Insights & Musings

Nobody ever said, ‘bring back the minipreps.’ Yet science still has them—endless drafts, word counts, and spreadsheets on repeat.

A recent MIT report found that 95% of AI pilots fail. The winners won’t be the ones with the biggest models. They’ll be the ones that solve the stitching problem—wiring data, workflows, and expertise into systems that actually work.

In healthcare and biotech, the teams that win clean their data first, then apply AI tuned for science.

What Adobe built for digital transformation applies just as well to life sciences. In this post, I break down the DDOM framework—and how we help biotech, diagnostics, and medtech teams apply it to bring alignment and real results.

Lasers, AI, and promises that outpace infrastructure—sound familiar? In health tech, big visions often race ahead of the systems and data needed to support them. This post breaks down why that happens, what to watch for, and how to build what your story actually promises.

AI is changing how work gets done, but not everyone has had a fair chance to keep up. A new UN report shows women’s jobs are nearly 3x more likely to be displaced. It’s not about skill. It’s about access. And unless we act, the gap will only widen.

Many small businesses aren’t ignoring AI—they’ve dabbled and seen its power. But they’re stuck, overwhelmed by tools that don’t fit and advice that assumes a tech team. In this post, I explore the gap they face, the tension I’ve felt as a founder, and the question I can’t shake: Should I stay focused on life sciences—or is there a way to serve more broadly, starting right in my own backyard?

Most teams using AI are just running prompts. At Savvyn, we’re building agent-driven systems that scale real domain expertise—because in life sciences, that will be the foundation of the future.