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The reason is straightforward: the data needed to start often already exists within enterprise systems. Companies are not building data infrastructure from scratch. They are applying AI to information they already capture through ERP, batch management and quality systems. That existing foundation compresses the timeline considerably.
Companies that have gained early value are also extending it. Almost two-thirds of those with completed initiatives have scaled benefits to new areas of the business, whether by applying the same solution to a different problem, expanding scope to another plant, or opening the solution to a broader team.
The real obstacles are knowledge & data quality, not resistance
One of the more useful findings for anyone building an internal case for AI is that the primary barriers are not fear or distrust. Fewer than a quarter of respondents cite fear of job loss. Only about a third report organisational distrust of AI.
The actual obstacles are more practical. 53 % cite a lack of AI knowledge. 47 % lack data science skills. 43 % say they do not have enough time to dedicate to it. On the data side, 55 % report data quality concerns, and nearly half say their data is spread across disconnected systems.
As Seatex CFO Andrew LeBlanc puts it:“ If you don’ t start with discipline around being precise on data entry, you don’ t have a chance to get value from AI”. 40 % also say their data is difficult to contextualise in ways that make it useful to AI. Interpreting chemical process data requires deep domain understanding, which is why industry-specific knowledge and tools matter.
This distinction matters because knowledge and data quality gaps are
Figure 2
addressable. They require investment in training, time allocation, and data governance. Only 18 % of respondents cite insufficient executive support, suggesting that leadership commitment is largely in place even where capability is not.
Where to start
The research points toward a practical entry sequence. First, target repetitive manual work. 62 % of respondents are looking to AI to automate manual tasks and 58 % want to eliminate manual data entry. These applications carry low risk, produce measurable time savings and build organisational confidence without touching safetycritical processes.
Second, improve how teams find and use data. 67 % of companies are pursuing better data for decisionmaking and 52 % want to improve their ability to locate information. For R & D and quality teams, reducing the time spent searching for specifications, historical batch data or regulatory documents frees capacity for highervalue analytical work.
Third, treat AI readiness as a data discipline exercise. The companies making progress are not necessarily the ones with the most advanced technology. They are the ones that have invested in data quality, consistent data entry practices and connected systems.
The full report includes detailed findings on how companies are enabling AI improvements, where early leaders are scaling value, and the readiness gaps still holding the industry back. Download it at datacor. com / ai-report.
About Datacor
Datacor has served chemical manufacturers and distributors with purpose-built software since 1983, spanning ERP, CRM, formulation management, MES and distribution operations. That deep domain expertise is now being applied to embed AI directly into the solutions chemical companies already rely on, so customers can start capturing value without starting from scratch. To learn more, visit datacor. com.
About the research
Tech-Clarity surveyed 274 process industry companies and further analysed 124 participants in the chemicals industry. The full methodology, including respondent demographics, is available in the complete report. ●
MAY / JUN 2026 SPECCHEMONLINE. COM
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