Author: Abby Lockwood

By Laura Farmer, President, Opus Strategy

JPM Week in San Francisco is always a useful barometer for the life sciences industry. Not for what’s “trending,” but for what is moving from possibility into execution. The conference itself matters, but the real signal often comes from the adjacent meetings, the smaller sessions, and the conversations that happen when people stop pitching and start problem-solving.

This year, the tone felt more constructive than it has in a while. Not exuberant. But steadier. Investors were asking more disciplined questions about differentiation, durability, and proof. Operators were sharper about what they need in order to win the next phase. And in many rooms, the conversation shifted away from “big ideas” toward pragmatic plans: how to generate evidence efficiently, how to scale manufacturing, how to secure coverage, and how to make adoption predictable.

For pharma leaders, that shift matters. It suggests the industry is entering a phase where the winners will be the teams that can translate scientific promise into repeatable value creation, across portfolios and across therapeutic areas.

As went to events and spoke with colleagues, there were three themes I heard repeatedly:

First, the funding environment is reopening, selectively. Capital remains scarce for programs with weak data or unclear clinical utility. But for companies with credible differentiation and a coherent path to adoption, the window feels less constrained than it did even 12 months ago. Importantly, “strong data” is no longer interpreted narrowly as a positive trial readout. It increasingly includes clarity on the patient definition, the biomarker strategy, the regulatory plan, and the pricing and access narrative. In other words, teams are being rewarded for reducing uncertainty across the full development and commercialization arc.

Second, large pharma is in pipeline-building mode, unmistakably. Between patent expirations and growth expectations, the pressure to build and buy is real. What stood out this year is how central precision medicine and novel modalities have become a pipeline strategy. These are not side bets. They are core to how many organizations are thinking about the next decade of growth, particularly in areas where payer and provider expectations are tightening and where “me too” products face a rapidly shrinking runway.

Third, scalability has become a litmus test. The industry is no longer satisfied with brilliant therapies that cannot be produced at scale, delivered efficiently, or reimbursed sustainably. The conversations are increasingly integrating manufacturing, logistics, evidence requirements, and workflow adoption into the scientific story. Breakthrough biology is necessary, but it is not sufficient on its own. If you cannot make the therapy, deliver it, and get it used in real-world settings, the science will not matter as much as it should.

Precision medicine as the connective tissue

These themes converge most clearly in precision medicine. Leading into JPM, the field has been moving from “promise” to more visible proof. We are seeing better multi-omics integration, improved biomarker rigor, and a faster feedback loop between discovery, development, and clinical decision-making.

AI is clearly a catalyst here, but the most credible narratives were not “we used AI.” They were “we used AI to reduce uncertainty.” Better patient selection. Faster identification of responders. Earlier signals. A more precise understanding of heterogeneity. Pharma teams have heard the AI hype. What they are looking for now is a measurable advantage that shows up in development timelines, trial efficiency, label strength, or commercial adoption.

At the same time, expectations for commercialization are rising. Precision medicine programs are increasingly expected to show early thinking on access, evidence, and adoption. The best teams are building with payer logic and clinical workflow reality in mind from the beginning, not bolting it on after publication. For medical directors and product teams, this is a meaningful shift. It changes when and how you engage on evidence generation, and it raises the importance of aligning clinical development with the future label story and the future value story.

A session that went deeper: Penn Center for Innovation on Precision Health

One of the most substantive discussions I attended was a Precision Health session hosted by the Penn Center for Innovation. It was a welcome contrast to the usual surface-level conversations and got into the hard truths that will shape what happens next. Three themes stuck with me:

“Best of times, worst of times” for academic medicine

Dr. Jonathan Epstein, Dean of the Perelman School of Medicine and Executive Vice President of the University of Pennsylvania for the Health System, was candid about the strain on academic research. Funding pressure is real, and it directly affects the biomedical pipeline that industry ultimately depends on. At the same time, the opportunity is enormous, especially as AI-enabled discovery and translation help compress timelines from insight to intervention. The tension is clear: the science is accelerating, but the ecosystem that generates and validates that science is under increasing stress.

For pharma leaders, this is more than a macro observation. It will shape access to partnerships, the pace of external innovation, and the availability of high-quality translational research. It also underscores the value of academic-industry collaboration models that are built for speed and rigor, not for optics.

Rare and ultra-rare therapeutics need a better commercial path

Dr. Peter Marks, Senior Vice President of Molecule Discovery and Head of Infectious Disease at Eli Lilly and former Director of the FDA’s Center for Biologics Evaluation and Research, highlighted a reality that the industry continues to struggle with: if we want a full complement of rare disease therapies, the path must become faster and less expensive without sacrificing rigor.

One idea that resonated was the concept of process-based approvals. Instead of forcing each molecular variant through a full regulatory “toll booth,” a validated end-to-end lab and manufacturing process could potentially be reused across individualized therapies. For regulated science, this is a meaningful reframing. It shifts the value from a single asset to a scalable platform and challenges us to rethink what we define as “the product” when therapies are increasingly personalized.

For medical and commercial teams, rare disease strategy has always required careful choreography between evidence, access, and trust. What changes now is the scale of the ambition. If the industry wants broader rare disease coverage, the system must evolve to support it. Otherwise, the economics will remain structurally misaligned with the clinical need.

Immune health as a transformational frontier, with AI as an enabler

Dr. Allison Rae Greenplate, Adjunct Assistant Professor of Systems Pharmacology and Translational Therapeutics and Director of Immune Health at Penn Medicine, described a vision that felt both ambitious and increasingly plausible: decoding an individual’s immune profile at scale and using it to match patients to therapies with far less trial-and-error.

This is where precision medicine starts to look less like a niche approach and more like a broader operating system for care. Stratify better. Treat smarter. Reduce waste. Improve outcomes that matter clinically and economically. For pharma, this is a reminder that the future of differentiation may be less about “a new mechanism” and more about “a better match,” supported by evidence and operationalized through diagnostics, data, and workflow integration.

The practical question precision medicine keeps forcing us to answer: How do we build trust?

Precision medicine is fundamentally a trust business. Trust that the data are sound. Trust that stratification is meaningful. Trust that outcomes translate to real-world settings, not just curated cohorts. This is where medical affairs and evidence strategy become central to success, not supportive functions downstream.

Why this matters now

The next phase of precision medicine will not be won by the loudest storytellers. It will be won by teams that translate complexity into clarity:

• Clear clinical utility
• Clear evidence strategy
• Clear market access pathway
• Clear operational plan for scale

The science of precision medicine is moving fast. The healthcare system is still the healthcare system. Translation is the work.

By Laura Farmer, Opus Strategy

Recently, I attended the AI in Medical Affairs Conference in Philadelphia, and came away with a clear impression: the conversation around AI in #MedicalAffairs has entered a more serious phase. The focus is no longer on potential or experimentation alone. It’s on execution: how organizations integrate new capabilities responsibly, sustainably, and in ways that truly support scientific and business objectives.

Here are the five lessons that stood out most, and that I believe matter most for leaders navigating this transition.

1. The challenge is no longer technological—it’s organizational

Most Medical Affairs organizations are past the point of asking whether AI belongs in their function. Tools have been piloted, use cases explored, and early value demonstrated. Yet many teams remain stuck in pockets of experimentation rather than moving toward scaled impact.

What’s holding them back is rarely the technology itself. Instead, the real work begins with aligning workflows and understanding decision rights, governance, and expectations. AI introduces new ways of working, but organizations often try to layer those capabilities onto existing structures and workflows without rethinking how work actually gets done. Leaders who recognize this as an organizational #transformation (not a digital add-on) are the ones seeing progress.

2. Quality of scientific exchange is becoming a deliberate capability

Several discussions focused on the evolving understanding of Medical Affairs interactions, particularly between MSLs and HCPs. These conversations were framed not as isolated events, but as cumulative experiences shaped by listening, empathy, credibility, and relevance over time.

Here’s what’s shifting: The ability to support this type of development around the interactions between MSLs and HCPs systematically. AI-enabled tools are beginning to provide structured feedback and insights that help teams reflect on how they engage, identify patterns, and improve consistency. When used thoughtfully, this kind of support elevates practice across a team without reducing interactions to scripts or metrics. It enables professional growth while preserving scientific judgment.

3. Adoption depends on leadership clarity and change discipline

Adoption emerged repeatedly as the differentiator between organizations making progress and those stalled in repetitive pilot modes or adoption purgatory. Tools alone do not change behavior. What does change behavior is clarity and connections with end users, understanding and educating teams on why a tool exists, how it fits into daily work, and what success looks like.

Organizations seeing momentum have invested in change management fundamentals: training grounded in real workflows, internal champions who model new behaviors, and leadership messaging that signals both permission and expectation to adopt. Without this foundation, even well-designed tools struggle to move beyond early adopters.

4. Governance is enabling trust, not slowing progress

A consistent theme across sessions was the importance of verification. AI-generated outputs are widely treated as inputs that require review, context, and traceability, particularly in regulated environments.

Rather than creating friction, clear governance and guardrails have helped teams move faster. When guardrails are explicit, users know where automation is appropriate and where human oversight remains essential. This clarity builds confidence, supports compliance, and reinforces scientific integrity. Governance, when designed intentionally, becomes an enabler rather than a constraint.

5. Value is being measured in capability, not just efficiency

Conversations around ROI reflected a more mature understanding of value. While time savings still matter, leaders increasingly emphasized outcomes such as improved quality, greater consistency across teams, and the ability to scale best practices.

These measures resonate because they connect #AI investments to long-term capability building. Leaders are asking whether AI helps teams make better decisions, engage more effectively, and operate with greater confidence—not just whether it speeds up existing tasks. This shift in measurement is helping organizations build more durable business cases and align investments with strategic goals.

What does this mean for Medical Affairs?

The organizations that will stand out in the next phase of Medical Affairs transformation are not those with the most advanced tools, but those that approach adoption deliberately. Success depends on leadership alignment, thoughtful change management, clear governance and communications, and metrics that reflect real value.

AI is becoming part of the operating fabric of Medical Affairs. The opportunity now is to shape how it’s embedded—intentionally, responsibly, and in service of both science and strategy.

What Leaders Should Do Next

For leaders, the next phase of AI adoption success by the Medical Affairs team will be more about intentional design than continual experimentation. A few actions can help organizations move from pilots to sustained impact.

1. Anchor AI initiatives in clear business and scientific priorities. Teams should be explicit about the problems they are trying to solve—whether improving consistency of scientific exchange, accelerating insight synthesis, or strengthening decision-making. Clarity at the outset prevents tools from becoming disconnected from real needs.
2. Invest in change management as deliberately as in technology. Adoption requires training that reflects actual workflows, visible leadership support, and internal champions who help translate strategy into practice. Without this foundation, even strong tools will struggle to gain traction.
3. Define governance early and communicate it clearly. Establishing expectations around verification, oversight, and acceptable use builds confidence and trust across teams. When guardrails are understood, users can engage more freely and responsibly.
4. Rethink success metrics. Move beyond short-term efficiency gains and track indicators that reflect capability building—quality, consistency, scalability, and decision support. These measures better capture the long-term value of AI in Medical Affairs.
5. Plan for how work will shift. AI changes not just how quickly work is done, but where time and expertise are required. Leaders who anticipate this shift and adjust capacity accordingly will be better positioned to realize meaningful returns.

The organizations that act with intention now—aligning leadership, people, and processes—will be the ones that turn AI into a durable advantage in Medical Affairs.

How change management determines whether AI becomes an enterprise capability or remains a permanent pilot.

By Laura Farmer, President, Opus Strategy

The biopharma industry has never had a shortage of ambition when it comes to AI. What it has a shortage of is follow-through.

According to a December 2025 Deloitte report, only 22 percent of life sciences leaders report successfully scaling AI initiatives beyond pilots, and just 9 percent report achieving significant financial returns.

The problem isn’t the technology. The tools work. The problem is organizational, and it’s one the industry continues to underestimate.

AI Transformation Is Different From Every Other Change Biopharma Has Managed

Most biopharma change is vertical. AI is horizontal.

Biopharma organizations are experienced with change. Asset launches, system implementations, and functional reorganizations are difficult, but they are contained. They occur within a function, a program, or a geography. The underlying operating model remains largely intact.

AI doesn’t work that way.

It cuts horizontally across the enterprise, touching manufacturing, quality, regulatory, clinical, and commercial functions simultaneously. It doesn’t simply introduce new tools; it changes how evidence is interpreted, how risk is evaluated, and who is accountable for decisions supported by algorithmic systems. That’s a fundamentally different kind of change, and the playbooks organizations have relied on historically don’t fully address it.

There is also a cultural mismatch at the center of this challenge. Biopharma organizations are built around stability. Protocols are locked. Processes are designed for audit readiness. Change control exists for good reason.

AI systems operate differently. Models update as new data becomes available. Managing that tension requires structured lifecycle governance for AI models, including validation checkpoints, documentation standards, and defined escalation paths. Not because bureaucracy demands it, but because without those guardrails, teams simply will not trust the outputs even when the underlying technology performs as intended.

Most Organizations Can Prove AI Works. Far Fewer Can Make It Stick.

Most companies can demonstrate value in an AI pilot. The problem is what happens next.

Pilots prove that technology works. They rarely prove that the organization is prepared to use it at scale.

Three structural breakdowns tend to appear repeatedly when organizations attempt to move from experimentation to enterprise capability.

The first is messaging that focuses on features rather than workflow. Teams learn what a tool does, but no one shows them what work will change, which tasks can be discontinued, or how daily decision-making will be affected. Without that clarity, adoption remains superficial. People use the new system when required and revert to familiar workflows when oversight disappears.

The second structural breakdown is late governance. Regulatory, quality, and compliance teams are often engaged only after pilots demonstrate value. This positions them as gatekeepers rather than collaborators. Governance introduced after the fact creates friction. Governance embedded from the beginning creates confidence.

The third, and least discussed, is the accountability gap. Biopharma professionals remain personally responsible for the rigor and defensibility of their work. When AI assists in analysis or decision-making, that accountability does not transfer with it. It remains with the human.

When individuals are responsible for conclusions generated through systems they do not fully understand, control, or trust, quiet resistance becomes the rational response. Teams do not openly reject the technology. They simply return to familiar workflows.

Scaling AI Is Not a Technology Challenge. It’s a Workflow Design Challenge.

Workflow redesign is the most underinvested lever in AI adoption.

Layering AI tools onto existing processes rarely produces meaningful change. Organizations must map workflows honestly, remove redundant steps, and decide which tasks should be automated, simplified, or eliminated. If AI adds steps rather than removing them, adoption quickly disappears. This work is not primarily a technical challenge. It is organizational design.

Governance architecture must also be established before deployment, not after. This includes defining validation pathways, documentation standards, and clear boundaries around what AI informs, what it automates, and where human approval remains required. When these guardrails are clear, innovation moves faster because teams understand what is permitted.

Leadership alignment must be anchored in outcomes rather than aspiration. Statements such as “we are adopting AI” are not strategies. Transformation requires measurable objectives: faster development timelines, improved consistency of analysis, higher quality insights, or more efficient decision cycles.

Organizations must also address the skills gap directly. A GlobalData survey of 109 biopharma professionals found that 49 percent identified skill shortages as the leading barrier to digital transformation. These capabilities do not develop automatically. Companies need individuals who can operate across technical, scientific, regulatory, and commercial domains simultaneously.

The Real Competitive Question

The competitive question is no longer who experiments with AI. It’s who operationalizes it.

AI tools in biopharma are maturing quickly. Competitive advantage increasingly will not come from which platforms organizations purchase or which pilots they run. It will come from which organizations build the governance models, operating structures, and workflows necessary to use these technologies responsibly at scale.

Most organizations don’t lack the will to do this work. They lack the infrastructure and experience to do it well in a regulated environment where the cost of getting it wrong is high.

Technology vendors can deploy systems. Building the organizational capability to use them, and sustain that use over time, is a different kind of work entirely. At Opus Strategy, that is the work we do alongside leadership teams: translating AI ambition into the governance models, operating structures, and change programs that turn promising pilots into durable enterprise capability.

By Christian Hayden, Opus Strategy

Precision medicine has moved from scientific ambition to commercial reality, but for many organizations, the gap between promise and practice remains wide. Despite rapid scientific progress and billions of dollars in investment, precision medicine has delivered uneven results beyond select successes, particularly in oncology. Many biopharma companies continue to struggle to operationalize and commercialize precision-driven innovation at scale. It is tempting to blame the science, but in practice, the limiting factor is more often execution.

What problem is precision medicine actually solving?

At its core, precision medicine is an operating upgrade to the traditional biopharma model: instead of treating the average patient with the average disease, it aims to match the right intervention to the right biology at the right time. Done well, it can address four structural constraints that have historically limited both R&D productivity and commercial impact.

Where is precision medicine most ready to scale?

Precision medicine is most operationalized in oncology and select genetically defined diseases, with growing (but uneven) progress in neurology and mental health.

Oncology remains the most mature environment for precision approaches due to established regulatory precedent, reimbursement pull, and the routine use of biomarker testing in clinical workflows. Biomarker testing for EGFR, ALK, BRAF, KRAS, and HER2, among others, increasingly guides targeted therapy selection across tumor types. Yet even here, scaling remains constrained by workflow integration, testing logistics, and the difficulty of linking data across fragmented systems.

Outside oncology, the path to scale is slower even when the underlying science is compelling. In neurology, clinical development is often constrained by heterogeneous presentations, long timelines, and endpoint ambiguity. In mental health care, there is enormous unmet need and rising interest in digital phenotyping and response prediction. However, clinical validation standards, implementation pathways, and reimbursement models remain immature.

Across these domains, the pattern is consistent: progress correlates less with quality of innovation and more with organizational readiness, evidence strategy, and workflow integration. In many cases, the science is moving faster than operating models.

How investors and innovators can course correct

Organizations attempting to move from promise to practice repeatedly encounter the same execution bottlenecks. In our work across the ecosystem, we see seven recurring organizational gaps:

• No clear capability owner accountable for prioritization, reuse, and scalability
• Inadequate focus on HCP adoption of companion diagnostics and workflow barriers
• A persistent asset-by-asset execution mindset
• Limited cross-functional integration across R&D, clinical, medical, commercial, and data teams
• Underdeveloped evidence and value translation strategies
• Unrealistic timeline expectations
• Talent gaps in program leadership and orchestration

These gaps appear not only in the clinic but also post-launch, where precision assets often require ongoing evidence generation, stakeholder alignment, and operational rigor to sustain adoption.

Recent therapeutic examples illustrate how quickly execution risk becomes enterprise risk. When Applied Therapeutics’ govorestat failed to secure FDA approval due to deficiencies in its clinical evidence package, the company’s valuation reset and external confidence eroded. The lesson? In rare disease, regulatory strategy and data execution are not downstream considerations. Sarepta’s Elevidys gene therapy for Duchenne muscular dystrophy reinforces the point from another angle: severe safety concerns prompted revised labeling and narrowed indications, shifting the narrative from clinical promise to governance, monitoring, and stakeholder trust.

The operating model changes required to scale

These risks begin to fade when companies treat precision medicine as an enterprise capability, not a set of one-off programs. Core operating model changes include:

• Moving from an asset-centric approach to a capability-centric strategy, building reusable playbooks across portfolios
• Designing for HCP adoption, including test logistics, ordering pathways, turnaround times, and patient access
• Making data usable for decisions, including fit-for-purpose RWD, prospective/retrospective collection aligned to decision points, and appropriate use of external controls
• Investing in reusable evidence and analytics platforms, built for longitudinal reuse rather than one-off pilots
• Establishing cross-functional ownership earlier, recognizing that precision programs require aligned decisions upstream
• Integrating medical and commercial teams early, ensuring efforts are co-owned beyond R&D and innovation groups

Why now?

The urgency is rising, and with it the opportunity. Precision medicine’s operational barriers persist even as market tailwinds strengthen:

• Broader availability and linkage of real-world data (RWD) across care settings
• Increasing regulatory openness to RWE, external controls, and pragmatic designs
• AI-enabled approaches that improve phenotype extraction, signal detection, and data harmonization
• Rising payer and provider expectations for measurable outcomes and value demonstration

Capitalizing on these trends requires more than better science. It requires an operating model that integrates evidence, diagnostics adoption, and cross-functional execution into a repeatable system. At Opus Strategy, LLC we help biopharma leaders build capabilities that allow precision medicine to scale across portfolios, not just succeed asset by asset.

For organizations willing to treat precision medicine as a scalable operating model rather than a series of one-off bets, the opportunity is substantial. The future belongs to those who can turn precision from promise into repeatable practice.

By Laura Farmer, Founder and President, Opus Strategy

The pharmaceutical industry is at a pivotal moment. Amid unsustainable cost pressures and growing macroeconomic uncertainty, AI and digital health are shifting from experimental tools to enterprise-wide drivers of change. No longer confined to pilots or innovation labs, these technologies are now reshaping how pharmaceutical companies operate across the value chain: transforming commercial engagement, enabling personalized medicine, streamlining workflows, and accelerating evidence generation. The opportunity is enormous. However, realizing its full potential requires more than adopting new tech. It demands a clear strategy, cross-functional alignment, change management, and the discipline to turn digital investments into measurable patient and business impact.

Precision Medicine

Advances in genomics, biomarkers, computational biology, AI, and real-world data are transforming personalized medicine. AI is unlocking deeper insights into human biology, revealing disease subtypes, guiding diagnostics, predicting response, and optimizing dosing so that therapies fit each patient’s biology and deliver better efficacy, tolerability and safety.  

Precision medicine is no longer aspirational — guiding diagnostics it is becoming operational. It is shaping how drugs are developed, which assets to move into later phase trials, and how treatment is delivered at the point-of-care. This evolution demands a strategic rethink: how clinical programs are designed, which biomarkers to prioritize, how novel biomarkers inform pipeline decisions, and how commercial requirements like diagnostic accuracy and clinical workflow integration must be considered early. Companies that integrate AI-driven insights into early R&D position themselves to unlock faster, more effective clinical development and better treatments for improved patient outcomes (1,2).

AI for Commercial Teams

Generative and agentic AI are revolutionizing how biopharma engages physicians, patients, and other key stakeholders. What began as digital content optimization is now evolving into intelligent orchestration, driven by generative and predictive AI that can tailor messaging, optimize timing, and inform channel strategy in real time.

By seamlessly integrating with CRM systems, field force tools, and marketing automation platforms, AI enables a unified intelligence layer that delivers hyper-personalized content and adaptive engagement strategies across the digital, in-person, and patient support channels.This goes beyond efficiency:  it’s about agility, relevance, and the ability to act on insights faster than the competition.

For commercial teams, success will increasingly depend on the ability to translate complex scientific and clinical data into actionable, customer-centric narratives at scale. AI doesn’t just automate workflows like MLR review, it amplifies reach, precision, and impact of commercial efforts in a rapidly evolving and competitive market (3,4).

Direct-to-Patient (DTP) Platforms

Direct-to-patient platforms are redefining how patients discover, access, and stay on therapy, enabling biopharma companies to bypass traditional gatekeepers and engage patients directly. Through telehealth partnerships, fulfillment, digital patient support services, and app-based adherence support, these platforms offer an end-to-end experience that biopharma can shape to improve access, adherence, and outcomes for patients.

Beyond convenience, DTP models offer something far more strategic: greater control over patient engagement, data collection, and brand experience. By reducing reliance on intermediaries like pharmacy benefit managers and retail pharmacies, biopharma gains earlier touchpoints with patients and the ability to personalize interventions, and capture real-world insights.

To capitalize on this shift, biopharma must rethink traditional access strategies and forge new partnerships with digital health platforms, retail disruptors, and tech providers. The goal: meet patients where they are while maintaining regulatory, clinical, and data integrity (5,6).

Automation and Workflow Efficiency

AI and automation are easing operational bottlenecks across the biopharma value chain—from regulatory submissions and SOP management to pharmacovigilance, quality documentation and literature reviews. These tools don’t just speed things up, they reduce risk, improve consistency, lower costs, and unlock capacity at scale.

More strategically, automation frees teams to shift their focus from operational tasks to scientific innovation and strategy. In an industry where time-to-market defines competitive advantage, intelligent automation is no longer optional or  a nice-to-have, it is a strategic imperative (7,8).

Evidence Generation and Clinical Trial Optimization

AI and data  platforms are reshaping how clinical evidence is generated, analyzed, and applied. These tools are accelerating the identification of enriched biomarkers, optimizing inclusion/exclusion criteria, supporting dynamic trial design based on real-world data, and supporting the validation of surrogate endpoints for regulatory and payer use. Evidence synthesis, historically manual and fragmented, is becoming increasingly automated, dramatically reducing development timelines and costs.

For medical affairs and clinical teams, this translates into faster insights, smarter trial design, and strong narratives to support regulatory and payer decisions. Companies that embed these capabilities into their R&D and medical affairs strategies stand to lead not only in speed, but also in scientific credibility and commercial readiness (9,10).

What Success Requires

The promise of digital transformation is real, but execution remains uneven. Many organizations face what I call “prioritization paralysis”— the inability to decide which technologies deserve scarce capital and leadership attention. Too often, companies chase trends without completing robust ROI evaluation or building a strong business case.

Digital transformation is not a checklist; it is a disciplined strategy. Leaders must separate hype from impact, align across R&D, commercial, and medical functions, and focus on initiatives that deliver measurable patient and business value (11,12).

Partnering for Impact

At Opus Strategy, we help pharmaceutical and biotech leaders bridge the gap between innovation and execution. Our team brings deep expertise across biopharma, digital health, MedTech and AI to help clients identify the right opportunities, validate ROI, and prove the value of their investments. We know how to separate hype from impact, and we focus on solutions that deliver measurable business results while advancing patient care.

Biopharma is indeed at a digital inflection point. Companies that lean in thoughtfully and embrace these technologies as strategic imperatives — rather than experimental side projects — will define the next era of medicine. The future is not merely about adopting technology; it is about creating transformation that improves outcomes, strengthens connections, and drives sustainable growth.

References

1. Deloitte. AI in pharma and life sciences. Deloitte. https://www.deloitte.com/us/en/Industries/life-sciences-health-care/articles/ai-in-pharma-and-life-sciences.html

2. McKinsey. Generative AI in the pharmaceutical industry: Moving from hype to reality. McKinsey & Company. https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality

3. Deloitte. Future of pharma field force: AI-driven agility. Deloitte. https://www.deloitte.com/us/en/Industries/life-sciences-health-care/articles/future-pharma-field-force-ai-agility.html

4. McKinsey. Early adoption of generative AI in commercial life sciences. McKinsey & Company. https://www.mckinsey.com/industries/life-sciences/our-insights/early-adoption-of-generative-ai-in-commercial-life-sciences

5. Deloitte. Future of artificial intelligence in health care. Deloitte. https://www.deloitte.com/us/en/Industries/life-sciences-health-care/articles/future-of-artificial-intelligence-in-health-care.html

6. arXiv. The digital transformation in health: How AI can improve the performance of health systems. arXiv. https://arxiv.org/abs/2409.16098

7. McKinsey. Gen AI: A game changer for biopharma operations. McKinsey & Company. https://www.mckinsey.com/industries/life-sciences/our-insights/gen-ai-a-game-changer-for-biopharma-operations

8. Deloitte. The convergence of AI technologies and human expertise in pharma R&D. Deloitte. https://www.deloitte.com/uk/en/Industries/life-sciences-health-care/research/the-convergence-of-ai-technologies-and-human-expertise-in-pharma-r-and-d.html

9. McKinsey. Accelerating clinical trials to improve biopharma R&D productivity. McKinsey & Company. https://www.mckinsey.com/industries/life-sciences/our-insights/accelerating-clinical-trials-to-improve-biopharma-r-and-d-productivity

10. McKinsey. A vision for medical affairs 2030: Five priorities for patient impact. McKinsey & Company. https://www.mckinsey.com/industries/life-sciences/our-insights/a-vision-for-medical-affairs-2030-five-priorities-for-patient-impact

11. McKinsey. Scaling gen AI in the life sciences industry. McKinsey & Company. https://www.mckinsey.com/industries/life-sciences/our-insights/scaling-gen-ai-in-the-life-sciences-industry12. Deloitte. Realizing the value of artificial intelligence in life sciences. Deloitte. https://www.deloitte.com/us/en/Industries/life-sciences-health-care/articles/value-of-genai-in-pharma.html

On April 15, I had the privilege of attending the AI Healthcare Leadership Summit, an invite-only event hosted by Bessemer Venture Partners, Bain & Company, and Amazon Web Services. This summit convened many of the industry’s leading minds — CEOs, founders, clinicians, investors, and technologists — who are actively shaping the trajectory of artificial intelligence in life sciences, clinical medicine, and healthcare financing. One of the most valuable components of the summit was the unveiling of the Healthcare AI Adoption Index, a data-rich analysis of how more than 400 healthcare organizations are approaching AI adoption. The findings reflect what many of us are seeing firsthand: AI adoption is not only accelerating, it’s becoming central to enterprise strategy.

As the founder of Opus Strategy, a firm that works with investors and large companies in the pharma industry, this event offered essential insights into how AI is evolving, particularly in the life sciences, and what healthcare leaders and innovators must prioritize to stay ahead.

AI Applications in Life Sciences

Only 15% of current AI projects are categorized as vertical applications, a fact that leaves immense whitespace for startups and healthcare innovators to co-create tools tailored for a range of healthcare applications.

Within pharma specifically, AI is being utilized in preclinical applications such as molecule identification and indication selection, along with clinical applications like protocol design and even New Drug Application (NDA) submission. The technology also is being leveraged in pharma for marketing and sales.

Despite the current and potential applications for AI, trust in the technology is not quite robust. And trust will be paramount to capitalizing on AI’s potential. Additionally, outcomes continue to drive procurement decisions, creating fertile ground for strategic partnerships built on transparency, data ownership, and performance.

AI Co-Development within the Pharma Industry

While much of the early focus on healthcare AI revolved around startups, what’s emerging now is a more complex and collaborative model. Many of the most promising AI applications are being developed not just by health tech startups, but are being co-developed by internal provider teams working closely with large technology firms and cloud providers.

According to the report released by Bessemer, 57% of pharma executives believe AI will help drive new therapies over the next decade. With security concerns, costly integrations, and the need for AI-ready data (especially in pharma), executives do believe collaboration will be key. Indeed, many believe adoption risk is mitigated when collaboration exists between traditional industry players and innovators. This opinion underscores a key strategic shift: AI is no longer seen as a siloed. It’s now deeply embedded in core corporate strategy.

Despite this momentum, adoption remains somewhat limited. Only about 30% of AI pilots make it beyond the proof-of-concept stage, and within pharma specifically, fewer than 24% of innovators have reached that milestone. This lag, especially when compared to adoption rates in the payer and provider sectors, highlights the urgent need for a more agile, “test and learn” approach to drive real-world implementation.

AI Budgets and IT Spend

The Bessemer report demonstrates AI has moved beyond the experimental phase. It is now a core element of competitive healthcare strategy with 60% of healthcare executives allocating more resources to AI than to traditional IT at a time when budget authority is increasingly consolidated at the C-Suite level.

This shift marks a broader institutional commitment: 65% of AI initiatives are now funded through centralized corporate budgets, while the remaining 35% are supported at the departmental level. In the past, IT budgets were the primary obstacle to advancing AI. Now that’s not the case. Most respondents to the Bessemer survey indicated budget constraints are no longer a primary obstacle to scaling AI from pilot to production, signaling a shared understanding that AI adoption is a strategic imperative, not a discretionary expense.

That said, executives do have a preference with whom they work.

While big tech companies are often seen as leaders in generative AI, nearly half (48%) of executives say they prefer to partner with startups, especially those that bring flexibility and a collaborative mindset. Moreover, 64% of executives are open to co-developing AI solutions with early-stage companies, particularly when those partnerships offer clear, measurable value and demonstrate alignment with clinical or operational needs.

Once again, being open to collaboration is key.

Pioneers Shaping the Future

Throughout the summit, participants heard from pioneers who are already shaping this future, including former FDA deputy commissioner Janet Woodcock. The message was consistent: success in healthcare AI will be defined not only by technical sophistication, but by deep integration and collaboration, shared accountability, and measurable value.

What excites me most is the collective momentum. Healthcare organizations are no longer asking if AI should be adopted — they’re defining how it can be implemented responsibly, efficiently, and equitably. As someone who works closely with clients on strategy, market insights, and innovation in the life sciences industry, this summit reaffirmed the importance of staying informed and being engaged within the ecosystem. We need to work collaboratively to turn promise into progress.

I’m grateful to the organizers for creating such an insightful and inspiring environment, and I look forward to continuing this important work with many of the brilliant minds I met last week.

For those interested, the full Healthcare AI Adoption Index is well worth the read: https://www.bvp.com/atlas/the-healthcare-ai-adoption-index