data entry, AI will simply scale that mess. Garbage in, garbage out isn't just a technical term — it's a business reality.
Evaluate your data quality before investing in AI on top of it. It doesn't need to be a full data quality audit. Ask: Is our data structured and consistent? Do we have defined fields and standards for how data is entered? Who owns data quality, and is there an active process for maintaining it? If the answers are uncertain, data cleansing is an important first step before AI implementation.
Question 4: What are the success criteria at 90 days?
Define concrete, measurable success criteria before you sign anything. Not "people use it more" or "we're more efficient" — but specific, quantifiable targets: "The sales team's average meeting preparation time decreases from 2.5 hours to under 1 hour within 90 days." Or: "Our monthly reporting process is reduced from three days to under four hours within two months."
These success criteria serve two purposes. First, they give you a clear evaluation framework — you know whether it's working. Second, they force you to think precisely about what "success" actually means for this specific tool and this specific use. Many AI implementations fail not because the tool is poor, but because no one ever defined what success looked like — and therefore never noticed when you were getting close to it.
Question 5: What is the exit strategy if it doesn't work?
This is the question no one asks in the sales meeting — and that's precisely why you should ask it. Vendor dependency is real. Business-critical data migrated into an AI platform isn't always easy to retrieve. Enterprise contracts with two to three year terms and six months' notice are increasingly common in the AI market.
Understand the exit conditions before you sign. What happens to your data if you cancel? Can you export it? In what format? What does it cost to migrate to an alternative solution? And what is the real notice period — not the marketing-labelled one, but the legal one?
These five questions aren't designed to stop AI investments. They're designed to ensure that the AI investments you make are the right ones — at the right time, for the right purpose, with the right organisational readiness. An AI investment that answers these questions satisfactorily is one you're likely to see a positive return on. An investment that can't answer them is a risk you should understand before you take it.
The AI tools market will continue to grow and mature. The best decisions aren't made by those who are fastest to adopt — but by those who are sharpest at identifying which tools solve exactly the problems they have, and are ready to succeed with the implementation.