A decade into the “AI revolution” in biotech, massive investments totaling upwards of $50 billion haven’t yet resolved uncertainty about the technology’s true impact on the biotech and pharmaceutical industries. While a new wave of tech-driven biotech leaders like Demis Hassabis and Daphne Koller have set their eyes on reshaping the industry, innovation brings volatility and mindset clashes between tech-centered and science-focused views. Future biotech leaders must bridge tech and industry realities, focus on smart AI use cases and data strategy, and center their approach around AI literacy while balancing human and machine skills.

The AI Revolution of Pharma and Biotech – New Technologies, Old Challenges

Despite breakthroughs like AlphaFold2 and major investments into the technology, in the absence of hard milestones, AI’s promise in biotech remains uncertain. While an ever-increasing number of AI-discovered molecules enteres clinical trials, no AI-developed drug has been approved, and AI-discovered targets and molecules still look very similar to the ones that were found without AI.

With rising investment but few concrete proof points, the industry faces a crucial question: Will AI really pay off, or are we in a bubble that’s about to burst?

Technology Hype, Disillusionment, and the Need for Differentiation

While 2024 saw record investments in AI technology, venture capitalists also began voicing skepticism about generative AI’s maturity and integration into real-world applications. We see the same trend in biotech and pharma, with a recent McKinsey survey showing that only 5% of pharma and MedTech leaders see generative AI as a competitive differentiator. In an age where almost every pharma and biotech company has its own AI department, platform, and partnerships, biotech founders must find ways to differentiate AI assets and teams to establish the impact of the technology beyond hyped claims of an “AI revolution.”

Focus on Use Cases – Clarity on What to Expect from AI

The first wave of AI-native biotechs started with strong claims about the revolutionary potential of AI, leading to large early-stage funding rounds and massive pharma partnerships. Many are now facing the harsher reality of drug development—with clinical failures leading to stock drops, failed pharma partnerships, job cuts, and investor skepticism. Namedropping AI alone isn’t enough anymore—companies need clear use cases to distinguish their AI assets.

Taking a cue from oncologist, author, and entrepreneur Siddhartha Mukherjee, those use cases can be distinguished into:

Productivity (automation, efficiency): AI optimizes existing processes to save time and money, offering a lower-hanging fruit with a more immediate pay-off.
Creativity: AI expands the option space to drive true innovation, representing a higher-hanging fruit with a slower return on investment.

Focus on Data – Is It Good Enough?

AI needs high-quality, multi-dimensional, well-annotated data. While biotech and pharma are inherently “data-heavy” industries, most public biomedical datasets don’t meet this standard. Companies like Genentech, Recursion, and Insitro invest heavily in creating proprietary AI-ready datasets, but such massive data investments are likely not feasible for startups. Nevertheless, having a clear data strategy—access, trustworthiness, validation, processing, and privacy protection (if patient data is involved)—will be key to the credibility of AI applications.

Focus on Mindset – Balancing New Solutions with Old Problems

The rise of tech-driven biotech leaders with solution-focused strategies brings fresh energy but also clashes with traditional industry caution. The "move fast and break things" attitude of tech meets the long, regulated cycles of drug development. Biotech founders should bridge these mindsets, combining tech-driven enthusiasm with pragmatic industry knowledge to avoid overpromising on what technology can deliver while also avoiding the “can’t do” trap. Adopting a problem-centric approach, in which AI is framed as a solution to a specific problem rather than as the perfect hammer for every nail, can help differentiate an AI application in biotech.

Focus on Understanding – AI Literacy as a Key to Success

While the EU AI Act highlights the need for AI literacy, ensuring that employees of companies working with AI understand the technology’s risks and capabilities, only 25% of German companies have an AI strategy, and 79% of employees lack basic AI knowledge and skills. However, AI literacy should extend beyond employees to key stakeholders—including investors. Biotech founders can help increase AI literacy among stakeholders by providing clear and concise communication about the technology and ensuring transparency about the potential shortcomings of current AI models.

Focus on Managing AI – Integrating Human and Machine Intelligence

As AI tools become a central part of our working lives, we’ll have to learn to manage AI tools just as we manage people, requiring good managerial skills and common sense to differentiate tasks at which humans excel from those at which AI excels and integrate them effectively. A good understanding of AI’s current limitations (lack of reasoning, memory, and planning) helps define its best applications. While human intuition and critical thinking remain irreplaceable—especially in leadership and decision-making—AI can enhance these areas by quickly assessing large decision-making spaces. However, over-reliance on AI poses risks, such as reducing critical thinking capacities and oversimplifying complex challenges by iterating biased and vague information.