Dubai, UAE — Only one in five life sciences organizations are consistently deploying artificial intelligence at scale and generating meaningful business value, according to new research from Bain & Company and venture capital firm Mayfield Fund.
The study, The Human Imperative: Scaling AI Across Life Sciences, found that while AI investment continues to accelerate across the sector, most companies remain trapped in pilot programs and isolated experiments rather than achieving enterprise-wide transformation.
Researchers concluded that the organizations creating the most value from AI are not necessarily those deploying the most technology. Instead, they are redesigning workflows, modernizing operating models and preparing employees for new ways of working.
Focused ambitions generate stronger returns
According to Bain, companies successfully scaling AI tend to concentrate resources on a small number of high-impact initiatives linked directly to strategic priorities.
Rather than pursuing dozens of disconnected use cases, these organizations identify areas where AI can fundamentally improve competitiveness, productivity, growth and customer outcomes.
The report found that leading adopters are more likely to use a “future-back” approach, defining a vision of the AI-enabled organization they want to become and then building a roadmap to achieve it.
They also increasingly embed AI performance indicators into broader business reporting systems, allowing executives to measure enterprise value rather than technology deployment alone.
Legacy workflows limit AI’s impact
Bain’s research suggests that one of the biggest obstacles to AI value creation is not technological capability but organizational inertia.
Many companies continue to add AI tools to existing processes in pursuit of incremental efficiency gains. While such efforts can improve productivity, Bain argues they rarely deliver transformational results.
Organizations generating significant returns from AI instead redesign workflows from the desired outcome backwards, determining where technology can automate tasks, support decision-making or accelerate operations.
This approach often requires new operating models built around cross-functional collaboration, rapid experimentation and clearer accountability structures.
Workforce transformation becomes competitive advantage
The report highlights workforce readiness as a critical differentiator between successful and unsuccessful AI programs.
Companies that scale AI effectively are more likely to involve human resources teams early, undertake long-term workforce planning and invest in training employees for AI-enabled environments.
At the same time, executives surveyed identified workforce adoption and behavioural resistance as the most significant barriers to realizing AI’s potential.
In response, leading organizations increasingly view change management as a core capability rather than a one-time initiative, emphasizing continuous communication, leadership sponsorship and trust-building.
Five priorities for AI leaders
Bain outlined five actions for life sciences executives seeking to accelerate AI adoption:
- Focus investments on a limited number of transformative initiatives.
- Redesign workflows around outcomes rather than existing processes.
- Build organizational flexibility to adapt during implementation.
- Plan future workforce requirements and invest in new capabilities.
- Lead behavioural change through communication, training and visible executive support.
The findings suggest that as AI technologies become more widely available, competitive advantage will increasingly depend less on access to algorithms and more on how effectively organizations reshape work and enable people to operate alongside new technologies.
