Somewhere in your organisation, right now, someone is sitting through an AI vendor pitch.
Discovery, clinical, regulatory, pharmacovigilance, medical affairs, market access, insights, commercial, supply chain, manufacturing – every business unit in pharma is now an AI buyer, and every one of them is being sold to constantly. If you add up the pitches, demos, “quick fifteen minutes,” and inbound decks landing in inboxes across your organisation this quarter, the number is not small. It’s not unusual for it to run into the dozens or greater.
The average vendor deck runs to about ten slides. Strip away the branding, the case-study logos, and the founder’s origin story, and almost all ten collapse into the same four moves: a big number, a logo wall, a live demo, a roadmap. And here’s the uncomfortable part – none of those four things is evidence. Not because the vendors are dishonest. Because a big number, a client list, a polished demo, and a set of future promises are exactly what you’d see from a genuinely excellent product, and exactly what you’d see from a mediocre one with a good deck. Ten slides don’t discriminate. They were never designed to.
That’s the asymmetry worth sitting with. The vendor in front of you has given this pitch five hundred times. They know which question stumps which kind of buyer, which objection to pre-empt, which slide to slow down on. You are buying this once, maybe twice a year, and you’re doing it on top of a full-time job that has nothing to do with vendor evaluation. That is not a fair situation, and no amount of general business acumen closes the gap on its own – because the skill required here isn’t business judgement, it’s a specific kind of judgement your organisation has simply never had to build before now, even from procurement teams as AI is a new ballgame for them as well.
You already have a world-class process. Just not for this.
Pharma is exceptionally good at buying things. Decades of rigour go into how you evaluate a molecule, a manufacturing site, a CRO, a piece of capital equipment, a clinical services partner. That machinery works, and it works precisely because those categories of purchase share a property that makes diligence tractable: the thing you’re buying holds still. You test it once, hard, and the thing you tested is substantially the thing you end up with.
An AI vendor pitch breaks that property in five specific ways, and it’s worth naming them, because each one quietly disables a piece of the diligence process you already trust.
The product is not deterministic. “It works” stops being a binary claim and becomes a statistical one. There is no clean acceptance test you can run once and be done with, the way you might test whether a piece of equipment meets spec.
The vendor does not fully control what they’re selling you. Underneath many AI products today sits a foundation model built by someone else entirely. The vendor’s roadmap, cost base, and even the product’s behaviour can move whenever that underlying provider ships something new – a dependency your existing supplier-qualification process was never built to price in.
The benchmark is written by the seller. There’s no independent, audited bake-off the way there might be for other categories of technology. Every number in the pitch deck is, in a very real sense, marked homework.
The product changes after you sign. What you evaluate in the room is a snapshot. Vendors in this category ship constantly, that’s table stakes for staying competitive, which means the diligence you complete today has a shorter shelf life than almost anything else you buy.
Your data is the consideration. In much of pharma, especially R&D, what you’re handing over in a pilot isn’t just usage data, it can be unpublished target biology, proprietary methodology, competitive strategy. A leak in this category isn’t a fine. It can be a lost priority date, and no amount of contractual boilerplate fully undoes that once it’s happened.
None of these five things are reasons to slow down on AI. They’re reasons your existing diligence instincts, however sharp, are answering the wrong questions when they’re pointed at this category of vendor.
Why smart people still buy badly
If the five broken assumptions above explain why AI vendor evaluation is structurally harder, they don’t fully explain why so many experienced, capable pharma teams still end up with a disappointing outcome. That part comes down to a small set of recurring patterns, and it’s worth being clear that not one of them is a technical failure. Every one is a buying failure.
Solution shopping: evaluating a capability in the abstract rather than starting from the specific decision you need to change. A vendor who can do “AI for insights” sounds compelling right up until you ask what decision, made by whom, actually improves.
Demo blindness: grading the polish of the interface rather than the substrate underneath it. A beautiful demo tells you about the vendor’s design team. It tells you almost nothing about the data foundation the product is reasoning over.
Pilot as consensus-avoidance: running a pilot not to test a hypothesis, but because running a pilot lets everyone avoid making an actual decision. This one is worth dwelling on, because it’s the pattern that quietly costs the most.
The wrong buyer in the room: the person holding the budget is often not the person who understands the data the tool will depend on, and the gap between those two people is exactly where weak vendors thrive.
Compliance theatre: a certificate or a compliance statement mistaken for an actual control. “We’re SOC 2 certified” and “we’re GxP-ready” are both sentences that sound like answers and frequently aren’t, once you ask what they actually cover.
Sunk-cost scaling: rolling out a pilot that didn’t really work, because too much has already been spent to say so out loud.
What a demo actually tells you, and what it doesn’t
It’s worth pausing on demo blindness specifically, because it’s the pattern that catches even experienced buyers. A demo is, by construction, the vendor’s best five minutes, the exact query their product handles beautifully, on data curated to make it shine. That’s not deception; it’s just what a demo is for. The real question a demo can’t answer is what happens on the query it wasn’t built to handle, on your data, with your edge cases.
A related trap worth watching for is what a client logo wall is actually telling you. “Used by eighteen of the top twenty pharma companies” is a genuinely impressive-sounding sentence, and it’s often completely true, and it frequently means one scientist on a free trial in one affiliate office, which is a very different fact than a company-wide production deployment that’s been renewed. The honest question underneath the slide is simple: how many of those logos are in full production, and how many have actually renewed? Most vendors have that answer ready if you ask directly. Very few volunteer it unprompted. I’ve seen this exact trick played straight: a logo wall for a product that’s just launched, quietly carried over from an earlier, unrelated one. Nobody in the room thought to ask which product those logos actually used.
The roadmap slide deserves the same instinct. Future capability described in the present tense, “we handle X” when X ships next quarter, is one of the easiest things to catch and one of the most commonly missed, simply because nobody in the room asked for the date in writing.
None of this requires a technical background to catch. It requires knowing to ask, and building the habit of not letting the room’s momentum carry a claim past the point where it’s actually been tested.
The cost of getting this wrong isn’t the licence fee
Get the evaluation wrong and the failure mode in pharma is rarely a dramatic one. It’s usually a pilot that quietly never ends. Pilots stall for four structural reasons, and every one of them is set at the very start, often without anyone noticing: nobody has been named as the person who will actually decide; there’s no defined condition under which the pilot is declared a failure, so “not yet” stays available indefinitely and costs nothing to say; there’s no budget line waiting for what happens if the pilot succeeds, so success has nowhere to go; and success was defined loosely enough as “learnings,” or “insights”, that it was never possible to fail in the first place.
The licence fee is genuinely the cheapest part of a pilot like that. The real cost shows up afterward, in the next AI conversation your organisation has, because a dead pilot doesn’t fail quietly and it teaches the room that AI doesn’t really work here, and the next vendor, the genuinely good one, starts the conversation twenty points down before they’ve said a word.
The vendor that’s hardest to score isn’t the bad one
It’s worth naming which vendor actually causes the most wasted pharma AI spend, because it’s not the one you’d expect. It isn’t the obvious wrapper product or the demoware with nothing underneath, as those are relatively easy to catch once you know what to look for, and most experienced teams get there eventually. The money is lost on the vendor in the middle: genuinely competent, well-funded, real customers, a real product doing something real, and still the wrong signature this quarter, for reasons that have nothing to do with whether they’re good at what they do. There’s no villain in that room and no obvious tell. It’s exactly the case where a systematic approach earns its keep, because judgement alone tends to wave a confident, capable vendor straight through.
In the full training, I score a real, complex AI vendor end to end, live, in public, not a strawman, not a hypothetical, a genuine, well-regarded AI vendor product put through the entire rigorous process from claim to score. It’s deliberately the hard case rather than the easy one, because the hard case is where most of your actual calendar lives.
Ten slides, ten domains
Here’s where the “ten slides” at the start of this piece earns its keep. A vendor’s ten-slide deck is built to be persuasive. What you actually need in the room is something built to be diagnostic, and in the training, that instrument also happens to have ten parts, though they have nothing to do with the vendor’s slides. They’re the ten domains a vendor should be scored against, regardless of what order the deck presents them in: Problem Fit & Value Thesis, Data Foundation, Model & Method Transparency, Evidence & Validation, Governance & Regulatory Readiness, Security & Privacy Architecture, Integration & Interoperability, Human Oversight Model, Commercial & Contractual Terms, and Viability & Support. Each is scored, weighted differently depending on which business unit is buying, and four of the ten carry a hard gate, a zero in any one of them ends the evaluation outright, regardless of how strong the total looks.
Ten slides are what the vendor built to move you. Ten domains are what actually protects you. Only one of those tens is yours to control.
Where good instinct stops being enough
Everything above is true and useful, and none of it, on its own, is a complete answer to the vendor sitting across the table from you right now. Knowing that a logo wall can mislead doesn’t tell you exactly what to ask instead, for your specific business unit, with your specific regulatory exposure. Knowing that demos are curated doesn’t tell you what a genuinely strong vendor’s answer to a hard question actually sounds like, as distinct from a well-rehearsed one that sounds almost identical. Knowing the four structural causes of a stalled pilot doesn’t hand you the contractual language that prevents one before it starts.
That gap, between recognising the pattern and having the instrument to act on it in the room, live, in real time, is exactly where a framework earns its keep. The vendor across the table has heard every one of your instincts before; they have a rehearsed, reasonable-sounding answer ready for the obvious question before you’ve finished asking it. What actually decides the outcome is the second move: knowing what a genuinely strong answer sounds like against a plausible-but-fatal one, which of the areas that answer touches matters most for your specific function, and how to grade what you just heard rather than simply how to prompt it.
Where to go next
Where this goes next
The full version of this, a structured way to score any AI vendor across the areas that actually matter, weighted differently depending on your business unit, so whether you sit in Discovery, Clinical, Pharmacovigilance, Regulatory, Medical, Value and Access or Commercial; the specific questions that turn a well-rehearsed answer into a genuine test, live, in the room, in under twenty minutes; and the contractual terms that make a pilot structurally incapable of drifting into purgatory, is exactly the kind of thing that’s worth having as an instrument you can actually use, not just a set of things to watch out for.
That’s the version built out in full inside the AI Enablement Institute, under Foundations · “Evaluating, Buying & Building AI”, including the complete live scoring of that real, complex AI vendor referenced above, worked in detail across each of the 10 domains, done domain by domain, so you can see the instrument used on a genuine case rather than a hypothetical one.
It’s kept current the way the vendor landscape itself moves, new modules land monthly as new claims, new tactics, and new categories of AI vendor show up in pharma’s inbox, and the live monthly Q&A with me is where you can bring your own vendor’s actual pitch deck and get a direct read on what it’s really telling you, and what it isn’t.
(Eularis takes no referral fees, revenue share, or promotional arrangements from any vendor it evaluates which ensures complete impartiality, which is worth knowing, given the subject.)
Before your next AI vendor meeting
You almost certainly have one of these on your calendar already. That’s the moment this stops being an article and starts being useful.
Start with the free video clip on the Institute page here – https://eularis.com/institute/ a genuine, unedited few minutes from inside the Ten-Domain Scorecard module of the vendor evaluation training, no form to fill in first.
That is only one of the modules in that training which encompasses areas like Why Pharma Buys AI Badly , Build Vs Buy Vs Partner, The Ten-domain Vendor Scorecard, Judging Claims, Escaping Pilot Purgatory, Governance, Security & Regulatory, Worked Example: Scoring A Real Vendor, Same Scorecard, Different Weights
If this is what you need, the next step is a twenty-minute call with me directly: I’ll walk you through the full curriculum for your function, the support materials, and the ask-a-question AI search, so you know exactly what you’d be putting your team into before anyone signs anything.
Watch the clip, then book your twenty minutes here to find out if this can help you.