AI For Pharma Growth

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  E237: Beyond the Pill: How AI is Unlocking the Preventative Medicine Opportunity Pharma Can't Afford to Miss 29.09.2026 30мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with cancer biologist and health educator Rebecca Maff about how AI could help shift healthcare from treating disease to preventing it.Rebecca’s interest in prevention began after seeing women in her own family diagnosed with late-stage cancers and serious autoimmune conditions. Her subsequent work has included machine learning models designed to identify patients at higher risk of cancers and encourage overdue screening before disease progresses.The conversation explores what prevention really means in practice, from early biomarkers and personalised risk modelling to screening, lifestyle, metabolic health and behavioural change. Rebecca explains how AI can help analyse electronic health records and large patient populations to identify signals that would be difficult for humans to find manually.They also discuss the commercial opportunity for pharma. If late-stage disease becomes more preventable, the industry may need to think beyond treating established illness and consider new models built around earlier detection, intervention and maintaining health.Trust remains a major challenge. Rebecca argues that patients and clinicians need to see credible, successful AI implementations before confidence grows, particularly when systems are using highly personal health, genomic and behavioural data.Topics CoveredMoving healthcare upstream towards preventionAI-powered cancer risk stratificationEarly screening and disease detectionBiomarkers and personalised riskUsing electronic health records to identify signalsPrevention as a future pharma opportunityLifestyle, metabolic health and chronic diseasePatient data, privacy and trust in AIWhy proof of concept matters before scalingAbout EularisEularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies.Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work.The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges.AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny.AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint.Start with the Institute → https://eularis.com/institute/Everything else → https://eularis.comAbout the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates LinkedIn | Facebook | X
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  E236: Why 90% of Trials Still Fail — and What AI Can (and Can't) Fix 22.09.2026 34мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Raviv Pryluk, co-founder and CEO of PhaseV, about why so many clinical trials still fail and where AI can genuinely improve the odds.Raviv argues that failure is often not because the drug itself is wrong. Trials can fail because of the wrong patient population, indication, dose, design, sites, monitoring or interpretation of the data. PhaseV uses causal machine learning, adaptive trial design and large-scale simulation to help sponsors make better decisions across those areas.The conversation explores why explainability and validation matter in clinical development. Raviv explains that a 95% prediction is not enough on its own. Sponsors and regulators need to understand why a recommendation is being made, which evidence supports it, and whether the result is statistically and clinically defensible.They also discuss using existing trial data to identify responder subgroups, stress-testing designs before patients are enrolled, adapting trials mid-flight and connecting protocol design more closely with clinical operations.Topics CoveredWhy clinical trials still failCausal ML versus predictive modellingPatient selection and responder subgroupsAdaptive trial designSimulating trials before enrolmentSite selection and recruitmentValidation, explainability and statistical guaranteesGo/no-go portfolio decisionsAbout EularisEularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies.Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work.The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges.AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny.AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint.Start with the Institute → https://eularis.com/institute/Everything else → https://eularis.comAbout the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates⁠⁠ LinkedIn⁠⁠ |⁠⁠ Facebook⁠⁠ |⁠⁠ X
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  E235: AI created a trust crisis and nobody is talking about it 15.09.2026 31мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Dan Pratl, founder and CEO of Quadron Inc, about a trust problem emerging as AI moves deeper into pharma: how do organisations preserve confidence when more work is generated, shaped and accelerated by machines?Dan argues that verified output alone is not enough. Pharma already has human review, MLR, regulatory sign-off and quality controls, but trust also depends on understanding where data came from, which models were used, where human judgement entered the process and whether that chain can be audited.The conversation explores why human expertise may become more valuable, not less, as AI spreads. Dan discusses shadow AI, the limits of forcing employees onto a single approved model, and why organisations need to reward people for curating, verifying and applying judgement across tools rather than treating AI usage itself as productivity.They also examine the risk of losing the junior work that traditionally builds expertise, the need for stronger audit trails, and why redesigning systems around AI may matter more than simply adding another governance dashboard.Topics CoveredAI and pharma's emerging trust problemWhy verified output is not the whole answerHuman judgement as a scarce resourceShadow AI and unsanctioned modelsAudit trails across humans, models and dataThe danger of equating token use with productivityBuilding future expertise in an AI-enabled workforceWhy trust requires incentives as well as governanceAbout EularisEularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies.Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work.The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges.AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny.AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint.Start with the Institute → https://eularis.com/institute/Everything else → https://eularis.comAbout the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates LinkedIn | Facebook | X
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  E234: The contrarian case for physics over data: can deterministic, training-free models beat ML in lead optimization? 08.09.2026 43мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Dr. Jacek Marczyk, co-founder and CEO of BioDynLab, about a contrarian view of computational drug discovery: that the next leap may come not from more data and bigger models, but from physics.Dr. Marczyk brings a background in aerospace engineering, automotive, Silicon Graphics and complexity science. His work led to quantitative complexity theory, which he now applies to molecules through BioDynLab’s deterministic, training-free approach.The conversation explores why high precision and high complexity cannot coexist, and why throwing more compute at biological problems does not automatically produce useful knowledge. Dr. Marczyk argues that machine learning can produce impressive outputs, but without explainability, teams may get a result without understanding the physics behind it.He explains how BioDynLab uses molecular dynamics and complexity theory to study how atoms and amino acids move, how information flows through molecules, and which residues act as key “hotspots” in that dynamic system. Instead of treating molecules as static structures, this approach looks at the motion and information patterns that help determine biological function.The key message is that AI and physics should not be seen as enemies. In data-sparse areas such as rare diseases, novel targets and first-in-class chemistry, physics-led methods may offer a complementary route to insight, especially where machine learning has little or no training data to rely on.Topics CoveredWhy pharma’s AI gold rush may miss key biologyThe principle of incompatibilityPhysics-first drug discoveryQuantitative complexity theoryWhy explainability mattersMolecular dynamics and information flowAtomic and amino acid participation factorsComplexity hotspots in moleculesStatic structures versus molecular motionRare disease and data-sparse discoveryAbout EularisEularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies.Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work.The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges.AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny.AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint.Start with the Institute → https://eularis.com/institute/Everything else → https://eularis.comDr. Andree Bates⁠ LinkedIn⁠ |⁠ Facebook⁠ |⁠ X
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  E233: The Diagnostic Room: The AI Capability Problem Pharma Hasn't Named 01.09.2026 43мин

  In this solo episode of AI For Pharma Growth, Dr Andree Bates explores the AI capability problem pharma has not properly named: training that works in the room, but fails to hold inside the organisation.Dr Andree explains why one-off workshops, generic AI fluency programmes and broad learning platforms are not enough. They may teach people what AI is, what it can do and where it can fail, but they rarely teach the exact workflows, judgement calls and regulatory context people need for their own roles.The episode looks at why AI capability fades over time. Some people leave training and build valuable new workflows, while others forget how to apply what they learned within weeks or months. In pharma, that matters because many AI use cases depend on cognitive, accuracy-based judgement: deciding whether a generated summary faithfully represents a source, whether a claim is substantiated, or whether an output can safely enter a regulated workflow.Dr Andree also explains why generic training can create risk. If usage rises faster than judgement, teams may become more confident with AI without becoming more capable in the workflows where mistakes carry regulatory, compliance or patient safety consequences.The key message is clear: AI capability needs to be maintained, role-specific and grounded in pharma reality. Training once, or training generically, is not a capability plan.Topics CoveredWhy AI training often fails to holdThe difference between awareness and capabilityWhy generic AI fluency is not enoughRole-specific AI workflows in pharmaSkill decay and why 90 days mattersCognitive judgement and regulatory riskWhy confidence can outpace competenceShadow AI and unmanaged tool useWhat real AI capability support must includeThe Pharma AI Enablement InstituteThe Pharma AI Enablement Institute is the structure this episode describes.Foundations everyone starts with, because the regulated reality is common. Then tracks that split by function - every function, from discovery and clinical through regulatory, safety, medical affairs, market access, manufacturing and commercial, up to leadership. Monthly live office hours with Dr Andree Bates. Prompt libraries maintained as the models change. Per-person records a functional sponsor can act on and show an auditor.Hit a problem mid-workflow and your team asks the library in plain language, then lands on the exact video and timestamp where it has already been answered.One price per business unit, banded by size. No per-seat charges — because per-seat pricing is what causes the failure this episode is about.See what the curriculum contains for your function →https://eularis.com/institute/ Read the long-form argument, including what changed in Article 4 of the EU AI Act in July → eularis.com/your-ai-training-worked-thats-the-problem-the-ai-capability-problem-pharma-hasnt-named About the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates LinkedIn | Facebook | X
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  E232: The Early Readout: Upgrading the Interim Analysis to Catch Futility and Success Years Sooner 25.08.2026 28мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Tom Coates, CEO of Presentient, about why interim analysis in clinical trials is ready for a major upgrade.Interim analyses allow sponsors to look at trial data mid-flight and assess whether a study is likely to succeed or fail, using pre-specified rules. But Tom explains that many phase two and three commercial trials still do not include a pre-planned interim analysis, meaning sponsors often wait far longer than necessary to detect futility or act on early signs of success.The conversation explores how Presentient is working on next-generation interim analysis and readout strategies, including the BRX platform, which is designed to handle unblinded data while protecting trial integrity. Tom explains why it is not enough to have a powerful algorithm. Sponsors also need secure architecture, audit trails and methods that regulators and data monitoring committees can trust.Tom also discusses where AI does and does not belong. For high-stakes stop or go decisions, explainability, reproducibility and regulatory confidence matter more than hype. But model-based methods, synthetic data and subgrouping engines may help sponsors better understand which patients benefit, who does not, and how to design trials around more meaningful treatment signals.The key message is that interim analysis should not be an underused checkpoint. Done well, it can help sponsors stop failing trials earlier, prepare for success sooner and make better decisions with greater confidence.Topics CoveredWhy interim analysis is underusedStopping trials early for futility or successProtecting blinding and trial integritySecure handling of unblinded dataWhat data monitoring committees need to seeWhere AI fits, and where it does notSubgrouping and individual treatment effectsSynthetic data and trial simulationRegulatory confidence and audit trailsThe future of continuous trial monitoringEularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharmaAbout the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates LinkedIn | Facebook | X
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  E231: The Diagnostic Room: You didn't have an AI problem. You had a capability problem. 18.08.2026 36мин

  In this solo episode of AI For Pharma Growth, Dr Andree Bates explores why many pharma teams do not have an AI problem at all. They have a capability problem.Dr Andree starts with a simple question: when was your team last properly trained on AI for their specific role? Not when they were given access to tools, licences or a generic use policy, but when they were trained to use AI effectively, safely and compliantly in their actual workflow.The episode challenges the usual explanations for disappointing AI results: the model was not good enough, the vendor was wrong, the data was not ready, or the organisation resisted change. In many cases, the tools work, the pilots are useful and the training lands. But the working knowledge needed to use AI well is uneven, fragile and decays over time.Dr Andree explains why this matters so much in pharma. High-value AI work is often judgement-led: medical information responses, payer materials, safety narratives, regulatory documents and MLR-compatible content. AI can support these tasks, but only when users can tell the difference between a strong draft and a merely plausible one.She also discusses the research behind skill decay, including why cognitive and accuracy-dependent skills fade faster than simple speed-based or physical skills. That is especially important in pharma, where the cost of a confident but wrong output can become a compliance, regulatory or patient safety issue.The key message is clear: AI capability is not something you achieve once. It has to be maintained. The functions that lead in AI will not simply be the ones with the most licences or training events. They will be the ones that treat capability as something with a rate of decay and build systems to keep it current.Topics CoveredWhy AI underperformance is often a capability problemThe difference between access, policy and real trainingWhy confident AI use varies across teamsAI in judgement-led pharma workflowsSkill decay and why 90 days mattersWhy high-value AI workflows are often forgotten fastestThe risk of outdated working knowledgeWhy training is ignition, not maintenanceThe limits of AI champions and internal portalsThree questions to ask your function this weekEularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharmaAbout the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates LinkedIn | Facebook | X
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  E230: The Last Untouched Dataset 11.08.2026 23мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Nijat Ahmadov, CEO of Nucs AI, about molecular imaging as one of pharma’s most underused data assets.Nijat explains why PET, CT and other molecular imaging data remain largely “untouched”: clinically valuable and created at scale, but still too often trapped in qualitative reads rather than structured, standardised data that can support decision making. As radioligand therapies expand in oncology, that gap becomes harder to ignore.The conversation explores how AI can help turn molecular imaging into computable, decision-grade data for patient selection, response monitoring and companion diagnostic strategy. Nijat argues that AI is no longer a nice-to-have in this space. Without it, pharma risks losing confidence in the outcomes that affect adoption, reimbursement and commercial success.They also discuss what it will take for AI-derived imaging biomarkers to become regulatory grade: analytical validation, reproducibility, diverse data sets, clinical validation and evidence that endpoints are meaningful, not just technically impressive.The key message is that imaging is not only diagnostic. Once structured properly, it can reveal predictive signals about disease behaviour and treatment response, making it a powerful asset for pharma teams building the next generation of oncology trials.Topics CoveredWhy molecular imaging is still underusedTurning PET and CT scans into structured dataRadioligand therapy and patient selectionMoving beyond eligible vs not eligibleAI-derived imaging biomarkersClinical validation and regulatory trustImaging data as a competitive moatWhy prediction matters more than diagnosisEularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharmaAbout the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates LinkedIn | Facebook | X
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  E229: From Reactive to Proactive: What a QP's Job Should Actually Look Like in 2026 04.08.2026 33мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Jitesh Halai, founder and CEO of OneSC, about what the Qualified Person role should look like in a more proactive, digitally connected pharmaceutical supply chain.Jitesh explains how many QPs are still forced into reactive work: chasing documents, checking versions, searching inboxes, reconciling batch data across disconnected systems and trying to work out what is holding up release. In virtual pharma environments, where much of the supply chain is outsourced, that burden becomes even heavier.The conversation explores how platforms like OneSC can create a single source of truth across supply chain partners, giving QPs live visibility of batch status, documentation, review progress and quality signals. Instead of waiting weeks for all documents to arrive before spotting a packaging, leaflet or batch data issue, automated checks can flag risks much earlier.Jitesh also discusses how AI, OCR and automation can reduce repetitive administrative work, without replacing human judgement. The aim is not to remove the QP from the process, but to give them more time for the work they were trained to do: critical review, risk assessment and patient safety decisions.The key message is clear: the future QP should not be fighting their mailbox. They should have consolidated batch information, automated signals and the confidence to move from reactive release management to proactive quality oversight.Topics CoveredWhy QPs are stuck in reactive workBatch review, release and document chasingThe burden of disconnected systemsCreating a single source of truthAutomated checks for earlier risk detectionAI, OCR and automation in quality workflowsReducing cognitive burden for QPsWhy human judgement still mattersHow real-time auditing may evolveEularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharmaAbout 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.Dr. Andree Bates LinkedIn | Facebook | X
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  E228: The Diagnostic Room: "We're Doing AI" Is Not a Board Answer 28.07.2026 40мин

  In this solo episode of AI For Pharma Growth, Dr Andree Bates explains why “we’re doing AI” is not a credible board answer, and why activity, pilots and steering committees are not the same as strategy.Dr Andree breaks down two common answers leadership teams give when asked about AI strategy. The episode explores why crowdsourced use cases often become “use case copying” rather than genuine internal innovation. A pain point may be real, and a pilot may work, but that does not mean it is one of the highest-value AI opportunities for the organisation. Without financial modelling, business-unit submissions are only inputs, not prioritisation.Dr Andree also outlines four structural conditions that explain why AI investment often fails to realise value: the value prioritisation gap, the decision rights gap, the data ownership conflict, and incentive misalignment. These issues are connected, and if they are diagnosed in the wrong order, the strategy usually fails at the next layer.The core message is clear: boards do not need a list of AI activity. They need a strategy they can govern, with clear priorities, financial assumptions, sequencing, ownership and metrics that can be tested over time.Topics CoveredWhy “we’re doing AI” is not a board answerActivity, demand and value: the difference that mattersWhy business-unit use cases are not strategyUse case copying and internal innovation theatreThe value prioritisation gapDecision rights between pilot and productionData ownership and access conflictsIncentives, adoption and rational resistanceWhat finance needs to see before funding AIWhat a real board-level AI answer sounds likeEularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharmaAbout 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
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  E227: From Bench to Boardroom: How One Geneticist is Quietly Reshaping the Future of Healthcare 21.07.2026 33мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Bret Bostwick from Breyer Capital about the rare path from genetics, clinical medicine and drug development into venture capital, and what that perspective reveals about the future of healthcare innovation.Bret shares how the release of the Human Genome Project first pulled him into genetics, and how clinical work with patients made the science deeply practical. As a medical geneticist, he saw families finally receive a diagnosis, but often without a treatment option. That experience led him towards programmable therapeutics, RNA-based medicines and the translational work required to move from biological insight into human trials.The conversation explores what makes a therapeutic company investable beyond the science alone. Bret explains why breakthroughs often fail not just because of technical risk, but because the right people, culture, operating experience and business model are not around the table. For him, one of the first questions is not simply “does the science work?” but “what problem is this company really solving, and is this the most elegant solution?”They also discuss where AI is overhyped and underestimated in medicine. Bret is sceptical of claims that AI can compress a 12-year clinical development journey into two years, because biology still requires time to evaluate safety and efficacy. But he sees enormous potential in agentic AI across the full healthcare and pharma stack, from discovery and preclinical design to manufacturing, commercialisation and patient finding.The key message is that the future of healthcare will belong to people and companies that can bridge disciplines: genetics, computation, medicine, product development and investment. The biggest opportunities may sit at the intersections, where scientific insight, platform thinking and practical translation come together.Topics CoveredMoving from genetics and clinical medicine into venture capitalLessons from RNA therapeutics and translational medicineWhy target genetics matters in drug developmentWhat investors look for beyond the scienceWhy the right team and culture are criticalPlatform companies vs single-asset thinkingWhere AI can and cannot compress drug developmentAgentic AI across pharma and healthcare workflowsFounder mistakes when pitching healthcare investorsEularis 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.AI platforms and tools solve specific problems. Strategy makes sure you’re solving the right ones, in the right order. If you want help mapping priorities as you evaluate what to roll out next, send me a LinkedIn DM starting with ‘PRIORITIES’ and two lines: what’s already in flight, and the decision you’re trying to make next.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
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  E226: The AI Deal Scout: How Machine Intelligence Is Reshaping Biopharma Business Development 14.07.2026 28мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Smbat Rafayelyan, founder and CEO of Bioneex, about how AI is reshaping biopharma business development, deal scouting and asset evaluation.Business development has traditionally relied heavily on relationships, conferences, databases, analyst reports and manual search. But as therapeutic pipelines, publications, patent filings and global biotech activity expand, that old model is becoming harder to sustain. Smbat explains why teams that only rely on their network risk missing valuable assets before they even know they were available.The conversation explores how AI can act as a deal scout, helping biopharma and VC teams identify, structure and evaluate opportunities faster. Smbat explains how Bioneex allows biotech companies to submit non-confidential asset information, while AI extracts, validates and compares that data against external sources, curated databases and market intelligence.They also discuss where AI is most useful in the BD process. The aim is not to replace human judgement, but to reduce the overwhelming search space. If AI can narrow thousands of potential assets down to a small, relevant shortlist, expert teams can spend their time on the work that matters: diligence, strategic fit, deal judgement and human relationships.Smbat also warns that AI is not magic. General-purpose language models are not enough for serious deal sourcing. Effective AI scouting requires structured data, validation, multiple specialised models, human review and infrastructure built specifically for biopharma business development.Topics CoveredWhy traditional deal scouting is too slowThe limits of relationship-led deal flowHow AI supports asset search and evaluationMatching biotech assets with pharma and VC prioritiesWhy structured and validated data mattersAI use cases beyond drug discoveryNarrowing thousands of assets into focused shortlistsFailure modes of general-purpose AI in BDHow BD teams may change over the next few yearsWhy human judgement still matters in due diligenceEularis 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.AI platforms and tools solve specific problems. Strategy makes sure you’re solving the right ones, in the right order. If you want help mapping priorities as you evaluate what to roll out next, send me a LinkedIn DM starting with ‘PRIORITIES’ and two lines: what’s already in flight, and the decision you’re trying to make next.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
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  E225: The 80% Nobody Talks About: Building AI Governance That Survives a Pharma Audit 07.07.2026 31мин

  In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Nuno Valério, Head of Innovation, R&D Quality at Merck Healthcare, about the part of AI governance most organisations rarely talk about: the operational 80% that decides whether AI survives a pharma audit.Nuno explains why governance cannot stop at policies, committees, risk frameworks and model registers. Those visible elements matter, but they are only the start. In a GxP environment, auditors will want to reconstruct how a decision was made, which data was used, which model version was involved, what validation evidence exists, and where the human decision trail sits.The discussion explores how AI governance is moving from strategy decks into implementation. Pharma teams are under pressure to turn AI into value, but in regulated environments the margin for error is close to zero. That means adoption, trust, validation, traceability and operational discipline all matter just as much as the model itself.Nuno also shares a practical way to pressure test readiness: take an AI tool already in production, pick a decision from a few months ago, and try to fully reconstruct the inputs, model version, validation status, review trail and evidence. If that takes more than 48 hours, the system is probably not audit ready.The key message is that AI governance is not just a compliance function. Done properly, it becomes a competitive capability, helping organisations deploy AI faster, safer and with greater trust.Topics CoveredWhy AI governance is more than frameworks and policiesThe visible 20% vs the operational 80%What auditors actually want to reconstructGxP expectations for AI systemsValidation, traceability and change controlHuman oversight and decision accountabilityWhy governance must include how people use AIVendor selection and audit-ready AIWhy trust by design could become competitive advantageEularis 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.AI platforms and tools solve specific problems. Strategy makes sure you’re solving the right ones, in the right order. If you want help mapping priorities as you evaluate what to roll out next, send me a LinkedIn DM starting with ‘PRIORITIES’ and two lines: what’s already in flight, and the decision you’re trying to make next.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
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  E224: The Diagnostic Room: The Financial Case Your CFO Actually Needs to See Before Approving Your AI Strategy 30.06.2026 33мин

  In this solo episode of AI For Pharma Growth, Dr Andree Bates tackles one of the biggest reasons pharma AI strategies stall: they are not presented in the financial language a CFO can approve.Dr Andree argues that the issue is rarely whether AI has value. The issue is that most organisations cannot quantify that value in a way that connects to business outcomes, baselines, investment priorities, timelines and return. Too often, teams arrive with pilots, use cases, vendor proposals and enthusiasm, but no financial model that can survive board-level scrutiny.With the patent cliff putting hundreds of billions in revenue at risk, AI is being positioned as a strategic lever across R&D, commercial, medical, regulatory and market access. But activity is not strategy. A use case that looks impressive in isolation may not be the one that moves the business most.This episode explains what a credible AI financial case needs to include: initiative-level modelling, realistic cost assumptions, adoption scenarios, redeployment of time and talent, technology cost changes, the cost of inaction, and sequencing. Dr Andree also explains why implementation order can change return, because some initiatives create the data, governance and organisational readiness needed for others to work.The core message is clear: AI strategy must be treated as a financial discipline, not just a technology ambition. If you cannot explain which AI initiatives matter most, why they come first, what return they should generate and when, you do not yet have the case your CFO needs.Topics CoveredWhy AI business cases often fail with CFOsThe difference between AI activity and AI strategyWhy pilot ROI is not enoughPatent cliff pressure and financial rigourComparing AI initiatives against business prioritiesRedeployment and real financial returnTechnology cost modellingThe cost of not actingWhy sequencing changes AI ROIEularis 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.comIf this episode described your situation, send me a LinkedIn DM starting with ‘SENSECHECK’ and two things: the question you’re trying to answer internally, and what’s currently in flight. I’ll reply with what I’d need to see to turn that activity into a defensible plan, and the next step.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
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  E223: From Population Models to Personal Intelligence: Rethinking Biological Data 23.06.2026 35мин

  For most of modern medicine, biological data has been built around population averages: what people like you might experience, rather than what you personally will. In this episode, Dr Andree Bates speaks with Ken Clark, co-founder and CEO of Enigma Genetics, about moving from population models to personal intelligence, and what it could mean for patients, pharma and the future of biological data.Ken shares the personal medical experience that led him into this work. After multiple surgeries and a persistent infection, he found that the data needed to understand what had changed in his own biology simply wasn’t available in a useful way. That sparked the idea of creating a personal biological “version history”, a healthy baseline that can be compared against future changes as science and computation improve.The conversation explores Enigma Genetics’ vision for an AI that lives within a personal health profile, continuously aggregating genetic data, medical records, imaging, wearables and other health information to help build the most comprehensive picture possible for the individual and their doctor. The goal is not to replace medical judgement, but to give clinicians a richer, more personalised foundation for diagnosis and decision making.Ken also discusses why consent, identity and data ownership need to be rethought. Instead of medical and genetic data being locked inside institutions, Enigma’s model imagines a system where individuals control access, grant or revoke consent, and become active participants in how their data is used.The key message is that precision medicine cannot fully mature if it is still built on population-level thinking. To unlock the next stage, biological intelligence may need to become more personal, consented, longitudinal and controlled by the individual.Topics CoveredWhy population models fall short for individual biologyCreating a personal biological “version history”Personal AI profiles for health data and clinical supportGenetic data, imaging, medical records and wearablesConsent, identity, access control and data ownershipReal world evidence and individual-level consentClinical trials, rare disease data and patient participationThe future of personalised biological intelligenceEularis 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.AI platforms and tools solve specific problems. Strategy makes sure you’re solving the right ones, in the right order. If you want help mapping priorities as you evaluate what to roll out next, send me a LinkedIn DM starting with ‘PRIORITIES’ and two lines: what’s already in flight, and the decision you’re trying to make next.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
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  E222: Why Most Pharma AI Will Fail Without This One Thing 16.06.2026 29мин

  Most pharma companies are racing to apply AI across drug discovery, development and commercialisation, but many of those efforts will fail for one simple reason: the data underneath is not good enough. In this episode, Dr Andree Bates speaks with Lisa Downey, CEO of DrugBank, about why trusted, structured biomedical intelligence is the foundation pharma AI cannot succeed without.Lisa explains how DrugBank has spent 20 years building and continuously curating a biomedical knowledge layer across drugs, targets, diseases and trials. With more than 156 million structured data points and over 60,000 academic citations, DrugBank is not just another dataset. It is a continuously maintained reference system designed so AI can reason over biomedical knowledge with traceability and trust.The conversation explores why most pharma AI projects fall short. Lisa argues the blocker is rarely the model. Instead, teams hit the wall because internal data lakes are not harmonised, licensed third-party data may not be AI-ready, and public data sources are incomplete or not maintained for enterprise use. Brilliant ML teams then spend most of their time cleaning and reconciling data instead of creating real scientific or commercial value.Lisa also breaks down what pharma buyers should test before trusting any AI vendor: interoperability, harmonisation, evidence lineage and continuous validation. She explains why human pharmaceutical expertise still matters, introducing DrugBank’s “human over the loop” approach, where experts set scientific boundaries, validation criteria and judgement so AI can scale inside trusted guardrails.Topics CoveredWhy most pharma AI projects fail before they scaleData quality as the foundation of trustworthy AIDrugBank’s 20 years of curated biomedical intelligenceInternal data lakes, third-party data and public data limitationsWhy hallucinations often start upstream of the modelHow to evaluate data quality: interoperability, harmonisation, lineage and validationHuman over the loop vs human in the loopWhy defensible AI needs traceable sourced factsThe difference between confident AI and grounded AIWhy proprietary context matters more than raw dataEularis 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.AI platforms and tools solve specific problems. Strategy makes sure you’re solving the right ones, in the right order. If you want help mapping priorities as you evaluate what to roll out next, send me a LinkedIn DM starting with ‘PRIORITIES’ and two lines: what’s already in flight, and the decision you’re trying to make next.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
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  E221: The Diagnostic Room: The AI Governance Timeline Moved. Your Governance Exposure Didn't 09.06.2026 28мин

  On 7 May 2026, the EU reached a provisional agreement to push back the hardest deadlines in the EU AI Act. Many leadership teams heard one message: “we’ve got more time”. In this solo episode, Dr Andree Bates explains why that exhale is dangerous. The timeline moved, but the governance exposure did not.Dr Andree breaks down what the delay does and does not change. The dates may shift, but the architecture of the AI Act remains intact: risk classification, documentation, oversight, robustness, logging, conformity assessment, and post market monitoring. These are not last minute checklist items. They are operational capabilities you have to build, test, and keep running.The real risk, she argues, is misreading “more time” as permission to wait. The hardest work is operational: finding every AI system across the enterprise, including vendor embedded AI inside platforms like CRM and workflow tools, distinguishing genuine AI from marketing labels, classifying systems properly, assigning ownership, and building processes that still hold when vendors update models or features under the hood.She also tackles a costly misconception for US based pharma: the EU AI Act is deliberately extraterritorial. Scope follows where outputs are used, not where the company is headquartered. If AI outputs touch EU employees, regulators, clinicians, or patients, you may be in scope, even if the system is built and operated in the US.Dr Andree’s bottom line: the companies that treat this runway as time to build will compound governance maturity and deploy faster with less risk. The ones that wait will hit 2027 under compression, with more shadow AI, more remediation, and less credibility when scrutiny arrives.Topics CoveredWhat moved in the EU AI Act timeline, and what did notWhy AI governance is an operating model, not a deadline projectThe real work: inventory, classification, ownership, documentationVendor embedded AI and shadow AI as hidden exposureHigh risk obligations and why you can’t assemble them lateExtraterritorial scope and why US pharma is still in scopeWhat to do with the runway: build maturity, not delayEularis 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.If this episode described your situation, send me a LinkedIn DM starting with ‘SENSECHECK’ and two things: the question you’re trying to answer internally, and what’s currently in flight. I’ll reply with what I’d need to see to turn that activity into a defensible plan, and the next step.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.
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  E220: The Intelligence Gap: Why Pharma's Biggest Deals Are Being Lost Before They Even Know They're Competing 02.06.2026 23мин

  In pharma, the biggest deals are increasingly won or lost before a formal process even begins. In this episode, Dr Andree Bates interviews Andrey Doronichev, co-founder and CEO of Bioptic, about the “intelligence gap” in business development, licensing, and corporate strategy, and why many companies are losing opportunities before they even know they’re competing.Andrey shares how his background building products at Google, including launching the YouTube mobile app, shaped his obsession with making messy, unstructured information usable at speed. He argues that pharma intelligence suffers from a similar problem: critical signals exist across scientific, regulatory, and business sources globally, but traditional approaches rely on relationships, conferences, spreadsheets, and slow manual synthesis.A key theme is competitive asymmetry. Deal teams are under pressure to source external innovation while the signal landscape expands rapidly, including an increasing share of patents and assets emerging outside the US. Andrey describes a common pattern: teams work from partial databases and manually maintained lists, then discover a competitor has already secured a preferred position with an asset they never saw coming, often in markets where information is harder to access.Bioptic’s thesis is cadence. If the same landscape work that takes weeks via consultants or days internally can be done in minutes, the operating model changes. Instead of humans acting as data gatherers, they can spend time on the human work: judgement, relationship building, negotiation, and structuring deals. Andrey describes Bioptic as a “self evolving operating system” that can build new integrations and analyses on demand, closing the gap between questions and actionable intelligence.Topics CoveredWhy pharma’s biggest deals are lost before the process startsThe intelligence gap: relationships vs anticipatory signal captureGlobal complexity, China signals, and why databases lagCadence as competitive advantage in BD and strategyFrom spreadsheets to continuously updated intelligence“Operating system” thinking and building capabilities on demandTurning analysts from data gatherers into decision makersWhat changes for BD teams over the next five yearsEularis 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.If this episode described your situation, send me a LinkedIn DM starting with ‘SENSECHECK’ and two things: the question you’re trying to answer internally, and what’s currently in flight. I’ll reply with what I’d need to see to turn that activity into a defensible plan, and the next step.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.
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  E219: Bridging the Data-Use Divide: How QuadraticMed’s Dr. Danielle Bower Bridges Medicine and Data Science to Unlock Real-World Evidence 26.05.2026 35мин

  Real world evidence (RWE) could transform drug development and clinical care, but most organisations still struggle to turn messy clinical data into decisions they can trust. In this episode, Dr Andree Bates speaks with Dr Danielle Bower, CEO of QuadraticMed, about bridging the “data use divide” between clinical expertise and data science, so real world data becomes usable evidence rather than noise.Danielle explains why real world data is both more valuable and more difficult than clinical trial data. It reflects broad, diverse patient populations over longer timelines, with richer signals across labs, medications, imaging, pathology, and increasingly digital sources like wearables. But it’s also incomplete, inconsistent, siloed, and collected through real clinical judgement rather than strict protocols.A core message is that tools don’t replace domain expertise. Danielle shares how data processing without medical context can silently change the meaning of clinical variables, producing flawed conclusions even when the analytics look “correct”. Trustworthy outcomes require the right clinical question, appropriate comparisons, careful handling of missingness, and validation against biological reality.They also unpack what’s real versus hype in healthcare AI. GenAI is already helping with documentation and summarisation, but the bigger value is using RWE at scale to personalise treatment, detect risk earlier, and improve care efficiency. The main constraint is rarely the model. It’s data quality, governance, and cross functional communication.Topics CoveredRWE vs clinical trial dataWhy real world data is messy but essentialDomain expertise and clinical validationData quality, missingness, and contradictionsThe real bottleneck: workflow + communicationWhat GenAI can and can’t do todayRegulation, privacy, and trustMeasuring success in pharma and healthcare systemsEularis 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.If this episode described your situation, send me a LinkedIn DM starting with ‘SENSECHECK’ and two things: the question you’re trying to answer internally, and what’s currently in flight. I’ll reply with what I’d need to see to turn that activity into a defensible plan, and the next step.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
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  E218: How astrophysics methods used to study dark matter are now being applied to model cancer biology 19.05.2026 31мин

  Some of the most powerful breakthroughs happen when methods built for one discipline get turned on another. In this episode, Dr Andree Bates interviews Dr Irina Babina, CEO of Concr, on how computational techniques originally developed in astrophysics are being applied to oncology, helping predict how individual cancer patients will respond to treatment.Irina shares her journey from genetics and targeted cancer therapy into building applied solutions, driven by a frustration many scientists recognise: good science doesn’t always reach patients fast enough. A pivotal patient experience reinforced her focus on personalised biology, because behind every dataset is a person, and oncology cannot be solved purely through averages.Concr’s approach is built around Bayesian computation and uncertainty-aware modelling. Instead of assuming clean, complete datasets, the system is designed to work with missingness and fragmentation, updating predictions as new evidence comes in. Irina explains how Concr connects mechanistic biological modelling and preclinical drug perturbation data to patient multi-omics, imaging, treatment response, and outcomes data from both clinical trials and real-world settings.A key application is Concr’s patient-level digital twin (“Farsight Twin”), which simulates an individual’s probability of response across therapies, estimates likely benefit, and helps stratify patients earlier in development. Irina shares a use case where Concr supported indication ranking from cell line data, then helped interpret phase 1 signal by estimating which patients benefited from the novel drug versus standard of care, enabling sharper inclusion and expansion planning.Looking ahead, Irina argues we’re moving toward personalised oncology where population-level protocols fade, and decision-making becomes confidence-based, adaptive, and informed by longitudinal monitoring as tumours evolve over time.Topics CoveredApplying astrophysics-inspired methods to cancer biologyBayesian computation and modelling uncertaintyIntegrating multi-omics, imaging, trials, and real-world evidenceTranslational modelling from preclinical to clinical outcomesPatient-level digital twins and therapy response simulationStratification, enrichment, and reducing early-stage uncertaintyPan-cancer modelling to improve rare cancer predictionThe future of personalised oncology and dynamic monitoringEularis 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.If this episode described your situation, send me a LinkedIn DM starting with ‘SENSECHECK’ and two things: the question you’re trying to answer internally, and what’s currently in flight. I’ll reply with what I’d need to see to turn that activity into a defensible plan, and the next step.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