October 2026 • PharmaTimes Magazine • 24-25
// AI PHARMA //
Bold rush
Finding the real competitive advantage in pharma’s AI race
McKinsey estimates AI use cases could generate $60 billion to $120 billion a year in economic value for pharma and medical-product industries by boosting efficiency and innovation throughout the sector’s value chain. This trend aligns with an increase in pharma firms experimenting with AI use cases in their R&D processes.
Over the next three to five years, experts predict that AI-enabled efficiencies could halve early-stage development timelines and costs.
But that is not to say AI is without its challenges. Ultimately, any LLM or other AI-based technology is only as good as the quality, completeness and context of the information on which it is trained and applied. In pharma, where decisions are expensive to reverse, inputs are everything.
While AI speeds up analysis, competitive advantage comes from unique data, not commoditised models. If we look at many of the pharma marketing trends of the past, failures can most often be traced back to the idea that technology was viewed as a substitute for understanding prescriber behaviour.
AI may ultimately prove more transformative than any of those shifts. That is precisely why getting the operating model right matters.
The current wave carries a familiar risk, only faster. Healthcare AI spending nearly tripled from an estimated $480 million in 2024 to $1.4 billion in 2025.
Anecdotal signals suggest a meaningful share of that spend is shifting from proven primary research towards internal builds, synthetic data and increasingly sophisticated models on the argument that AI can serve as a substitute for physician, patient and payer research.
Yet replacing primary research with synthetic data or internal AI builds remains largely unproven. The risk is simple: spend first; measure later, and discover the gap in brand performance.
The question isn’t whether to use AI, but whether we are using it for sustainable value or short-term efficiency gains that create systemic problems later.
What AI can’t tell you
Consider the questions that brand teams need answered to make a launch call, respond to a competitive entry or defend a resource allocation: how is a specialist reasoning through a narrow indication right now? How does a biologic-experienced patient weigh a treatment switch? Where does a payer stand on a newly approved agent this quarter, not last year?
That information isn’t published anywhere, and it isn’t sitting in a claims feed waiting to be scraped. A general-purpose model can synthesise what is already public, but it can’t manufacture behavioural evidence that doesn’t yet exist.
It can tell you what a market looked like last year, and not why prescribing has shifted over the last six months among the specialists who matter to your brand.
That kind of evidence has to be gathered directly from the field, kept current and generated independently from the public and manufacturer-sponsored information that increasingly forms the common knowledge base available to company-sponsored AI systems.
And one part of this is structural, not a matter of degree: at launch, there is no historical data to synthesise. A more powerful model operating without evidence doesn’t create evidence, it creates a more sophisticated inference.
That distinction matters. AI is extraordinarily good at finding, connecting, synthesising and interrogating information. But it is not inherently an evidence-generation system.
When the question is what physicians, patients or payers actually think and do today, somebody still has to create the evidence.ord are intended to address fragmented health information across England.
Where synthetic data sits
Synthetic data has real uses. Synthetic data can extend existing evidence, simulate scenarios, fill gaps and stress test hypotheses. What it cannot do on its own is establish new empirical evidence about how real physicians, patients or payers are behaving today.
Synthetic data can help generate hypotheses; primary research tests whether those hypotheses survive contact with reality. A synthetic respondent can generate a surprising answer. What it cannot establish is whether that answer reflects a real physician, patient or payer in the market.
In our research, the surprise is often the whole point: the physician whose reasoning breaks the pattern; the patient barrier the existing evidence didn’t predict, or the market shift that becomes visible only when new data is collected.
‘AI is extraordinarily powerful at finding and connecting information, but it is not an evidence-generation system’
Ask synthetic data to carry a launch decision alone, and you are still reasoning from what was already known.
There is a broader risk here as well. AI systems largely reason from what has already been observed, documented or generated. If organisations increasingly replace primary evidence with synthetic outputs derived from existing evidence, the system risks becoming recursive: increasingly sophisticated analysis of an increasingly closed information set.
Primary research does something fundamentally different: it introduces new observations into the system.or AI applications and agents to work across different parts of the organisation.
Beyond the algorithm
In January 2026, the FDA and EMA jointly issued Guiding Principles on Good AI Practice in Drug Development, emphasising data quality, validation rigour and human oversight.
We are not in drug development ourselves, but the direction is the same: even in the highest-stakes corner of pharma AI, regulators are converging on the standard I would hold our own team to, regardless of what they require of us specifically.
I want to know what the model is reasoning from, who reviewed the output and who is accountable if it is wrong. We establish documented workflows for how AI is used in client-facing processes, including where expert review and accountability sit.
Every AI-assisted output at Spherix passes through a human expert before it reaches a client. Not because technology can’t be trusted, but because human expertise isn’t simply the last quality-control step.
It determines which questions are worth asking, which signals deserve scrutiny, what the evidence can and cannot support and what a finding actually means for a brand.
Judgement matters most when the stakes and ambiguity are highest.
Where the advantage lives
That is not to argue against AI, far from it. Rather, it is to warn firms of the serious implications of rushing head-first into AI adoption purely for efficiency gains, with insufficient consideration of the role of human expertise and judgement.
From data wrangling and first-pass synthesis to pattern recognition across decades of studies, AI’s ability to automate and accelerate analytical work is unparalleled. But the real opportunity lies in what we do with the time it creates, reinvesting it in deeper reflection, richer inputs and sharper judgement on the decisions that carry real consequence.
Do this well, and the next five to ten years could mark a significant shift in pharmaceutical decision-making, characterised by differentiated evidence, technologies capable of extracting greater value from that evidence and experts equipped to interpret its implications within the broader clinical and commercial context.
That, ultimately, is where the advantage lives.
Dan Barton is CEO at Spherix Insights
PharmaTimes
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