Over the last decade, connected product initiatives achieved what they set out to do. Machines came online, telemetry stabilized, and teams gained visibility into performance across distributed fleets.
That foundation matters. Without secure connectivity, lifecycle management, and structured data, there is no operational insight to build on. Many organizations have successfully established this base. What happens next is where progress often slows.
As fleets expanded, signal volume increased with them. More metrics were tracked. More alerts were generated. Visibility improved, but the ability to interpret those signals consistently did not scale at the same rate.
Connected infrastructures now surface more data than teams can confidently act on. Eseye’s 2025 State of IoT Report reflects this gap: 36% of respondents say they struggle to gather timely and accurate data, leading to poor decisions. The issue is not access to data. It is the distance between signal and sustainable action.
The Failure Point Sits Between Data and Decision
The visible symptoms show up in dashboards and alarms, but the pressure point sits deeper in the analytics layer that converts telemetry into alerts and recommendations.
Most connected architectures follow a rational progression: raw data flowing into rule-based systems, statistical models, or narrowly trained machine learning tools. Those systems flag anomalies against predefined thresholds or learned patterns, then surface alerts for human review. This design narrows search space and guided expert attention, but it was never built to resolve ambiguity on its own.
In stable environments, this approach works. Known deviations are identified against established baselines. Recognizable failure patterns are surfaced. Experts review and act.
Real-world deployments introduce variability those systems struggle to reconcile.
Load shifts, environments vary, firmware versions differ across customers, and sensors drift over time. A spike that appears anomalous in isolation may be expected within a specific configuration. Behavior that remains within tolerance may still indicate early degradation when viewed against longer-term trends.
Rule-based systems perform reliably within clearly defined parameters, but as operating context expands, their accuracy begins to weaken. False positives increase, subtle signals blend into background variation, and alerts gradually become less trustworthy.
Over time, the issue is not volume alone, but rather the erosion of confidence in the signal layer itself.
Expert Judgment Became the System of Record
When alerts lack sufficient context, interpretation shifts to people.
The individuals best equipped to evaluate machine behavior are often highly trained subject matter experts, responsible for escalations, service strategy, and product improvement. As signal volume grows, more of their time moves toward validation. They cross-reference configuration states, historical performance, and service records to determine whether action is required.
Throughput becomes constrained by expert bandwidth. Some alerts escalate unnecessarily. Others remain unresolved because the effort required to validate them outweighs perceived impact. From the outside, the system appears responsive. Internally, progress is governed by human review capacity.
Eseye reports that nearly 40% of IoT leaders believe these gaps damage reputation and customer trust. When detection does not translate consistently into follow-through, customers experience that inconsistency directly.
Most connected product strategies were justified by improvements in uptime, service efficiency, or recurring revenue. Achieving those outcomes depends on a reliable link between detection and execution. Observability does not create that link on its own.
When Interpretation Does Not Scale
At a modest scale, manual interpretation works. A limited set of alerts can be reviewed by experienced operators who apply context. As fleets grow and variability increases across environments and customers, interpretation does not scale proportionally.
Context rarely resides in a single system. Operators move between dashboards, ticket histories, firmware records, asset hierarchies, and CRM systems to reconstruct what is happening. That reconstruction depends heavily on tacit knowledge accumulated over time.
Eseye reports that 34% of organizations say these limitations slow IoT innovation and product development. When action depends on sustained human interpretation, automation carries risk and expanding workflows often requires additional headcount.
Connectivity and observability remain essential. They form the baseline. What determines whether connected products generate durable value is whether the signal layer can interpret complexity with enough context to drive consistent execution.
From Contextual Interpretation to Embedded Execution
The next phase of connected products is not about more telemetry. It is about strengthening the layer between signal and workflow.
Instead of generating isolated alerts that depend on manual review, systems must evaluate signals alongside operational context such as configuration state, asset history, degradation patterns, and workflow implications. When interpretation remains external to the system, expert review becomes the constraint. When it is embedded into the architecture, execution can be scaled.
AI agents introduce a structural shift in this layer. They reason across clusters of machine data and business context rather than evaluating signals in isolation. They apply defined evaluation criteria consistently and determine appropriate next steps within operational systems. The objective is fewer ambiguous alerts and clearer execution paths.
Mesh has long focused on building production foundations for connected products. Our newest solution, MeshInsights, extends that foundation by encoding expert evaluation logic directly into AI agents that assess machine data in context and execute within established workflows.
When the connection between signal and action is embedded directly into the system, connected products move beyond visibility and begin delivering the measurable outcomes they were originally designed to produce.
Kurt Neuens is Vice President of AI Solutions at Mesh Systems.
Learn more about MeshInsights, our solution to applying expert judgment at scale across connected products: