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 Covered

• AI and pharma's emerging trust problem

• Why verified output is not the whole answer

• Human judgement as a scarce resource

• Shadow AI and unsanctioned models

• Audit trails across humans, models and data

• The danger of equating token use with productivity

• Building future expertise in an AI-enabled workforce

• Why trust requires incentives as well as governance

About Eularis
Eularis 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.com

About the Podcast
AI 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

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 Covered

• Why pharma’s AI gold rush may miss key biology

• The principle of incompatibility

• Physics-first drug discovery

• Quantitative complexity theory

• Why explainability matters

• Molecular dynamics and information flow

• Atomic and amino acid participation factors

• Complexity hotspots in molecules

• Static structures versus molecular motion

• Rare disease and data-sparse discovery

About Eularis
Eularis 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.com

Dr. Andree Bates⁠ LinkedIn⁠ |⁠ Facebook⁠ |⁠ X

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 Covered

• Why AI training often fails to hold

• The difference between awareness and capability

• Why generic AI fluency is not enough

• Role-specific AI workflows in pharma

• Skill decay and why 90 days matters

• Cognitive judgement and regulatory risk

• Why confidence can outpace competence

• Shadow AI and unmanaged tool use

• What real AI capability support must include

• The Pharma AI Enablement Institute

The 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 Podcast
AI 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

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 Covered

• Why interim analysis is underused

• Stopping trials early for futility or success

• Protecting blinding and trial integrity

• Secure handling of unblinded data

• What data monitoring committees need to see

• Where AI fits, and where it does not

• Subgrouping and individual treatment effects

• Synthetic data and trial simulation

• Regulatory confidence and audit trails

• The future of continuous trial monitoring

Eularis 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-pharma

About the Podcast

AI 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

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 Covered

• Why AI underperformance is often a capability problem

• The difference between access, policy and real training

• Why confident AI use varies across teams

• AI in judgement-led pharma workflows

• Skill decay and why 90 days matters

• Why high-value AI workflows are often forgotten fastest

• The risk of outdated working knowledge

• Why training is ignition, not maintenance

• The limits of AI champions and internal portals

• Three questions to ask your function this week

Eularis 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-pharma

About the Podcast
AI 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

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 Covered

• Why molecular imaging is still underused

• Turning PET and CT scans into structured data

• Radioligand therapy and patient selection

• Moving beyond eligible vs not eligible

• AI-derived imaging biomarkers

• Clinical validation and regulatory trust

• Imaging data as a competitive moat

• Why prediction matters more than diagnosis

Eularis 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-pharma

About the Podcast

AI 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

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 Covered

• Why QPs are stuck in reactive work

• Batch review, release and document chasing

• The burden of disconnected systems

• Creating a single source of truth

• Automated checks for earlier risk detection

• AI, OCR and automation in quality workflows

• Reducing cognitive burden for QPs

• Why human judgement still matters

• How real-time auditing may evolve

Eularis 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-pharma

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.

Dr. Andree Bates LinkedIn | Facebook | X

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 Covered

• Why “we’re doing AI” is not a board answer

• Activity, demand and value: the difference that matters

• Why business-unit use cases are not strategy

• Use case copying and internal innovation theatre

• The value prioritisation gap

• Decision rights between pilot and production

• Data ownership and access conflicts

• Incentives, adoption and rational resistance

• What finance needs to see before funding AI

• What a real board-level AI answer sounds like

Eularis 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-pharma

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

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 Covered

• Moving from genetics and clinical medicine into venture capital

• Lessons from RNA therapeutics and translational medicine

• Why target genetics matters in drug development

• What investors look for beyond the science

• Why the right team and culture are critical

• Platform companies vs single-asset thinking

• Where AI can and cannot compress drug development

• Agentic AI across pharma and healthcare workflows

• Founder mistakes when pitching healthcare investors

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.

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 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

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 Covered

• Why traditional deal scouting is too slow

• The limits of relationship-led deal flow

• How AI supports asset search and evaluation

• Matching biotech assets with pharma and VC priorities

• Why structured and validated data matters

• AI use cases beyond drug discovery

• Narrowing thousands of assets into focused shortlists

• Failure modes of general-purpose AI in BD

• How BD teams may change over the next few years

• Why human judgement still matters in due diligence

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.

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 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

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 Covered

• Why AI governance is more than frameworks and policies

• The visible 20% vs the operational 80%

• What auditors actually want to reconstruct

• GxP expectations for AI systems

• Validation, traceability and change control

• Human oversight and decision accountability

• Why governance must include how people use AI

• Vendor selection and audit-ready AI

• Why trust by design could become competitive advantage

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.

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 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.

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