AI agents in industrial value chains: what needs to be in place before autonomy makes sense

AI agents are starting to move from demos into serious operational discussions. For industrial companies, the opportunity is clear: better decisions, faster workflow execution, less manual handling, and more consistent work across complex value chains.

But there is a problem with the way AI agents are often discussed.

Too much of the conversation jumps straight to autonomy. That is not where most companies should start.

Direct answer: AI agents become valuable in industrial value chains when they help people and systems make better decisions and execute workflows across ERP, data, documents, planning tools, and connected business systems. Before autonomy makes sense, companies need trustworthy data, clear orchestration, validation, explainability, permissions, logging, and human responsibility.

The practical question is not: "How autonomous can the agent be?"

The practical question is: "Which part of the value chain is ready for an agent to support, prepare, or perform work safely?"

Why industrial value chains are a strong fit for AI agents

Industrial value chains are full of work that crosses system boundaries.

Customer orders, supplier documents, production plans, warehouse constraints, quality data, transport information, product data, finance flows, and demand signals rarely live in one clean place. They move through ERP, emails, PDFs, spreadsheets, portals, planning tools, data platforms, and local process knowledge.

That creates friction.

People spend time finding information, checking details, comparing sources, correcting data, asking for missing input, and deciding what should happen next. In many cases, the work is not difficult because one task is hard. It is difficult because the context is spread across the value chain.

This is where AI agents can become useful.

AI Sweden's report on multiagent systems for improved decision making in industrial value chains points to needs that match this reality: trustworthy systems with control functions, efficiency and automation, better information management, decision support, and reusable components. It also highlights risks around reliability, quality, transparency, legal and ethical issues, and cost versus benefit.

That balance matters. Industrial AI agents are not only a technology topic. They are an operating-model topic.

Start with the decision or workflow, not the agent

RISE makes a simple but important point in its article on the opportunities and pitfalls of AI agents: the starting point should be the organisation's needs, the problems to solve, and the role humans should have in relation to the agent.

That is the right starting point for industrial companies too.

If a workflow is straightforward, a simpler automation, integration, dashboard, or model may be enough. An AI agent is most relevant when the workflow involves multiple steps, changing inputs, varied documents, several data sources, and decisions that need context.

Good candidates often share a few traits:

• the workflow is repeated often enough to matter
• information is spread across several systems or documents
• people spend time validating, correcting, or preparing work
• the process has clear owners and known exception patterns
• the action can be reviewed before it affects business-critical data
• the business can measure whether quality, speed, or control improves

Poor candidates are usually the opposite: unclear ownership, weak data quality, undefined rules, high-risk actions, and no agreement on what a good outcome looks like.

An agent should not be used to hide a process problem. It should be used where the process is understood well enough to improve.

The three autonomy levels that matter

Agentic AI should not be treated as a choice between manual work and full autonomy. For most industrial workflows, the useful discussion has three levels.

1. Decision support

The agent gathers, structures, validates, and explains information so a person can make a better and faster decision.

This is often the safest starting point for ERP, master data, planning, procurement, finance, order handling, and support workflows. It reduces manual search and checking without giving the agent control over business-critical actions.

Example: an agent reviews item, supplier, or customer data and returns missing fields, duplicate risks, inconsistent values, and suggested corrections for a user to assess.

2. Human-in-the-loop execution

The agent prepares an action, but a person reviews or approves before the final update.

This level is useful when the workflow is structured enough for the agent to prepare work, but the business impact still requires human approval.

Example: an agent reads a customer order document, checks available data, prepares an order in the ERP flow, flags exceptions, and asks a user to approve before submission.

3. Selected autonomous execution

The agent performs a clearly defined action where the risk is low, the data is reliable, permissions are tight, monitoring is in place, and the process boundaries are understood.

This should not be the default. It can make sense for narrow, repeatable, low-risk actions. It is not the right starting point for complex operational decisions with unclear rules or high business impact.

What needs to be in place before autonomy scales

The more an agent is allowed to do, the more the surrounding control model matters.

AI Sweden's industrial multiagent report identifies quality and validation, management and orchestration, explainability and trustworthiness, and integration with existing industrial domain knowledge and systems as important areas for future work. That maps directly to what companies need before they move from experiments to recurring operational use.

Trustworthy data

Agents depend on the data they can access.

If product data is incomplete, supplier records are duplicated, order history is inconsistent, or planning data is poorly governed, the agent may work quickly but still produce weak output. That is why Data Intelligence is not a side issue. It is part of the agent foundation.

A useful question is: would we trust this data if a person used it to make the decision today?

If the answer is no, autonomy should wait.

Clear orchestration

An industrial agent does not work alone. It needs to know which systems to read from, which tools it can use, which step comes next, which rule has priority, and when to escalate.

In practice, this means designing the workflow around orchestration: inputs, context retrieval, validation, tool calls, approvals, write-back, exception handling, and logging.

Without orchestration, the agent becomes a loose interface beside the business. With orchestration, it can become part of the workflow.

Validation and explainability

Industrial work needs validation before action.

An agent should be able to show what it used, what it checked, what it concluded, what uncertainty remains, and why a suggested action is reasonable. That does not mean every model step must be fully transparent, but it does mean the business needs enough traceability to review, challenge, and improve the workflow.

RISE warns that agent systems can amplify issues such as hallucinations and that explainability, security, and transparency need careful consideration. That is especially relevant when agents use tools, retrieve information, or interact with several systems.

Permissions and access control

Agents should not have broad access because the technology can use it.

They need least-privilege permissions tied to the workflow. Read access, prepare-only rights, approval requirements, and write-back permissions should be separated. Sensitive data should be limited. High-risk actions should require review.

This is basic governance, but it becomes more important when the agent can act across systems.

Logging and monitoring

If an agent supports a recurring operational process, the company needs to know what happened.

Logging should cover inputs, retrieved context, checks performed, suggested actions, approvals, write-backs, errors, overrides, and exceptions. Monitoring should show quality, adoption, failures, cycle time, and whether human review is catching the right issues.

This is how an agent moves from a demo to something that can be improved over time.

Human responsibility

AI agents do not remove responsibility from the business.

They make responsibility more explicit. Someone must own the workflow, the data, the approval model, the allowed actions, the risk level, and the operating rhythm after launch.

The more autonomous the agent becomes, the clearer that ownership needs to be.

How this connects to ERP and CloudSuite M3

For many industrial companies, ERP is still the operational backbone. It holds much of the process logic, transactional data, master data, finance structure, and business rules that agents need to respect.

That makes ERP a practical starting point for agentic AI, especially in companies running Infor CloudSuite M3. M3 processes often sit close to the real value chain: order handling, item data, customer setup, procurement, warehouse, planning, finance, production, and distribution.

But the point is not to add an AI layer on top of ERP and hope for value.

The point is to connect the agent to the actual workflow. That may include ERP data, emails, PDFs, transport data, customer documents, planning signals, a modern data platform, and other connected systems.

Our article on AI agents in Infor M3 for order management, item data and customer setup shows the practical pattern: agents can gather information from multiple sources, interpret structured and unstructured data, validate against business rules, and suggest or perform actions with the right level of human oversight.

That same pattern applies across industrial value chains. Start where the process is valuable, recurring, and close enough to the data to be governed.

A practical readiness framework

Before moving an industrial workflow into an AI agent pilot, evaluate it across seven areas.

1. Business value

What problem are we solving?

Look for slow handovers, avoidable manual checking, poor data quality, delayed decisions, high exception volume, repeated document handling, or recurring process friction.

2. Workflow clarity

Can the team explain the current process?

The normal flow, exceptions, business rules, decision points, and expected output should be clear enough to design around.

3. Data readiness

Can the agent trust the inputs?

Check data quality, ownership, availability, master data, document quality, API access, and whether important context sits outside the main system.

4. System integration

Which systems, tools, documents, APIs, and data sources are involved?

An agent that cannot access the right operational context will either stay superficial or create manual work around itself.

5. Permission model

What is the agent allowed to do?

Separate read access, prepare-only actions, approval points, write-back permissions, and blocked actions.

6. Review and responsibility

Where should people stay in control?

Define expert review, approval, escalation, and accountability before the pilot moves into production-like use.

7. Measurement

How will the business know it worked?

Useful measures include reduced manual handling, fewer errors, faster cycle time, better data quality, improved exception handling, higher user adoption, and clearer auditability.

The implementation choice also matters

Not every AI initiative needs a new model, and not every agentic workflow needs heavy custom AI development.

AI Sweden's white paper on the AI implementation spectrum argues that sustainable AI adoption depends on knowing when to reuse, fine-tune, train, or integrate existing systems. For many organisations, the practical route is not model training from scratch. It is intelligent integration using existing models, retrieval-augmented generation, orchestration, and hybrid intelligence.

That is an important point for industrial agents.

The value usually does not come from building the biggest model. It comes from connecting the right model, data, business rules, user flow, and operating controls to the workflow that needs to improve.

In some cases, a simple automation, deterministic validation rule, integration, or dashboard may solve the problem. In other cases, an agent is useful because the input is varied, the context is distributed, and the next step requires interpretation.

The right architecture should follow the workflow, not the other way around.

Where Elvenite fits

Our position is practical: AI agents should support, prepare, and perform operational work where business value is clear and control can be designed in.

That fits the work we already do across ERP, data, integrations, documents, workflows, permissions, logging, human review, and connected business systems. It also fits the reality of operationally complex companies, where AI only creates value when it works with process knowledge and reliable data.

For companies running CloudSuite M3, the opportunity is often close to daily operations: master data, order handling, procurement, finance, supplier documents, planning, warehouse flows, customer operations, and exception handling.

For companies building broader AI readiness, the work often starts with Data Intelligence: data quality, governance, access, architecture, analytics, automation, and AI-ready workflows.

And for teams still building their understanding of the category, our AI agents guide gives a broader introduction to what agents are and how they differ from assistants and traditional automation.

Autonomy should be earned

AI agents can improve industrial value chains. They can help companies make better decisions, reduce manual handling, respond faster, and execute workflows with more consistency.

But autonomy should be earned.

Start with the workflow. Check the data. Define the permissions. Build validation. Keep human review where it matters. Log what happens. Measure the result. Improve from there.

The companies that get value from industrial AI agents will not be the ones that automate the most on day one.

They will be the ones that understand where agents can safely support decisions, where they can prepare work for approval, and where selected autonomous execution actually makes business sense.

FAQ

What are AI agents in industrial value chains?

AI agents in industrial value chains are AI-enabled workflows that can gather context, use tools or APIs, validate information, and support or perform selected tasks across ERP, data platforms, documents, planning tools, and connected business systems.

Where can AI agents create value in industrial companies?

They can create value in recurring workflows where information is spread across systems and documents, such as order handling, master data, procurement, supplier invoices, planning, warehouse processes, customer operations, and exception management.

Should industrial AI agents be fully autonomous?

Not by default. Most companies should start with decision support or human-in-the-loop execution. Selected autonomous execution is only suitable when the task is narrow, risk is low, data is reliable, permissions are clear, and monitoring is in place.

What needs to be in place before AI agents can scale?

Companies need trustworthy data, clear workflow ownership, system integration, orchestration, validation, explainability, permissions, logging, monitoring, and defined human responsibility.

How do AI agents connect to ERP systems such as Infor CloudSuite M3?

ERP systems often hold the process logic, master data, transactions, and business rules that agents need to respect. In a CloudSuite M3 context, agents can support workflows around orders, item data, customer setup, supplier documents, planning, finance, warehouse processes, and connected operational data.

What is the best first AI agent use case?

The best first use case is usually a recurring workflow with visible business value, known data sources, manageable risk, clear ownership, and an action that can be reviewed before it affects business-critical systems.