The recently released TechnoVision 2026 by Capgemini clearly shows that AI and generative AI are no longer experimental technologies, but are moving to become the backbone of enterprise operations.
When encountering this report, it is easy to think of IT companies or the digital services industry, but the isol trend analysis team believes that this message is much more directly connected to the chemical, materials, and precision process industries.
The chemical industry is inherently data-intensive, has complex processes, and is an industry where small variables can have a significant impact on quality and cost. In other words, it is an industry where experience and judgment are accumulated, and therefore, there is a great potential for AI to function not just as simple automation but as a 'tool for structuring judgment'.
The AI that Capgemini speaks of for 2026 is not an automation tool attached to specific equipment. It is closer to a stage where it becomes a structural component that permeates the entire operation of the factory, going beyond one-off PoCs (Proof of Concept) or some pilot projects. In the context of chemical processes, this means that AI is not just adjusting single process conditions but is moving towards a role that looks at process, energy, raw material, quality, and maintenance data together, organizing and suggesting directions for operators to make judgments. This represents a shift from traditional process control to intelligent operations.
The areas where AI is first applied in the field are also closely related to this point. Existing chemical processes have been primarily operated stably around PID control (Proportional–Integral–Derivative Control), which is a basic automation method that corrects process errors through proportional, integral, and derivative calculations. While PID control is a validated method, it has limitations in predicting situations where multiple variables, such as raw material lot variation, environmental changes, and equipment aging, act simultaneously.
At this point, AI operates not by replacing existing controls but by learning from past process data to detect anomalies earlier, explaining what risks are increasing under current conditions, and suggesting recommended actions to operators. In other words, it is closer to a Human-in-the-loop structure where humans make the final judgment and AI assists in that judgment rather than complete unmanned automation.
The role of AI in quality management is becoming increasingly clear. In the chemical and materials industry, the largest costs are defects, rework, and off-spec products. AI analyzes process data, quality results, and equipment condition data together to predict the likelihood of quality risks occurring in the current batch through predictive quality analytics. This can be seen as a representative area that goes beyond just having 'tried AI' to actually reducing losses and improving yields, leading to the Proof of Impact stage.
The application of AI in energy and cost structures is also evolving in a more realistic direction. MDI, polyurethane, and precision chemical processes are energy-intensive and highly cost-sensitive industries. AI is being utilized not just to reduce power consumption, but to propose the most cost-optimal operating scenarios by considering production planning, load balancing, planned maintenance timing, and feedstock conditions together.
The phrase "AI is eating software" mentioned in TechnoVision 2026 also applies directly to chemical processes. In the past, MES (Manufacturing Execution System), APC (Advanced Process Control), QMS (Quality Management System), and EMS (Energy Management System) operated as siloed systems. Now, AI is breaking down these boundaries, changing the operator's role from deciding 'which system's button to press' to defining what the process intent is. The operational method is shifting to a structure where AI integrates data, simulates scenarios, and generates recommendations.
The keyword of tech sovereignty is also becoming important in the chemical industry. Process data is a core asset of the company, control logic is know-how, and quality prediction models are competitive advantages. A structure that relies entirely on external public clouds or vendor lock-in for all data can pose risks in the medium to long term. Therefore, in practice, a pragmatic sovereignty strategy that combines private clouds, hybrid clouds, and on-premise AI is becoming increasingly important.
The ISOL trend analysis team does not believe that AI will replace chemical processes. Instead, AI is likely to become a supportive yet essential tool that structures expert judgment, codifies experience into data, and manages process variability. Future competitiveness will depend more on how naturally AI is integrated into process operations than on how much AI is adopted.
The direction shown by Capgemini TechnoVision 2026 is clear. Moving from experimentation to operation, from automation to intelligence, and from individual processes to entire processes. ISOL plans to continue focusing on the practical application of AI combined with processes rather than the technology itself amid this change.