The biggest misconception about AI is that finding information is the same as understanding it. It’s not. Consultants don’t just gather data. They weigh it.
Over 30 years' experience in the pharmaceuticals industry, specialising in commercialization
Imagine walking into a pharmacy, asking for painkillers, and being handed the entire contents of the shelf. No guidance, no priority, just everything. Paracetamol next to morphine. A children’s syrup right next to a cancer therapy.
It’s all medicine. But without context or ranking, it’s dangerous at worst, and useless at best.
That’s what working with most AI feels like in market access. You ask a question, and it surfaces everything it can find. Studies. HTA summaries. Payer comments. Clinical endpoints.
But it doesn’t tell you what’s most relevant.
It doesn’t tell you what’s defensible.
It doesn’t think like you do.
And that’s the problem.
The biggest misconception about AI is that finding information is the same as understanding it. It’s not.
Consultants don’t just gather data.
They weigh it.
They test it against client objectives.
They look at whether it’s timely, specific, regionally relevant, and strategically useful.
That’s what makes good work good.
And that’s exactly what most AI tools can’t do.
Let’s say you’re refining a TPP. You ask your AI assistant to pull relevant endpoints from the literature. It finds 15 papers. Looks impressive.
But five of them are from US studies when you’re working in the EU.
Three are outdated.
Two contradict each other.
And one is a preprint without peer review.
What a consultant would do is filter all that.
They’d bring forward the two or three most strategic, defensible pieces of evidence and link them clearly to the value proposition.
Generic AI? It gives you all 15.
And now you have to clean up its mess.
In market access, time isn’t your only constraint. Attention is.
If a senior client or internal stakeholder is reviewing your draft GVD, you don’t have space for ten semi-relevant claims. You need three strong ones that matter.
And when a tool can’t rank those for you, it becomes a liability.
This is where most general-purpose AI fails. It pulls from broad, noisy sources and presents everything with equal weight. It can’t distinguish between background noise and business-critical insight.
The result? More content, less clarity.
Ranking isn’t just about putting the best result at the top.
It’s about understanding why something matters.
For that, you need:
Without that, AI becomes a search engine with a nicer interface.
When your consultants spend half their time sifting through AI output, you’re not saving time. You’re wasting it.
When a weak claim slips into a deliverable because the AI didn’t flag the strong one, your quality takes a hit.
And when every project starts with unranked noise instead of structured clarity, your delivery slows down and your margin disappears.
Ranking is not a luxury.
It’s the backbone of quality, speed, and defensibility in consulting.
The best consultants don’t just know more.
They know what matters most.
That’s what makes their work strategic, not just correct.
The future of AI in market access will belong to the systems that can do the same.
That don’t just retrieve information, but prioritise it.
That don’t just answer questions, but answer them the way a consultant would.
Because in this business, what you surface is only as valuable as what you choose to lead with.
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