Manufacturing just got its first serious set of AI agents designed for the real complexity of industrial operations. Infor and AWS today announced new industry-specific AI agents built natively on AWS, targeting manufacturing and distribution enterprises with the kind of workflow automation that general-purpose tools have never reliably handled.

The announcement covers three distinct operational domains — project profitability, delivery management, and process intelligence — each addressed by dedicated AI agents that reason and act across complex cross-departmental workflows.

What the Agents Actually Do

Profitable Project Management agents continuously compare a project’s baseline plan against real-time financial performance. Instead of waiting for a monthly management report to reveal a cost overrun, the agent surfaces margin variances as they develop, forecasts revenue implications, and flags schedule or cost issues before they compound. For any manufacturer running project-based work, this is the difference between catching a problem at the 10% mark and finding out at 80%.

On-Time Project Delivery agents monitor milestones, dependencies, and cross-project performance simultaneously. When a delay risk appears, the agent coordinates an intervention response rather than simply issuing a warning. It’s the difference between an alert system and one that actually helps you respond.

Process Mining and Operational Intelligence agents use event log data to map real-world workflows as they actually operate, not as they were designed to operate. They surface bottlenecks, detect process variations, and prioritise the highest-impact changes. For manufacturers running complex production lines, this kind of continuous process visibility has historically required a dedicated analyst team and weeks of analysis time.

These agents are available through Infor’s Velocity Suite — an all-inclusive package covering AI solutions, tools, and expertise for CloudSuite customers across distribution, industrial manufacturing, and process manufacturing.

Real-World Evidence

Xpress Boats, a US-based manufacturer of aluminium fishing and pontoon boats, has already deployed Infor Velocity Suite across its operations. The result: a 50% reduction in expedited shipping costs. Expedited shipping is typically the canary in the coal mine for operational problems — it reflects poor forecasting, supply chain surprises, and production delays. Cutting it in half does not happen without fundamentally better operational intelligence.

Why Manufacturing Is the Right Domain

Manufacturing has always had a data problem. The data exists — in ERP systems, production logs, supply chain records, financial reports — but accessing it across systems in real time, at the right moment in a workflow, has been the hard part. AI agents change this because they can operate across multiple systems simultaneously, pulling relevant data, making decisions, and taking action without waiting for a human to bridge the gap.

This is different from dashboard analytics, which show what happened. It is also different from basic automation, which repeats a defined task. Agentic AI for manufacturing sits between those two: it observes complex operational state, reasons about what it means, and acts on that reasoning.

That is why enterprise ERP vendors like Infor are the right players to deliver this. They already hold the operational data. The integration problem is solved. What they are adding now is the intelligence layer.

What This Means for Business

If you operate at enterprise scale in manufacturing or distribution, the relevant question is not whether to adopt AI agents. It is which workflows to start with and whether your data infrastructure can support them.

The pattern playing out across enterprise software right now — SAP, Oracle, Salesforce, and now Infor — is that every major platform is adding an agentic layer to the operational data it already holds. This means the companies with the best-structured, most reliable data will extract the most value from these tools. Companies with fragmented or inconsistently maintained data will get inconsistent results.

Building the data foundations that make AI agents effective is exactly the kind of work Enterprise DNA has been helping organisations do — whether through EDNA Learn for developing internal data capability, or through Omni by Enterprise DNA for deploying AI agent workforces tailored to specific operational needs. The tools are now available at enterprise scale. The question is whether your team can use them well.