Putting AI to work for people in chemical processing

At Bayer Crop Science in Muttenz, Switzerland, shift teams were generating valuable operational data around the clock — but much of it was not getting used. Critical insights were buried in shift handover notes or lost in translation between operational teams. By adding artificial intelligence (AI)-powered search to their plant process management system, they turned a growing archive of plant knowledge into a real-time decision-making tool that has dramatically improved productivity and slashed problem resolution time.

That is the kind of practical, people-focused AI the chemical industry needs more of — and it is already delivering results. Yet for many chemical processors, AI has yet to fulfill its promise. By working within existing workflows and designing AI systems around the needs of people, chemical processors can harness the power of AI on the plant floor now.

AI in chemical processing: Adoption vs. reality

AI is quickly gaining ground in the chemical industry. Since 2021, the rate of digitalization has grown by 56%, and 94% of industry leaders say AI will be critical to their success over the next five years. The global market for AI in chemicals is projected to grow from $500 million in 2023 to nearly $10 billion by 2032, with use cases ranging from predictive maintenance and process control to quality assurance and compliance. Some of the most promising uses of AI in chemical processing include:

Knowledge management: NLP and GenAI preserve institutional knowledge and make it searchable using natural language queries.
Root cause analysis. AI mines historical logs and shift notes to identify recurring issues and suggest proven solutions faster.
Quality assurance: Machine learning models detect anomalies in production data early, improving consistency and reducing batch failures.
Process optimization: AI adjusts process parameters in real time to boost yield, lower energy use and minimize waste.
Predictive maintenance: ML algorithms analyze sensor data to predict equipment failures and schedule maintenance before downtime occurs.
Inventory and supply chain management: AI forecasts material needs, tracks usage trends and automates restocking to prevent shortages and overstock.
Safety and compliance: Pattern recognition tools flag potential safety risks, while NLP supports accurate, automated documentation.

Early results are promising — for example, some companies have reported a 20–40% boost in energy efficiency through AI-driven process optimization. But despite the momentum, many plants are still struggling to realize full value. A CAS report predicts that while 70% of companies will pursue digital transformation in the next decade, only 30% will meet their business goals.

The biggest barriers? Many chemical processors face roadblocks to AI adoption:

Dirty or incomplete data. AI is only as good as the data it is trained on, and many plants still struggle with inconsistent recordkeeping, offline/analog shift notes and maintenance records, or outdated systems.
Disconnected systems. Information lives across MES, QMS, LIMS, historians and spreadsheets, making it hard to get a complete picture.
Cost and complexity. Integrating AI into legacy infrastructure can be expensive and time-consuming, especially if the solution is not tailored to the plant’s specific needs.
Poor user fit. Many AI tools are designed for data scientists, not shift supervisors. If a tool does not reflect the language, workflows and responsibilities of the people using it, adoption stalls.

To deliver real value, AI needs to fit the plant — not the other way around.

Realizing the potential: AI that works for people

The real promise of AI in chemical processing is not just about automation — it is about enabling people to do their jobs better. That’s the core principle behind Industry 5.0: putting human expertise at the center while using intelligent tools to enhance decision-making, communication and continuous improvement.

In many plants, AI adoption has stumbled because solutions were built for technologists, not for the people actually running production. Complex dashboards, disconnected systems and generic tools often add friction instead of removing it. To truly unlock AI’s value, process manufacturers must flip the model: start with the work, then apply the technology.

That means embedding AI into familiar workflows and focusing on use cases that support how operators, engineers and supervisors actually solve problems. For example:

Smart search tools powered by natural language processing allow teams to query plant records in plain English, eliminating the need to know exact terms or trawl through years of shift notes.
AI-driven solution suggestions can surface potential root causes and recommend proven fixes, shortening investigation cycles and reducing repeat issues.
Generative AI tools help preserve institutional knowledge, summarize trends and generate draft reports or shift summaries, freeing up time and making it easier for new employees to get up to speed.

To get real value from AI, plants first need a solid digital foundation. That starts with digitizing shift notes, inspection logs and maintenance reports, replacing paper with structured, searchable data. A centralized plant process management (PPM) system is essential for bringing together inputs from MES, QMS, historians and human-generated records. Finally, AI tools must be trained on industry-, company- and site-specific data to reflect real workflows, language and operational priorities, ensuring insights are relevant and actionable. With information accessible in one place, AI can deliver faster insights, support better decisions and turn day-to-day data into long-term value.

The Bayer Crop Science team is already seeing these benefits in action. By pairing digital shift handovers with AI-powered search, they have significantly reduced time spent on troubleshooting and investigations, turning multi-hour problem-solving efforts into quick, confident decisions. They have also improved safety and quality, preserved institutional knowledge and made onboarding faster for new staff.

When AI is built around real plant language, processes and workflows, it becomes a trusted partner. It does not replace human expertise — it amplifies it. The result is a smarter, more connected workforce that can respond faster, improve quality and keep operations running more smoothly with less waste. This is how chemical processors can finally close the gap between AI’s promise and its performance: by building solutions around the people who will use them.