Pharma's AI is missing the "Why”

Artificial intelligence (AI) is reshaping the pharmaceutical industry, driving advances from drug discovery and clinical trial acceleration to manufacturing optimisation and commercialisation. And it’s easy to see why – AI’s potential is staggering, with its adoption increasingly reflected in the KPIs of every major pharma company. But here’s the problem: for all the hype, many in the industry still don’t fully understand what AI is – or its capabilities.

This gap in understanding has opened the door for noise. Companies eager to capitalise on AI’s popularity overstate their capabilities, AI-washing by slapping the “AI label” onto tools that barely scratch the surface of the technology’s potential. (Think: chatbot wrappers masquerading as groundbreaking solutions.) This dilutes the conversation, making it harder for meaningful advancements to get the recognition they deserve.

So, how can companies developing real, high-impact solutions stand out? By shifting the focus away from the "how" and onto the "why".

Learning from the Recursion case study

Recursion Pharmaceuticals, for example, has mastered the art of staying above the AI hype by keeping their mission front and centre. Their goal is clear: develop a portfolio of clinical-stage assets in oncology and rare diseases. To achieve this, they’ve built an infrastructure that blends automated lab systems with machine learning (ML) capabilities.

But, while their technical “how” is impressive, Recursion’s messaging consistently reinforces their “why”. They aren’t selling AI for AI’s sake – they’re using AI to create novel drugs for patients in need. Their ability to communicate this purpose allows them to stand out in a crowded space, without losing sight of solving real problems – the foundation of pharma’s true mission.

This approach isn’t just about branding, it also impacts adoption. Investors, regulators, and researchers alike are more likely to trust and support AI-driven initiatives that are transparent and backed by purpose. In contrast, the companies that overhype their AI capabilities without a clear end goal often struggle to gain long-term traction.

The 4 "Whys"

For any company developing AI-powered solutions, the “why” should guide every decision. The technical sophistication of your solution (the "how") won’t gain traction unless your "why" resonates with the industry’s pain points, KPIs, and larger mission. To make a real impact, companies need to answer four fundamental questions:

1. Why should a customer care?

• Does your product address a significant pain point or unmet need? AI-powered digital twins, for example, are making a real impact in clinical trial design by reducing patient enrolment numbers, directly tackling pharma’s need to streamline timelines and resources.

2. Why should a customer buy it?

• What measurable value does your solution offer? Whether it’s accelerating trial completion, reducing costs, or improving patient outcomes, the impact should be clear and demonstrable.

3. Why should a customer buy it now?

• How does your offering align with immediate industry priorities? The cost of developing a new drug now exceeds $2 billion on average, and AI has the potential to cut that significantly. Amid rising costs and regulatory pressures, AI tools that improve efficiency and reduce patient burdens are more critical than ever.

4. Why should a customer buy it from you?

• What makes your company uniquely capable of delivering consistent, scalable, and reliable solutions? Whether you’re running a fleet of high-end GPUs or leveraging more humble resources, differentiation is key. More often than not, the answer lies in operational excellence.

Operational excellence: The overlooked differentiator

In AI-driven pharma, operational excellence is what separates a promising idea from an industry-changing solution. Operational excellence isn’t just about having flashy tech; it’s about execution. For AI to gain broader adoption in pharma, particularly in process-driven areas like clinical development, manufacturing, and commercialisation, operational excellence will become the ultimate differentiator.

Take digital twins, for example. While these models hold immense promise for optimising clinical trials, their success depends entirely on execution. The ability to implement them effectively at scale – while meeting stringent regulatory requirements – – is ultimately what will determine their broader adoption. The companies that master this level of execution will be the ones driving real change.

Regulators are already taking notice. The FDA has begun engaging with AI-driven clinical trial methodologies, including digital twins, as part of its efforts to modernise drug development. Companies that proactively demonstrate compliance and rigorous validation of their AI models will have a significant advantage, as these technologies become more embedded in the regulatory landscape.

AI’s future in pharma depends on the “Why”

The success of AI in pharma won’t be determined by the most complex algorithms or the flashiest technology – it will be determined by purpose and execution. Whether developing drug discovery tools, clinical trial solutions, or manufacturing optimisations, the companies that align their innovations with a clear “why”, and demonstrate operational excellence, will rise above the noise.

AI is already reshaping drug development, and its influence will only grow. The real question isn’t whether AI will change pharma – it’s who will drive that change, and why.