Across dozens of conversations we had with pharma and biotech leaders, spanning R&D, medical affairs, clinical development, market access, digital health, and real-world evidence, one thing has become clear: AI is no longer a curiosity or side project. It’s a business mandate.
Whether during executive roundtables, 1:1 meetings, or informal catch-ups at recent conferences, a consistent pattern emerged: teams are being pushed to move from experimentation to execution. The pressure is on to prove that AI can deliver measurable value at scale and in the real world.
Here's what we’re hearing most often—and where the smartest teams are focusing their energy.
From Curiosity to Business Mandate: AI is Now Being Judged on Performance
AI is no longer in the sandbox. In every conversation, from digital to RWE to clinical teams, leaders are now accountable for ROI. The exploratory phase is over. What matters now is: Will this scale? Will it deliver value? Will it stick?
The WSJ article covering Johnson & Johnson’s AI pivot (“J&J Pivots its AI Strategy”) triggered broad industry reflection. After piloting nearly 900 use cases, J&J retired 90% - not because the technology failed, but because only 10–15% of use cases delivered 85% of the value. Those that stayed focused on embedded, outcome-driven tools: internal knowledge assistants, AI-enhanced sales coaching, and selected discovery tools.
That example mirrors what we’re hearing across the industry. The question is no longer what AI can do, but how to scale and implement it meaningfully. From R&D to medical affairs, teams are being asked: Does it work in the real world? Will people or more frequently healthcare providers use it? And can it show measurable business or clinical impact?
The most grounded teams are now tracking three success metrics: first, can the use case be successfully deployed in real workflows; second, will it see adoption beyond a pilot; and third, does it deliver ROI or clinical value.
AI is no longer something to pilot for its own sake. It’s about outcomes, integration, and impact. The bar has been raised from hype to performance.
This is especially true for clinical AI use cases. Whether it’s surfacing missed diagnoses in rare disease, improving patient journey understanding in oncology, or identifying care gaps in chronic conditions, pharma teams want partners who can deliver outcomes, not just algorithms.
One striking trend: large pharma companies are investing in "Data Strategy" or "RWD Strategy" teams. These teams don’t just aggregate data sources, they're tasked with making sense of an increasingly crowded and chaotic vendor landscape.
Many describe it as data chaos: the explosion of structured, semi-structured, and unstructured sources, from EHRs and labs to registries, PROs, claims, and social data, has made it harder, not easier, to navigate. What once promised insight now risks duplication and waste without clear alignment to business needs. The challenge isn’t lack of data; it’s knowing what data to use for which question.
We hear this especially from medical affairs and HEOR teams trying to define real-world comparators, understand population characteristics, or identify treatment patterns. Without a clear framework, they’re left guessing what sources are fit-for-purpose.
Teams that get this right are building dynamic maps of:
That’s a big mindset shift: from asking "Do we have data?" to "Do we have the right data, for this question, in this population, at this point in time?"
When pharma teams say they "need data," they mean it in service of a goal. No one wants data for data’s sake. They want to:
The problem? Much of what matters is missing from claims or structured EHR data. Patient populations have grown more nuanced. Treatments have grown more specific. And the information that differentiates one patient from another often lives in free-text clinical notes.
To truly understand real-world patient populations, you need to read the story between the lines. For example:
We often explain it this way: structured data gives you the shadow. Narrative data gives you the whole figure – rich with context, nuance, and meaning.
This depth of insight isn’t just academic – it allows teams to target patients more precisely, explain payer decisions more clearly, and surface unmet needs more reliably.
Even a year or two ago, the focus was on surfacing the right patients. Today, the conversation is evolving:
We’re supporting clients in:
These are not abstract problems. They are real barriers to effective drug development, equitable access, and market performance.
The AI conversation in life sciences has matured. We’ve moved from proof-of-concept curiosity to enterprise-level implementation. But the winners in this next phase won’t be those with the flashiest technology. They’ll be the ones who:
If your team is navigating these same challenges—whether around data strategy, unstructured data, or scaling clinical AI—let’s talk. The stakes are high, the tools are ready, and we know what it takes to go from proof-of-concept to practice.