AI For Pharma Growth

Dr Andree Bates
  • 3.9 (8)
  • TECHNOLOGY
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AI For Pharma Growth is the podcast from pioneering Artificial Intelligence entrepreneur Dr. Andree Bates created to help Pharma, Biotech and other Healthcare companies understand how the use of AI-based technologies can easily save them time and grow their brands and company results. This show blends deep experience in the sector with demystifying AI for biopharma execs from biotech start-ups right through to big pharma. In this podcast, Dr Andree will teach you the tried and true secrets to building results in a pharma company using AI and alert you to some fascinating new tools and applications to benefit you and your company. As the author of many peer-reviewed journals in pharma AI, and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI, futuretech, healthcare and pharma to help you to navigate through the, sometimes confusing, but magical world of AI powered tools to achieve real-world results. This podcast features many experts who have developed powerful AI-powered tools that are the secret behind some time-saving and supercharged revenue-generating business results. Those who share their stories and expertise show how AI can be applied to Discovery, R&D, clinical trials, market access, medical affairs, regulatory, market research, business insights, sales, marketing, including digital marketing, and so much more.

  1. E213: The Diagnostic Room: We have AI initiatives, but do we have a strategy? The quickest self-test

    5D AGO

    E213: The Diagnostic Room: We have AI initiatives, but do we have a strategy? The quickest self-test

    Many pharma and life science organisations have been investing in AI for years: pilots across commercial, medical, regulatory, and R&D, innovation labs, steering committees, vendor spend, and genuine effort from smart teams. And yet the same story keeps showing up in boardrooms: ROI is unclear, adoption is patchy, and leaders struggle to explain how all the AI activity connects to strategic goals. In this solo episode, Dr Andree Bates steps into “The Diagnostic Room” to explain why this happens, and why it’s usually not a technology, talent, or speed issue. It’s a diagnosis issue: organisations often haven’t identified what is actually constraining value, so they end up executing hard on the wrong problem. Dr Andree shares a real example from a mid-sized pharma company that believed its AI programme was failing due to lack of velocity. On the surface, it was a reasonable hypothesis. But a focused diagnostic revealed three hidden structural blockers: unclear decision rights for scaling pilots into production, fragmented data ownership preventing access to the best datasets, and incentive misalignment where the people expected to adopt AI tools were not rewarded for the behaviours those tools required. She then clarifies what a diagnostic is and is not. A diagnostic is not a strategy, roadmap, vendor shortlist, financial model, or implementation plan. Instead, it provides evidence-based clarity: what’s broken, how you compare to peers, what’s at stake, and what questions have been opened that cannot responsibly be answered in ten days. That clarity creates a shared language for leadership, replacing vague frustration with a precise problem statement. Finally, Dr Andree explains why the next step after diagnosis is not “faster action”, but smarter action: a full strategic AI blueprint with proper financial modelling, governance design, sequencing, and adoption architecture. The organisations pulling ahead are not simply those with the biggest budgets, but those willing to find what’s actually broken before trying to fix it. Topics Covered Why AI initiatives can grow without creating measurable ROI The gap between pilots and a true AI strategy Misdiagnosis: executing brilliantly on the wrong problem What a diagnostic sprint is (and what it is not) Three hidden blockers: decision rights, data ownership, incentive misalignment Why working groups can’t fix structural AI constraints What a full strategic AI blueprint includes Why many AI business cases are untested projections How to improve board confidence with evidence, governance, and measurement Why diagnostics create speed by creating shared clarity Eularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes. If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort. The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan. Details at eularis.com. About the Podcast AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results. This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more. Dr. Andree Bates LinkedIn | Facebook | X

    27 min
  2. E212: The Ethics of AI

    APR 7

    E212: The Ethics of AI

    AI ethics has moved from theory to urgent necessity, especially as AI systems become embedded in healthcare, business decisions, and society at large. In this episode, Dr Andree Bates is joined by Dr Nadia Morozova, founder of Enriched Insights, to unpack what ethical AI really means in practice, and how organisations can innovate quickly without creating risk, bias, or governance failures. Nadia shares insights from the global conversation on AI ethics, including discussions at Davos, and explains why trust is becoming the true competitive advantage. She argues that organisations should use AI to build stronger, more open relationships with customers and stakeholders, where technology acts as an enabler rather than the centrepiece. The conversation then gets practical. Nadia outlines a human-centric framework for high-quality AI outcomes, covering accurate sampling, futureproofing (because models are trained on the past), data connectivity across sources, and responsible blending of human and synthetic data. She warns that leadership teams often treat AI as “magic”, assuming tools will solve complex problems like data harmonisation without the hard work of ontology, governance, and expert oversight. A real-world example brings this to life: the Zillow case, where initial success collapsed as market dynamics shifted and the model failed to adapt in time, leading to huge losses. For Nadia, the lesson is clear: ethical responsibility is not a checkbox at launch, it requires ongoing monitoring, review, and culture change. Nadia closes with a strategic message for leaders: start with business goals and targeted use cases, involve data experts early, build governance upfront, and keep humans in the loop throughout the AI lifecycle. Done properly, ethical AI is not a constraint on innovation, it is how you protect long-term value and trust. Topics Covered Why AI ethics is now an urgent business and societal issue Trust, transparency, and accountability in AI deployment Human centricity as the foundation of high data quality Accurate sampling and avoiding “biased reality” in models Why futureproofing matters when algorithms learn from the past Data connectivity, governance, and the ontology problem Responsible blending of human and synthetic data Dangerous leadership assumptions about AI “magic” The Zillow case and what happens without ongoing oversight Strategy first: KPIs, targeted use cases, and right-sized models Skills gaps: technical roles, business acumen, and cross-functional teams Culture change and post-deployment monitoring About the Podcast AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results. This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more. Dr. Andree Bates LinkedIn | Facebook | X

    18 min
  3. E211: Precision Monitoring: How Digital Biomarkers Are Changing Medicine

    MAR 31

    E211: Precision Monitoring: How Digital Biomarkers Are Changing Medicine

    Digital biomarkers are turning everyday movement into clinically useful data, giving doctors a clearer picture of what’s happening between appointments, and giving pharma new ways to measure drug impact earlier and more precisely. In this episode, Dr Andree Bates interviews Dr Quique Llaudet, CEO and co-founder of Ephion Health, about precision monitoring and how AI-driven mobility analysis is changing both clinical care and drug development. Quique shares his journey from academic research into entrepreneurship, driven by a desire to turn science into real products that help patients. Ephion Health grew out of early work with paediatric hospitals in Barcelona, where sensor technology used in rehabilitation and exoskeleton projects revealed a bigger opportunity: objective, high-sensitivity gait and movement analysis that can detect disease signatures and track progression over time. The conversation breaks down what a digital biomarker actually is: a measurable signal of health captured via connected devices and analysed with digital methods. Ephion’s platform integrates multiple validated, off-the-shelf sensors to capture rich movement data in a short test, replacing blunt measures like the six-minute walk test with something both more sensitive and less stressful for patients. The system then combines key parameters into a single composite score to track progression and treatment response. Quique also tackles the “black box” concern head on. He explains how their models are developed alongside clinicians, with clinical relevance checked throughout, and how doctors can inspect the underlying parameters behind the biomarker score in a dashboard. For rare diseases with limited data, he highlights deep collaboration with clinicians and patient associations, and the use of synthetic data to support modelling and testing. Finally, Quique outlines the economics: reducing specialist assessment time, enabling more frequent remote monitoring, supporting earlier treatment adjustments, and helping pharma generate evidence in real-world settings. The long-term vision is continuous monitoring that helps clinicians act earlier, plus AI-assisted diagnosis and eventually prevention. Topics Covered What digital biomarkers are and how they differ from traditional biomarkers Turning mobility data into clinically meaningful signals Multi-sensor monitoring: IMUs, pressure insoles, and EMG Why short tests can beat the six-minute walk test Composite biomarker scoring and tracking treatment response AI patterns clinicians may sense but cannot quantify Explainability and building models “hand in hand” with doctors Data challenges in rare disease and the role of patient associations Synthetic data for modelling and validation Economic impact: time savings, remote monitoring, and better treatment adjustment Pharma use cases: real-world evidence and earlier efficacy signals in trials About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results. This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more. Dr. Andree Bates LinkedIn | Facebook | X

    33 min
  4. E210: Beyond Alzheimer’s: Scaling Digital Twins Across Disease Areas

    MAR 25

    E210: Beyond Alzheimer’s: Scaling Digital Twins Across Disease Areas

    Digital twins have become one of the most promising tools in Alzheimer’s research, but the bigger story is what happens when they scale across disease areas. In this episode, Dr Andree Bates interviews Aaron Smith, Founder and Head of Machine Learning at Unlearn AI, about how “digital twin generators” can transform trial design by modelling realistic patient progression and improving statistical power without compromising the fundamentals of randomised controlled trials. Aaron shares his journey from academic mathematics into computer vision and machine learning, then into biopharma, where Unlearn began by building generative models that learn the joint distribution of clinical variables. In practice, that means the model can take baseline patient measurements and generate likely future progressions that are as indistinguishable from real clinical records as possible. The conversation dives into a key misconception: digital twins are not only about replacing control arms. Aaron explains a regulatory friendly approach where you keep standard trial structure, but add counterfactual information for every patient into the analysis. Unlearn’s best known method, ProCOVA (prognostic covariate adjustment), summarises a predicted control outcome per patient and uses it for covariate adjustment, creating more efficient treatment effect estimates. The headline result is simple: you can increase power, or reduce recruitment burden while maintaining power, potentially speeding time to results. Finally, Aaron explains why scaling across diseases is genuinely hard. Data structures differ wildly by indication, missingness can block transfer learning, and areas like oncology require modelling complex treatment histories. He also highlights that combining sources is not just “more data”, it demands careful harmonisation and context modelling to avoid biased predictions, especially when bringing in real world evidence. Topics Covered What “digital twin generators” are in clinical trials Generative modelling of clinical records and disease progression Counterfactual prediction under standard of care Why replacing control arms is not the only use case ProCOVA and prognostic covariate adjustment Getting more statistical power and reducing trial size FDA openness to digital twins in trials and what it enables Why scaling across disease areas is not just parameter tuning Missing data, confounding context, and data harmonisation CNS versus oncology modelling challenges Real world evidence and how to validate digital twin models About the Podcast AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results. This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more. Dr. Andree Bates LinkedIn | Facebook | X

    32 min
  5. E209: Beyond Failure Prevention: How AI is Redesigning the Drug Discovery Pipeline

    MAR 18

    E209: Beyond Failure Prevention: How AI is Redesigning the Drug Discovery Pipeline

    AI in drug development is moving beyond “failure prevention” into something much bigger: redesigning how we discover, develop, and deliver medicines. In this episode, Dr Andree Bates speaks with Vitalay Fomin of Numenos about biomarker discovery, patient stratification, and why the next breakthroughs come from breaking down data silos across diseases, modalities, and even species. Vitalay shares his background across biotech and pharma, including work in biomarker discovery, translational medicine, and data science, and how frustration with existing approaches led her to build a new architecture for clinical genomic insights. A core theme is that traditional methods often oversimplify biology by forcing outcomes into binary labels and treating each disease area as an isolated box, even when the available data is too limited to answer meaningful questions well. The conversation explores how foundation model approaches can unify clinical, genomic, transcriptomic, proteomic and imaging signals to create a fuller “biological fingerprint” of each patient. Vitalay explains how this can enable earlier insight from single-arm trials by effectively benchmarking against standard-of-care cohorts, helping teams enrich later-stage trials with the right subpopulations sooner, and reducing time and cost. They also discuss the real blockers to adoption: not only scientific conservatism, but commercial uncertainty around how Big Pharma structures deals with tech-bio companies that bring platforms rather than single assets. Vitalay argues that explainability is non-negotiable in this space, because clinicians, scientists, patients, and regulators will not trust black-box predictions. Topics Covered Why AI is shifting from failure prevention to pipeline redesign Biomarker discovery beyond binary responder vs non-responder labels Breaking disease silos to learn across indications Multimodal integration: DNA, RNA, protein, imaging, and clinical data Using foundation models to bridge trial data and real-world data Patient stratification and trial enrichment from early studies Reverse translation and identifying unmet need before target hunting Explainability, trust, and regulatory readiness Adoption barriers: culture, champions, and deal structures for tech-bio Misconceptions about AI in drug development and why “press a button” is a myth About the Podcast AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results. This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more. Dr. Andree Bates LinkedIn | Facebook | X

    47 min

  6. MAR 11

    E208 : The future of enterprise AI: agents, automation, and trust

    Enterprise AI is shifting from experiments to infrastructure, and that changes everything. In this episode, Dr Andree Bates interviews Jocelyn Houle, Senior Director of Product Management at Securiti.ai, to explore the future of enterprise AI, agents, automation, and the single biggest blocker to scale: trust. Jocelyn shares what she is seeing across highly regulated industries as organisations move beyond proof of concept into production. She explains why the agent era raises the stakes: when you add LLMs into workflows, systems become non-deterministic and harder to trace end to end. In her words, once the data goes in, you cannot easily untangle it, so organisations need stronger controls around permissions, auditing, and policy enforcement. A major theme is that data foundations matter more than ever. Jocelyn warns that agents will not magically repair messy data, they will expose weak data quality immediately. From there, she outlines how trust can be won or lost at the prompt layer, both outbound (what the model says to customers) and inbound (what users share with the organisation). She also discusses “toxic combinations”, where overlapping access can accidentally leak sensitive information, plus the growing need for prompt screening and tracking to reduce risk. The conversation also digs into explainability and auditability, with Jocelyn being refreshingly honest that the perfect solution is not here yet. Instead, enterprises are using practical approaches like benchmarking releases side by side, cataloguing AI agents in use, and building governance that is starting to look more like modern cybersecurity: baked in from the start, not added as an afterthought. Jocelyn closes with clear advice for leaders: start “left” with raw data controls, build a truly cross-functional team, and begin setting up auditability even with imperfect tools, because regulators are catching up and they will expect responsible behaviour. Topics Covered Where enterprise AI adoption really stands today What makes AI agents different from traditional automation Non-determinism, traceability, and why permissions matter Data mapping, policy controls, and reducing sensitive data leakage Prompt security: outbound and inbound trust risks “Toxic combinations” and exposure through agent workflows Explainability, benchmarking, and parallel release testing AI governance becoming as essential as cybersecurity Top 3 pieces of advice for CTOs starting their AI journey Why enterprise AI will become so embedded we stop talking about it AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results. This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more. Dr. Andree Bates LinkedIn | Facebook | X About the Podcast

    31 min
  7. E207: The economics of clinical trials and the relationship to AI

    MAR 4

    E207: The economics of clinical trials and the relationship to AI

    Clinical trials are a massive industry with brutal economics, long timelines, and failure rates that would be unacceptable in almost any other sector. In this episode, Dr Andree Bates is joined by Dr Joseph Geraci of NetraMark to break down why trials fail so often, how patient heterogeneity drives cost and uncertainty, and where AI can realistically shift the economics. Joseph shares his unusual path from mathematics and mathematical physics into oncology and medical science, including a decision to move into hospital research rather than follow a more traditional academic route. That shift shaped his focus: not just discovering more molecules, but understanding why the same drug can work brilliantly for some patients and fail for others. A central theme is that clinical trials are not “one disease, one patient type”. In many areas, disease definitions are too broad for trial design, making trials feel like trying to hit multiple dartboards with one dart. Joseph explains how NetraMark’s approach aims to identify meaningful subpopulations inside small datasets, finding the “pocket” where a drug’s true advantage shows up, without discarding patients as outliers. The conversation also touches on regulators, including growing interest in innovation pathways, but also the fear pharma teams have about changing protocols and risking setbacks. Joseph argues that AI’s biggest economic value in trials is speed, using better insight from limited trial data to guide enrichment strategies, smarter substudy decisions, and faster iteration, especially in oncology and rare disease where time is everything. Topics Covered Why clinical trial economics are becoming unsustainable Patient heterogeneity and why disease definitions break trials Finding “pockets” of responders within small datasets Trial enrichment and substudies that reveal a drug’s advantage Why pharma adoption can be slow, even when failures are constant Regulatory interest, guidelines, and sponsor risk aversion Large language models vs mathematically augmented AI approaches Speed as the biggest economic lever in trials Practical examples across depression, schizophrenia, oncology, and beyond What clinical trials could look like in five years with AI-driven insight This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.  Dr. Andree Bates LinkedIn | Facebook | X

    33 min
  8. EP206: Why Your Pharma AI Strategy Is Probably Broken — And What a Real Blueprint Looks Like

    FEB 25

    EP206: Why Your Pharma AI Strategy Is Probably Broken — And What a Real Blueprint Looks Like

    AI capability has never been higher, yet most pharma AI programmes are still failing to create measurable business impact. In this solo episode, Dr Andree Bates breaks down why many pharma and biotech AI strategies are “broken before they even begin” and what a real AI strategic blueprint needs to include if you want adoption, scale, and outcomes, not just impressive pilots. Dr Andree explains the core paradox: AI can now synthesise literature at speed, accelerate discovery, and outperform human experts in specific tasks, but the business results are often disappointing because the failure is rarely technical. It is strategic. She describes the “technical obsession trap”, where organisations spend months optimising models and benchmarking competitors while adoption remains low and teams are not operationally ready to act on the outputs. She outlines three common failure modes: Innovation Theatre, where disconnected pilots never compound into enterprise value Competitor benchmarking, where companies copy use cases that do not fit their context Technology first strategy, where tools are bought before priorities are defined From there, she maps what a strong pharma AI blueprint must cover: grounding in business objectives, end to end deployment architecture (data, governance, capability, change), leadership and culture, rigorous financial modelling tied to revenue and ROI, and alignment across functions including commercial, medical, regulatory, R&D, market access, insights, and tech teams. Dr Andree closes with a clear challenge for leadership: competitive advantage will come to organisations that build the most intelligent operating model around AI, not those with the biggest budgets. She also offers a 45 minute AI strategic diagnostic for pharma and biotech leaders who want an honest read on what to fix before investing further. Topics Covered Why pharma AI impact often disappoints despite powerful tools The “technical obsession trap” and the AI strategy blind spot Innovation Theatre, competitor benchmarking, and technology first mistakes What a pharma AI strategic blueprint must include Governance as a foundation for scale and regulatory trust Leadership, culture, and adoption as the real differentiators Financial modelling and prioritisation based on ROI and revenue impact Organisational alignment across the full pharma value chain Choosing the right advisory partner and avoiding generic frameworks Why strategy must come before technology to build durable advantage AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results. This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more. Dr. Andree Bates LinkedIn | Facebook | X

    37 min
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Ratings & Reviews

3.9
out of 5
8 Ratings

About

AI For Pharma Growth is the podcast from pioneering Artificial Intelligence entrepreneur Dr. Andree Bates created to help Pharma, Biotech and other Healthcare companies understand how the use of AI-based technologies can easily save them time and grow their brands and company results. This show blends deep experience in the sector with demystifying AI for biopharma execs from biotech start-ups right through to big pharma. In this podcast, Dr Andree will teach you the tried and true secrets to building results in a pharma company using AI and alert you to some fascinating new tools and applications to benefit you and your company. As the author of many peer-reviewed journals in pharma AI, and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI, futuretech, healthcare and pharma to help you to navigate through the, sometimes confusing, but magical world of AI powered tools to achieve real-world results. This podcast features many experts who have developed powerful AI-powered tools that are the secret behind some time-saving and supercharged revenue-generating business results. Those who share their stories and expertise show how AI can be applied to Discovery, R&D, clinical trials, market access, medical affairs, regulatory, market research, business insights, sales, marketing, including digital marketing, and so much more.

Information

  • Creator
    Dr Andree Bates
  • Years Active
    2022 - 2026
  • Episodes
    215
  • Rating
    Clean
  • Copyright
    © Dr Andree Bates
  • Show Website
    AI For Pharma Growth

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