What 250 + chemical companies actually report about AI in the plant & the lab
Independent research commissioned by Datacor and conducted by Tech- Clarity surveyed chemical manufacturers and distributors to find out where the industry is with AI, what is working, and what is getting in the way. The findings challenge several assumptions
The chemical industry has never been one to chase technology trends. When a process deviation can mean a failed batch, a compliance issue or a safety incident, adopting anything unproven carries real risk.
But caution is not the same as inaction. When Tech-Clarity surveyed over 250 process industry companies, with in-depth analysis of the chemicals sector specifically, a clear picture emerged: the industry is already moving on AI and the companies furthest along are finding value in areas that matter most to formulators and process professionals.
Formulation & process optimisation lead the R & D agenda
The findings most relevant to speciality chemical operations sit in the R & D data. 60 % of companies with AI plans for R & D are targeting recipe optimisation; 54 % are focused on simulating and predicting process outcomes; and the same percentage are working on optimising recipes based on variable ingredients and identifying existing recipes and processes for reuse.
These are not abstract ambitions. Survey respondents described goals such as finding lower-cost, lowertoxicity synthesis routes for existing products and designing solvents and coatings that maintain performance with reduced environmental impact. For companies managing hundreds or thousands of formulations across multiple product
Figure 1 lines, the ability to search, compare and adapt existing recipes rather than developing from scratch represents a significant efficiency gain.
About half of respondents are also looking to AI to augment technical data: pulling together supplier specifications, process parameters and lab results that currently sit in separate systems. 40 % plan to use AI to find and summarise external technical data such as standards and regulatory specifications. For R & D teams that spend significant time locating information before they can act on it, this is practical, not futuristic.
Inside the plant, quality dominates
Among companies with productionfocused AI plans, 71 % identified quality as a primary target. That figure outranks cost( 57 %), yield( 43 %) and uptime( 43 %) by a wide margin. Respondents described applications such as correlating temperature, pressure and raw material variability to predict reaction drift and identifying root causes of quality deviations in complex formulations.
Error-proofing and support for lean and continuous improvement initiatives each appear at 43 %, reinforcing the finding that the industry sees AI as a tool for tightening existing processes rather than replacing human judgement. Only about one in five companies is currently looking to AI to make autonomous decisions. The strong preference for keeping a human in the loop reflects the safety-critical nature of chemical manufacturing.
Pilots are delivering results faster than expected
The assumption that AI requires years of investment before producing returns does not hold in this data. Among companies that completed at least one proof of concept, 83 % report gaining value in under a year, while 60 % achieved measurable results within a single business quarter.
26 SPECIALITY CHEMICALS MAGAZINE ESTABLISHED 1981