There has never been more help available for buying AI, and less help available for deciding about it. Vendors are genuinely good at what they do: they present options, compress complexity into demos, and make the next step feel obvious. That is their job. The problem is that presenting options is not the same as building a decision.
Two research findings cut through the noise. MIT’s State of AI in Business analysis found that purchasing specialised tools from vendors succeeds about 67 percent of the time while internal builds succeed only a third as often, so buying is often right, but only against a defined use case[M]. And McKinsey’s 2026 operating-model research notes that platform capabilities that differentiate a vendor today often become standard within months, which is precisely why a tool-first decision logic keeps expiring[O].
What vendor noise actually is
Vendor noise is not lying. Most vendor claims are technically defensible. The noise is structural: every conversation is shaped by what the vendor can sell, not by what your organisation needs to decide. Feature comparisons crowd out the prior questions: whether this problem is worth solving with AI at all, whether your data and workflows can carry it, and who will own the result when it behaves unexpectedly.
Inside the noise, a subtle shift happens. The organisation stops asking what should we do and starts asking which one should we buy. The second question feels like progress. It is actually a narrowing of the first question, performed by someone with a commercial interest in the narrowing.
The decision logic that belongs to you
No vendor can supply your decision logic, because it is made of things only you hold: your constraints, your risk tolerance, your data reality, your people, and your view of where value comes from. A workable AI decision logic answers four questions in order. What business problem, stated without any product name. What would have to be true for AI to pay off here. What it costs in effort versus what it returns in value. Who owns the decision and its consequences.
Written down, these four answers turn every vendor meeting from a persuasion event into an evaluation. The demo does not get to define the criteria. It gets measured against them.
Independence has a specific value
This is also the honest case for independent advice in AI: not superior technical knowledge, but the absence of a commercial stake in your answer. An advisor who earns nothing from your tool choice can afford to tell you that the right decision this quarter is no tool at all.
| Vendor question | Your question |
|---|---|
| Which plan fits your needs? | Is this problem worth solving with AI? |
| When can we start the pilot? | Can our data and workflows carry this? |
| Which features matter most? | What must be true for this to pay off? |
| Who signs? | Who owns the outcome when it misfires? |
The practical takeaway
Before the next vendor meeting, write your decision criteria on one page and date it. If the meeting changes the criteria, that is worth noticing. Criteria that move with every demo were never criteria.
How much of the noise is even real
Gartner put a number on the noise itself: of the thousands of vendors now selling agentic AI, it estimates only around 130 offer genuinely agentic capabilities, the rest are rebranding existing assistants, RPA and chatbots, a practice it calls agent washing. The same analysis predicts over 40 percent of agentic projects will be canceled by end of 2027 on cost, value or risk grounds[GA]. Read those two numbers together and the decision logic writes itself: the scarcest resource in this market is not technology. It is a buyer who knows what problem they are solving.
What I see in the field
Every leadership team I meet is being sold to, constantly and competently. The ones that stay calm have something the vendors cannot supply: their own decision logic: use cases ranked by business value, readiness honestly assessed, and a rule for when buying beats building. With that in hand, vendor meetings become short and useful.
We are on no vendor payroll and sell no software. Our only product is your decision logic: where AI moves your results, what to buy against which use case, and when to walk away. That independence is the point.
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Sources
- MIT NANDA, The GenAI Divide: State of AI in Business 2025; reporting by Fortune, August 2025. fortune.com
- McKinsey & Company, The Operating Model Advantage: Why AI Winners Are Rewiring Their Organizations, July 2026. mckinsey.com
- Gartner, Press Release: Over 40 Percent of Agentic AI Projects Will Be Canceled by End of 2027, June 2025. gartner.com
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