Pharma races to seize the $250bn AI opportunity
We expect the future of pharma and healthcare to be personalized and digital, with increasingly blurred boundaries between prevention and treatment. Artificial intelligence (AI) is accelerating this convergence of pharma, broader healthcare, technology and consumer products and generates great benefits for each sector. Pharmaceutical companies can lead the way and incorporate AI into new products and services directly, or profit indirectly by using AI to make processes more productive and efficient. The focus of this report is the indirect value from AI and the respective AI use cases.
This report is based on an analysis of more than 200 AI use cases with 25 experts and thought leaders from healthcare, pharma, and technology.
Key findings
- Pharma companies that industrialize AI use cases across their organizations have the potential to double their operating profit
- AI use cases in operations account for 39% of the impact by boosting efficiency on the production, material, and supply chain costs
- R&D accounts for 26% of the impact, followed by commercial at 24%, with AI increasing efficiencies in developing new medicines and opening up new ways of interaction
- Pharma’s enabling functions contribute 11%, with AI increasing the speed and efficiency of supporting processes such as IT, finance, HR, and legal and compliance
- In total, pharma companies could gain an additional $254bn in annual operating profits worldwide by 2030, assuming a high degree of industrialization of AI use cases
AI will generate significant value along the pharma value chain
The use cases analyzed in this report were evaluated for their indirect value contribution to a typical innovative pharmaceutical company with an operating margin of 20%. Each use case was connected to a business function such as operations, research and development (R&D), commercial, and enabling functions.
AI use cases in operations account for 39% of the impact as they affect the greatest cost baseline including production, material, and supply chain costs. R&D accounts for 26%, closely followed by commercial with 24% – at both, AI does not only increase efficiency but also revenues by shaping new medicines and new ways of interaction with the market. Enabling functions contribute another 11% of the potential by driving speed and efficiency in supporting processes such as IT, finance, HR, and legal and compliance.
Overall, pharma companies that industrialize AI use cases completely across their organizations have the potential to double today’s operating profits by boosting revenues and reducing costs. We expect that this industrialization process will begin to be fully realized by companies prioritizing AI by 2030. Our model also takes into account different speeds of AI adoption, with the US leading the way, followed by emerging markets, and Europe.
In total, pharma companies could gain an additional $254bn in operating profits worldwide by 2030, assuming a high degree of industrialization of AI use cases. This additional AI value would include $155bn in the US, $52bn in emerging markets, $33bn in Europe and $14bn in remaining countries. This extrapolation is based on a 5.7% CAGR of the pharmaceutical industry without the effects of AI.
AI global uplift in adoption year (bn US$)
Three steps to overcome challenges and realize AI value
Our observations show that a major share of the industry is already getting started and has agreed on high priority AI use cases. But only very few companies are successful at operationalizing selected use cases at scale. We identified three critical steps that pharma companies should follow in order to realize the full AI potential:
- 1Organize for delivery: Pharmaceutical companies need to assess and build organizational structures to execute their priorities fast. So far, hybrid delivery models with cloud hyperscalers and an implementation partner are delivering faster than internal, IT-led or vendor-led constructs.
- 2Establish incubators: The creation of processes for incubating innovation and the setup of dedicated teams to experiment with the rapidly evolving models and adjacent technologies (e.g., LLM Ops platforms) is separating leaders from followers.
- 3ROI follows adoption: As AI products are delivered, the way that business is executed will fundamentally change with great impact on the workforce. Products only capture value if they are used with responsibility and impact. Top-down programs are required to address concerns and drive adoption.
The AI race is not only a sprint but also marathon. Both quick wins and long-term AI value realization are possible with precise priorities and bold investments. Pharmaceutical companies should recognize the responsibility they hold both for the competitiveness of their offerings and the immense potential that AI can unleash for human health. The integration of AI technologies into all parts of the pharmaceutical value chain, products, and services can re-invent healthcare and its surrounding ecosystem – pharmaceutical companies have the unprecedented opportunity to lead the way.
Hans-Fabian Ahrens, Johannes Dizinger, Jonathan Müller and Christelle Azar co-authored this report.
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