AI For Pharma Growth

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 CoveredWhy clinical trial economics are becoming unsustainablePatient heterogeneity and why disease definitions break trialsFinding “pockets” of responders within small datasetsTrial enrichment and substudies that reveal a drug’s advantageWhy pharma adoption can be slow, even when failures are constantRegulatory interest, guidelines, and sponsor risk aversionLarge language models vs mathematically augmented AI approachesSpeed as the biggest economic lever in trialsPractical examples across depression, schizophrenia, oncology, and beyondWhat clinical trials could look like in five years with AI-driven insightThis 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

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 valueCompetitor benchmarking, where companies copy use cases that do not fit their contextTechnology first strategy, where tools are bought before priorities are definedFrom 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 CoveredWhy pharma AI impact often disappoints despite powerful toolsThe “technical obsession trap” and the AI strategy blind spotInnovation Theatre, competitor benchmarking, and technology first mistakesWhat a pharma AI strategic blueprint must includeGovernance as a foundation for scale and regulatory trustLeadership, culture, and adoption as the real differentiatorsFinancial modelling and prioritisation based on ROI and revenue impactOrganisational alignment across the full pharma value chainChoosing the right advisory partner and avoiding generic frameworksWhy strategy must come before technology to build durable advantageAI 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

Healthcare accessibility is still being held hostage by phone queues, missed calls, and clunky portals. In this episode, Dr Andree Bates sits down with Josh Taylor of TxtSquad to explore why simple, secure texting can remove friction for patients and providers, and how AI can support the conversation without making care feel robotic.Josh explains why SMS works where apps often fail: it is familiar, low effort, and asynchronous. That means patients can reply on their own time, while care teams can manage conversations in a way that fits real clinic workflows instead of forcing everyone into yet another platform.They dig into the pharma and adherence angle too. Josh shares how two-way messaging helps teams move beyond generic reminders and uncover the real reasons people stop taking medication, opening the door to earlier support, smarter interventions, and better outcomes.Finally, Josh outlines where AI adds value inside messaging: triage for common questions, translation, summarisation, and staff copilots. He also highlights the importance of compliance, clear boundaries, and human oversight when dealing with sensitive health information. The future, he suggests, is smarter communication across SMS, voice, and phone-based assistants, designed to meet patients where they already are.Topics CoveredWhy portals and apps struggle with adoption in healthcareSMS as a low-barrier, asynchronous engagement channelBuilding a two-way patient to provider communication loopMedication adherence: understanding drop-off, not just sending remindersPatient assistance programmes and support at scaleClinical trial recruitment and retention using messagingAI in messaging: triage, translation, summarisation, and staff copilotsCompliance considerations and handling sensitive informationWhat comes next: voice agents and deeper integration with phone workflowsAbout the PodcastAI for Pharma Growth is a podcast focused on exploring how artificial intelligence can revolutionise healthcare by addressing disparities and creating equitable systems. Join us as we unpack groundbreaking technologies, real-world applications, and expert insights to inspire a healthier, more equitable future.This show brings together leading experts and changemakers to demystify AI and show how it’s being used to transform healthcare. Whether you're in the medical field, technology sector, or just curious about AI’s role in social good, this podcast offers valuable insights.AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates created to help organisations understand how the use of AI based technologies can easily save them time and grow their brands and business. This show blends deep experience in the sector with demystifying AI for all pharma people, from start up biotech right through to Big Pharma. In this podcast Dr Andree will teach you the tried and true secrets to building a pharma company using AI that anyone can use, at any budget.As the author of many peer-reviewed journals and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI and futuretech to help you to navigate through the, sometimes confusing but, magical world of AI powered tools to grow pharma businesses.This podcast features many experts who have developed powerful AI powered tools that are the secret behind some time saving and supercharged revenue generating businessDr. Andree Bates LinkedIn | Facebook | X

As artificial intelligence becomes increasingly woven into everyday life, the real question isn’t just what AI can do — it’s what it should do for humans. In this episode, Dr Andree Bates interviews Dr Morteza Zihayat of Heisenberg Network to explore Human-Centred AI: designing intelligent systems that prioritise welfare, autonomy, dignity and trust over pure technical capability.Morteza explains how his background in data mining led to a key insight: numbers can be incredibly powerful, but they still “miss the mark” when human values are ignored. That perspective shaped his work in Human-Centred AI and helped inspire Heisenberg Network, a decentralised infrastructure designed to transform fragmented, messy data into high-quality, AI-ready intelligence — in minutes rather than weeks — while dramatically reducing cost.The conversation digs into what human-centred design looks like in practice. Morteza introduces a “ladder of priorities” to manage conflicting values — from safety and laws at the top, through ethics and platform policies, down to individual preferences — and argues that making this ladder public gives people a right to debate and appeal decisions when something feels wrong.Finally, Morteza tackles the unavoidable trade-offs between performance and fairness, sharing a real example from mental health research where reducing model accuracy produced a fairer system that clinicians felt more comfortable using. His key message is urgent: powerful AI is coming either way — making it human-centred is a choice we need to commit to now, with transparency becoming non-negotiable.Topics CoveredWhat Human-Centred AI means in practiceTurning user stories into concrete design checksThe “ladder of priorities” for managing conflicting valuesDesigning systems for welfare, autonomy, dignity and trustPower caps and keeping AI controllablePersonalisation vs wellbeing (and avoiding filter bubbles)Where bias enters: data, models, and feedback loopsDebiasing pipelines and fairness as real-world data shiftsLearning from users without storing raw personal dataExplainability challenges with reasoning modelsWhy transparency is becoming non-negotiableWhen less accuracy can be the more responsible choiceAbout the PodcastAI for Pharma Growth is a podcast focused on exploring how artificial intelligence can revolutionise healthcare by addressing disparities and creating equitable systems. Join us as we unpack groundbreaking technologies, real-world applications, and expert insights to inspire a healthier, more equitable future.This show brings together leading experts and changemakers to demystify AI and show how it’s being used to transform healthcare. Whether you're in the medical field, technology sector, or just curious about AI’s role in social good, this podcast offers valuable insights.AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates created to help organisations understand how the use of AI based technologies can easily save them time and grow their brands and business. This show blends deep experience in the sector with demystifying AI for all pharma people, from start up biotech right through to Big Pharma. In this podcast Dr Andree will teach you the tried and true secrets to building a pharma company using AI that anyone can use, at any budget.As the author of many peer-reviewed journals and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI and futuretech to help you to navigate through the, sometimes confusing but, magical world of AI powered tools to grow pharma businesses.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.Dr. Andree Bates LinkedIn | Facebook | X

Designing proteins that have never existed in nature is no longer sci-fi — it’s becoming a real drug discovery strategy. In this episode, Kashif Sadiq, Founder & CEO of DenovAI Biotech, explains how AI is powering a shift from searching for biologic binders to intentionally designing new proteins from scratch.Kashif shares his journey from studying physics at University of Cambridge into computational biophysics, and how breakthroughs like AlphaFold from DeepMind helped unlock the next frontier: de novo protein design. Instead of hoping evolution has already produced a usable molecule, Kashif describes how modern AI can engineer bespoke proteins for specific functions, including challenging targets where traditional approaches come up short.The conversation dives into the sheer scale of “protein space” and why evolution has only explored a tiny fraction of what’s possible. Kashif outlines how this opens the door to targeting diseases and biological mechanisms that have historically been considered undruggable, especially where flat protein interfaces or complex signalling pathways have made small molecules ineffective.Finally, Kashif explains why combining generative AI with physics-based methods is essential to reduce false positives, improve real-world binding performance, and enable “one-shot design” — where discovery and optimisation become a single integrated process. He also shares what keeps him up at night: clinical trial attrition — and why designing better earlier may be the key to improving success later.Topics CoveredDe novo protein design vs traditional biologics discoveryWhy evolution explored only a tiny fraction of protein space“Programmable biologics” and intentional molecular designAlpha Design and designing proteins from the inverse problemAntibodies, nanobodies, and therapeutic protein engineeringCombining generative AI with physics-based validationReducing false positives in protein binding predictions“One-shot design” and compressing discovery timelinesUndruggable targets, flat interfaces, and intracellular signallingClinical trial attrition and what’s missing at the preclinical stageWhen the first de novo-designed therapeutic could enter trialsAbout 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, no-fluff conversations that demystify AI for biopharma execs — 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, market access, medical affairs, regulatory, insights, sales, marketing, and more.Dr. Andree Bates LinkedIn | Facebook | X