As AI begins to influence experiments, drug programmes and clinical decisions, scientific judgment may become as important as model performance.
The model has produced an answer.
It looks convincing. It performed well against the benchmark. The technical team can explain how it arrived there. But would you allow that answer to change an experiment, deprioritise a drug programme or influence which patients enter a clinical trial? And who inside the company has the authority to say no?
Some of the most interesting searches I work on begin with a question like this, rather than an established job title. The founder can see that an important responsibility is currently spread across the technical team, scientific advisers, customers and the founders themselves. What is less obvious is who should own it, what experience that person needs and where they should sit within the company.
I think scientific AI is approaching one of those moments. As the models become more capable, companies will need people who can determine when an output is scientifically meaningful, when it is misleading and when the available evidence simply is not strong enough to justify a decision.
That person may become one of the most important hires in the business.
A technically correct answer may still be the wrong answer
Scientific AI is getting better at finding patterns, generating hypotheses and predicting outcomes. But generating a plausible answer is not the same as making a defensible scientific decision.
A model can identify a statistically valid relationship that has little biological importance. It can perform well against historical data but struggle with a different patient population. It can produce a confident answer in an area where the underlying evidence is incomplete or biased. Sometimes the model may be technically correct, but answering the wrong question.
This becomes more important as AI moves beyond supporting research and starts influencing what happens next. If a system is helping a scientist explore possible targets, an imperfect output may still be useful. It creates another hypothesis to investigate. If that same output is being used to select a programme, determine an endpoint or decide which patient population should enter a trial, the consequences are very different.
The definition of good enough changes with the decision being made and the cost of being wrong. Somebody needs to understand that distinction.
The real challenge is deciding when to act
Companies are already using AI to simulate aspects of clinical trials, compare patient populations and challenge assumptions before expensive human studies begin.
The value is not that these systems will replace clinical trials. It is that they may expose weaknesses while there is still time to change the protocol, reconsider the population or stop a programme before significantly more capital is committed. But a prediction is only useful if the company understands how much confidence to place in it.
If an AI system suggests that a trial will fail, should the programme be stopped? Should the design be changed? Should the result be treated as one piece of evidence among many? What would need to be true for the company to disregard the model entirely? The answer cannot come from the confidence score alone. It requires someone who understands the disease, the available evidence, the limitations of the data and the commercial and clinical consequences of the decision. This person does not need to know with absolute certainty when a model is wrong. Nobody can offer that.
They need to know how to test the answer, where to look for weaknesses and when to prevent a plausible output from acquiring more authority than it deserves. That is a different capability from building the model itself.
Better models will not remove the need for judgment
There is an understandable belief that better models and larger datasets will gradually reduce the need for human scientific judgment. I suspect the opposite will happen.
Initiatives such as the Virtual Biology Initiative are intended to create the large, standardised biological datasets needed to support more capable scientific AI. AstraZeneca’s collaboration with insitro similarly combines human data, cellular models and machine learning to identify potential targets in difficult cardiometabolic diseases. These developments address one of the biggest constraints in scientific AI: the quality and relevance of the data available to train and evaluate models.
But better data will not remove uncertainty. It will allow companies to generate more hypotheses, discover more relationships and apply AI to more consequential questions. The number of apparently credible answers will increase. The challenge will move from producing possibilities to distinguishing between those that are interesting, those that are reproducible and those that are strong enough to influence a real decision.
The better the models become, the more valuable that judgment may be.
Evaluation cannot be left until the end
In many companies, evaluation is still treated as something that happens after the model has been built.
The technical team develops the system, performance is measured against a benchmark and scientific or clinical specialists are then asked whether the result appears useful.
That may be sufficient for an early research tool. It becomes increasingly risky when the system starts influencing drug development or clinical decisions.
Evaluation needs to begin with the intended use of the model.
- What decision is it supposed to improve?
- What would a scientifically meaningful answer look like?
- Which errors would be tolerable and which could send the programme in completely the wrong direction?
- Does the benchmark reflect a genuine research or clinical problem, or merely a task for which accessible data already exists?
- What happens when the system encounters a disease, patient population or data type it has not seen before?
These are not questions that should be answered by an ML team in isolation. But they also cannot be outsourced indefinitely to occasional advisers who have little ownership of the product.
If evaluation determines whether customers trust the system, it is not simply quality control. It is part of the product. For many scientific AI companies, it may also become part of the moat. Models will improve. Technical capabilities will spread. Competitors may gain access to similar tools and infrastructure. A company’s ability to demonstrate where its system works, explain where it fails and connect its outputs to meaningful scientific decisions will be much harder to reproduce.
The difficult part is that the role does not have an agreed title
From a search perspective, this is where the problem becomes particularly interesting.
Should the company hire a Chief Medical Officer, a head of translational medicine, a clinical development leader, a computational biologist or a scientific product executive? Any of those answers could be right. They could also produce an expensive mismatch. A highly experienced clinician may understand the consequences of a poor recommendation but have little experience evaluating probabilistic systems. An exceptional AI scientist may understand the model better than anyone else in the company but lack the biological or clinical authority to decide whether its output should change a programme. A senior pharmaceutical executive may bring considerable credibility and pattern recognition, but struggle in an early-stage environment where the evaluation framework, product and customer proposition are all being built at the same time.
The strongest candidates often sit between established disciplines. They may have worked across translational medicine and computational biology, clinical development and biomarkers, or experimental science and AI product development. Their value is not simply that they understand several technical subjects. It is that they can translate between them, challenge specialists on both sides and take responsibility for a consequential judgment.
These people are difficult to find partly because the market has not settled on what to call them. Their current job title may not reflect the responsibility a founder needs them to assume. For candidates, that creates an unusual opportunity. A role that appears to be scientific product leadership or translational medicine may actually provide responsibility for determining how an entire AI platform earns scientific and commercial trust.
For founders, it means the search should not begin with a familiar title. It should begin with the judgment the company needs someone to own.
Define the authority before defining the person
The answer is not to search for a mythical individual who combines world-class AI research, drug development, clinical medicine, regulatory strategy and product leadership. That person probably does not exist. If they do, they are unlikely to be available precisely when the company needs them.
The more useful exercise is to decide which forms of judgment must exist inside the business and which can remain with external advisers, collaborators or customers. A company developing toxicity models may need deep human-biology and regulatory credibility internally. A business building scientific agents may need someone who can define benchmarks that measure genuine scientific reasoning rather than performance on an academic task.
Another company may already have excellent validation, but lack a leader who can translate that evidence into something a pharmaceutical customer will trust, adopt and expand. The right structure depends on the decision, the consequences of getting it wrong and the evidence the customer will require. Before writing the job description, I would want to understand what this person must be able to stop.
- Which assumptions must they be willing to challenge?
- What evidence should cause them to change their mind?
- Where does the model’s authority end?
And will the founders genuinely allow this person to influence the programme when their judgment conflicts with the direction the company wants to take?
Those questions tell you much more about the required hire than a conventional list of qualifications. They also reveal whether the company is genuinely ready to appoint the person.
Scientific trust is becoming a company building capability
The most credible scientific AI companies will not claim that their systems are always right.
They will be able to explain which questions their models are qualified to answer, what evidence supports those claims and under which circumstances a human must remain responsible. That honesty should not weaken the commercial proposition. In a market full of broad claims about what AI will transform, it may become one of the clearest signs that a company is ready to work with serious pharmaceutical and healthcare customers.
The next generation of scientific AI companies will still need outstanding ML researchers, computational scientists and engineers.
But one of their most consequential hires may be the person who can look at a compelling output and ask the question everyone else has become too invested to ask:
What if the model is wrong?
Not because they are resistant to the technology.
Because they understand what the company needs to establish before allowing that technology to influence something that matters.
About the author
James Trott is a recruitment and talent advisory specialist with more than 18 years of experience across Life Sciences and related sectors. He works with early-stage to mid-sized biotech, TechBio, pharmaceutical, and healthcare organisations, supporting leadership teams on strategic hiring decisions across C-Suite, VP, Director, and specialist technical functions.
As Co-Founder of Aspire, established in 2013, James has helped build the firm into a trusted partner across Biotechnology, Pharmaceuticals, Medical Devices, CROs, Research, Consumer Healthcare, and Healthcare Technology.
James is known for his relationship-led approach, pragmatic market perspective, and focus on helping companies think carefully about capability, team structure, and long-term growth, particularly during the critical early stages of company building.