This article and the accompanying slide deck are part of a series exploring how companies in specific industries can adopt the mindset, expertise, and ambition required to win in an AI-first world.
As AI reshapes the biopharmaceutical industry, the unimaginable is becoming tangible: faster discovery, smarter trials, more reliable manufacturing, and new ways of engaging with health care providers and patients.
AI-first biopharma companies are already starting to prove what’s possible. Leaders have shown that they can cut drug early discovery and candidate identification from four or five years to just eight months, accelerate clinical trials by up to 20%, improve manufacturing yields by more than 20%, and boost the efficiency of sales representatives by 20% to 30%. For many companies, these results may seem aspirational but are achievable.
Most biopharma companies are running AI pilots, and some are starting to scale those efforts. But few have embraced an AI-first mindset, treating AI as the organization’s central nervous system. For those that take that bold step, the rewards can be profound: a 5% to 15% revenue uplift and a decisive edge in a competitive industry.
How to Move Toward AI-First
The transformation toward becoming AI-first follows a familiar trajectory: deploy, reshape, and invent.
In the deploy phase, companies start small, using off-the-shelf tools to automate everyday tasks, such as summarizing meetings. These early wins prove that AI can work in practice and build confidence, but they are largely productivity plays.
In the reshape phase, real breakthroughs begin as companies reimagine how work gets done and processes are redrawn. In R&D, trial documentation has required legions of people and months of effort. Today, AI agents can orchestrate the entire workflow, generating documents four times faster and at a fraction of the cost.
In operations, algorithms running virtual experiments can uncover subtle drivers of yield, delivering double-digit gains in productivity and tens of millions of dollars in new revenue. On the commercial side, AI is transforming how companies engage with physicians. Conversational AI agents can now interact with field teams, providing insights and coaching and performing tasks autonomously via integration into CRM, ERP, and other systems. These interactions improve coverage, boost productivity, and help patients access therapies more quickly.
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In the invent phase, companies create entirely new business models. Recursion Pharmaceuticals, for example, is pairing algorithms with robotics to accelerate discovery. Others are experimenting with computer-simulated patients and virtual trials that could one day reduce or even eliminate the need for traditional control groups. In commercial functions, conversational AI platforms are emerging that allow healthcare providers to query complex medical information in real time.
Altogether, these shifts have the potential to deliver double-digit revenue growth while simultaneously reducing costs in R&D, operations, and corporate functions. But beyond the numbers, AI promises to give life-saving therapies to patients faster, more reliably, and at lower cost.
The AI-First Foundations
AI-first companies succeed by embedding AI into the core of how they work. They focus on fewer, higher-value use cases and scale them across the enterprise, rather than scattering resources across pilots. They back AI with long-term capital commitments, dedicating a higher share of revenue to digital and AI initiatives than their peers. They build elite teams with scarce AI skills, while upskilling the broader workforce so business units can own and deploy AI themselves.
These companies follow the 10/20/70 principle: 10% of the challenge is about the algorithms, 20% is about the data and technology, and 70% is about people and processes. Culture, governance, and change management are what bring the models to life.
Without AI-ready data, biopharma companies will struggle to move beyond pilots. Leaders invest in modern data platforms and stewardship to ensure everything from trial protocols to manufacturing records is AI-ready, so that initiatives can scale.
Getting Started
Is your organization on an AI-first path? Leaders can perform a rapid assessment by answering the following questions and defining the specific capabilities and use cases to pursue.
- Leadership and Strategy. Is AI a clear strategic priority with executive sponsorship and visibility at the top?
- Funding and Investment. Do we have defined processes and budgets to fund AI-ready data, talent, and scaling of proven use cases?
- People and Ways of Working. Are teams upskilled and empowered to work with AI, supported by strong change management?
- Data Readiness. Do we have named data owners, integrated datasets, and AI-ready data that’s compliant, clean, and accessible?
- Solutions and Business Value. Have we identified high-impact AI use cases with measurable outcomes and clear ownership?
- Governance and Responsible AI. Is a responsible AI framework in place, backed by governance to ensure safe, ethical deployment?
Biopharma has thrived at the intersection of science, risk, and possibility. AI does not eliminate the risks, but it does change the odds. By weaving AI into discovery, development, manufacturing, and commercialization, the industry can unlock unprecedented value.
