How artificial intelligence is accelerating breakthroughs, transforming workflows, and redefining the future of pharma and life sciences — in conversation with Silvia Ondategui-Parra.
AI is no longer a theoretical concept in healthcare; it’s revolutionizing real-world applications in ways we could only imagine a decade ago. From accelerating drug discovery to redefining patient engagement, artificial intelligence is at the heart of innovation in the life sciences sector.
Today we’re surfing with Silvia Ondategui-Parra, Managing Partner, Global Life Sciences Leader at BIP.
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How is AI currently transforming the health and life sciences industries, and what are some of the most significant breakthroughs you've seen so far?
AI is no longer at the periphery of pharma—it’s rapidly becoming its core innovation engine. From drug discovery to access strategy, we’re seeing a leap from potential to proof of concept.
Some of the most significant breakthroughs include AI-designed molecules, such as those from Alphabet’s Isomorphic Labs, expected to enter clinical trials in 2025. These assets, developed entirely through generative models, mark a new frontier in speed and precision for drug development.
At the same time, companies like J&J are piloting autonomous AI agents to optimize manufacturing—deciding in real time when to switch solvents in chemical synthesis.
We’re also witnessing a shift from AI adoption to AI adaptation. It’s no longer about plugging in technology—it’s about rethinking workflows and decision-making around it. As emphasized by Dave Williams (MSD) at BioAsia, the industry must shorten the innovation-to-adoption cycle and anchor AI across three archetypes:
- Human-led, AI-enhanced
- AI-led, Human-supervised
- AI-driven and managed
By mapping use cases to these archetypes, organizations can strategically assess ROI, risks, and readiness—both from a capability and compliance standpoint.
What are the key challenges organizations face when integrating AI into areas like clinical trials, regulatory compliance, or research, and how can these be addressed?
While the ambition around AI is sky-high, execution often lags behind. Why?
Because AI integration isn’t just a tech issue—it’s a multidimensional change journey.
Key challenges include:
- The Trust Deficit: Stakeholders ask, “Can we rely on AI?” Especially in regulatory, medical, and clinical spaces, there's hesitance to delegate critical decisions to opaque algorithms. This is where Explainable AI (XAI) becomes essential—ensuring every output is transparent and defensible.
- Skill Gaps: Many professionals feel overwhelmed or fear displacement. As Dr. Reddy, AIG Chairman, aptly said, “AI won’t replace doctors, but AI-augmented doctors will.” Upskilling and building AI fluency across functions is vital.
- Data Chaos: "Garbage in, garbage out" still holds true. Without structured, harmonized data inputs, AI models fail. Organizations must prioritize high-quality, interoperable, and secure datasets.
- Regulatory Tightening: 2025 is a pivotal year. The FDA has released draft guidance on AI governance, and the EU AI Act will begin enforcement in August. Lifecycle monitoring, human oversight, and controls to prevent algorithmic drift will become baseline expectations.
How does AI help improve the efficiency of processes like data analysis, literature reviews, and competitive intelligence in life sciences?
AI is acting as a “strategic accelerant” across core knowledge workflows:
- Literature reviews powered by natural language processing can screen and synthesize thousands of publications, identifying trends and evidence gaps in real time.
- Competitive intelligence is evolving into a predictive capability. AI continuously monitors clinical trials, pricing dynamics, and HTA outcomes—alerting teams to emerging threats and opportunities across indications and markets.
- Market access teams are now leveraging AI to simulate payer behavior using RWE inputs. One example: AI-enabled dynamic pricing models that adapt in real time based on symptom evolution and adherence captured via digital companions.
What impact does real-world data (RWD) have on Pharma?
RWD is becoming indispensable—not just for validation, but for value creation. Its role has expanded from supplementing clinical evidence to informing pricing, access, and contract structures.
For instance:
- Payers now expect long-term effectiveness data in real-world settings to support reimbursement.
- RWD enables adaptive reimbursement and supports outcome-based contracts, particularly in complex, chronic, or multi-indication diseases.
- AI-enhanced patient support programs become more targeted and scalable when RWD is used to refine patient segmentation, cost-risk models, and treatment adherence insights.
In 2024, Demis Hassabis, co-founder of Google DeepMind, won the Nobel Prize in Chemistry for his innovative use of AI in the pharmaceutical sector. What role do you think artificial intelligence will play in the future of healthcare?
Hassabis’ Nobel marks a turning point—the recognition that AI will not just support science, but co-create it.
Looking ahead, AI will:
- Accelerate drug discovery by simulating preclinical models and optimizing molecular design.
- Transform care delivery through digital twins and real-time, AI-powered clinical decision support.
- Enable dynamic access models, where payer agreements adjust continuously based on real-world outcomes.
- Embed intelligence into the system itself—making commercial, clinical, and operational decisions increasingly AI-managed.
To navigate this, life sciences organizations must invest not only in tech—but in governance, trust, and adaptability. Because in the end, AI will not replace the industry—it will reshape the way we lead it.
Silvia Ondategui-Parra is currently Managing Partner, Global Life Sciences Leader at BIP. With over two decades of experience in life sciences and strategic consulting, she has held leadership positions in the Life Sciences Strategy division at top firms.
Her expertise spans business strategy, competitive analysis, digital transformation, market access, and innovation in the pharmaceutical and biotech sectors. Silvia has worked closely with pharmaceutical companies, regulatory agencies, and investors, helping shape growth strategies and optimize operations.
Beyond her corporate career, Silvia is an active researcher and lecturer, with a strong academic background that includes a medical degree and multiple advanced degrees in public health, management, and pharmaceutical sciences. She is a frequent speaker at international conferences and has authored numerous publications in healthcare and business strategy.
Based in Zurich, Switzerland, Silvia is recognized for her ability to lead multidisciplinary teams and develop innovative solutions to address the challenges of the life sciences sector.