Manufacturers have spent the last decade connecting products, collecting machine data, and building dashboards. Most now have more information than they can reasonably act on.
Connected products generate a constant stream of telemetry, events, alarms, and performance data. Teams can see what is happening across their installed base with far greater visibility than ever before. Yet many organizations continue to struggle with the same questions they faced years ago: Which issues matter most? What action should be taken? Who should take it? And how quickly can it happen?
The challenge is no longer visibility. The challenge is turning machine data into decisions and decisions into action.
That is why AI agents have become one of the most important developments in connected products.
Unfortunately, they have also become one of the most misunderstood.
AI Agents Are Not What Most People Think
When many people hear the term AI agent, they picture a chatbot.
That assumption makes sense. Most of the public conversation around artificial intelligence has focused on conversational experiences that answer questions, generate content, or help users find information.
For connected products, however, value is rarely created through conversations.
A chatbot waits for a user prompt. Someone must decide to ask a question before anything happens. Connected product operations work differently. Machine conditions emerge continuously across fleets of assets, often requiring action before a human even realizes there is a problem.
This is why the most valuable AI agents for connected products are not chatbots at all. They are background agents.
Background agents continuously monitor machine data, evaluate operational context, and take action when specific conditions occur. Rather than waiting for someone to review a dashboard or investigate an alert, they apply expert reasoning automatically and trigger the appropriate next step.
The distinction is important because many organizations already have systems that tell people something happened. What they need are systems that determine what should happen next.
AI agents are also frequently confused with other technologies that support connected products. They are not dashboards with a conversational interface attached. They are not reporting tools that summarize trends. They are not a replacement for secure connectivity, device management, or the infrastructure required to support connected products at scale.
Those capabilities remain essential. AI agents build on top of them.
What AI Agents Actually Do
At their core, AI agents apply expert reasoning at scale.
Most manufacturers already have people inside the organization who know how to interpret machine behavior. Service leaders understand failure patterns. Product experts recognize abnormal operating conditions. Reliability engineers know which signals require immediate attention and which can be safely monitored.
The challenge is that these experts are limited resources.
As connected fleets grow, experts become responsible for reviewing increasing volumes of telemetry, alarms, service events, and operational exceptions. Eventually, human capacity becomes the constraint.
AI agents help manufacturers extend that expertise across every connected asset.
Instead of simply surfacing machine signals for human review, an agent continuously evaluates telemetry alongside business and operational context. Depending on the use case, that context may include service history, warranty status, customer commitments, operating conditions, asset configuration, parts availability, or internal business rules.
The goal is not simply to identify a condition. The goal is to determine and execute the appropriate next action.
An AI agent may:
- Prioritize issues based on operational or financial impact
- Determine likely failure modes
- Decide whether a situation requires monitoring, escalation, or intervention
- Route work to the appropriate team
- Create service tickets or work orders
- Trigger customer communication
- Document the rationale behind decisions
In effect, the agent performs the same evaluation process that an experienced subject matter expert would perform, but does so continuously and at scale.
Why Manufacturers Need AI Agents
Most connected product programs begin by solving the right problems. Devices are connected. Data is collected. Dashboards are deployed. Alerts are configured. Organizations gain visibility into equipment performance that was previously unavailable.
Initially, these capabilities create meaningful value. Teams can identify issues faster, understand asset behavior more clearly, and begin making more informed decisions.
As deployments expand, however, a different challenge emerges.
More connected assets generate more telemetry. More telemetry generates more alerts. More alerts create more decisions that require review and interpretation. Organizations often respond by adding additional dashboards, analytics, reports, and alert thresholds. While these tools can improve visibility, they rarely address the underlying constraint. The real bottleneck is decision-making.
A connected product platform may successfully identify thousands of events across a fleet, but someone still needs to determine what those events mean, prioritize them against competing demands, and decide what action should occur next.
This is where many connected product initiatives stall. Visibility improves, but outcomes fail to scale at the same rate because expert attention remains limited. AI agents close that gap.
By applying expert reasoning continuously and consistently, they allow manufacturers to move beyond monitoring and begin automating the operational decisions that drive measurable business results.
Where AI Agents Create Value
The strongest AI agent use cases are tied directly to business outcomes. They focus on decisions that occur frequently, require expertise, and have measurable operational or commercial impact.
For connected product manufacturers, common starting points include:
- Early Warning: Identifying degradation, abnormal behavior, and failure risk before downtime occurs.
- Service Resolution: Determining what should happen next when an issue emerges, including remote fixes, technician dispatches, escalation paths, and parts recommendations.
- Performance Management: Finding inefficiencies, utilization gaps, throughput issues, and operational waste across the installed base.
- Aftermarket Growth: Identifying replacement timing, upgrade opportunities, maintenance packages, and other revenue-generating moments hidden within machine behavior.
- Customer Health: Recognizing adoption challenges, unresolved issues, churn risk, and other signals that influence long-term customer success.
Each of these use cases shares a common objective: turning machine data into action that creates measurable value.
How to Get Started
The most successful AI agent initiatives rarely begin with a broad transformation effort. They begin with a single, high-value decision.
The best first use cases tend to share several characteristics. They occur frequently, have clear economic impact, rely on available data, and follow decision criteria that experts can articulate and validate.
For one manufacturer, that may mean determining which assets are most at risk of failure. For another, it may involve deciding whether a service event requires dispatch, escalation, or remote resolution. Others may focus on identifying churn risk, replacement opportunities, or service prioritization.
The objective is not to automate everything at once. The objective is to identify one decision that matters, apply expert reasoning at scale, and prove measurable outcomes.
Manufacturers that take this approach often discover that the greatest opportunity in connected products is not collecting more data. It is creating systems that consistently turn machine data into action.
If your organization already has connected products in the field and is looking for ways to move beyond dashboards and alerts, download our ebook, The Last Mile of Connected Product ROI. It provides a practical framework for identifying where connected product programs stall, how to evaluate the decisions that drive value, and where AI agents can have the greatest impact on operational and commercial outcomes.