AI is everywhere in the life sciences conversation right now, and for good reason. Across discovery, development, and clinical operations, it is rapidly shifting from experimental to foundational.
The opportunity is enormous. But the true determinant of AI’s impact in 2026 will not be the sophistication of the models. It will be the quality, completeness, and representativeness of the data used to train them.
Why is AI looking so exciting?
AI can potentially scan vast molecular libraries, predict drug behaviour, and model clinical outcomes before any patient enrols. Companies adopting these tools are compressing discovery cycles from years to months and reshaping decision-making across the value chain.
Industry pressure is rising too. Pipeline gaps, global tariff volatility, and increasing regulatory scrutiny mean organizations need faster, more productive R&D. AI is ideally positioned to help by enabling:
Screening of millions of compounds in days, not months.
Simulation of trial outcomes pre‑enrolment, improving protocol design and reducing costly trial amendments.
Earlier detection of safety signals, improving patient protection and reducing late‑stage failures.
For a sector criticised for long timelines and high costs, this could represent a significant 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, creating models that perform less effectively for underrepresented populations. This is not just a statistical challenge but also an ethical one. Without addressing this issue, we risk developing treatments that work better for some groups than others.
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 Data from failed trials is often unpublished or under-reported, despite being just as scientifically valuable as positive outcomes.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 and distorts 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.
How is your organisation preparing for this? Are you confident in the data behind your AI?
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MHA Predictions 2026
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