AI Agents, Process Mining, and Robotic Process Automation: How AI is Used in Manufacturing Today

In 2026, 49% of manufacturing and distribution leaders still remain in the early stages of AI adoption, either conducting pilots only, pausing projects, or not yet getting started (Infor). This trend isn’t driven by a lack of AI tools available on the market, but by the absence of clear use cases and measurable ROI. Often, AI feels more like tinkering and less like real transformation.

This article shares a brief, jargon-free breakdown of four AI-enabled technologies manufacturers use today, how they drive value, and practical steps forward.

Four Leading AI-Enabled Technologies: Definitions and Use Cases

#1. Generative AI

Generative AI (GenAI) is a category of artificial intelligence that produces new content, such as text or images, based on learned patterns from large data sets. It is considered a reactive technology because it requires user input. GenAI is most helpful for summarizing reports and documents, explaining exceptions, and translating text between languages.

Manufacturing Use Cases

GenAI is an everyday tool that is widely applicable across the organization. It can support strategic decisions by using real operational data to answer questions like:

• “Which orders had the best margin?”
• “Are there any orders likely to be late?”
• “Do I have any late orders for Customer XYZ?”
• “Which suppliers are driving delays?”

Other manufacturing use cases include summarizing engineering change orders (ECOs), documenting service knowledge, drafting a safety data sheet (SDS), and creating allergen change explainations.

#2. AI Agents

AI agents are digital systems designed to execute tasks and adapt to changing circumstances. Created to help organizations automate internal workflows, agents are a proactive tool that act autonomously within defined boundaries.

Manufacturing Use Cases

AI agents are highly customizable and can be role-specific. For example:

• An Accounts Payable Agent can collect vendor invoices, open balances, and payment batches.
• A Non-Conformance Agent can monitor non-conformance reports and track associated orders, customer claims, and follow-up actions for suppliers and projects.
• An Inventory Agent can track inventory levels and validate against open purchase orders.
• A Project Agent can examine underlying causes of project delays to maintain timelines and margins.

Sources: Infor, Industry AI Agents

AI agents can also be tailored to a specific industry. For instance:

• Industrial manufacturers can use AI agents to deactivate compromised machinery and divert production to a different line.
• If an ingredient’s quality does not meet tolerance levels, food and beverage manufacturers can use AI agents to immediately stop production, switch suppliers, and change recipe parameters.

#3. Process Mining

Process mining is not a new technology, but it has been drastically enhanced with embedded AI. As a modern alternative to manual process mapping, process mining uses algorithms to analyze current workflows and highlight areas of non-compliance or operational friction. With process mining, companies can visualizes how work actually gets done based on their real operational data. Embedded AI take process mining to the next level by generating actionable summaries and reducing manual analysis.

Manufacturing Use Cases

Companies can use process mining to drive greater efficiency accross the organization, from production, logistics, finance, and more. Users can:

• Benchmark process performance (i.e. procure to pay cycle time) against internal or industry standards
• Map the flow of ingredients and products from sourcing to production and manage deviations (Food and Beverage)
• Enable proactive equipment maintenance (Industrial Manufacturing)

Marine equipment manufacturer Xpress boats used process mining to optimize their procure to pay, order to cash, and demand to build workflows. With process mining, they achieved a 75% reduction in process issue reporting and were 98% faster to identify process issues. Check out their story below.

Real-World Use Cases in Automation and Process Mining

Discover how two manufacturers leverage AI, automation, and process mining to predict demand and eliminate process inefficiencies.

Read More

#4. Robotic Process Automation

Robotic process automation (RPA) are software bots that mimic human interactions (such as clicking buttons and entering data). RPA is helpful for extracting content from one source (emails, PDFs, spreadsheets, legacy systems) and inputting it into another system. When paired with AI capabilities like natural language processing (NLP) and ML, RPA can also suggest next steps and adapt to problems.

Manufacturing Use Cases

RPA can automate tedious and error-prone work across teams. Examples include:

• Automating purchase orders
• Real-time logistics and shipping monitoring
• Monitoring stock levels
• Invoice processing
• Payroll management

Artificial intelligence has enhanced RPA by allowing it to process more complex processes and unstructured data. In the future, RPA will become more proactive and autonomous.

The Next Step

Companies using Infor CloudSuite Industrial SaaS can implement and scale all of the technologies mentioned above through Velocity Suite. Velocity Suite is an all-inclusive set of AI and automation solutions with use cases for specific manufacturing microverticals.

“Infor’s Velocity Suite delivers value by packaging AI, process mining, and automation into a single, ready-to-use solution that works natively with CloudSuite Industrial, so manufacturers can quickly identify inefficiencies and act on them without adopting 3^rd party tools.” – Nick Perry, AI Technology & Innovation Account Executive at Infor

With Velocity Suite, users can diagnose current gaps, automate away everyday friction, and keep optimizing to stay ahead.

We’re here to help you turn AI into tangible business outcomes. Let’s get in touch!