How is process manufacturing taking advantage of the AI opportunity? We analyzed over 250 companies in the chemical, food, animal nutrition, and engineering industries to understand their plans and progress. We found broad plans, varied readiness, and a small number of companies who have made substantial progress beyond proofs of concept and pilots.
Please see a summary of the chemical report, below. You can access all three reports, AI in the Chemicals Industry, AI in Food and Animal Nutrition, and AI for Engineering in addition to a related webinar here.
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Table of Contents
- The Business Case for AI
- Goals for AI
- AI Focus Areas
- Improving Production with AI
- Improving R&D with AI
- Improving Overall Business Performance
- Enabling AI Improvements
- Progress with AI
- Early Leaders Gaining Value
- Early Leaders Scaling Benefits
- Time to Value with AI
- Organizational Readiness
- Data Readiness
- AI Adoption Challenges
- Business Impact of AI Challenges
- Seeking Support with AI
- Recommendations and Next Steps
- About the Research
- About the Author
The Business Case for AI
AI Has Arrived
Artificial Intelligence (AI) has matured beyond specialized applications. In its many forms, AI is changing how companies operate, helping them generate new insights, predict outcomes, streamline work, optimize decisions, and manage by exception. It also frees employees from repetitive, low-value tasks, such as manually producing documents like certificates of analysis, allowing companies to reinvest employees’ time to drive innovation and offer white-glove customer service.
Chemical Companies Don’t Do Hype
Chemical manufacturers and distributors approach AI the way they approach any technology: practically. They are experienced at leveraging technology to optimize performance and are always open to doing things better, faster, or cheaper. They aren’t enamored with technology for its own sake; rather, they need to understand the business value before they invest. And for now, they’re still figuring AI out. Seatex CFO Andrew LeBlanc shares, “We believe AI is a big opportunity, but we don’t know how to tap into it yet.”
The Opportunity is Broad
The opportunity spans both front and back-office functions across the entire business: plant operations, R&D, distribution, supply chain, quality, compliance, and sales and marketing. And the use cases are still being discovered.
What the Data Shows
Tech-Clarity spoke with Datacor customers and surveyed over 250 companies with under $300 million in revenue to understand how chemical companies are approaching AI, where they’re finding success, and what’s holding others back.
The findings tell a clear story: chemical manufacturers and distributors are making meaningful progress with AI and doing so more quickly than many might expect.
- Those that have started are finding ways to scale their AI advantage.
- The barriers are real but addressable: skills gaps, data readiness, and knowing where to begin matter more than distrust or fear.
- The companies leaning on existing trusted partners are getting further, faster.
AI won’t transform a business overnight, but it is becoming a key differentiator in the chemical industry. Companies finding value in AI are already pulling ahead, meaning the cost of waiting is rising.
Goals for AI
Cost and Efficiency Lead the Way
When asked about their primary AI business goals, chemical companies most commonly reported wanting to reduce cost and drive efficiency. This goal is consistent with how chemical companies approach technology broadly: they operate on thin margins, and cost-consciousness is built into how they run their business.
But cost and efficiency are only part of the picture. Over one-third prioritize improving product and service quality, and a similar percent target reducing associated costs. About one-third target increased business resilience to disruption. Only 4% of companies reported having no business goals for AI at all.
Customer-Facing Goals Rank Alongside Operational Ones
Chemical companies report goals for improving customer-facing processes and driving revenue growth at nearly the same rate as their operational goals. Two of the most commonly reported goals directly impact the customer: improving service quality and enhancing the customer experience. Investing in efficiency isn’t just about reducing costs and headcount — it’s about serving customers better.
AI Opportunities Span Every Corner of the Business
Perhaps the most telling finding isn’t any of the individual goals, but the sheer breadth of them. Companies reported goals spanning nearly every corner of the business. AI is not being treated as a point solution — it’s being pursued as a broad operational capability. As one chemical company put it, “Ideally, we’d like to use AI to streamline operations, gain better insights from our data, and create more scalable, consistent processes — allowing our teams to focus on higher-value, creative, and strategic work.”
The Path Forward
Start with the Practical, High-Return Applications
Chemical companies are pursuing AI for a wide variety of reasons, from reducing cost and driving efficiency in the plant and R&D to improving customer service and growing revenue. As one respondent put it, their goals are to “make more money and provide a better customer experience.”
For most, the practical entry point is the same: free people from repetitive work, streamline how work gets done, and give employees better information to make decisions. “We’re trying to take something painful and make it easier,” shares Hubbard-Hall’s Kielbowicz. AI at this stage is a productivity multiplier for existing resources. The data and process knowledge to get started is, in most cases, already in place. As respondents shared, their goals include “better using the data we have” and “improving on workflows and empowering employees to work smarter.”
Close the Knowledge Gap
The biggest barrier to AI adoption is not distrust or fear of job loss — it is a lack of knowledge and capability. Chemical companies need to invest in training and give employees time to explore what AI can do for their specific operations. Supplementing internal knowledge with expertise from existing vendors and consultants is equally important. The barriers are well understood and addressable; what is required is commitment, not just interest.
The Data Makes the Case for Action
Many chemical companies are still in planning mode or early pilot phases, and organizational, data, and implementation challenges are slowing them down. Those challenges must be addressed.
But the data is clear: companies that complete pilots are achieving their stated goals, and 60% do so within a single business quarter. The challenges are real, but they are addressable — and the companies proving that are already pulling ahead.
Early Movers are Widening Their Lead
Chemical companies that have gained value from AI are scaling those gains to new problems and new parts of the business, building organizational capability that competitors cannot quickly replicate. The window to close the gap is open, but early movers are not standing still.
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