Author’s Note: This post is inspired by themes explored more deeply in our ebook, The Last Mile of Connected Product ROI, which examines why connected product initiatives often stall between visibility and operational action. Download it here.
For years, the connected product conversation centered on connectivity, visibility, and data collection. Today, most large OEMs already understand how to connect assets and monitor equipment remotely. The conversation is changing.
What many organizations are wrestling with now is operational scale. Service teams are overloaded with alarms and exceptions. Product and support teams are buried in fragmented data. AI initiatives are generating interest, but many companies still struggle to translate insights into consistent operational outcomes across large fleets and customer environments.
That gap is becoming one of the defining challenges of the connected product market.
Most Organizations Have Already Solved For Visibility
Over the last decade, connected product investments focused heavily on building the technical foundation for IoT at scale. Organizations invested in connectivity, cloud infrastructure, remote monitoring, telemetry pipelines, and device management. Those efforts created visibility into asset behavior that did not exist before.
Today, many organizations can monitor thousands of devices across customers, facilities, and geographies in near real time. That foundation remains critical to any connected product strategy.
However, visibility alone rarely creates measurable business outcomes. Dashboards can show that a machine is behaving abnormally, but they do not automatically determine what should happen next, who should act, or how work should be prioritized across the business.
As connected fleets scale, operational complexity grows with them. Signal volume increases. Edge cases multiply. Context becomes fragmented across IoT platforms, service systems, warranty data, customer history, and operational workflows.
AI Did Not Remove the Operational Bottleneck
AI has accelerated interest across the industrial market, but many organizations are discovering that deploying AI into real operational environments is more difficult than expected.
The operational stakes are significant. Siemens’ 2024 True Cost of Downtime report estimates that the world’s 500 largest companies lose approximately $1.4 trillion annually to unplanned downtime, equal to roughly 11% of annual revenues.
Industrial operations are rarely clean or standardized. The same fault code may mean different things depending on the asset configuration, operating conditions, firmware version, customer environment, or service history. Operational teams often rely on experienced personnel who understand these nuances and can interpret signals in context.
As a result, organizations continue depending heavily on human expertise to make operational decisions such as:
- prioritizing service dispatches
- validating alarms
- escalating warranty issues
- identifying high-risk assets
- recommending replacement timing
- determining when intervention is necessary
This creates a scaling problem. As fleets grow, expert review does not scale efficiently with the number of devices, signals, and operational scenarios.
Many organizations expected AI to reduce this burden immediately. In practice, AI initiatives often stall because the surrounding operational structure is not clearly defined. Data may lack consistency, workflows may vary across teams, and organizations may not agree on what a “correct” operational decision looks like.
Expert Dependency Becomes a Growth Constraint
One of the least discussed challenges in connected products is expert dependency.
Many connected product programs rely on a small group of specialists who understand how to interpret telemetry, correlate operational context, and determine the right course of action. These individuals often become the operational bridge between connected systems and real business outcomes.
That model works at smaller scale. It becomes difficult to sustain as deployments expand.
Service organizations begin struggling with alarm fatigue and prioritization. Product teams lose visibility into repeat operational issues across the installed base. Customer success teams lack actionable insights that help them proactively engage customers. Valuable operational knowledge remains trapped in spreadsheets, dashboards, inboxes, and institutional memory.
The result is a connected environment that appears digitally mature while still relying heavily on manual interpretation and human throughput.
The Market Is Moving Toward Operational Execution
The next phase of connected products is focused less on monitoring and more on operational execution.
Organizations are beginning to prioritize workflows that connect machine signals directly to business processes. This includes areas such as service triage, uptime prioritization, maintenance planning, warranty prevention, and aftermarket growth opportunities.
These workflows require more than telemetry. They require systems capable of reasoning across operational context and supporting repeatable decisions at scale.
This is where agentic AI is gaining attention across industrial markets. The value comes from systems that can evaluate signals in context, apply operational constraints, and support workflow execution inside the tools teams already use.
The organizations seeing the most progress are typically starting with narrow, high-value operational problems. Examples include reducing unnecessary truck rolls, prioritizing critical service events, identifying recurring failure patterns, or surfacing proactive maintenance opportunities.
These initiatives tend to create measurable value faster because they focus on operational outcomes rather than broad transformation goals.
Closing the Last Mile
Connected product programs rarely fail because organizations lack data. More often, they stall because the path between sensing and acting remains fragmented, manual, and difficult to scale.
As AI adoption continues to accelerate, the organizations creating meaningful ROI will likely be the ones that operationalize decision-making more effectively across service, operations, and commercial workflows.
Our latest ebook, The Last Mile of Connected Product ROI, explores why connected product initiatives often stall after achieving visibility and outlines a practical framework for moving from signals to measurable operational outcomes at scale.