Sit through any AI vendor pitch and you will hear about model size, benchmark scores, and a feature list longer than your arm. None of it tells you whether the tool will work in your business. There is a better way to evaluate, and it comes straight out of how the leaders who actually adopt technology think about it, which my research found is very different from how the people selling it think.
Two different lenses
My 2014 doctoral work was a qualitative descriptive multiple-case study built on in-depth interviews with 19 people across adopting and resisting Fortune 500 organizations, plus employees from a technology provider. A clear theme emerged in how the two sides evaluated the same technology. The provider employees focused on infrastructure: bandwidth, scalability, security, performance, the specifications. When asked what mattered most in evaluating technology, all of the provider employees named scalability and security. The leaders and the people who would actually use the tool focused on something entirely different. They led with ease of use, perceived value, and user experience.
Both lenses are valid, and both matter. The mistake leaders make is borrowing the provider’s lens. They walk into the evaluation and get pulled into a specification contest they are not equipped to judge and that does not actually predict whether the tool will get used. The provider is right that bandwidth and security matter. But a tool can be flawless on every specification and still sit unused because nobody found it valuable or easy enough to adopt. The questions that predict real adoption are the leader’s questions, not the engineer’s.
The questions that actually matter
When you evaluate an AI tool as a leader, the useful questions are not technical. They are these.
- What specific problem does this solve? If you cannot answer in one sentence, you are shopping, not solving. This is the same problem-first discipline that separates adoption from waste.
- Will my team actually use it? A tool that is technically brilliant and quietly ignored is worth nothing. Ease of use beats raw capability almost every time, which is exactly what the adopting leaders in my research led with.
- What does it replace? If it adds to the pile instead of removing something, it is making you slower, not faster.
- Who owns the data, and what happens when we leave? Trust and exit terms matter more than features, because you are choosing a dependency, not just a tool.
- What does it cost in time, not just dollars? Setup, training, and upkeep are the real price. The subscription is the small part.
None of these require an engineering background. They require knowing your business, which you already do. Notice that the infrastructure questions the vendor leads with are not absent from this list, they are folded into the data-ownership and total-cost questions, where a leader can actually weigh them. You do not ignore security and scalability. You just refuse to let them become the whole conversation.
Why this matters more with AI
AI vendors compete on benchmarks harder than almost any technology before them, and the benchmarks are improving so fast that any spec advantage is temporary. Meanwhile the actual barrier to value has not moved. McKinsey found that 88 percent of organizations use AI but only 7 percent have scaled it, and the differentiator is rarely the model. It is whether the work around the tool was redesigned and whether people actually use it (McKinsey, 2025). Boston Consulting Group quantifies where the effort actually belongs: in successful AI work, only about 10 percent goes to the algorithms and 20 percent to the technology, while 70 percent goes to people and process (BCG, 2024). The specifications the vendor sells you are inside that smallest slice. Evaluating on specifications optimizes for the thing that does not predict success and ignores the things that do.
This is one application of a larger pattern in the 5 drivers of AI adoption, and it depends on the same foundation as everything else, enough literacy to know which questions to ask, covered in you cannot adopt what you do not understand.
The move
Next time a tool is in front of you, set the spec sheet aside for a moment and run those five questions. If it cannot clear them, the benchmarks do not matter. If it clears them, the benchmarks were never the point. Lead the evaluation like a leader, not like an engineer auditioning vendors.
To get a clear read on where to focus first, the Omnine AI Readiness Assessment takes about three minutes.
References
Goodwin, M. R. (2014). A qualitative descriptive multiple-case study: Fortune 500 leaders’ social business platform adoption (Doctoral dissertation, University of Phoenix). ProQuest Dissertations Publishing (UMI No. 3648813).
McKinsey & Company. (2025). The state of AI in 2025: Agents, innovation, and transformation. McKinsey Global Survey on the state of AI.
Boston Consulting Group. (2024). Where’s the value in AI?