Datacor, Inc., a premier software provider for process industries, has released a series of research reports detailing the state of artificial intelligence (AI) adoption in chemical manufacturing, food and animal nutrition, and engineering. The study, conducted by independent research firm Tech-Clarity, surveyed 274 companies with annual revenues under $300 million. The findings indicate that while mid-market organizations face practical implementation hurdles, those successfully navigating the pilot phase are realizing economic value much faster than industry benchmarks previously suggested.
Research indicates that 60% of chemical companies saw measurable AI value within a single quarter of completing a proof of concept.
In the food and animal nutrition sector, 45% of companies achieved similar rapid ROI.
Two-thirds of companies that made initial AI progress have successfully scaled those gains across other business units.
The primary barriers to adoption are lack of data science skills and data quality, rather than fear or distrust of the technology.
A significant portion of the market is opting to receive AI support through existing software providers rather than building in-house solutions.
The reports suggest a widening competitive gap between "early movers" and those who have not yet initiated AI strategies.
Unlike the broader tech sector, which often focuses on theoretical AI capabilities, process manufacturers are prioritizing disciplined, outcomes-focused investments. The research highlights that the mid-market is moving away from technology for technology’s sake, instead seeking clear business cases. This pragmatic approach is allowing smaller manufacturers to cut through the noise of the generative AI hype cycle and focus on specific operational efficiencies.
"Our customers do not invest in technology for its own sake," said Tom Jackson, president of Datacor. "They need to understand the business value first. This research gives them the data to make informed decisions about where AI fits in their business."
The Tech-Clarity data clarifies that the bottleneck for AI adoption is largely structural. Organizations cited a significant lack of internal knowledge and data science expertise as the leading challenge. Additionally, the quality of legacy data remains a major hurdle, preventing many firms from establishing the reliable baselines required for AI training. However, companies that leverage their existing ERP and software providers appear to be overcoming these obstacles more efficiently, with over 60% of food and nutrition firms taking this integrated approach.
A defining trend identified in the reports is the "lead extension" by early movers. Once a company successfully completes an AI pilot, the institutional knowledge gained allows them to replicate success across different departments—such as supply chain, formulation, and quality control—at an accelerated pace. This suggests that the cost of delay in the process manufacturing sector may result in a permanent loss of competitive positioning as leaders continue to widen the gap.
About Tech-Clarity
Tech-Clarity is an independent research firm dedicated to making the business value of technology clear. The firm analyzes how companies improve innovation, product development, design, engineering, manufacturing, and service performance through the use of digital transformation, best practices, software technology, industrial automation, and IT services.
About Datacor
Datacor is a leading provider of industry-specific solutions that help process manufacturers, chemical distributors, and engineers grow, run, and elevate their business. Backed by deep industry experience and a commitment to continuous improvement, we go beyond software to act as true partners in solving complex process challenges. For those seeking greater visibility and control, Datacor is the trusted source for proven technology and expertise to achieve great outcomes, together.
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