AI is everywhere in the life sciences conversation right now…and for good reason. It will be more deeply embedded across the value chain: helping researchers identify promising molecules, modelling how drugs behave in different populations, and streamlining trials that are increasingly complex and expensive to deliver.
It could help researchers identify promising molecules, design smarter trials, and bring new treatments to market faster and cheaper than ever before.
The opportunity is enormous. But there’s a catch - and it isn’t the algorithms. It’s the data that powers them.
Why is AI looking so exciting?
AI can scan millions of compounds in days, predict how they’ll behave, and even model trial outcomes before a single patient signs up. For an industry under pressure to innovate, that’s a game-changer, and could cut years off drug development.
The pressure on this sector has never been greater. Pipelines are under scrutiny, global tariff changes are squeezing margins, and regulatory expectations around transparency are rising. Yet, advances in AI mean models can now:
- Screen millions of compounds in days rather than months,
- Simulate trial outcomes before a single patient is enrolled, and
- Flag safety signals earlier and with greater precision.
For an industry criticised for long timelines and high costs, this represents a genuine step-change in productivity
The dilemma in the data
AI is only as good as the data it learns from. Much of that data comes from old clinical trials and fragmented sources. It’s often incomplete, inconsistent, and unfortunately, biased.
Many datasets lack diversity; therefore this means AI models might not work equally well for all patient groups. This is not just a technical issue; it’s an ethical one. If we don’t fix this, we risk creating treatments that fail large parts of our population.
Compounding the issue is a longstanding industry habit of success-bias. Data from failed trials is often unpublished or under-reported, despite being just as scientifically valuable as positive outcomes. This skews the evidence base, limits what AI can learn, and ultimately gives us an incomplete picture.
What can we expect in 2026?
We can expect that AI adoption will accelerate as companies chase speed and efficiency, and data quality will take centre stage, with investment flowing into cleaning, enriching, and diversifying datasets.
Regulators will also step in, demanding transparency and fairness in AI-driven decisions. The companies that lead will be the ones fixing the data first, before fixating on AI.
In a nutshell: AI will transform life sciences R&D, but the real race is to build better, unbiased data foundations.
Related content
