By Adrian Wood, director of strategy and marketing at DELMIA
Artificial intelligence is opening new possibilities for how manufacturers use knowledge, make decisions and turn insight into action. Applied effectively, Industrial AI can help engineers, planners and operators evaluate more information, explore alternatives faster, reuse expertise and automate routine work. The opportunity extends beyond productivity. It can speed up decision-making, improve responsiveness to change and help organisations make better use of the knowledge they have accumulated over years of industrial experience.
As industrial operations become more complex, this ability becomes increasingly valuable. Products are more configurable, production networks more distributed and supply chains more dynamic. Critical knowledge is often spread across experienced employees, enterprise systems, documents, spreadsheets and plant-specific processes.
This is where agentic AI in manufacturing becomes particularly important. Agentic AI can interpret intent, take context into account and coordinate multi-step workflows across applications and processes. Rather than simply returning information, an agent can help determine what needs to happen next and initiate or coordinate actions within a governed workflow.
Generative AI is an important related technology. It is particularly suited to creating and adapting content, processes and proposed outputs. The two can work together. An AI agent may call on generative AI when something needs to be created, just as it may draw on simulation, optimisation or predictive intelligence when a task requires those capabilities. The distinction is important: generative AI primarily creates, while agentic AI coordinates capabilities around an objective.
The opportunity is therefore to bring different forms of intelligence together so people can understand complex situations, explore more possibilities and act on trusted information with greater confidence.
Why does general-purpose generative AI fall short in critical manufacturing environments?
By general-purpose generative AI, we mean broadly trained tools such as ChatGPT or Microsoft Copilot that can answer questions, summarise information and generate content across many topics. They have demonstrated significant value for everyday productivity, but moving from experimentation to dependable operational use has proved more difficult. Gartner reported that at least half of generative AI projects had been abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs and unclear business value. That helps explain why general-purpose models alone are not enough for industrial environments, where AI must operate within specific products, processes, constraints and operating conditions.
In manufacturing, an answer that merely sounds plausible is not enough. Decisions may affect product quality, worker safety, compliance, machine utilisation, material availability, production schedules, costs and customer commitments.
That means industrial AI must be grounded in more than text. It needs access to governed information, validated engineering knowledge, operational constraints and models that represent how industrial systems actually behave. Generative AI still plays an important role, but its outputs become far more valuable when they are based on trusted industrial context and connected to the systems in which work takes place.
Generative AI can create new content, proposed processes, instructions or alternatives. Agentic AI can interpret an objective, determine which capabilities are required and coordinate a workflow that may include generative AI alongside predictive models, optimisation, simulation and other technologies.
Human supervision remains essential. AI may analyse, propose, generate or coordinate actions, but people need visibility into the information being used, the assumptions being made and the potential consequences before critical decisions are approved.
How can manufacturers use agentic AI to retain practical knowledge?
One of the most immediate industrial opportunities for agentic AI involves knowledge.
Many manufacturing processes still depend heavily on experienced individuals who understand why certain decisions were made, which exceptions matter and how formal processes need to be interpreted in practice.
That creates a growing challenge as experienced workers retire, organisations struggle to recruit people with specialist skills and newer employees need to become productive more quickly.
AI agents can help make enterprise knowledge easier to access in the context of the work being performed.
Instead of searching through documents or trying to find the person who remembers a particular decision, an engineer might ask why a manufacturing process was configured a certain way, what standards apply or what previous decisions are relevant to the current situation.
An agent can connect that request to available enterprise knowledge, operational context and historical information, helping bring relevant guidance directly into the workflow.
AI helps organisations make industrial expertise easier to discover, apply and reuse. Knowledge that was once difficult to find or concentrated in a few individuals can become more accessible across teams, programmes and locations.
Agentic approaches can therefore help organisations move beyond using AI simply to retrieve information. They offer a way to put accumulated knowledge to work by combining context, reasoning and actions within industrial workflows. Read more about AI-powered virtual companions and industrial knowledge.
Shifting from disconnected data to integrated industrial world models
Industrial AI becomes significantly more valuable when it understands the environment in which decisions are being made.
General AI systems may begin with documents, databases or individual data sources. Industrial operations require a richer representation that includes products, production systems, supply chains, resources, processes, constraints and real-world operating conditions.
An industrial world model represents the relationships between different elements of an industrial system. It provides context for understanding what each element is and how it relates to the rest of the system.
Virtual models of products, production environments and supply chains can provide an important part of that foundation. Connected to operational information, they allow organisations to experiment, simulate behaviour and evaluate the potential consequences of decisions before implementing them in the real world.
Agentic AI provides a practical way for people to interact with this richer industrial context.
For example, a planner responding to a production disruption might begin with a simple question. Predictive intelligence could identify the likely impact, optimisation could evaluate alternative schedules and simulation could help assess the consequences.
The agent can coordinate the workflow and bring the relevant information back to the user, drawing those capabilities together around the user’s objective.
As industrial AI develops further, this relationship between AI agents, structured industrial knowledge and richer models of the physical world could become increasingly important. Industrial world models offer a way to ground AI in relationships, constraints and behaviour rather than relying on disconnected information alone.
What are the key performance indicators and return on investment for industrial AI?
The value of industrial AI ultimately comes from improving how quickly and effectively organisations can turn information into decisions and decisions into action.
At an operational level, that can mean less manual work, faster responses to disruption, better use of scarce expertise, improved quality, more consistent execution and better planning and use of resources.
The benefits can be significant. Representative examples from manufacturing engineering and machining include:
- Up to 50 per cent improvement in manufacturing efficiency
- Up to 25 per cent reduction in industrialisation lead time
- 90 to 95 per cent reduction in the effort needed to create and update manufacturing bills of materials (MBOMs)
- Up to 50 per cent reduction in machine set-up time
These examples illustrate the potential at the task and process level, but the greater value comes from what those improvements enable across the business.
Faster engineering and planning cycles can help companies bring products to market sooner. More reliable decisions can improve delivery performance and customer responsiveness. Better access to expertise can reduce dependence on scarce skills.
At the corporate level, the cumulative effect is the ability to operate with greater speed, resilience and flexibility. That can strengthen competitiveness by helping companies innovate faster, respond to customers more effectively, protect margins and make better use of their existing knowledge and resources.
The value of industrial AI is measured both in time saved on individual tasks and in how effectively the organisation can convert knowledge into better business outcomes.
Shifting from simple AI assistance to trusted industrial transformation
Agentic AI represents an important next step in the evolution of industrial AI.
AI agents can help users interpret intent, navigate complex situations and coordinate actions across industrial workflows. Their potential comes from bringing together the right information, applications and specialist capabilities around a specific objective.
That may include generative AI to create or adapt content, predictive AI to anticipate risks, optimisation to evaluate complex trade-offs and simulation to test potential outcomes.
In this way, agentic AI can help people use an increasingly sophisticated range of industrial technologies together.
But the value of that capability depends on trust. Agents need to operate within governed workflows, draw on reliable industrial context and give people appropriate visibility and control over important decisions.
The goal is to make industrial knowledge and advanced capabilities easier to access, coordinate and apply.
When that happens, agentic AI can help industrial organisations move beyond isolated AI assistance and use their knowledge to make better decisions, act on them and measure the results.
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