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Only 20% of life sciences organizations are consistently deploying AI at scale and achieving meaningful, measurable value, according to Bain & Company’s latest research, The Human Imperative: Scaling AI Across Life Sciences, conducted in partnership with Mayfield Fund. The findings suggest that organizations creating the most value from AI are not necessarily deploying more technology, they are redesigning workflows, modernizing operating models, and preparing their workforce for new ways of working.
While AI investment continues to accelerate across the industry, many organizations remain stuck in experimentation mode. Bain’s research finds that successful AI leaders distinguish themselves through a small number of transformative business bets, a willingness to fundamentally rethink how work gets done, and an organizational model designed to accelerate adoption at scale.
Transformative ambitions drive disproportionate value
Most life sciences organizations do not lack AI activity. What many lack is focus. Bain’s research finds that organizations successfully scaling AI concentrate resources on a small number of transformative initiatives tied directly to enterprise priorities, rather than spreading investment across dozens of disconnected use cases. Instead of asking where AI can be deployed, leading companies are asking where AI can fundamentally reshape competitiveness, productivity, growth, and customer outcomes.
These organizations take a future-back approach to strategy, starting with a clear vision of the AI-enabled enterprise they want to become and then building the roadmap required to get there. They are also more likely to embed AI metrics into core business reporting and decision-making, helping leaders measure enterprise value rather than technology activity.
Workflow redesign separates leaders from followers
Bain’s research suggests that the greatest barrier to AI value creation is often not technology, but legacy ways of working. Many organizations continue to layer AI tools onto existing processes in pursuit of efficiency gains. While this approach can generate incremental improvements, it rarely delivers transformational outcomes. Organizations realizing meaningful value from AI take a different path, redesigning workflows from the desired outcome back and determining where AI can automate, augment, or accelerate decision-making.
This shift requires new operating models built around cross-functional collaboration, rapid iteration, and clear end-to-end accountability. It also demands a more deliberate approach to human involvement, distinguishing between situations where direct human decision-making is required and those where oversight is sufficient.
Workforce transformation is becoming a critical differentiator
Redesigning work inevitably changes the workforce required to deliver it.
Successful AI scalers are significantly more likely to involve HR early in workforce planning, engage in future-back talent strategies, and invest in capability development to support AI-enabled ways of working. As organizations adopt more AI-driven workflows, new roles and capabilities are emerging that cut across traditional functional boundaries.
At the same time, executives identified workforce adoption and behavioral change as the most significant barriers to AI value realization. Bain finds that leading organizations are responding by treating change management as a continuous capability. Visible leadership sponsorship, ongoing communication, workforce development, and trust-building are becoming essential components of successful AI transformations.
For life sciences executives seeking to scale AI successfully, Bain identifies five priorities:
- Focus on a small number of transformative bets tied directly to enterprise priorities.
- Redesign workflows and operating models around outcomes rather than existing processes.
- Build organizational flexibility to address challenges that emerge during implementation.
- Plan workforce requirements future-back and proactively invest in new capabilities.
- Lead the behavioural shift required for AI adoption through continuous communication, workforce development, and visible leadership sponsorship.
As AI capabilities continue to advance rapidly, competitive advantage will increasingly depend not on access to technology itself, but on how effectively organizations redesign work and enable people to operate in new ways.