Business question
How do AI systems evaluate and recommend vendors?
In stages. An AI first forms an opening list for a broad category question, then narrows it as the buyer adds requirements: cutting companies that do not visibly meet each new requirement, moving the leader when a requirement exposes it, and settling on the companies that survive the full sequence. The final recommendation reflects all the requirements added along the way, not the opening question.
The problem
Treated as a black box, AI recommendation looks random. Seen as a staged process, it becomes something you can place yourself inside: where you enter, where you are removed, and where you survive.
Why it matters
Understanding the stages is what makes the other questions answerable. Each stage is a place where a company can be won or lost, and we study each one on its own.
What the research studies
The components behind this question.
The evidence so far
What we have run, and what we have seen.
- E-045Video conferencing software: When the field survives and keeps its names
- E-043Accounting software: When the whole field is wiped out and replaced
- E-044Accounting software: When the same kill repeats on a second evaluator
- E-041CRM software: When the recommendation set survives but its members do not
- E-034Clinical trial management software: When the recommendation set collapses
- E-040Project management software: When the recommendation set holds
- E-002Healthcare CDP and the HIPAA flip: the program's one causally verified gate
- Observation · Candidate · 2026-07-20The same elimination appeared on two different evaluators
- Observation · Held · 2026-07-20The category leader was removed at the first requirement it could not meet
- Observation · Candidate · 2026-07-20Survival depended on the category, in at least four ways
- Observation · Candidate · 2026-07-20Survival appeared in two modes, and their cause is confounded
What we can conclude
We have observed this staged narrowing across several categories: an opening list that is not the final answer, companies cut at specific requirements, the leader exposed at the first requirement it cannot meet, and fields that survive, collapse, or move to a different tier depending on the category. Every observation is held at its honest confidence level, and we have now seen the pattern on both ChatGPT and Google AI.
What remains unknown
How closely this resembles the way a human buying committee evaluates, and how much of it holds across different AI systems and over time, are open questions the research program is built to keep testing.
How Upstream Zero helps
From the evidence to your answer.
Upstream Zero studies each stage and turns that into a diagnosis for your company: where you enter, where you are cut, and what must become true to survive. The research is public; the diagnosis is the product.