AI-native intelligence can change how pharma makes strategic decisions

Executive summary

I think we are approaching a rather important change in pharmaceutical intelligence. AI makes analyses faster and less expensive, and gives us the ability to keep them current. That changes which questions companies can afford to investigate before they make a decision. But it does not happen by giving a scientist a chat window and expecting the rest to take care of itself.

1. Speed changes which questions a company has time to investigate. The first answer can reveal a question the client could not have formulated beforehand—and there may now be time to follow it.
2. Lower costs make exploration possible before committing to a course. If analysis becomes less expensive, teams can compare more possibilities before selecting which one deserves a major investment.
3. Intelligence is much more useful when it does not go stale after delivery. Continuous monitoring matters only if the new evidence is interpreted and the original conclusions are reconsidered.
4. As agents do more information work, scientists can focus on what requires judgment. Experts preserve the client’s nuances, keep the investigation on track, challenge the findings and discover the next questions.
5. The models enable the change; the knowledge graph, agents and learning loop make it work. The additional technology connects specialized agents and a knowledge graph with scientists and clients. Retail AI alone does not provide that system.
6. Pharma and frontier labs are investing in AI, but strategic decisions are a different problem. Acquisitions and partnerships span discovery, development and deployment. Deciding what deserves investment requires different evidence and expertise.

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I think our expectations of software have changed dramatically. We increasingly expect it to do our work, not just help us do it. We want it to be fast, correct, and good enough to share with our colleagues without spending hours fixing it. Ideally, we want it to read our minds.

And AI makes that expectation seem reasonable. But anyone who has spent a serious afternoon working with ChatGPT or Claude on a complex question knows that there is still a large difference between a good first answer and a complete piece of work. A convincing answer can be missing important evidence, sometimes in ways that are hard to predict. In pharma, where a small nuance can change the conclusion, that is a serious problem.

At Epistemic AI, we have seen work that used to take roughly three months move to two weeks across successive engagements, with delivery costs about a third of what they used to be. The work is also kept current by agents and our scientists. I mention these numbers because I believe their consequence is much more interesting than the productivity gain itself: they change which investigations are feasible while the decision is still open.

1. Speed changes which questions a company has time to investigate

A pharmaceutical client asked us to characterize the white space in a particular indication. That sounds like one question, but the white space was large and therefore quite difficult to describe in a useful way.

Our initial analysis showed that what looked like one big white space was actually many smaller ones. Once the client could see that structure, they were able to focus on a particular opportunity that they would otherwise have struggled to identify in time.

I want to be precise about this. There was nothing intellectually impossible about the work. A good consulting team could have done it. The problem was the time and effort it would have required, compared with the calendar for the decision.

And that is, I think, the most important consequence of speed. The first question is often not the right question, or at least not the last one. You learn something, you ask the next question, and sometimes you discover something you had not thought of at all. But if the first answer arrives three months later, there may be no time left to do anything with it.

So one change I would suggest to pharma teams is to stop treating every intelligence engagement as a report whose contents must be fully specified in advance. Specify the decision and the questions you know, of course. But leave time for the questions you do not yet know you should ask.

2. Lower costs make exploration possible before committing to a course

The cost of intelligence has also shaped the way it is used. When an analysis is expensive and takes a long time, you naturally become very selective. Often you commission the work only when you already have a reasonably clear hypothesis and need to confirm it.

There is nothing wrong with that. But it means a lot of potentially interesting avenues never receive a serious look, not because the science is uninteresting, but because investigating them is not economical.

Take a target product profile, or TPP: the description of what a drug will need to look like to be successful. It is useful to have one early, because it forces the team to understand the competition, the expected standard of care and the attributes that will actually matter. Yet it can take months to develop a good one, so companies cannot simply produce detailed TPPs for every candidate and possible indication.

Now imagine being able to investigate preliminary TPPs across an entire preclinical portfolio, then going into much greater depth on the most promising possibilities. That would change not only the volume of analysis, but when and how a company makes its choices.

That broader use across portfolios remains a prediction. What we have already seen is the first step: once the cost and turnaround fall, clients begin to ask questions that would not have been practical before.

3. Intelligence is much more useful when it does not go stale after delivery

There is another peculiar thing about intelligence reports. They begin to depreciate as soon as they are delivered. A competitor publishes a clinical result, a trial is amended, an asset disappears from a pipeline, or a new deal changes the landscape. What was a perfectly good analysis last Monday may need to be revisited on Wednesday.

Historically the client either pays to update it or, more often, continues using a report while knowing that parts of it are becoming stale. That is not a particularly satisfactory way of working.

What I believe is changing is the nature of the product itself. At Epistemic AI our agents continuously monitor the evidence behind the analyses our clients receive. When something relevant changes, they alert our team and propose modifications. Our scientists examine them, and only after formal approval is the online analysis updated.

This is important: continuous monitoring does not mean letting an AI rewrite conclusions without anyone checking. It means you no longer need to assemble a new team and start from scratch every time the world moves. The intelligence can evolve, and confidence in an earlier conclusion can go up or down as the evidence changes.

It also changes the relationship with the client. Instead of delivering a report and disappearing, we can continue the conversation as the scientific and competitive landscape evolves.

4. As agents do more information work, scientists can focus on what requires judgment

I have heard the concern that if agents do more of the analytical work, the scientists will become less important. I think the opposite is true, provided we use their time differently.

An extraordinary amount of work in traditional intelligence services goes into finding papers, navigating databases, checking which asset belongs to which company, reconciling terminology and preparing the material. These tasks are necessary, and they can absorb weeks of time. They are also exactly where machines can help enormously.

What scientists contribute is something else. First, they recognize when the agents have missed a nuance of what the client really wants. Second, like a good manager of a team, they keep the work focused and avoid spending time on directions that are unlikely to be useful. Third, they make sure the results can sustain rather harsh criticism and scrutiny. And finally, they often identify new questions to bring back to the client.

In other words, scientific experts do not sit at the end of the process merely to proofread a machine-generated report. They participate in deciding what should be investigated, how, and what the findings actually mean.

There is a practical implication here too. If information gathering becomes less costly, we risk drowning ourselves in analyses nobody has time to read. The scarce resource moves toward judgment: knowing which questions are worth asking, which evidence matters and when we have enough to make a decision. More analysis is useful only if it helps us make better choices.

5. The models enable the change; the knowledge graph, agents and learning loop make it work

So why not just give your scientists ChatGPT or Claude, or use the AI platform your company already has? It is a fair question. I use these models every day, and their progress has been remarkable. But a retail AI service on its own is not the system I have been describing.

A model can only work with the information it can reach, within the limits of its attention and the way the task is framed. In pharma, much of the difficulty lies in knowing what you have not found: the same compound has several names, an asset has changed hands, a result appeared first at a conference, a trial amendment has disappeared behind the current record. Larger context windows help, of course. They do not organize all of this information for you.

AI is very good at molding the work to the question the client has. But to mold something you need a skeleton. Otherwise you get goo. That skeleton is not another clever prompt.

This is why we have spent years building a biomedical knowledge graph, now with more than 10 billion nodes and 25 billion edges, integrating over 100 curated sources with normalized entities and provenance. But the graph alone is not the answer either. Complex work products have many interdependent questions that must be investigated independently and then put back together without contradictions.

We therefore bring together dozens of specialized agents, the graph, and our scientists in a coordinated workflow. The agents perform and reconcile the information work. Scientists establish the methods, direct the investigation, and challenge the conclusions. Clients bring their own context, react to the findings and ask new questions. We capture those decisions and use them to improve the methods and the next round of work. That learning loop matters as much as the individual agents.

We also tested the difference in practice. In March 2026, we evaluated a target product profile for Crohn’s disease alongside output from another large pharmaceutical company’s internal AI platform and a frontier-model system. Our analysis included a calculated current efficacy baseline and a more extensive, traceable set of references; neither comparison output provided the baseline. This was one particular exercise, assessed by our own team, not a ranking of the models. The internal system had strengths of its own. But it is a good illustration of why having a very capable model is not equivalent to having a complete scientific analysis.

Some of the engineering we built in the early days existed only because the models were not very good. We expect that layer to become less important, and frankly we are happy when it does. We are model-agnostic: if a better model becomes available tomorrow, we want to use it. What takes much longer to reproduce is the organized evidence, the historical record, the methods and the process by which scientists and clients keep improving the work.

Toward the end of 2024, we started reorganizing how we delivered our services around this approach. Our partnership with ClearView Healthcare Partners, which began then and was publicly announced in January 2025, gave us the opportunity to test and refine the model through actual projects. Others, including Bessemer, were articulating a similar vision for AI-powered services around the same time. For us, the work of making it real was already under way.

The result is something companies can buy as a service, with the customization and scientific attention they expect from consulting, rather than spending years building all the machinery themselves. And there is a natural opportunity for existing consultancies and frontier AI developers to combine their strengths with an established, specialized capability.

6. Pharma and frontier labs are investing in AI, but strategic decisions are a different problem

Pharma has already started putting serious money behind AI. BioNTech acquired InstaDeep in 2023 after years of working together, to strengthen its ability to discover and design medicines using AI. Merck’s April 2026 partnership with Google Cloud, valued at up to $1 billion, is broader: R&D, manufacturing, commercial work and other company operations. These are not just companies buying more chatbot seats.

And I think what the frontier AI companies are doing is perhaps even more telling. OpenAI agreed to acquire Tomoro in May to bring in people with experience deploying AI inside other businesses. That same month, an AI services company backed by Anthropic and investors including Blackstone acquired Fractional AI for its engineering and delivery expertise. Anthropic has also been developing Claude Science for research. It seems even the companies building the models understand that the technology alone does not make the work happen.

Then, on October 20, OpenAI acquired Orchestra Bio, whose technology helps scientists and program leaders coordinate the work of drug development, and is bringing it together with Rosalind, its tools for life sciences research. I find this particularly interesting because it is about the rather messy reality of getting a drug through development, rather than improving a model’s ability to answer a biology question.

But there is a distinction worth making. Finding a target, understanding biology and designing a new molecule is scientific discovery. Coordinating studies and programs so that a candidate advances is drug-development execution. Deciding which candidate deserves investment, which indication to pursue, what a drug must achieve to be successful, or which white spaces are worth exploring is strategy and intelligence. Of course they overlap. But they involve different evidence and different questions. Much of that last category is still performed by consultants and intelligence teams, and it is precisely where we have concentrated our work.

I think we are only at the beginning. If AI lets us discover more candidates faster, what happens next? More assets to evaluate, more potential indications, more changing competition, and decisions that still have to be made on time. It would be strange to invest so heavily in discovery and in AI deployment, and then leave the decisions about what to develop on the old calendar. That is the opportunity I find most interesting.

Conclusion: The question is what companies can now afford to investigate

I do not think every analysis will be better simply because it uses AI. There will be plenty of fast, inexpensive, incomplete work. What matters is whether the intelligence helps a team explore possibilities it might otherwise have missed, and arrive at an answer that can withstand scrutiny while there is still time to use it.

That requires changes on both sides. Those delivering intelligence must put together the models, the data, specialized technology and scientists who can stand behind the conclusions. Those commissioning it should leave room for the question that emerges once the first answer is understood. Otherwise we may have spent an extraordinary amount of money making the old process faster.

The question I would leave pharma executives with is simple: which investigations are you not doing today because they would take too long or cost too much? And what would happen if you could finally do them, and keep the answers current?

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Source note: The delivery improvements and anonymized customer example describe Epistemic AI’s experience. The March 2026 TPP comparison was run and scored by Epistemic AI; the large-pharma internal-system output was evaluated from its own demonstration. It was a qualitative comparison of a specific exercise, not an independent benchmark or a claim about current frontier-model rankings. Customer identities and clinical specifics are omitted.

Public references: ClearView / Epistemic; Bessemer (2024); BioNTech / InstaDeep (2023); Merck / Google Cloud (2026); OpenAI / Tomoro (2026); Anthropic-backed services firm / Fractional AI (2026); Anthropic / Claude Science (2026); OpenAI / Orchestra Bio (2026).