The AI Vendor Mistake That Sets Organisations Back by Years

I have been involved in more AI vendor selection processes than I can count — as the buyer, as the evaluator, and occasionally as the person brought in to explain why a recently completed selection was going to produce a poor outcome. The mistakes I see are remarkably consistent. They are also remarkably predictable once you understand the incentive structure that shapes how vendor selection processes are conducted.

Here is what most organisations get wrong — and how to do it better.

The Demo Problem

The centrepiece of most AI vendor evaluations is the product demonstration. The vendor brings their best people, demonstrates their most polished capabilities, and shows the prospective customer a version of their product that has been specifically prepared for this engagement. The demonstration is impressive. The decision committee is persuaded. The contract is signed. And then the implementation begins — and the gap between the demonstration environment and the actual operational environment becomes apparent.

Demonstrations are not evaluations. They are sales presentations. The evaluation question is not "does this product look impressive in a controlled demonstration?" It is "does this product work reliably in our specific operational context, with our specific data, integrated with our specific systems, operated by our specific team?" These questions can only be answered by a structured proof of concept in the buyer's actual environment — not by a vendor demonstration.

The Reference Check Failure

Most vendor selection processes include reference checks — conversations with existing customers of the vendor. Most reference checks are conducted perfunctorily and produce limited useful information. The vendors select which references to provide. The references are aware they have been selected and typically provide positive endorsements. The questions asked are often generic.

The reference check question that produces the most useful information is not "how has the product performed?" It is "what did you wish you had known before you started?" This question surfaces the friction points, the implementation challenges, the support limitations, and the gap between vendor promises and delivery reality that positive references typically omit.

The Total Cost of Ownership Blindspot

AI vendor pricing structures are often designed to make the initial investment appear modest while the total cost of ownership over a multi-year engagement is significantly higher. Licensing fees that scale with usage, implementation costs that are underestimated in proposals, integration work that falls outside the vendor's scope, ongoing maintenance and model retraining costs, and the internal resources required to manage the vendor relationship — all of these contribute to a total cost that frequently exceeds the initial evaluation by a factor of two or three.

Evaluating vendors on initial price rather than total cost of ownership over a realistic deployment horizon is one of the most reliable paths to budget overruns and disappointed stakeholders. Build the full five-year cost model before making the selection decision.

What a Good Vendor Evaluation Looks Like

Define the specific use case before engaging any vendor. Require all vendors to demonstrate their product on a sample of the buyer's actual data — not a demo dataset. Conduct structured reference interviews with customers who have completed full implementations, not just pilots. Build a total cost of ownership model for a five-year horizon. Evaluate the vendor's implementation methodology and support infrastructure as rigorously as the product itself. And assess the vendor's financial stability and long-term product commitment — a technically excellent product from a vendor that will not exist in three years is not a sound investment.

This process takes longer than a standard demo-and-proposal cycle. It produces decisions that are significantly better — and it is the approach that separates organisations that build durable AI capability from those that spend years cycling through vendors trying to find the solution that the selection process failed to evaluate properly.

Dr. Sunny Okonkwo

AI Strategist · Head of Data Analytics at one of Africa's largest energy and utility companies. Author of 7 books including the #1 Bestseller The AI Alchemist. Keynote speaker at IIBA, Big Data Summit Canada, Global Summit, and UNICAF.