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