In 2026, the competitive advantage will come from AI that actually runs end-to-end. From data to decision to execution.
In 2026, the competitive advantage will come from AI that actually runs end-to-end. From data to decision to execution.
I think this is the year AI in process chemistry shifts from something you “ask” to something you can operate. Less analysis in isolation. More systems wired into how work actually gets done.
Here are the three shifts I’m watching:
1) Interoperability becomes the real moat
Most teams aren’t blocked by a lack of algorithms - they’re blocked by brittle data flows. When instruments, analytics, ELNs/LIMS, and optimization tools don’t connect cleanly, you end up with workarounds: manual exports, spreadsheets, and one-off scripts. The labs that move fastest in 2026 will be the ones that treat connectivity as a first-class project - because once the underlying data infrastructure is robust, every experiment becomes a usable learning signal, with traceability built in.
2) Closed-loop optimization moves from “pilot” to “default”
More teams will run closed-loop campaigns as a standard way of working - not as a special project. In practice, the bottleneck is often the analytics: turning chromatograms/spectra into structured, trusted feedback quickly and consistently. I expect tighter integration here: machine-readable outputs, standardized impurity/peak reporting, more automated assignment with QC checks, anomaly detection, and fast capture straight into the dataset.
3) Agent workflows quietly remove a ton of busywork
LLMs get useful when they’re connected to tools, not when they’re used for generic chat. In practice, that means automating the glue work: structuring messy experiment logs, automatically checking constraints, generating worklists, summarizing runs, extracting insights, and packaging results in a way teams can actually act on. It shifts the bottleneck away from “who can code this?” and towards “what’s the goal?” - which is exactly what you want if you’re trying to scale output without scaling headcount.
If these three land together, the impact is big: AI won’t just suggest experiments. It’ll make process development faster, cleaner, and more repeatable — because trusted, traceable decisions will come with validated execution built in.
Curious about these developments?
Follow us at @ReactWise as we’re tackling these challenges with our partners, and turning them into a platform that connects data to make decisions and execute them.
Let’s make chemistry smarter, together.
