For much of the past two years, life sciences R&D has been described as an obvious beneficiary of advances in generative AI. The logic sounded simple. If large language models could summarize documents, answer questions, and draft text, then they could accelerate the work of scientists who spend much of their time navigating literature, data, and prior evidence.
In practice, that promise has largely failed to materialize inside research organizations. AI tools have spread rapidly across enterprises, yet their impact on core scientific work remains limited because chat-style systems simply don’t work there. This is not because scientists are resistant to change or because the technology lacks raw capability. It is because most early deployments were built on assumptions that do not hold, once scientific standards of evidence and reasoning come into play.
The first wave of adoption treated AI as a productivity layer. Organizations rolled out generic chat systems or copilots and expected them to support research in the same way they support email, presentations, or internal documentation. In R&D, that approach quickly breaks down.
Scientific work is not simply about retrieving information or generating plausible text. It requires explicit provenance, an understanding of causal relationships, and the ability to assess whether an output is trustworthy enough to inform a decision. Without those capabilities, scientists fall back on what they know best. They stop relying on the system. What at first appears to be a failed adoption is often a rational response to tools that cannot meet the epistemic bar of science.
This gap becomes most visible when chat-style systems are pushed into research workflows. A language model can produce an answer to a biological question that appears coherent and confident, but that surface fluency masks how little the system reveals about evidence, reasoning, or uncertainty.
Trust in R&D depends on traceability and the ability to interrogate reasoning step by step. Without that, the output could be useful for drafting or brainstorming, but it cannot be used to inform decisions that shape experiments, portfolios, or clinical bets.
Why building intelligence turned into a maintenance problem
Many organizations attempted to solve this gap by building their own systems. They assumed that combining a foundation model with internal data and custom logic would create an AI assistant tailored to their needs. That assumption reflects an older software era, when systems could be built once and left largely untouched for years. Agentic AI does not behave that way.
Architectures evolve rapidly, evaluation becomes central, and small changes in system design can have outsized effects on output quality. Keeping pace requires continuous iteration and deep expertise in both AI engineering and scientific evaluation. For most research organizations, that cost was underestimated.
This experience has begun to reshape how leaders think about the build-versus-buy question. The answer is not binary. What has become clear over time is that the economically viable approach is to buy the core capabilities and build selectively at the edge. The core includes the infrastructure required to reason over complex scientific evidence while preserving transparency and evaluability. The edge is where organization-specific workflows, conventions, and decision criteria live, shaped by how a particular team actually works. This split matters because scientific workflows vary widely even within the same therapeutic area. Attempting to internalize the entire stack slows adaptation when the underlying technology is moving fast.
When productivity is framed for the wrong buyer
Early adoption has stalled because of how productivity is typically framed inside pharma. Workplace productivity is typically treated as the CIO's remit, tied to enterprise tools and broad enablement. Scientific productivity belongs to R&D leadership, which remains accountable for the quality and integrity of scientific outcomes. The CIO usually justifies large-scale deployments that increase knowledge workers’ efficiency across the organization. R&D leaders, by contrast, tend to evaluate tools through the lens of specific use cases.
While that mindset is understandable, scientists end up with isolated tools that address narrow tasks while lacking a shared system that supports reasoning across the research lifecycle. The result is fragmentation.
As attention shifts toward agentic AI, there is a risk of repeating the same mistake under a new label. The term “agentic” has quickly become overloaded, applied to systems that behave very differently when put to work. Many products now claim it, yet they diverge in how they operate and in the quality of their outputs. For buyers, this creates real confusion. From the outside, two systems may appear similar: both can take a goal, break it into steps, and generate a result. The difference becomes clear when outputs are judged against scientific standards. In R&D, architecture matters only insofar as it produces results that can be trusted, scrutinized, and acted upon.
From assisting scientists to owning workflows
The more meaningful shift underway lies in the ownership of work. Early deployments focused on supporting individuals by helping them search, summarize, and draft. The phase we are now entering is different: systems are beginning to take responsibility for bounded segments of workflows, coordinating agents to assemble evidence, apply predefined reasoning patterns, and produce work products that once required sustained human effort. Rather than eliminating the role of the scientist, this shift changes where expertise is applied. Humans move upstream to define intent, constraints, and evaluation criteria, and then downstream again to review outputs and make judgments that still require domain understanding and accountability.
There is a useful parallel in software engineering, where much of the routine work of writing code is increasingly automated, allowing experienced engineers to focus more on architecture, requirements, and review. Biology, of course, is far more complex, and progress will likely be slower. Experimental systems are less standardized, and the evidence scientists work with is often incomplete or ambiguous – factors that make careful scientific oversight even more important. Even so, the broader pattern is familiar. As systems become capable of executing well-defined reasoning tasks, the real productivity gains come not from replacing experts but from freeing them to concentrate on the decisions that matter most.
As we move through 2026, the organizations that benefit most from AI in R&D will be those that align technology with scientific reality. Instead of optimizing for labels or feature lists, they will evaluate systems based on the quality and transparency of outputs. Trust, in this context, is earned through explicit reasoning and evidence, not through confidence or speed. Adoption strategies will increasingly be designed around workflows rather than isolated use cases, with a clear understanding that some parts of the process can be automated while others must remain firmly human.
The first wave of AI adoption in pharma underdelivered because it asked the wrong question. Too much attention went to what the technology could generate, and not enough to what science actually requires. There is now an opportunity to recalibrate. By grounding AI systems in scientific standards and reshaping how work is allocated between humans and machines, R&D organizations can move beyond prolonged experimentation and start seeing real effects on decision-making.
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