#data #biology #biotech #food #ai #ML This is such an important issue! First, if you’re not already subscribed to Jesse's newsletter Jesse Johnson, you should be! It’s packed with extremely interesting and educational content, especially for #data and #R&D managers and users (which means all of us, right?). This specific post is incredibly relevant in our company too, especially with teams that aren't traditionally data-oriented. Our solution in my data team along with Omer Genkin? We co-create internal tools with our users, leveraging ML and other cutting-edge techniques to solve real day-to-day challenges. Our mission is to make these tools as intuitive and accessible as possible – with the simplest UI/UX (if any at all) and ensuring the insights are crystal clear. It's all about #empowering everyone, not just the data scientists, to harness the power of #data and #AI.
Everyone's busy predicting how AI tools will change the way we work. But in biotech, the biggest change will continue to be not a "how" but a "who". While AI/ML has been creeping into biotech for decades, it has taken on a different nature in the last 5-10 years, even before ChatGPT. But it's not about the tools. It's that bench scientists are no longer managing experiments end-to-end from bench through analysis. Instead, they're increasingly handing off the data to a computational biologist or data scientist for the last steps. And while this may sound simple, it requires bench scientists to communicate a huge amount of context that they don't even realize they have. Plus it requires new ways of coordinating work and organizing projects. AI/ML is just the seed of this much deeper organizational transformation. The latest generation of AI models could begin to shift some of this work back to bench scientists. Or it could accelerate the existing trend. But either way, it's the organizational reconfiguration, not the technology, that will determine the future of biotech.
