Not Every Molecule, Not Every Cross, Not Every AI Pilot Makes It to the Field...

Not Every Molecule, Not Every Cross, Not Every AI Pilot Makes It to the Field
Agriculture R&D runs on a hard truth: most of what you start doesn't make it — and that's the point. In crop protection, we screen tens of thousands of molecules to register a single product. In breeding, we make thousands of crosses to advance a handful of elite lines. Neither pipeline works because of the discovery; both work because of the discipline that comes after it — the stage gates, the multi-environment testing, and the ruthless culling of everything that doesn't earn its place in the field. We are very good at saying no to promising things that won't perform at scale. It's in our scientific DNA.
Most enterprises run AI the exact opposite way. They let a thousand pilots bloom, celebrate the experimentation, generate a flurry of proofs-of-concept — and then wonder why so little ever reaches production, why budgets are spread thin across dozens of half-funded experiments, and why "we're doing AI everywhere" somehow hasn't moved the business. The hard part of innovation was never starting. It's choosing what to kill.
At Syngenta, we stopped treating our AI portfolio differently from our science and applied the logic we already trust: broad exploration up front, then real selection pressure toward the few "lighthouse" use cases that demonstrably move the business — the ones worth concentrating talent, data, and capital behind until they reach full scale. We went from sprawling, scattered experimentation to a focused set of high-impact initiatives, and the shift was less about technology than about organizational courage: the willingness to defund good ideas in service of great ones.
In this session, I'll walk through how we made that transition as a global agricultural enterprise — the selection criteria we use to decide what advances, the stage-gate discipline borrowed directly from our crop development model, the way we define and protect a lighthouse so it actually reaches scale, and the hard culling decisions (and internal resistance) that came with it. I'll be candid about what worked, what we got wrong, and what I'd do differently.
You'll leave with a practical framework you can apply to your own AI portfolio on Monday morning — a way to decide which pilots deserve acreage, which to let go, and how to build the organizational muscle to tell the difference.
Three takeaways:

A stage-gate framework for deciding which AI pilots advance to scale and which get culled — before they quietly drain budget and attention.
Why broad experimentation is the easy half of innovation, and how to build the discipline for the hard half: concentration and the courage to defund.
How the screen-test-advance logic that governs both our chemistry and genetics pipelines translates directly to enterprise AI portfolio management.