After several years of heavy investment in industrial IoT, many manufacturers and asset-intensive operators are reassessing programmes approved when borrowing costs were lower and technology budgets more flexible. Boards and finance teams now expect a clear link between connected assets and operating margin, and projects that cannot show direct financial impact are coming under review.
During the earlier phase of digital expansion, pilot initiatives often spread quickly across plants and business units. Sensors were added, data platforms were built, and analytics tools were deployed with the expectation that value would emerge once systems were connected. Today, that broad portfolio is being narrowed as companies focus on projects that show measurable operational benefit.
Large-scale rollouts under review
Predictive maintenance programmes are among the initiatives most often re-evaluated. Many were designed around large sensor deployments and wide equipment coverage, with savings projected from reduced downtime and fewer failures. In practice, some installations have struggled to prove that those savings outweigh the cost of hardware, connectivity, and data management. Where failure rates were already low, incremental improvements have not always justified the investment.
Digital twin projects face similar challenges when the models remain separate from daily operations. Simulation tools can provide insight into how assets might behave, but unless those insights affect maintenance schedules, production planning, or work orders, their financial impact remains limited. In several organisations, twins built for analysis have stalled because they never became part of operational decision-making.
Standalone condition monitoring systems are also being questioned. Alerts that flag potential issues may add visibility, yet if they do not trigger automatic actions or revised maintenance plans, they can increase reporting effort without reducing downtime. Environmental and energy monitoring projects linked to sustainability reporting are under pressure as well, particularly when companies struggle to connect the data to clear cost savings.
Data lake initiatives launched ahead of defined use cases are another area of concern. Storage and processing capacity were often approved quickly, while the organisational changes needed to act on the data received less attention. As a result, some firms now hold large volumes of operational data without a clear path to improved productivity or cost reduction.
Integration and operational follow-through
When industrial IoT programmes fall short, the causes often relate less to technology performance and more to how systems were implemented. Many deployments relied on fragmented vendor environments, with connectivity, device management, analytics, and visualisation tools sourced separately. Integration between those layers consumed more time and budget than expected, and the complexity increased cybersecurity and infrastructure costs, especially where legacy systems required upgrades.
Another common issue is the gap between analytics and action. Plant managers may receive dashboards and alerts, but without revised maintenance procedures, staffing plans, or incentives tied to the new insights, daily routines remain unchanged. In those cases, the connected system produces information but not operational improvement.
Targeted projects still win support
Despite the broader reassessment, industrial IoT investment has not stopped. Projects that continue to receive funding tend to share a different approach: they focus on a defined cost centre, establish a baseline before rollout, and tie outputs directly to operational workflows.
In one manufacturing group, predictive maintenance was applied only to a limited set of high-value assets with documented failure costs. Sensor data triggered automatic work orders within the existing maintenance system, ensuring that alerts led to action. Over time, downtime fell, spare-parts inventory was adjusted, and the savings were recorded within the maintenance budget rather than estimated broadly across operations. Because the financial effect could be traced to specific equipment and decisions, the programme retained support while wider initiatives were paused.
This shift reflects tighter financial discipline across the sector. Many firms now expect industrial IoT deployments to show a payback period within roughly eighteen to twenty-four months. Improvements in overall equipment effectiveness are being measured against historical performance and linked to connected interventions rather than general operational change. Maintenance savings must be validated against past costs, and inventory reductions are tracked in working capital terms.
Narrower scope, clearer outcomes
The current review cycle does not suggest that manufacturers are abandoning digital transformation or connected operations. Instead, it marks a transition from exploratory expansion to targeted execution. Programmes framed around broad goals such as “insight generation” are harder to justify than those linked to specific operating expenses or risk reduction.
Capital allocation teams increasingly ask for conservative projections, sensitivity analysis, and clear ownership of operational changes tied to each deployment. Industrial IoT remains part of long-term strategy, yet it is being treated more as a practical operational tool than a signal of innovation.
For manufacturers and asset-intensive operators, the next phase of industrial IoT adoption may depend less on how many devices can be connected and more on how tightly each connection links to measurable outcomes. Projects that reduce downtime, cut maintenance costs, or free working capital are moving forward. Those that cannot show that link are being paused, redesigned, or scaled back as companies align technology investment more closely with financial performance.
(Photo by Rob Lambert)
See also: Industrial IoT scales as LoRaWAN hits 125M devices
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The post industrial IoT projects under review first appeared on TechToday.
This post originally appeared on TechToday.