AI Expert Urges Pharma to Adopt AI or Face Obsolescence

Published Date : 31/10/2025 

At CPHI Frankfurt, Bruno Fabre emphasized the need for pharmaceutical companies to view AI as a core business capability, not just a tool, to stay competitive. 

Pharmaceutical companies must fundamentally rethink their approach to artificial intelligence (AI) or risk being overtaken by AI-native competitors, according to a presentation at CPHI Frankfurt 2025. AI expert Bruno Fabre told attendees that the technology is growing at a double exponential rate and represents a competitive threat rather than simply a business tool.

Fabre, who has worked in AI since 1991, including a stint at IBM, pointed to the financial services sector as a cautionary example. Fintech companies are challenging traditional institutions by building AI into their core business model rather than adding it as an afterthought.

The presentation outlined three key areas where pharmaceutical companies should focus their AI efforts: data organisation, talent development, and digital health twins.

On data, Fabre emphasized the need to properly organize company information to make it machine-readable. He warned that data quality is essential to prevent biased or inaccurate AI models and noted that unique datasets can provide competitive advantages.

For talent development, he advocated against simply hiring external AI specialists. Instead, he recommended developing AI capabilities within existing employees who already understand the business, creating what he called “T-shaped” professionals who combine deep domain knowledge with AI competencies.

Fabre highlighted a significant market shift towards health maintenance rather than disease treatment. He cited compound annual growth rate projections showing AI and robotic process automation in healthcare growing at 18 to 44 percent, compared with six to 7.7 percent for traditional pharmaceutical activities.

He used Apple as an example of a company building “health twins” – digital copies of individuals’ health conditions based on data collected from devices. According to the presentation, Apple has accumulated approximately one gigabyte of health data per user over more than a decade.

Fabre said, “AI is not a tool. It’s your competitor. It’s building very quick, quicker than you can ever imagine.” He warned that new AI-powered companies could outpace traditional organizations with innovative, competitive, scalable models based on recurring revenue.

The presentation stressed four key considerations for AI implementation: data quality, explainability, compliance, and ethics. On ethics, Fabre urged companies to establish guidelines proactively rather than waiting for regulations. He posed three strategic questions for pharmaceutical executives: whether AI is a side project or the foundation of a new business model, how to capture valuable data, and whether the organization will be a rule-maker or rule-taker in AI ethics.

Fabre concluded, “If you are thinking in an ethical way, if you are incorporating ethics and human values in the development you are doing for your organization, then you are already preparing the new era centered around you, and not centered around technology or rules.” 

Frequently Asked Questions (FAQS): 

Q: Why is AI a competitive threat for pharmaceutical companies?

A: AI is a competitive threat because it is growing at a double exponential rate and can be integrated into core business models, potentially outpacing traditional pharmaceutical activities.

Q: What are the three key areas pharmaceutical companies should focus on for AI implementation?

A: The three key areas are data organization, talent development, and digital health twins.

Q: Why is data quality important in AI implementation?

A: Data quality is crucial to prevent biased or inaccurate AI models and to ensure that unique datasets provide competitive advantages.

Q: What is the significance of 'T-shaped' professionals in AI implementation?

A: T-shaped professionals combine deep domain knowledge with AI competencies, making them valuable for developing AI capabilities within existing employees rather than hiring external specialists.

Q: What are the four key considerations for AI implementation in the pharmaceutical industry?

A: The four key considerations are data quality, explainability, compliance, and ethics. 

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