The AI Efficiency Trap: Why Faster Tasks Alone Won’t Transform Life Sciences

Over the past few years, life sciences companies have found hundreds of ways to use generative artificial intelligence (AI). Although teams are able to complete individual tasks faster, if the work still moves through the same disconnected processes, the overall workflow is not meaningfully transformed.

This AI efficiency trap risks confusing faster work with fundamentally better ways of working and this opportunity is enormous. McKinsey has estimated that generative AI could unlock between $60 billion and $110 billion in annual economic value across pharmaceutical and medical products.¹ Yet research also points to this challenge: while pilots have proliferated, capturing transformational business value requires organizations to rethink how AI is scaled, not simply deploy more use cases.

The next phase of AI in life sciences should be less about adding AI to individual tasks and more about redesigning how work, intelligence and decisions move across the organization.

Moving From Faster Tasks to Better Workflows

Consider what is happening across a typical commercialization organization, with separate teams tackling their own tasks in isolation. Insights teams use AI to synthesize research. Strategy teams use it to develop audience insights. Marketing teams use it to accelerate content. Medical teams use it to interrogate scientific information. Field teams use it to prepare for HCP conversations. Analytics teams use it to evaluate performance.

Each function becomes more efficient, but what happens between those functions? If insights are still handed from one team to another, information still needs to be recreated, approved knowledge remains difficult to access and learnings from activation rarely make their way back into planning, then AI has accelerated the functions without necessarily improving the system connecting them.

The next unit of AI transformation should be the workflow, not the individual tasks themselves. Boston Consulting Group describes a similar evolution in its vision for the “AI-first biopharma company.”² Most biopharma companies are experimenting with AI, and some are beginning to scale it, but relatively few have reached the point where AI acts as something closer to an organizational “central nervous system.” As we all rolled out the first wave of enterprise AI, we asked, “where can AI help someone do this task faster?” For the next phase of transformation, we need to ask, “How should this entire process work differently now?”

From Disconnected Tasks to Connected Intelligence

Imagine a traditional commercialization process, where insights are handed over to inform strategy, which can then be used to guide content development, review and activation, followed by measurement.

Context can be lost at almost every handoff, but a more connected workflow would keep relevant evidence and insights available as work moves between teams. An emerging patient question identified through search or other audience signals could inform strategy and guide content development, with approved claims, references and medical knowledge available from the outset to support development and review. Field insights and performance data could then feed back into planning, helping teams refine future experiences and build a system that learns across the process.

Patient access offers another example of how this could work, as coverage questions, affordability concerns, onboarding difficulties and adherence challenges can reveal what patients are struggling to understand or accomplish. With appropriate governance and privacy protections, these insights could inform patient education, HCP engagement, support services and experience design across functions. Beyond answering individual access questions, the opportunity is to learn from recurring concerns and use that understanding to improve the information and support patients receive.

How Agentic AI Raises the Stakes

Whereas generative AI helped people create things, agentic AI allows AI to coordinate things: maintaining context, retrieving information, interacting with systems and executing defined components of multistep workflows.

McKinsey argues that agentic AI could help shift AI's role from a standalone tool toward something closer to a “coworker,” enabling organizations to rethink workflows end to end.³ Its analysis of 270 life sciences workflows found that 75% to 85% of pharmaceutical workflows contain tasks that could potentially be augmented or automated by AI agents.

That doesn't mean 85% of pharmaceutical work should suddenly run autonomously. Instead, as AI becomes more connected, human oversight becomes more important and should be used carefully to preserve sound decision-making.

In January 2026, the FDA and European Medicines Agency (EMA) published joint principles for good AI practice in drug development. While these principles are not commercial marketing rules, they offer useful guidance through their emphasis on human-centric design, a clearly defined context of use, risk-based oversight, multidisciplinary expertise, data governance, performance assessment and lifecycle management.⁴ These principles also highlight the need for more deliberate governance and clearly defined human responsibilities as enterprise AI becomes more capable and connected, without assuming that greater connectivity requires greater autonomy.

Understanding Where Work Needs to Improve

For leaders, the next step is to turn this ambition into a practical plan, starting with one priority workflow and a clear definition of success. Bring together the teams responsible for delivering it, agree who owns the outcome and establish a baseline against which progress can be measured. Alongside time saved, assess whether the changes reduce rework, improve quality and help teams respond more effectively to patient and HCP needs.

Sustaining that progress will require investment in people as well as technology, with training, support and incentives that help teams adopt new ways of working. Leaders should use the results to decide what to refine, what to scale and what to stop, building confidence through demonstrated value. For life sciences, that value should ultimately be judged by how effectively organizations help healthcare professionals make informed decisions and patients access the information and support they need.

Kristin Ryan is executive vice president of AI transformation & acceleration at Inizio Evoke.

References

1. McKinsey & Company. Scaling gen AI in the life sciences industry. January 2025. Accessed November 4, 2026. https://www.mckinsey.com/industries/life-sciences/our-insights/scaling-gen-ai-in-the-life-sciences-industry
2. Afkhami A, Chandran P, D’Avanzo I, et al. The AI-First Biopharma Company. Boston Consulting Group. January 2, 2026. Accessed November 4, 2026. https://www.bcg.com/publications/2026/scaling-ai-in-the-biopharmaceutical-industry
3. McKinsey & Company. Reimagining life science enterprises with agentic AI. September 8, 2025. Accessed November 4, 2026. https://www.mckinsey.com/industries/life-sciences/our-insights/reimagining-life-science-enterprises-with-agentic-ai
4. US Food and Drug Administration, European Medicines Agency. Guiding Principles of Good AI Practice in Drug Development. January 14, 2026. Accessed November 4, 2026. https://www.fda.gov/about-fda/artificial-intelligence-drug-development/guiding-principles-good-ai-practice-drug-development