AI Implementation in Life Sciences is not the Hard Part
The starting point for AI in Life Sciences is rarely the technology itself - the foundations of good data, good governance and good people engagement must come first.
One of the biggest lessons I’ve learned in biotech is that technology is rarely the hard part.
When I took on a CIO role 8 years ago, we were preparing for commercialization and the company had accumulated years of technical debt, fragmented systems, and processes that simply wouldn’t scale. Everyone across the business wanted new capabilities, new analytics, new tools, and eventually AI became part of those conversations as well.
But before we could launch all these new capabilities, we had to start with some more foundational transformations.
Before we could talk about advanced analytics, we needed trusted data.
Before we could scale commercial operations, we needed systems that worked consistently and together.
Before we could become SOX compliant, we needed governance and IT controls.
And before IT could accomplish any of these things, it had to earn the trust of the business.
Fast forward a few years later, our mission was built around using human data scientifically to improve pre-clinical drug development. AI wasn’t a future discussion—it was part of the platform from the beginning. But the strategy was the same.
The AI was only as good as the underlying data, processes, controls, and scientific rigor behind it.
The more time I spend working with AI, the more convinced I become that data governance, data quality, and organizational trust are becoming even more important—not less.
AI is incredibly powerful, but it tends to amplify whatever foundation already exists underneath it.
If the foundation is strong, AI can accelerate learning, decision-making, and productivity. But if not, you end up spiraling down a rabbit hole wasting time and energy.
I’ve learned that building trust, whether it’s with the market or with internal stakeholders requires small wins, investment into relationships and a well thought out sequence of goals that the team buys into.
As more biotech companies begin exploring AI, I think the biggest opportunity for technology leaders isn’t implementing the newest tool. It’s helping organizations build the data, governance, and trust required to use these technologies responsibly and effectively.
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