Agentic AI is moving into the workflows manufacturers actually struggle with
Manufacturing leaders often focus AI conversations on the factory floor. That is the wrong place to start.
The friction that slows most mid-market manufacturers is not on the machines. It is in the work around the machines: processing enquiries, chasing supplier confirmations, tracking tool inventories, preparing quotes, routing exceptions, and moving information between systems that do not talk to each other. That is where agentic AI is starting to deliver.
What BMW is doing – and why it matters
BMW Group has published details on two agentic AI deployments that are now running in live operations, not pilots.
The first sits in Alphabet, BMW Group’s European fleet business. Fleet enquiries arrive as unstructured emails, often requesting customised, multi-brand vehicle configurations that require data from multiple internal systems. BMW’s agentic workflow reads the incoming request, transfers data to internal applications, and initiates the required process steps – replacing around 90% of previously manual tasks in that process. Employees still review and refine outputs, but the system handles the coordination work.
The second example is in Purchasing and Supplier Network. BMW manages approximately 250,000 specialised tools worldwide – casting moulds, models, and templates used in component production and machine maintenance at suppliers. Its agentic system, built on AIconic (BMW’s internal multi-agent platform), supports inventory management by drafting orders, communicating with suppliers, reviewing responses, and approving routine cases.
The distinction between these examples and typical AI tools is worth noting. A language model can draft a reply. An agentic workflow reads the incoming request, identifies the required data, triggers steps in connected systems, prepares a proposal, and routes exceptions to people. That is a different category of automation – and a closer match to where manufacturing operations actually lose time.
The same pattern is attracting serious investment
Bremen-based Synera recently raised $40 million in a Series B round to scale its agentic AI engineering platform. The platform connects more than 80 hardware tools and enterprise systems, runs on-premises for engineering data security, and is already deployed at BMW, Airbus, Volvo Trucks, Hyundai, and NASA. The investment thesis is straightforward: engineering workflows are among the least automated functions in manufacturing, and most AI tools have approached them as a chat interface problem rather than an infrastructure problem.
Both cases point in the same direction. Agentic AI is becoming relevant in manufacturing not because it replaces physical work, but because it handles the coordination and information work that surrounds it.
Where mid-market manufacturers should look first
For companies with complex products, custom orders, and lean teams in DACH and Poland, the near-term opportunity is usually visible in a few recurring patterns: a sales engineer waiting on missing data before quoting, a purchasing team chasing supplier confirmations, a planner rekeying information between ERP and email, a service team manually sorting recurring complaints.
None of this looks as dramatic as a robot cell. But it directly affects lead time, margin, and how much of your experienced people’s time goes to coordination rather than judgment.
The best early targets share a few characteristics: the process is repetitive and rule-based enough to automate, the data involved is structured or can be made so, exceptions are clear enough to route to a person, and the business impact is measurable in hours saved or response time reduced.
Four things worth acting on
AI ownership cannot sit only in IT. The highest-value workflows cross sales, engineering, purchasing, and customer service. Someone with operational authority needs to identify where manual coordination is slowing the business.
Start narrow. RFQ intake, quote preparation, supplier document follow-up, tool inventory checks, and engineering validation loops often deliver faster business impact than broad transformation programs.
Keep humans in the decision loop. BMW’s examples preserve human review for commercial commitments, supplier obligations, and quality decisions. The goal is not to remove expert judgment. It is to stop using expert people as system integrators between email, ERP, and spreadsheets.
Fix the data before the AI. Agentic workflows depend on access to reliable internal information and clear process rules. If product data, supplier records, or customer requirements are incomplete, the first project may need to be process cleanup rather than deployment. That is not a reason to wait – it is a reason to start with a workflow where the data and accountability are already clear.
The manufacturers that answer the right question – which recurring workflow consumes skilled people, delays customers, or creates supplier friction – will not just automate tasks. They will shorten response times, protect engineering capacity, and make complexity easier to manage at scale.
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