AI's Quiet Takeover: Process Industries See Real Value, Widening Gap

FLORHAM PARK, N.J. – May 05, 2026 – While headlines often focus on artificial intelligence in consumer tech and finance, a quiet but profound revolution is taking place in the foundational sectors of the economy. A new report series reveals that in the disciplined, results-oriented worlds of chemical manufacturing, food production, and engineering, AI is moving beyond hype to deliver tangible value, creating a rapidly widening gap between early adopters and their competitors.

Datacor, Inc., a software provider for process industries, today released its "No Bull, No Hype" research series, conducted by independent firm Tech-Clarity. The study, which surveyed over 274 companies with revenues under $300 million, paints a clear picture: AI adoption is not only happening faster than anticipated, but it is also yielding measurable returns in remarkably short timeframes. The findings suggest that for many, the question is no longer if they should adopt AI, but how quickly they can catch up to those already scaling its advantages.

"Our customers do not invest in technology for its own sake," said Tom Jackson, president of Datacor, in the press release. "They need to understand the business value first. The industries we serve are disciplined and outcomes-focused, and this research gives them the data to cut through the noise and make informed decisions about where AI fits in their business."

The Accelerating Divide

The research underscores a critical competitive dynamic: early movers are not just experimenting, they are extending their lead. Nearly two-thirds of companies that have made meaningful progress with AI have successfully scaled those initial gains to other areas of their business. This ability to compound advantages is creating a formidable barrier for laggards.

In the chemical industry, the stakes are particularly high. The global market for AI in chemicals is projected to explode from $943 million in 2023 to $5.24 billion by 2030. Major players like Covestro are already targeting hundreds of millions in annual savings through digitalization and AI. The Datacor report provides evidence that this trend is not limited to giants. It found that 60% of smaller chemical companies that completed at least one AI proof of concept gained measurable value within a single business quarter.

This rapid return on investment is echoed in the food and animal nutrition sector, where 45% of companies reported similar quarterly gains. Real-world applications are proving transformative. While specific company names are often kept confidential, industry case studies report a Fortune 50 food manufacturer cutting inspection times by 45% using AI-driven tools, and another global producer recovering half a million dollars in weekly productivity losses by eliminating unplanned machine outages with an AI platform.

Overcoming Practical Hurdles, Not Fear

Contrary to common narratives that portray AI adoption as a battle against fear and distrust of the technology, the Tech-Clarity research reveals a more pragmatic set of obstacles. For companies in these process-driven industries, the primary roadblocks are a lack of in-house data science skills, challenges with data quality and accessibility, and simple uncertainty about where to begin.

This data-centric challenge is a recurring theme across industrial sectors. Independent research shows that data quality is a dominant constraint for nearly half of all engineers, who often struggle to manage and interpret the vast amounts of information generated by modern equipment. The talent gap is also a significant hurdle, as successful AI implementation requires a rare blend of deep domain expertise—in chemical processes or food science, for example—and advanced data analytics capabilities.

This is where the role of technology partners becomes critical. The report found a strong correlation between successful AI adoption and reliance on existing, industry-specific software providers. In the food and animal nutrition industry, 62% of companies took this approach, as did 47% in the chemical sector. These firms are finding that leveraging a trusted partner who already understands their business processes and data structures can dramatically lower the barrier to entry and accelerate time-to-value.

The Strategic Role of Specialized Software

The findings highlight a key strategic insight: for small and mid-sized manufacturers, the path to AI is often through their existing Enterprise Resource Planning (ERP) and process control systems. Instead of building a data science team from scratch, companies are turning to vendors that can provide AI capabilities embedded directly into the software they use every day.

The competitive landscape reflects this trend. While technology titans like SAP are embedding AI agents into their broad manufacturing solutions, more specialized providers like Infor are offering AI tools trained on datasets specific to the food and beverage industry. Datacor itself has integrated AI-powered features for predictive pricing, compliance automation, and process simulation into its CHEMCAD and Datacor ERP products, directly addressing the pain points identified in its research.

By offering AI-driven modules for tasks like least-cost formulation in animal feed or predictive maintenance on a chemical production line, these specialized providers are effectively democratizing the technology. They are translating the abstract power of AI into practical tools that solve concrete business problems, enabling companies without a team of PhDs to optimize yields, reduce waste, and enhance quality control. This approach de-risks AI investment and provides a clear, incremental path toward building a more intelligent and resilient operation.