AI For Pharma Growth

Pharma doesn’t have an AI experimentation problem. It has an AI execution, scaling, and ROI justification problem. In this solo episode, Dr Andree Bates names one of the most expensive failure patterns in the industry: pilot purgatory.
A key theme is misdiagnosis. When AI stalls, organisations often blame platforms, data science capability, training, vendor selection, or “resistance to change”. Dr Andree argues these explanations are usually incomplete because they ignore the structural constraints that determine whether AI gets trusted, governed, adopted, and tied to real decisions at scale.
She outlines the core blockers she sees repeatedly: governance ambiguity, unresolved decision rights between global and local teams, data ownership disputes, incentive misalignment across functions, and adoption friction caused by tools that were never designed around real workflows. Treating adoption as a comms issue or solving with yet another pilot simply keeps the constraint untouched.
Finally, Dr Andree explains what breaking out of pilot purgatory actually takes: clear executive ownership of business outcomes (not just technical delivery), defined decision points where AI changes action, governance that accelerates scale rather than blocking it, cross functional stewardship models, and defensible value logic that survives board scrutiny. The organisations pulling ahead aren’t running the most pilots. They’re confronting what’s structurally broken early, then building a strategy and sequencing plan that makes scale inevitable.
Topics Covered
What “pilot purgatory” is and why it’s so costly

The tell tale signs: pilots, duplication, uneven adoption, decorative AI

Why more pilots can make the stall worse

Common misdiagnoses: tools, training, vendors, “resistance”

The real blockers: governance, decision rights, data ownership, incentives

Why adoption is a downstream symptom, not the core problem

Rebuilding defensible ROI logic and board ready financial models

What “good” looks like when organisations break out of purgatory

Why delay compounds scepticism and weakens transformation capacity

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.
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 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 the use of AI-based technologies can save them time and grow their 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.
If you want, I can also pull 3 “quote graphic” alternates that are shorter and sharper from this solo transcript (there are loads of killer lines in here).

Dr. Andree Bates LinkedIn | Facebook | X

Cell therapies have huge potential, but cost, complexity, and centralised manufacturing have kept many of them confined to last-line use. In this episode, Dr Andree Bates speaks with Armon Sharei, Founder and CEO of Portal Biotechnologies, about what happens when AI meets cell engineering, and why point-of-care delivery could make personalised cell programming more practical, scalable, and safer.
Armon explains Portal’s core idea: cells are programmable machines. If you can reliably deliver multiple cargoes into cells, you can instruct new behaviours. Portal’s method briefly “squishes” cells through precision pores to disrupt the membrane so external material can enter, opening the door to complex cell engineering without permanent genome edits.
They explore where AI fits in: modelling cell behaviour. By combining perturbation experiments, rich readouts, and phenotypic screening, AI can help generate “virtual cell models” that suggest which RNA instructions to deliver to drive specific outcomes. The bottleneck is data at the right complexity, because many effects only appear when multiple pathways are changed at once.
A key takeaway is the safety and flexibility of transient RNA reprogramming. Unlike irreversible genetic modification, RNA fades within days, reducing long-term risk and making earlier-line use more realistic. Armon also discusses how point-of-care workflows may be regulated differently, with the machine treated as a device and the cargo as the drug.
Looking ahead, he paints a vision of infusion-centre cell programming: a compact system that takes blood, delivers tailored RNA instructions to immune cells, and returns them within hours, potentially bringing costs closer to mainstream biologics and expanding access.

Topics Covered
Why delivery is the unlock for programmable cell therapies
How Portal’s “cell squishing” delivery works
Using AI to model cells and generate functional programmes
The data bottleneck and why multi-perturbation datasets matter
Phenotypic screening and lab-to-clinic feedback loops
Transient RNA reprogramming vs permanent genetic modification
Regulatory implications of point-of-care engineering
Economics and scalability of point-of-care approaches
Near-term opportunities in oncology and autoimmunity
The future vision for infusion-centre personalised therapies
Eularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.
If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.
The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.Details at eularis.com.
About the Podcast
AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.
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.

Dr. Andree Bates LinkedIn | Facebook | X

Clinical trial site selection is one of the biggest hidden bottlenecks in drug development, and it’s still often driven by legacy relationships, spreadsheets, and habit. In this episode, Dr Andree Bates interviews Simon Arkell, founder of Ryght, Inc, about “AI Site Twins” and why the next era of site selection shifts from institutional memory to predictive, real-time analytics.
Simon explains why the current model produces terrible outcomes at scale: too many activated sites under-enrol, competition at sites is poorly understood, and sponsors often don’t see the failure until timelines have already slipped. He argues this is primarily a site selection problem, because “the easy button” of re-using familiar sites reduces data-driven decision making, even as trials get more complex and patient competition intensifies.
Ryght’s approach is to build AI-powered digital replicas of research sites, creating a unique identifier and a dynamic “twin” profile that continuously improves as new data arrives. Simon walks through how protocols can be matched to sites across countries, then enriched using harmonised public data, competitive trial context, and automated outreach that dramatically increases engagement. He also describes how different AI agents help fill missing information, find the right contacts, and capture context across email, portals, and voice interactions to improve future matching.
The upside is massive: faster feasibility, better site choices, shorter time-to-activation, earlier first-patient-in, and ultimately faster time-to-market. Simon links these operational gains to commercial reality: every month saved can mean earlier revenue, longer effective patent runway, and more lives impacted by getting therapies to patients sooner.
Topics Covered
Why site selection is still a major bottleneck in clinical trials

The true cost of underperforming sites and enrolment failure

What an AI Site Twin is and how it differs from legacy databases

Global protocol-to-site matching and competitive trial context

Data harmonisation from messy public sources

Agent workflows: enrichment, outreach, contact finding, and context capture

Engagement rates and accelerating feasibility timelines

Enrolment curve modelling and predicting site performance

Security, HIPAA/GDPR compliance, and sponsor data integration

Time-to-activation, first-patient-in, and time-to-last-patient-in KPIs

Why “execution speed” and flywheels create a moat in AI applications

Eularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.
If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.
The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.Details at eularis.com.
About the Podcast
AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

For many life sciences teams, the first wave of AI has looked like copilots: smart search, quick answers, and help on demand. Useful, but passive. In this episode, Dr Andree Bates is joined by Parth Khanna, CEO and co-founder of ACTO, to explore what comes next: moving beyond copilots into role-based AI agents that proactively close knowledge gaps, improve field readiness, and operate safely inside regulated environments.
Parth shares his path into life sciences and tech, including founding an early NLP company in 2012 and then building ACTO after speaking with over 100 life science companies about field force effectiveness. Today, ACTO supports tens of thousands of professionals and hundreds of brand launches, and Parth argues the industry is now entering the “agentic era” where the real differentiator is not just model access, but how organisations build context, control, and change management around AI.
A key theme is why generic AI tools often fail inside enterprises. Parth outlines four requirements for agent success: context (role and job-specific personalisation), connection (stitching data sources and agent-to-agent workflows), control (testing, monitoring, observability), and change management (reducing fear and driving adoption). Without these, he says, many copilots and assistants end up underused, with people quietly reverting to old workflows.
Parth then introduces ACTO’s concept of role-based “super agents”, designed around a real job description (for example an MSL). Rather than a disconnected swarm of task bots, a “queen bee” orchestrator agent delegates to worker agents, checks outputs against compliance guardrails, and can be assessed with exams to quantify risk before deployment. This approach, he argues, makes AI both more powerful and safer for regulated field teams.
Finally, the conversation looks ahead. Parth believes the future of work depends on pairing AI capability with distinctly human strengths: strategy, judgement, and human connection. The winners won’t be those who automate the most tasks, but those who redesign roles so humans and agents amplify each other.
Topics Covered
Why copilots are useful but fundamentally passive

The shift from AI that responds to AI that acts

Why generic tools fail: context, connection, control, change management

Adoption reality: why many AI assistants go unused

Quantifying risk and moving from black box to observable AI

Role-based super agents and the “queen bee” orchestrator model

Testing agents with exams before field deployment

Guardrails, compliance, and agent-to-agent quality checks

Human skills AI can’t replace: strategy, judgement, connection

The future of MSL and field excellence in an agentic era

Eularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.
If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.
The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.
Details at eularis.com.

About the Podcast
AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.
This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Many pharma and life science organisations have been investing in AI for years: pilots across commercial, medical, regulatory, and R&D, innovation labs, steering committees, vendor spend, and genuine effort from smart teams. And yet the same story keeps showing up in boardrooms: ROI is unclear, adoption is patchy, and leaders struggle to explain how all the AI activity connects to strategic goals.

In this solo episode, Dr Andree Bates steps into “The Diagnostic Room” to explain why this happens, and why it’s usually not a technology, talent, or speed issue. It’s a diagnosis issue: organisations often haven’t identified what is actually constraining value, so they end up executing hard on the wrong problem.

Dr Andree shares a real example from a mid-sized pharma company that believed its AI programme was failing due to lack of velocity. On the surface, it was a reasonable hypothesis. But a focused diagnostic revealed three hidden structural blockers: unclear decision rights for scaling pilots into production, fragmented data ownership preventing access to the best datasets, and incentive misalignment where the people expected to adopt AI tools were not rewarded for the behaviours those tools required.
She then clarifies what a diagnostic is and is not. A diagnostic is not a strategy, roadmap, vendor shortlist, financial model, or implementation plan. Instead, it provides evidence-based clarity: what’s broken, how you compare to peers, what’s at stake, and what questions have been opened that cannot responsibly be answered in ten days. That clarity creates a shared language for leadership, replacing vague frustration with a precise problem statement.
The organisations pulling ahead are not simply those with the biggest budgets, but those willing to find what’s actually broken before trying to fix it.

Topics Covered
Why AI initiatives can grow without creating measurable ROI

The gap between pilots and a true AI strategy

Misdiagnosis: executing brilliantly on the wrong problem

What a diagnostic sprint is (and what it is not)

Three hidden blockers

Why working groups can’t fix structural AI constraints

What a full strategic AI blueprint includes

Why many AI business cases are untested projections

How to improve board confidence with evidence, governance, and measurement

Why diagnostics create speed by creating shared clarity

Eularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.
If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.
The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.
Details at eularis.com.

About the Podcast
AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.
This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.
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.
Dr. Andree Bates LinkedIn | Facebook | X