Q&A: AI and digital transformation in healthcare & life sciences
September 2025 | SPECIAL REPORT: DIGITAL TRANSFORMATION
Financier Worldwide Magazine
September 2025 Issue
FW discusses artificial intelligence and digital transformation in healthcare & life sciences with Craig Wylie, Ben Enejo, Ben van der Chaff and Robert Albarano at Arthur D. Little, LLC.
FW: Could you provide an overview of the key opportunities and challenges associated with digital transformation in the healthcare and life sciences sectors? How is this process reshaping business models, product and service innovation, and patient engagement in healthcare?
Wylie: The biggest shift in healthcare and life sciences we are seeing is that the sector is finally being forced to think more like consumer businesses. That is especially true in clinical development. For a long time, clinical was mostly about process and data. Companies designed the trial, picked sites and hoped patients would come. But that approach just does not cut it anymore. Patients have more options than ever. They are better informed. They ask questions. And they are making active decisions about their care. Companies cannot expect patients to passively enrol in a study just because it exists. Clinical trial recruitment now has to feel more like a marketing campaign. Companies need to reach the right people, give them a clear reason to care and make it easy for them to say yes.
Enejo: The shift from compliance-focused process to patient-centred design is happening across the system. Patients expect more. They want experiences that are personal, transparent and easy to navigate. Providers and pharmaceutical companies need to work harder to earn trust and engagement. Of course, there are plenty of challenges. Digital tools are everywhere, but they often live in silos. A lot of companies are still running pilots instead of real transformation. And too many incentives are still tied to the old system. But change is coming. Digital transformation is not just about adding apps or collecting more data. It is about rebuilding how we work around the people we are here to serve. The ones who get that will move fast. The ones who do not will be left wondering why their trials are failing.
van der Schaaf: This digital transformation same dynamic is playing out across commercial functions, where digital transformation remains largely underleveraged. While customer expectations are evolving rapidly, most commercial models are still anchored in fragmented sales models, rigid contracting pathways and limited visibility across customer touchpoints. The result is a widening gap between what customers value and how companies show up in the market. The core opportunity is not simply digitalising existing activities – it is rethinking what we offer, how we price it and how we adapt in real time. Omnichannel engagement, intelligent bundling and more flexible deal architectures are all technically feasible today. But commercial teams often lack the infrastructure – or the mandate – to implement them in a way that drives sustainable differentiation. Tools are being deployed, but business model flexibility has not caught up.
Albarano: One of the most underappreciated challenges is the absence of commercial ownership in digital strategy. Transformation efforts are frequently led by enabling functions, with limited accountability for profit and loss impact. Without direct linkage to commercial metrics – such as revenue growth, margin expansion and customer retention – these initiatives drift into pilots that never commercialise. To move forward, organisations need to elevate commercial voice and accountability in transformation governance. The guiding question should be: how will this change how we create, price and deliver value? When that becomes the frame, digital stops being a parallel track and starts becoming core to growth.
“Compliance with evolving standards is where AI steps in. Especially language-aware systems that can actually understand regulatory language, not just search for keywords.”
FW: Which tasks are best suited to AI-driven processes, such as agentic AI? In which areas is it likely to have the most significant impact on patient outcomes?
van der Schaaf: It is easy to talk about artificial intelligence (AI) as if it is one tool, but that really misses the point. There are very different kinds of AI at play in healthcare and life sciences, and each one brings something unique to the table. Machine learning (ML) is great at spotting patterns in complex, noisy data. It is picking up early warning signs, predicting risks and surfacing trends that would be almost impossible to catch by hand. That is already changing how we think about diagnostics, trial design and even population health.
Enejo: Large language models (LLMs) do something different. They make systems easier to use. Instead of clicking through fixed dashboards and predefined reports, users can ask a question and get an answer in plain language. That kind of conversational interface is not just more convenient – it also lets more people engage with data in ways that used to be locked behind technical skills. Then there is agentic AI, which is a bit of a game-changer. These tools do not just respond, they operate on their own, behind the scenes. They can move through workflows, combine inputs from different systems and complete tasks without constant direction. Think of an agent that monitors clinical trial milestones, gathers updated data, drafts a summary and alerts a project manager only when something changes. That is coordination, not just automation.
Albarano: The real power comes when all of these AI-driven processes work together. ML brings the insight, language models handle the interaction and agents put it all into motion. It is making room for deeper thinking, faster action and better outcomes. The same architecture that enables better clinical decisions can – and should – be applied to commercial execution. Each AI modality serves a different function, and the most forward-leaning organisations are learning to match capability to commercial need. ML is already reshaping price-volume forecasting, bid optimisation and promotion-response modelling. It is helping teams detect shifts in customer behaviour before they show up in the numbers – whether that is a hospital reprioritising spend, a wholesaler shifting inventory or a payer signalling resistance to a contracting strategy.
Wylie: Language models are reducing the friction that has long held back commercial analytics. Teams no longer need to be fluent in structured query language or buried in dashboards to make data-informed decisions. In pricing, for example, we are seeing LLMs used to summarise competitive intelligence, translate policy shifts into pricing guardrails or generate ‘what-if’ narratives tailored to specific account archetypes. Where things get more interesting is agentic AI, especially when applied to time-sensitive, multi-party workflows like tenders, rebates or access programme deployment. Imagine a system that can monitor for changes in buyer behaviour or procurement terms, aggregate and evaluate competing offers, auto-draft a counteroffer or trigger escalation logic, and flag only high-impact exceptions for human review. That is no longer theoretical. It is how competitive advantage will be built. The opportunity is not just faster decision making – it is shifting the commercial model from manual orchestration to AI-augmented adaptability, where machines manage the noise and humans focus on leverage. That is where we see the most immediate value creation for commercial leaders willing to pilot real use cases, not just explore the tech.
“It is easy to talk about artificial intelligence as if it is one tool, but that really misses the point. There are very different kinds of AI at play in healthcare and life sciences, and each one brings something unique to the table.”
FW: In these highly regulated sectors, to what extent is compliance with evolving standards a growing focus? How is AI assisting in this area?
Enejo: The compliance landscape in healthcare and life sciences has become overwhelming. Global standards like the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, national regulators with competing priorities, even different agencies within the same country all expect different things. On top of that, companies have their own internal quality systems to manage. Even staying consistent within a single environment is hard. Trying to stay aligned across all of them is nearly impossible without the right support.
Albarano: Compliance with evolving standards is where AI steps in. Especially language-aware systems that can actually understand regulatory language, not just search for keywords. These tools can catch subtle contradictions between procedures and guidance. They help teams spot gaps, align with evolving standards and tailor submissions to different markets without starting from scratch every time.
Wylie: A few numbers show what is at stake. Compliance now accounts for nearly 25 percent of the total cost of bringing a new drug to market, according to a 2023 Tufts Center for the Study of Drug Development study. Around 68 percent of life sciences firms report experiencing regulatory delays caused by misaligned or inconsistent procedures, according to the Wall Street Journal. There are more than 60 active regulatory bodies worldwide overseeing pharmaceuticals, each with its own rules and expectations. Managing this kind of complexity by hand no longer works. AI gives teams the clarity and confidence they need to move faster, stay compliant and avoid costly rework. Compliance failures increasingly impact commercial outcomes. Common issues include offering confidential discounts in markets that require full net price disclosure under local transparency laws or deploying pricing that conflicts with internal governance thresholds.
van der Schaaf: AI can help prevent compliance breakdowns before they occur. Language models can cross-reference launch plans, offer terms and policy language against a shifting regulatory landscape to identify risks early – before contracts are signed or filings are submitted. The real unlock is upstream integration. Embedding compliance into pricing governance, deal review and access planning ensures that it becomes a source of speed and confidence – not a last-minute obstacle.
FW: Could you provide an insight into the impact of AI on the healthcare & life sciences workforce? What steps can companies take to overcome any resistance to change?
Albarano: There has been a lot of conversation about how AI might reshape jobs in healthcare and life sciences. But the real opportunity is not about eliminating roles or cutting costs. It is about freeing people from the procedural grind that slows everything down. Over the years, many parts of the industry, especially pharma, have become tangled in their own processes. Layer after layer of procedures, documentation requirements and internal sign-offs have turned the work into a box-checking exercise. Somewhere along the way, people stopped thinking about why something was being done or what outcome it was meant to deliver. The focus shifted to following the steps and staying compliant, no matter what.
Wylie: You hear people say ‘trust the process’ as if that is always a good thing. But that only makes sense if the process leads to the right outcome. If it does not, then the process is just a crutch. It gives the illusion of control without actually delivering value. AI can help change that. It can handle the repetitive parts, surface insights automatically and keep everything moving without needing constant manual effort. That creates time and space for people to focus on what really matters – not just doing the task, but thinking about whether it is the right task in the first place.
van der Schaaf: The companies that will move forward are the ones that use AI to cut through the noise. They will empower their teams to ask better questions and stay focused on the results, not just the routine. The commercial side of the business has its own version of this grind – manual pricing workflows, repetitive contracting reviews and spreadsheet-driven sales ops cycles that consume enormous time without moving the needle on margin or growth. AI offers a way out, but not if it is treated as a reporting tool. The shift comes when teams let AI shoulder the procedural burden and reserve human time for scenario analysis, trade-off evaluation and real customer decision making.
Enejo: Resistance to AI often stems from misalignment between the ambition and the operating model. Sales teams still compensated on volume will resist AI-driven margin optimisation. Pricing managers accustomed to annual list updates will view continuous pricing recommendations as a threat, not a tool. Overcoming that resistance means linking AI use cases to clear commercial outcomes – like faster bid response times, reduced leakage or higher close rates – and redesigning incentives to reward agility, not routine. The real shift is not automation for its own sake – it is enabling commercial talent to spend less time on mechanics and more time on judgment.
“The real shift is not automation for its own sake – it is enabling commercial talent to spend less time on mechanics and more time on judgment.”
FW: What advice would you offer to healthcare and life sciences companies on identifying and implementing strategies to manage the risks they are likely to face during a digital transformation process?
Wylie: The best way to manage risk in digital transformation is to stop treating it like a tech initiative. The essential transformations are the ones tied to the continued existence of the business. These are the shifts that are not optional. They are driven by a collapse in margins, a broken development model, a wave of disruption or a patient population that simply is not engaging the way it used to. If a company does not change, it will just fall behind and not survive. That is why the first move is not to build a roadmap; it is to define the case for action so clearly that no one can argue with it. The companies that succeed are the ones that can say, without hesitation, this is the change we must make to exist five years from now. When a company frames it that way, priorities come into focus. Distractions fall away and the transformation gains the energy it needs to push through the inevitable resistance.
van der Schaaf: Urgency is compounded by the technology landscape itself. Tools for analytics, AI, automation and data integration are moving fast. What was cutting edge two years ago is now baseline. Companies do not have five years to figure it out. They need to pick the right direction, move with speed and execute completely. Half-measures will get not get them there. Transformation without a clear existential driver will always drift. The effort fades, the teams lose interest and the business stays in place while the world moves on. When the need is undeniable, and the goal is tied to survival, the transformation becomes sharper, faster and far more likely to succeed.
Enejo: From a commercial perspective, the biggest transformation risk is not overreach; it is fragmentation. We see countless examples of pricing pilots, digital sales tools and AI-enabled segmentation engines launched in isolation, without a clear link to how the business actually captures value.
Albarano: Managing risk starts with anchoring transformation to specific commercial pressure points, such as margin erosion, tender losses, pricing leakage and channel conflict. Without that anchoring, teams chase innovation without accountability. The second layer of risk is sequencing. Too many organisations try to do everything at once – omnichannel rollout, pricing model overhaul and customer relationship management rebuild – without staging for integration or adoption. A better approach is to start with one or two value levers, define the minimum tech and process needed to improve them, and build outward from there. The final risk is internal misalignment. If finance is pushing for margin, sales is chasing volume and pricing is optimising list logic, no transformation will hold. The most successful efforts tie digital tools to a unified commercial ambition – measurable, financially grounded and cross-functionally owned.
“The opportunity is not just faster decision making – it is shifting the commercial model from manual orchestration to AI-augmented adaptability, where machines manage the noise and humans focus on leverage.”
FW: Could you provide any examples of digital projects that have significantly transformed healthcare & life sciences operations? What lessons can other companies learn from how these projects were rolled out?
van der Schaaf: One of the great unsolved problems in clinical trial operations is recruitment. Many trials miss their enrolment targets because sites do not recruit patients at the rate they forecast. Everyone knows it, and yet the system keeps running the same playbook. The focus on process over outcomes, the slow manual grind and rigid roles have quietly killed innovation in the space. Life sciences research and development has accepted a kind of defeatist mindset. Trials are often late. Setups routinely take longer than planned. Teams work in silos because that is how things have always been done. But it does not have to be that way. We have seen what happens when companies let go of that thinking.
Enejo: Companies need to be entirely data driven, focused on the outcome of successfully establishing the trial and recruiting as quickly as possible. Use every tool available. Use AI to analyse the protocol and recommend more precise patient profiles. Use advanced analytics to simulate recruitment pathways and site behaviour under different assumptions. Run a collaborative, cross-functional team that refuses to fall into the usual silos. Treat the problem like something that must be solved – which it usually is, given patent deadlines and investor expectations.
Albarano: Digital projects are what real transformation looks like. Not just a shiny dashboard or a long-term roadmap but a focus on outcomes, faster decisions and fewer excuses. The best organisations do not accept delays as normal, they do not let process take priority over results and they do not let complexity become an excuse for standing still. The lesson is simple. Companies do not need a five-year plan to get started. They need a real problem, a team willing to work differently, and the tools to move fast and smart. When that comes together, change happens. A common breakdown we see is not a lack of strategy or tools, but a failure to execute consistently across markets. Take pricing: many companies have solid strategies reflected in their annual operating plan (AOP), but real-world decisions get diluted by inconsistent governance, unclear escalation thresholds and market-level discretion that undermines intended guardrails. Discounts creep, margin leakage becomes normalised and accountability blurs.
Wylie: This disconnect between AOP and field execution is rarely a data problem – it is a structural one. What these situations need is not another dashboard but a disciplined operating model. That means clearly defined deal parameters, pricing councils with authority, and regular commercial reviews that track contribution margin and compliance, not just volume. The lesson is simple: most digital failures are operating model failures in disguise. If the commercial organisation lacks role clarity, aligned incentives and enforcement discipline, no AI layer will make the strategy stick. Start by closing the gap between what is on paper and what actually gets done, then digitalise what works.
FW: Looking ahead, how might the core principles of AI and digital transformation evolve in healthcare and life sciences? Are there any operational scenarios that remain underexplored?
Enejo: The next phase of digital transformation in healthcare and life sciences will not be led by those who perfect process. It will be led by those who are willing to challenge it, converse with it and explore options. LLMs are at the heart of this shift. For the first time, we can have real conversations with our systems and processes. We can ask why something is done a certain way, what else is possible and where the risk might actually be hiding. It is a move from following instructions to exploring ideas. And it is going to change everything. We have seen this dynamic play out in the worst way. One company was operating with every quality metric flashing green. Everything looked great on the dashboard. But the company still received a regulatory warning letter. Why? Because the team had learned how to turn the dashboard green. They were rewarded for compliance, not for quality. That mindset is more common than people think. It is the natural result of systems designed around control, not curiosity. LLMs and AI more broadly are offering a way out. They allow teams to explore, question and surface insights that do not show up in a status report. They make it easier to think, not just act.
Albarano: What is still underexplored is the operational layer. So much attention goes to the front-end breakthroughs around new molecules, such as digital health tools and diagnostic AI. But the real drag on performance often lives in the back office, in trial setup, in regulatory preparation and in supply chain planning. These are the places where AI can quietly deliver enormous gains, if companies are bold enough to look beyond the obvious and talk to their data. The future belongs to those who ask better questions, not just follow better workflows. That is the real promise of AI in this sector: not just smarter systems, but smarter organisations. One of the most overlooked frontiers is how commercial organisations make internal decisions – how they allocate resources, prioritise accounts and plan for demand under uncertainty. These are high-leverage activities that remain surprisingly manual, political or inertia-driven. For example, key account plans are often built with the same inputs year after year, despite major shifts in contracting dynamics, utilisation trends or local policy. Sales coverage models are rarely reoptimised unless performance collapses. Portfolio investment choices – where to push and where to hold – often reflect historical patterns more than real-time insight.
Wylie: AI has a clear role to play in not replacing judgment, but informing it. LLMs can consolidate field intelligence, competitor moves and policy signals into actionable briefings. Agentic tools can surface patterns in deal outcomes or channel performance that would otherwise be missed. ML models can simulate commercial scenarios and suggest resource allocations tied to likely outcomes.
van der Schaaf: Another underutilised application is return on investment assessment on prior spend. Direct-to-consumer campaigns, field events, conference sponsorships and access programmes often account for significant commercial investment – but post-hoc evaluation is rare. AI can help connect these outlays to downstream metrics like lead quality, formulary uptake or net margin contribution, enabling tighter feedback loops and smarter future deployment. This is not about dashboards. It is about enabling commercial teams to ask better questions. It is about their own logic, not just their customer’s needs. Companies that embrace that mindset will outperform not by scaling faster, but by thinking sharper.
Craig Wylie is the US managing partner for Arthur D. Little. With over 40 years of experience in the life sciences industry, and a background in computer science and statistics, he is focused on the use of advanced analytics, and now artificial intelligence, in the product life cycle. He can be contacted by email: +1 (617) 470 9609.
Ben Enejo is a partner at Arthur D. Little with over 15 years of experience in the life sciences industry and consulting. He specialises in pharma development transformation, commercial strategy, and introducing innovative products and services. He has led high-impact projects including clinical trial rescue operations, capability development for major pharmaceutical firms and international expansion strategies for clinical research organisations. He can be contacted by email: enejo.ben@adlittle.com.
Ben van der Schaaf has over 35 years of international finance and management experience, of which 15 have been in the pharmaceutical industry. He has led strategic commercialisation, supported asset transfers, reviewed clinical trial portfolios, worked on post-merger integration, and more across early-stage life sciences start-ups to top biopharmaceutical firms. His expertise also includes facilitating complex industry collaborations. He can be contacted by email: vanderschaaf.ben@adlittle.com.
Robert Albarano is a partner at Arthur D. Little, advising pharmaceutical and medtech clients on pricing strategy, go-to-market execution and transaction support. He applies artificial intelligence to shape launch and access strategies, stress-test commercial models and strengthen asset valuations – bringing strategic clarity to complex, high-impact decisions across the healthcare and life sciences landscape. He can be contacted by email: albarano.robert@adlittle.com.
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