AI procurement fails at selection, not deployment. Not because buyers are
incompetent — because the categories that would let a buyer decide what to
shortlist don't exist in a usable form yet. So buyers default to feature
comparison, feature comparison defaults to the best demo, and the best demo
defaults to whichever vendor spent the most on producing it.
The existing research products don't close this gap. Analyst quadrants rank
markets against each other — useful if you already know which market
you're in, which you usually don't. G2-style grids rank vendors against
each other — useful if you already know which criteria matter, which is usually
the thing you most need help with. Vendor sites describe what the vendor wants to
sell. None of them answer the actual question: what category should you be buying
in, and how do you evaluate within it?
Four formats, each answering one step. Market maps categorize the
market — the categories, buyers, failure modes, and selection criteria per
category. Buying guides decide within a category — weighted
criteria, shortlist process, reference script, TCO model, contract clauses.
Benchmarks show what the market collectively believes — annual
structured surveys with published methodology and sample. Vendor
comparisons — the format we haven't published yet, and explain below —
compare specific products against stated criteria, not against each other.
The discipline behind these is the point. Every format publishes methodology.
Every format rules out paid inclusion. Every format logs corrections in public.
Those aren't marketing claims; they're the terms of the product. A research
product that requires you to trust the researcher is a research product that
can't be audited. The public record is what closes that gap.