Bridging the gap between research excellence and business impact requires systematic training in business acumen—especially as AI reshapes the innovation cycle

When Pfizer scientists discovered that their failed heart medication caused unexpected erections in trial participants, they repositioned it as Viagra[1]. The drug created an entirely new therapeutic class, addressed a major unmet medical need, and became a multibillion-dollar franchise. This outcome depended on scientists who possessed uncommon skills: the ability to navigate the intersection of research insight and commercial awareness.

The pharmaceutical industry invests over $200BB annually in R&D[2], yet 90% of drug candidates fail[3] for a variety of reasons, including safety, efficacy, or a lack of commercial differentiation. Academic labs publish millions of papers, but technology transfer offices struggle to commercialize innovations[4]. As the model that guided research and development in the 20th century begins to shift, it is imperative that we adapt to these changing conditions. We must use resources more efficiently to explore new areas of science and to create innovations that reach more patients without increasing our spending. In an era of constrained resources, the gap between the number of exciting projects that we wish to undertake and the number we could afford to execute is often resolved by simply pausing promising projects. Funded projects tend to “do more with more” because of the heightened security associated with being part of the “selected” pool. Paradoxically, scientific rigor alone does not increase the overall probability of technical success or deliver go/no go answers more efficiently. The missing link is the absence of the systematic integration of business thinking into research decision-making.

Here, I argue that business awareness is now a core scientific competency, encompassing efficiency, stakeholder needs, competitive positioning, decision frameworks, portfolio optimization, and AI fluency. Drawing on personal experience and examples from neuroscience, drug discovery, and emerging AI applications, I outline six dimensions of business thinking that scientists must master to maximize research impact.

Efficiency Drives Discovery

Post-doctoral research fellowships impose critical time constraints that shape research strategy. Growing research and technical complexity amplify these constraints. For example, a multipronged and cross-disciplinary technical approach, e.g., combining behavioral, electrophysiological, and anatomical techniques, enables a richer understanding of a problem but limits the number of experimental cycles. When does the trade-off between time and complexity justify the expanded cycle time? The answer may depend on the effort and resolution associated with the outcome, i.e., who does the work and how will the results inform the next experiment. Whether I was a scientist at the bench or leading the work of others, I recognized I was engaged in team science and continually revisited what I vs. my colleagues were doing within the context of the project. Parallel work, if aligned, was more productive than sequential design-test cycles. Leveraging research associates and cross-lab collaborations can dramatically increase both depth and breadth of output. When I applied this approach to my post-doc, which yielded 4 first authored and 11 total publications in four years, it validated a principle I'd lean on throughout my career: efficiency is about identifying what actually matters and optimizing around it.

Efficiency optimization can expand research impact even within fixed timelines, consistent with the Theory of Constraints[5], which states that most systems are constrained by a few bottlenecks. As a scientist transitioning from academic to industrial research, I came to appreciate the drug discovery and development process (commercial insight would come later) as a complex system. “The Goal” influenced me early in my career because it helped me understand how pharmacology experiments contributed to the “flow” of small molecule screening through the Research Operating Plan. In research, these constraints are typically time, resources, or technical capacity. Identifying the most limited constraint enables strategic resource allocation at both individual and project levels.

In industry settings, this efficiency thinking scales to entire departments. Biopharmaceutical companies track “cycle time”—the interval from experimental design to actionable data—as rigorously as they track budgets. Reducing cycle time from three weeks to two weeks increases annual experimental capacity by 50%, fundamentally changing which projects are feasible.

I share this formative experience to illustrate an approach I benefited from, which other scientists can replicate. Each failed experiment or poorly designed study represents not just wasted reagents, but irrecoverable time—the scarcest resource in time-limited positions or projects. Scientists who systematically analyze their workflows to identify rate-limiting steps can often double their effective output. As we evolve towards AI as co-scientists[6], it will be increasingly important for human scientists to continuously evaluate the design, optimization and replacement of workflows. We must develop an awareness of where our time is best applied vs. the activities that will be better suited to virtual scientific assistants.

Business lesson #1: Optimizing efficiency is a key driver of performance. This lens supports project prioritization (which experiments are most important) and appropriate allocation of resources (who should do what work). Scientists who understand their constraints within the context of broader projects can systematically address bottlenecks. Rather than working harder within existing limitations, we can redefine and optimize the flows to fully leverage AI.

Know Your Stakeholders

Within biopharmaceutical research, entry-level PhD scientists are typically assigned a drug target and indication—the “what” of their work. However, considerable flexibility exists around the “how”: which assays to establish, what endpoints to measure, and how to balance competing priorities? Working in collaborative research settings requires understanding the needs of stakeholders.

For biologists supporting medicinal chemistry programs, this means knowing how assay data feed into design decisions. Tradeoffs between throughput and fidelity are inevitable. High-fidelity data—mechanistic studies revealing detailed molecular interactions—take longer to generate and typically provide lower throughput. Scientists can become enamored with elegant experiments; however, these assays are better suited for generating project-level insights than to supporting structure-activity relationship (SAR) decisions.

Data of lower fidelity need not be of low quality, provided their simple, timely reproducibility is sufficient to drive decision-making around SAR. The ability to automate data generation and analysis, combined with understanding make-test cycle times, becomes critical. In medicinal chemistry, rapid iteration is currency. A biology lab that delivers high-quality SAR data in two days versus two weeks can support 5-fold more design cycles annually.

Business lesson #2: Understand the key business drivers within your research context and design experiments to optimally support target goals. Recognize the interdependence between strategy and execution. Scientists who align their technical work with stakeholder needs will likely need to reframe the purity of scientific pursuit to match the realities of business. Getting this balance right “nice to have” vs. “must have” experiments can accelerate project timelines, yield more successes and thereby increase access to resources.

Commercial Awareness Enables Pivots

Much has been written on serendipity in drug discovery[7]. The goal is not to eliminate serendipity but to channel scientific curiosity into heightened commercial awareness. One example illustrates this principle. Peripherally involved in an anti-obesity program, my lab was tasked with establishing assays to evaluate tachyphylaxis (receptor desensitization following repeated agonist exposure). Classical organ bath pharmacology using isolated bladder muscle strips was ideally suited to this question, measuring contractility in vitro. This preclinical work de-risked the tachyphylaxis question; however, other data raised concerns about the target's suitability as an obesity treatment. Because of a commercially oriented mindset, the question was asked—could this molecule class be repositioned for overactive bladder (OAB)? Commercial questions emerged around market size and viability. While the market for OAB pharmacological treatments was modest, the adult diaper market in the US already exceeded $1BB in sales—indicating substantial unmet need. A pivot was triggered, and vibegron (GEMTESA) was ultimately approved to treat OAB, with sales potential estimated to exceed $1BB[8]. Without this mindset, perhaps the program and insights would have been shelved completely.

Strategic pivots can be even more difficult when catalyzed by clinical data, requiring teams to overcome the stigma of “clinical failure.” Sometimes a good molecule is tested in the wrong patient population or with an inappropriate clinical trial design. Consider one instance in which a product being developed for chronic obstructive pulmonary disease (COPD) failed to meet the target product profile established by a partner company (i.e., met a “business reality”). The question became: would the clinical data satisfy a different product profile? This required understanding both the clinical data and potential market opportunities. Patient-by-patient analysis of clinical results, integrated with preclinical findings, revealed product attributes distinct from existing COPD medicines. Specifically, an underserved population existed in institutional care settings where patients required multiple daily nebulizer treatments, each lasting 10 minutes with caregiver supervision. The investigational medicine was repositioned as a once-daily nebulized treatment providing 24-hour bronchodilation. Revefenacin (YUPELRI) became a product with more than $200MM in annualized sales[9]. These pivots share common elements: deep technical understanding combined with market awareness, a willingness to adopt a first principles mindset to question initial assumptions, and systematic analysis of alternative commercial opportunities. Scientists locked into their original hypotheses miss such opportunities.

Business lesson #3: Apply commercial understanding to guide R&D decision-making. The path to medicine is rarely straight, so allow R&D insights to guide commercial positioning, and vice versa. Commercial awareness transforms "failures" into opportunities by systematically exploring alternative value propositions.

Frameworks Guide Decisions

The ability to combine technical and commercial perspectives can be enhanced through structured decision-making frameworks. One approach is the Kepner-Tregoe (KT) method[10], which provides rational frameworks for problem-solving and decision-making. The KT method comprises processes enabling systematic assessment of situations, problems, and decisions. Within this framework, teams move beyond assumptions, focus on facts, and create action plans from complex situations.

Applied to annual research portfolio planning at a biopharmaceutical company, the KT method enabled systematic evaluation of dozens of projects spanning discovery through late-stage development. Rather than relying on intuition or politics, the process established explicit criteria: strength of biological rationale, differentiation from competitors, market opportunity, technical feasibility, and resource requirements. Each project was scored systematically, enabling objective comparison across therapeutic areas vs. a subjective assessment based on scientific passion areas which often emerge from a “herd” mindset. Other frameworks serve similar purposes. Stage-Gate® processes[11] break product development into discrete phases separated by decision points, with clear go/no-go criteria at each gate; whereas '5R framework' is designed to address R&D productivity based on 5 determinants (the right target, right tissue, right safety, right patient and right commercial potential)[12]. The specific framework matters less than using one, because it creates a shared language and explicit, objective criteria for decision-making.

Business lesson #4: Create or use frameworks to objectively guide research and commercial planning. Establishing criteria to advance or terminate projects not only helps prioritize activities but also enhances communication among stakeholders and improves the speed to reach go/no go inflection points. Systematic decision-making reduces bias and enables continuous improvement.

Portfolio Optimization Maximizes Value

R&D drives business growth by creating products that satisfy market demand. Some portion of sales is reinvested into R&D to sustain growth over time. The goal is to create more value from every dollar invested and to increase the likelihood of achieving this goal by building a resilient portfolio diversified by the risk/return profiles of individual projects.

Portfolio value is defined as eNPV / R&D Investment, where eNPV (expected Net Present Value) = Probability of Technical and Regulatory Success (PTRS) × NPV. Net Present Value is simply the difference between money invested and money returned, adjusted for discounted cash over time. Value creation occurs at three phases: discovery, development, and commercialization. Portfolio value increases through: (1) phase changes, focusing on activities that increase PTRS; and (2) efficient execution of studies that meet target objectives with less time and cost.[13] Ultimately, constraints of time, resources, and capacity are essential factors when considering which experiments, projects, or programs to undertake. Portfolio prioritization is not only about programs added. Subtracting programs enhances portfolio value and health by optimizing resource allocation and alignment.

In some instances, projects no longer strategically aligned can be spun out into newly created companies. As these companies advance the programs, additional value is created without putting additional internal R&D capital at risk. Such was the case with Aliada Therapeutics (formed through a partnership between Johnson & Johnson and RA Capital), which was acquired by AbbVie for $1.4BB in 2024[14]. Similarly, Rapport Therapeutics (formed through a partnership between Johnson & Johnson and Third Rock Ventures) went public in 2024 and now has a market capitalization of approximately $1.5BB[15,16].

These spin-outs illustrate portfolio thinking at the organizational level. Rather than terminating programs that didn't fit strategic priorities, the parent company created structures allowing those programs to succeed independently while retaining financial upside through equity stakes. This approach required sophisticated commercial awareness: understanding which assets had value outside the internal portfolio, structuring deals that aligned incentives, and knowing when to divest rather than terminate. It is equally valuable to recognize when spin-outs do not make sense, for example where low financial terms shift the cost-benefit analysis to pausing a project until additional capital is available for internal development.

Business lesson #5: Sustainable competitive advantage requires understanding the context within which one works, ensuring fiscal and research discipline, and translating insights into value proposition requirements. Portfolio thinking applies at all levels—from individual time allocation to institutional research strategy - and portfolio optimization is, ideally, an ongoing iterative process rather than something simply undertaken annually.

AI Reshapes the R&D Landscape

Each of the five business lessons discussed were learned in a “pre-AI era” but, as highlighted, one can now immediately appreciate the ways in which AI affects or optimizes each of the business lessons above. Because the integration of artificial intelligence into drug discovery and development is fundamentally changing how business awareness manifests in scientific work, AI literacy will become inextricably linked to what it means to be a scientist, in any and all research contexts. AI will enable new paths to target identification and validation. AI-driven drug discovery platforms will create drug candidates that could not be previously envisioned and/or will reduce the timelines for candidate identification from 4-5 years to 12-18 months[17]. Within neuroscience, companies and products will be built around the prospective use of digital phenotyping and other quantitative measures to stratify patients into biomarker-defined populations. This requires neuroscientists who understand both the biology and the commercial logic: Why does patient stratification create competitive advantage? What evidence validates a stratification approach? How do you design research programs to enable trials that generate both regulatory approval and commercial differentiation? This AI transformation creates new imperatives for all scientists.

Business lesson #6: Develop AI fluency not just as a technical skill, but as a business capability. Understand how AI tools change research economics, accelerate or de-risk programs, and create new forms of intellectual property and competitive advantages. The scientists who thrive will understand AI's R&D and commercial implications: how it changes drug discovery and development, time-to-market, alters resource allocation, and creates new competitive dynamics.

Building Business Competence

Integrating scientific excellence with business awareness is no longer optional; it's essential for research impact. Whether they are developing medicines, securing grants, or advancing academic careers, scientists must understand the economic context of their work (see Box 1).

Box 1: Business Awareness Diagnostic

Scientists can assess their business competency by asking:

Efficiency

  • Do I understand the cost and time requirements of my experiments?
  • Can I articulate tradeoffs between throughput and data fidelity?
  • Have I identified bottlenecks limiting research productivity?

Competitive Context

  • Who else is working on similar problems?
  • What approaches are competitors taking?
  • What is our differentiated value proposition?

Strategic Pivoting

  • If your hypothesis fails, what alternative value could your data provide?
  • Do you monitor how competitors approach similar problems?
  • Can you identify adjacent market opportunities for your work?

Stakeholder Alignment

  • Who are my key stakeholders and what do they need from my work?
  • How does my project fit within organizational priorities?
  • What defines success for my collaborators?

Decision Frameworks

  • What criteria determine if my project advances or stops?
  • How is resource allocation determined in my organization?
  • Can I articulate the risk/return profile of my work?

AI Fluency

  • Which parts of my research could be AI-accelerated?
  • How do AI tools change the economics of my experiments?
  • What new capabilities do AI partnerships enable?

Conclusion. Scientists who combine deep technical expertise with commercial fluency—who understand efficiency, stakeholder needs, competitive positioning, decision frameworks, portfolio optimization, and AI's transforming impact—will define the next generation of breakthrough discoveries. The question is not whether commercial awareness matters in science, but whether we will systematically develop it before the gap between research output and practical impact becomes unbridgeable. Historically, closing the gap between R&D and commercial expertise has been difficult because most biopharmaceutical companies separate these functions organizationally. As AI drives new ways of working, we should reconsider how to integrate them more closely. From serendipitous discoveries to systematic AI-driven drug design, moving innovations from laboratory to patient has always required navigating both scientific and commercial challenges. As research becomes more complex, costly, and competitive, those who master both dimensions will determine which discoveries ultimately improve human health.

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