Key Takeaways
· R&D organizations unlock greater scientific and clinical impact when AI is applied across integrated workstreams instead of in isolated functions.
· Scaling AI is not a technology-only effort. It requires simultaneous redesigning work, workforce, and workbench together.
· Real-world proof exists within Accenture’s own reinvention efforts, where enterprise-driven reinvention has already delivered $1.6B in savings.
· Out come-anchored redesign of your processes allows for true reinvention and delivers more impact than incremental cycle-time optimization.
The pharma industry faces a productivity paradox: artificial intelligence demonstrates transformative potential, particularly in pilots, yet bottom-line impact remains elusive. Prerana Pradhan and Shirley Masand from Accenture confronted this challenge directly, diagnosing an industry trapped in what they call "Thousand Flowers Bloom"- dozens of isolated experiments proving concept without proving scale.
"When you have dozens of isolated pilots, what we find is that AI works in theory, but it's failing to prove that it can scale to its full potential, "
Pradhan explained.
The diagnosis carries urgency: organizations missing this transformation window sacrifice extraordinary value. "If you don't solve for this gap to reinvention, you miss out on about 60% of your productivity gains, " she warned. The path forward requires rethinking not just technology, but the fundamental architecture of work itself.
The Scale Gap Trapping Pharma Innovation
Accenture's research partnership with Wharton University quantifies artificial intelligence's promise with precision. "We've done actually a joint research venture with Wharton, and we find that if you use AI in the right ways, you can free up between 49 to 63% of your operational capacity across pharma's value chain, " Pradhan stated. Yet only a handful of pharma companies have realized these gains at scale. Organizations miss productivity gains because they treat AI as a technology problem rather than an organizational reinvention challenge.
The pattern repeats across pharma value chains: clinical scientists teams can use AI to optimize protocol design within their function, and access historical trial data to improve enrollment strategies. Research scientists enhance molecular design within their functions, and access biomarker intelligence to refine target selection. Each captures localized value while missing exponential gains available through integration. But when these insights remain siloed, organizations forfeit the compound value of precision medicine informing precision trial design.
The presenters traced this failure to a fundamental misunderstanding. Pharma executives commission AI pilots, see promising results, then struggle to scale because they haven't addressed the prerequisite: clarity in desired outcome and then redesigning work processes, evolving workforce capabilities, and modernizing technology platforms. The technology works; the organization doesn't.
Accenture's internal transformation provides the counterfactual. The company's finance function initially pursued cherry-picked use cases- faster reports, better modeling- and achieved nominal gains. Real value emerged only after stepping back to connect SAP systems and data cores, creating real-time access across all business units. This wasn't technology deployment; it was work reinvention. "Over the past year, we've actually achieved about $1.6 billion in cost savings across finance, procurement, and some of our corporate functions, " Pradhan reported, citing productivity improvements of 25 to 35% in HR, payables, and marketing functions, plus contracting lifecycle reductions from 40 to 13 days.
The presenters emphasized this wasn't exceptional execution but rather adherence to a systematic framework. Organizations must start with outcome definition- improved probability of technical success, reduced cycle time, enhanced commercial feasibility- then work backward to redesign processes, evolve roles, and right-size platforms. Without this sequence, AI investments generate pilot success stories and strategic frustration.
Three Pillars Supporting Sustainable Transformation: Work-Workforce-Workbench
The Accenture framework emerged from hard-won lessons across multiple internal functions, each contributing distinct insights. Finance taught the importance of comprehensive data infrastructure over isolated use cases. Human resources demonstrated that optimizing existing processes misses opportunities visible only when starting with customer experience- in their case, employee experience. The result was Amethyst, a Workday-backed digital front door that created an internal culture of "Go to Amethyst" rivaling "Go Google it. "
Legal's contribution centered on right-sizing technology: standardizing contracts before layering automation, and recognizing that large language models aren't the only solution. Robotic process automation and existing tools still have roles. Success comes from matching capability to use case. "Your LLMs are not the only things. You can still use bots, you can still use some of your existing solutions. You just have to find the right cases where you put them together, " Pradhan noted.
These lessons crystallized into the three-pillar model.
"We move away from thinking about individual functions and we think about these value streams. What are the stitched-together capabilities that help us arrive at these more meaningful outcomes?"
Masand explained.
Work reinvention requires designing from outcomes backward, not optimizing forward from current state. HR's employee experience focus revealed entirely new workflows impossible to reach through incremental improvement.
Workforce transformation acknowledges that roles will evolve, some will be eliminated, and new capabilities will emerge. The presenters emphasized this isn't about headcount reduction but value redeployment. Pradhan offered a compelling example: "There was a data analytics organization that was made famous because of the standards, the measures that they're using and the types of modeling that they've done. Once they started bringing AI into the picture, their workflow changed. That team which was famous for building models moved up the value stream and they became more focused on deciphering insights. "
Workbench modernization questions legacy assumptions. Do clinical trial management systems, clinical data management systems, and electronic data capture platforms designed for document workflows support AI-first environments that deliver data-to-insights directly? "Do you now have AI-first capabilities that you then pull forward that goes directly from your data to your insights? And what does that mean in terms of managing the data in a different way or delivering data instead of documents?" Pradhan asked. The answer shapes architectural decisions worth hundreds of millions in platform investments.
Masand emphasized the hierarchy of these three pillars. "Workbench and the sort of tools and the data is almost not the problem. I think it's really thinking about how the workforce changes is sort of like the secret sauce that I think is going to separate the real winners here, " she stated. The presenters' experience suggests technology is nearly commoditized. "It's also just about the culture and mindset in terms of how we approach decision making, having more fluid boundaries, " Masand added. Workforce transformation- cultural mindset, decision-making approaches, fluid organizational boundaries- represents the true competitive differentiator.
Reimaging Clinical Development Architecture
The presenters reimagined clinical development's organizational architecture around four integrated value streams. Portfolio strategy and dynamic optimization serves as the nerve center, synthesizing internal and external signals for pipeline decisions and cross-program optimization. "This is really your nerve center for your pipeline. How are you taking your internal and external signals to make the right decisions, place the right bets?" Masand explained.
Trial design and optimization embodies a powerful principle. "This is our opportunity to define trial design once, but then express that intent everywhere, " Masand stated- a single source of truth propagating consistently across execution activities, eliminating redundancy while maintaining fidelity through amendments and site expansions.
The presenters referenced a Merck example about lean protocol design, "no longer using sort of previous legacy I/E criteria and SoAs. Coming in with a blank slate to really, again, develop that first-time-right protocol to reduce protocol amendments, get faster SSU. "
Patient, provider, and caregiver engagement shifts from transactional to relational, using digital channels while redirecting human time to focused interactions that matter. "This is our opportunity to do that. We do that in a couple of ways. There's of course, using digital communication channels to actually engage with, again, the external population. But there's also freeing up all that time that we now can use to now redirect those things to focused engagements that matter, " Masand noted.
Evidence generation, submission, and access creates a living data engine incorporating real-world-evidence, synthetic control arms, and automated cleaning to fuel rolling submissions and regulatory dossiers. "Bringing in multiple data sources, using real-world-evidence, acknowledging the fact that we could use synthetic control arms, bringing that to have supporting automated data cleaning and then supporting just rolling submissions and regulatory dossier creation, " Masand described.
This architecture breaks down functional silos that create friction when trial design insights should inform portfolio decisions, when real world evidence should shape protocols, and when patient engagement data should drive site selection. The presenters acknowledged this remains "an elegant simplification"- translational medicine, CMC advances, and manufacturing optimization also play crucial roles. But the pattern is clear: leading organizations are moving toward this model.
Accenture's clinical trial accelerator workbench demonstrates real-world viability, scaled to thirty priority studies with nine hundred active users at a live sponsor. The system brings together data sources supporting next-best action recommendations, moving beyond conceptual frameworks to operational reality. "We've seen this scale to about 30 priority studies. It's live with 900 people at a real-life sponsor, " Masand confirmed. The presenters are working with multiple clients on similar implementations while transforming their own operations arm to utilize this new thinking in safety operations, clinical operations, and medical writing.
Building Capability for Continuous Evolution
The presenters closed with pragmatic implementation guidance balancing aspiration with operational reality. "Start where the value is clear and the data quality is high, " Masand advised. "A lot of this is just about building trust in the process. So building some reinvention muscle is going to be super important. " Organizations should acknowledge that building trust matters as much as demonstrating return on investment. "Step one is of course about developing places where you can show value and have ROI, but it's also building that organizational reinvention muscle, getting people used to the change in the way of working, " she explained. This approach builds organizational capability for continuous adaptation as technology, science, and clinical strategy evolve.
But incremental changes won't suffice indefinitely. "Eventually you're going to have to think about some of those big T transformations that Prerana and I had both talked about, " Masand stated.
The question isn't whether to pursue comprehensive reinvention but when and how to sequence the journey. Masand reframed a fundamental question about clinical development optimization. When asked which development phase offers the greatest time reduction opportunity, she redirected attention from cycle time to decision quality.
"There still are pretty significant Phase 2, Phase 3 late-stage failures that many of our senior leaders believe could be avoidable if we just take a little bit of a different approach, " she observed. "I think there's maybe a bigger question, which is how do we get to faster fails. "
This perspective shift captures the transformation's essence. Digital simulation, digital twins, and computational power enable new approaches to the combinatorial optimization problem clinical development represents. "We can use things like digital simulation, use things like digital twins. We could use things like the ever-increasing computational power that we have to, again, really find new ways to reach some of those targets that we've been all after: 2X clinical impact, thinking about half the cost, half the resources, " Masand stated. Success metrics may need recalibration: celebrating intelligent termination decisions rather than viewing them as failures, rewarding rigorous upfront analysis preventing downstream waste rather than heroic execution of flawed programs.
The workforce evolution Pradhan described positions organizations for this future. As AI automates data cleaning and model building, human capability redirects to judgment-intensive work machines can't yet replicate: deciphering insights, building trust with academic partners and clinical sites, and making the complex decisions that determine which molecules reach patients. "There's going to be an increased focus on relationship building, on upfront strategy and really deciphering insights. And I think some of the more transactional operational activities will be slowly consumed or automated by some of the AI solutions, " Pradhan explained. The movement is toward insight-driven work, stakeholder management, and relationship building as transactional activities become automated.
The presenters' message resonates beyond their consulting practice. Accenture's $1.6 billion internal transformation validates the framework's viability. Their client work demonstrates scalability across diverse organizational contexts. The path from pilot proliferation to enterprise value realization is clear: start with outcomes, redesign work comprehensively, evolve workforce capabilities systematically, and right-size technology platforms strategically. Organizations that master this sequence will capture the productivity gains currently trapped in isolated experiments. Those that don't will watch competitors pull away, powered by the 60% of AI value they left unrealized.
In order to get you the highlights of Pharma Clinical Innovation USA 2025 faster, we are using generative AI technology to summarise the transcripts of the sessions. The conference organiser is checking the summary for accuracy. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com
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