The narratives on biopharma AI adoption tend to follow two familiar lines: reported improvements expressed in R&D metrics like shorter lead optimization times and number of new targets identified per year or qualitative assertions that AI has yet to produce clear gains.
But it is remarkably difficult to find reports of AI R&D integration efforts that have failed to improve predefined strategic outcomes, like an AI-enabled hit-to-lead workflow producing no meaningful reduction in design time compared with the original wet-lab workflow. Instead, scientists question the robustness and judgment of individual tools and utility of predictions generated by specific models.
While valid, these concerns can create a sense of failure without addressing the key question for AI integration into biopharma R&D: can these advances, with their shortcomings and faults, create real strategic value?
The difference between a company seeing material R&D gains from AI and a company continuing to deliberate whether AI has created concrete value is the operating level at which AI has been integrated.
Every biopharma company has an AI strategic window: the range of operating levels at which AI adoption has the potential to create strategic value. These operating levels represent layers of R&D complexity such as task, workflow, domain, and enterprise, e.g. library design, affinity maturation, antibody design and engineering, and company-wide R&D strategy, respectively, at a biologics company operating across discovery and development. These can break down into lower levels of granularity that depend on the company's strategic and operational scope, but their progression follows increasingly higher-stakes decision points and a sequential shift from technical outputs to strategically actionable outcomes.
Companies are not equally positioned to adopt AI across these levels. An AI adoption vision that is out of proportion to the company's state of AI preparedness can quickly result in strategic dilution and inability to deliver on strategic aims. But adoption at a level that cannot be anchored to existing strategically actionable outcomes will stagnate and falter due to lack of conclusive evidence on value.
The first prerequisite to initiating AI integration at a company is identifying its AI strategic window.
An industry-wide lower bound on the strategic window
There is a minimum viable level of AI adoption for any company: the first level at which outcomes can be measured by performance-based, implementation-agnostic success metrics. That is critical for comparing two alternative implementations and determining investment at an actionable decision point.
Taking the earlier biologics platform example, these metrics exist at the level of affinity maturation (“workflow”) but not at the more granular level of library design (“task”). Knowledge of the process details of affinity maturation is not needed to identify improvements in the process, as affinity fold improvement or the number of variants achieving a certain affinity threshold within a fixed number of maturation cycles are implementation-agnostic metrics that allow performance-based comparison, assuming comparable experimental conditions. Library design, on the other hand, does not in and of itself produce outcomes directly indicative of performance: different libraries may exhibit different distributions across sequence space; one may even be more diverse than another, but sequence diversity is no guarantee of better shots on goal. Library design has a technical output, the actual library, rather than an outcome measurable by a performance-based success metric that can inform a strategic decision.
Operating levels whose outcomes are measurable by implementation-agnostic success metrics permit head-to-head comparison of the original setup with an AI-enabled version. Pilots and proofs of concept can be scoped within existing strategic priorities and performance indicators, with the result that both direct performance impact and the return on implementation effort (which is informative of readiness for broader AI efforts) can be evaluated. Since platforms have different workflows and capabilities, there is inter-platform variability on which processes belong on each operating level; what is universally necessary is for these metrics to draw a clear line between investment and a strategically actionable result needed for a decision to maintain, expand, or reject an AI-enabled approach.
There is an important scientific benefit to operating at levels corresponding to such outcomes. Anchoring on performance metrics means that the process into which AI is embedded can be repeatedly benchmarked against a clear goal. This provides an empirical signal that can be fed back to models for subsequent improvement within and, where applicable, across campaigns: technical progress is directly driven by performance against strategic goals.
This capacity for iterative improvement has been driving industry interest in closed-loop wet-dry lab setups, which enable progressive performance gains by embedding models at the workflow level and iteratively exposing them to proprietary use-case-specific wet-lab data. Closed-loop setups provide an avenue for non-AI-native companies to develop differentiated wet-dry-lab infrastructure even without specialized proprietary models: tight integration of AI with wet-lab processes is platform-specific and involves expertise, biological intuition, and dynamic data acquisition. But these benefits and potential gains in productivity and efficiency require that AI integration be planned with respect to well-defined objectives.
Operationally, there is a psychological downside to bringing AI into an organization without attaching it to strategically actionable outcomes. When AI is explored ad hoc at the task or tool level (including off-the-shelf agentic infrastructure frameworks not embedded in a specific platform workflow), the use cases are either too granular or too exploratory to enable benchmarking against established R&D performance metrics. This is especially a concern when AI adoption strategy is anchored on usage-based adoption metrics (how many users, how often) rather than on platform-relevant outcomes. The risk is that the company's AI activities become a fractured landscape of test tools, unsupervised in silico implementations, and increased computational spend. Impact on key strategic outcomes is missing or impossible to quantify while the company believes that AI is now being used across multiple workstreams.
In other words, there is enough AI activity to produce the impression that AI is being adopted and too little to make a discernible difference in performance. This is where skepticism emerges and efforts falter.
A company-specific upper bound on the strategic window
At the other end of the AI adoption spectrum is a top-down enterprise-level rethinking of the traditional scientific and operational workflows of biopharma R&D. It envisions highly interconnected, nonlinear discovery and development approaches that can draw on diverse sources of information to provide new insights and drive cross-process compounding automation. This “AI first” vision goes beyond connecting every process to AI: it is about using AI to develop new processes. This is the emerging paradigm that has been shaping the discussion on automated end-to-end scientific discovery but that few have begun implementing in practice at this point.
Those in a position to implement this vision are the companies with the least baggage and the ones with the most resources. Companies only starting to build their platforms have the opportunity to weave AI into them ab initio: ensure that generated data is AI-ready, develop lab-in-the-loop setups that optimize both candidates and models, and streamline information flow across processes so that insights are not constrained by sequential workflow boundaries. Those with plentiful resources can rethink their workflows, create new initiatives and partnerships, overhaul outdated infrastructure, and acquire state-of-the-art in silico capabilities that put them at the forefront of AI drug discovery and development.
Most established biotech companies are not in this bucket, and there is substantial risk of strategic dilution in aiming for an enterprise-level AI-first transformation when AI organizational preparedness is low.
For an established wet-lab-first company, becoming AI-first is rarely about just hiring a team of ML scientists and engineers: a typically-sized ML team at a biotech company does not have both the technical breadth and depth (or the bandwidth) needed to implement an enterprise-level transformation of an existing platform. With every AI integration operating level climbed, the company is navigating a broader build vs. buy vs. partner optionality space. As the surge in pharma AI deals since the start of this year suggests, it takes a village to pursue an enterprise-level AI strategy.
To navigate these complex decisions a company must have access to expertise that can triangulate these choices on both strategic and technical levels and make confident decisions on R&D technical need and fit, level of investment, as well as the progression of adoption through proprietary development, specific partners, and vendor software. Companies that are only beginning to adopt AI do not have the in-house expertise to make informed decisions in this complex landscape or the proven case studies to justify the needed scale of internal and external investment.
The failure mode is aiming to pursue a strategy that sits at a significantly higher level than internal AI precedent and preparedness. What is at stake is a vision that remains abstract and the opportunity cost of not pursuing a targeted effort more likely to yield near-term results.
Shifting the window
The AI strategic window is not fixed: it expands to higher levels as a company's AI fluency matures. There are companies that are already AI integrated at the domain level: while their overall drug discovery and development process proceeds in traditional stages, one or more of these stages (like target discovery or candidate discovery and optimization) is strongly AI-integrated within and across workflows. Companies at this level of integration typically have AI-ready data practices, strong in-house AI expertise, and are often well-connected with other AI-focused companies that can become future partners.
A company with these variables is well-positioned to plan a cross-domain enterprise-level AI integration effort. Most resource-constrained biotech companies today, however, have a narrower AI strategic window that is focused on the workflow level: the level that matches their AI preparedness and has the potential to deliver measurable strategic change. As these efforts proceed and there is clear evidence of success, a company that started out at the workflow level can progress AI integration to higher operating levels.
AI adoption requires strategic vision: undirected experimentation at the task and tool level is highly unlikely to yield real strategic gains. But that vision should begin at the enterprise level only when the internal expertise, awareness, and network infrastructure are in place. When that preparedness is still several steps away, the AI strategic window is centered on cumulative and targeted workflow-level adoption steps that are symbiotic with the current technology.
That is not lack of transformative AI vision: it is creating a differentiated, value-generating technological stack that is led by outcome-driven strategy and survives execution.