For equipment manufacturers, the AI conversation is moving into a more difficult phase.

The question is no longer whether AI can identify a machine anomaly, summarize a service report or predict a maintenance event. Most OEMs have already seen what these capabilities can do in controlled pilots.

The harder question is: Can AI move from making recommendations to helping execute the work, and can that execution produce a measurable business result?

And there is when the strategy gets interesting with respect to agentic AI.

As described by Gartner, agentic AI refers to the transition from AI systems that can only plan, decide, and act at various levels of autonomy. For manufacturing organizations, it is important to understand that the goal should not be about autonomy in itself but rather about the use cases that matter.

For OEMs, this distinction matters. The value of an AI agent is not that it can act autonomously. The value is what that action does to downtime, inventory, quality, service cost, working capital, engineering lead time or revenue.

The Limitation of the AI Pilot

Think of a typical scenario where predictive maintenance is employed.

There is an identification through an AI tool that there is a high chance of equipment failing in a few days’ time. The alert is raised and the maintenance engineer gets notified about it.

That is helpful.

However, the engineer will still have to verify the alert, consider the history of the equipment, determine the failure mode, assess the availability of spare parts, and even arrange for a technician and raise a work order.

The AI has identified the problem. The organization still has to execute the response.

This is the gap between AI-assisted operations and agentic operations.

Workflow involving agency can be used to link up predictive modeling with the maintenance history, inventories, and service management and scheduling applications. The workflow would then use set thresholds of rules to investigate the problem, determine what is needed, and take action as necessary.

The objective is not simply autonomy.

The objective is shortening the distance between detecting a problem and resolving it.

OEMs Should Think in Workflows, Not AI Features

This changes how an OEM should approach AI investment.

Instead of asking:

“Where can we use an AI agent?”

A better question is:

“Which business workflow contains enough repetitive decision-making, delay or manual coordination to justify an intelligent agent?”

This is significant since the activities of OEM are inter-related. There can be an impact on schedules, warranty and customers’ commitments in case of quality issues. The delay in supplies will impact inventory management, production and delivery. The failure in the field impacts service cost, spare parts requirement and customer satisfaction.

This is also where an AI solution for Manufacturing needs to be evaluated differently from a standalone AI application. The strongest solutions connect manufacturing data, enterprise systems and operational workflows so that intelligence can influence an actual business process rather than remain isolated as an insight or recommendation.

Agentic AI becomes more valuable when it can work across those connections.

McKinsey’s research on agentic AI makes a similar point: organizations need to redesign workflows around human-agent collaboration rather than simply insert AI into existing processes.

Where OEMs Can See Measurable Impact

1. From Predictive Maintenance to Maintenance Execution

Predictive maintenance is already familiar to manufacturers.

The next step is connecting prediction to action.

An agent could evaluate a predicted failure against asset history, maintenance procedures, parts availability and technician schedules. Low-risk actions could be automated, while decisions requiring engineering judgment remain with people.

The potential business metrics are straightforward:

Unplanned downtime → maintenance response time → mean time to repair → maintenance cost → equipment availability.

McKinsey has reported an example involving a heavy-industry manufacturer that used generative AI to help technicians identify non-obvious causes of equipment failures. The resulting maintenance workload fell by 40%, while OEE increased by 3%.

The lesson is important: the value does not come from the AI model alone. It comes from connecting intelligence to the operational process that follows the prediction.

2. From Supplier Monitoring to Procurement Action

The procurement teams of OEMs devote considerable effort to tracking supplier performance, purchase orders, contracts, invoices and deliveries.

The agent can constantly track all of those data points, detect any exceptions and start executing predefined procedures.

For instance, there is a problem with delivery. Rather than just reporting about it, the agent will be able to evaluate its impact on production process, check other sources of inventory and detect any affected orders.

According to McKinsey, one aircraft OEM utilizes agents to automate order execution and inventory levels using data from production planning system, which resulted in 30% reduction in inventory levels and EBIT increase by almost $700 million.

This is how manufacturers, constrained by tight working capital requirements, should look at the agentic AI solutions through the prism of financial KPIs, rather than automation levels.

3. From Quality Alerts to Closed-Loop Resolution

Quality systems generate enormous amounts of information: inspection results, process parameters, defect codes, operator notes and historical production data.

An AI system can identify patterns in that information.

An agent can go further by connecting the finding to the corrective-action workflow.

For example:

Defect detected → production data reviewed → probable causes identified → relevant work instructions retrieved → quality engineer notified → corrective action initiated → outcome tracked.

The human quality expert still needs to be responsible for those decisions in which judgment and compliance play a role.

This agent streamlines the process of decision-making, thus making the transition from quality problem recognition to quality problem resolution much more efficient.

Manufacturing Dive has noted the increasing popularity of agentic workflows in the context of manufacturing, especially when it comes to exception management and detecting production problems.

4. From Engineering Information to Faster Configuration

There are cases when OEMs operate within a complex engineering environment involving product configuration, manufacturing process, documentation, and customer requirements.

Agentic AI could facilitate the coordination of these information processes.

Specifically, an engineering agent could analyze product configuration, find relevant specifications, recognize manufacturing limitations, and generate necessary documentation.

The bottom line of using such an approach does not involve merely quick generation of documents.

It can mean shorter engineering cycles, faster product configuration and reduced engineering effort per order.

For OEMs operating with highly configurable products, even modest reductions in engineering cycle time can improve responsiveness and capacity without proportionally increasing headcount.

The Real Transformation Happens Behind the Agent

There is an uncomfortable truth for OEMs:

Agentic AI exposes weak digital foundations very quickly.

An agent will not be able to coordinate the workflow if the key data is held in separate systems, master data is inconsistent or business rules are only known by humans.

Manufacturing Dive has pointed out that data quality, infrastructure, and integration are the main barriers to scaling AI and agents for manufacturers.

It implies that the transition from AI pilot to AI agent is more than just a model update.

It often requires:

Connected data → integrated systems → defined workflows → clear decision rights → AI agents → human oversight → measurable outcomes

This is why an AI initiative should begin with the business process rather than the technology.

If an OEM cannot clearly define the workflow, the decisions involved, the systems that support those decisions and the metric that needs to improve, adding an agent will not automatically solve the problem.

ROI Needs to Be Defined Before the Agent Is Built

OEMs should also resist the temptation to measure success by the number of agents deployed.

A better business case starts with a baseline.

If the objective is maintenance improvement, measure:

• Unplanned downtime
• Mean time to repair
• Technician hours
• Spare-parts availability
• Maintenance cost

If the objective is working-capital reduction, measure:

• Inventory turns
• Active inventory
• Stockouts
• Expedite costs
• Supplier lead times

If the objective is quality improvement, measure:

• First-pass yield
• Scrap
• Rework
• Defect escape rate
• Corrective-action cycle time

The ROI of such an investment will have to be assessed based on the changes in those key performance indicators.

This is increasingly significant as agentic AI enters production stage. According to McKinsey, organizations will have to assess the cost as well as value of agentic solutions on scale, beyond the mere technology assessment.

Thus, for an OEM, the successful agent is not always the one performing the most number of actions.

It is the one that moves a business metric in the right direction.

OEMs Do Not Need to Automate Everything

The most effective agentic approach to using AI could entail far less autonomy than what the technology implies at first glance.

The OEM could enable the agent to automatically categorize a service request, but leave it up to a person to authorize the assignment of a tech.

The OEM could allow an agent to identify a possible supplier risk, but leave it up to procurement to authorize the escalation.

The OEM could permit the agent to recommend a shift in the production schedule, but still leave it to the plant manager to make it happen.

The objective is to determine where autonomy creates value and where human judgment remains essential.

This human-in-the-loop approach is particularly important in manufacturing because decisions can have implications for safety, product quality, regulatory compliance and customer commitments.

The right question is therefore not:

“How much can we automate?”

It is:

“Where can AI take responsibility for the next step without compromising the decisions that require human accountability?”

From AI Experimentation to Business Outcomes

Phase two of AI integration into OEMs is no longer going to be about the number of pilots a company can run. It’s going to be about the level of effectiveness with which the technology integrates into the business model and enhances its key performance indicators.

For OEMs, this means getting past the idea of implementing AI in isolated examples and discovering where decision-making can help improve uptime, quality, service efficiency, inventory, engineering effectiveness, or revenue.

This is where the role of a digital transformation partner becomes important. Agentic AI should not be introduced simply because a process can be automated. The starting point should be the business problem, followed by the workflow, data, technology and level of human involvement required to solve it.

A consulting-led, AI-first approach keeps the focus where it belongs: on connecting technology investment to measurable business value.

For OEMs, Agentic AI is not the destination. It is another layer in the evolution of digital transformation, one that can connect data, decisions and execution across complex industrial workflows.

The organizations that capture the greatest value will not necessarily be those deploying the most sophisticated agents.

They will be the ones that identify the right problems, redesign the right workflows and measure whether AI actually changes the economics of the business.

The question is no longer whether an AI agent can act. It is whether the right AI action can produce a better business outcome.