After several years of heavy investment in industrial IoT, some manufacturers and asset-intensive operators are reassessing programmes approved when borrowing costs were lower and technology budgets more generous. Leadership now expects a clear link between connected assets and operating margin, and projects that cannot show direct financial impact come under review.
During the earlier phase of digital expansion, pilot initiatives could spread in 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.
Large-scale rollouts under review
Predictive maintenance programmes are among the initiatives most often re-evaluated. Many were designed around large sensor deployments and 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 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 are put into practice, their financial impact remains limited. In several organisations, twins built for analysis have stalled because they didn’t become part of operational decision-making.
Standalone condition monitoring systems are also being questioned. Alerts that flag potential issues may be of value, yet if they do not trigger automatic actions or revise maintenance plans directly, they merely increase report numbers without reducing actual downtime. Environmental and energy monitoring projects linked to sustainability reporting are under pressure too.
Data lake initiatives launched ahead of defined use cases are another area where intentions may have been good, but execution faulty. Storage and processing capacity can be approved quickly thanks to low relative cost, while the organisational changes needed to act on the data can receive 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 to how systems were implemented. Many relied on fragmented vendor environments, with connectivity, device management, control systems, and visualisation tools sourced separately. Integration of these layers consumed more time and budget than expected, and the complexity increased cybersecurity and infrastructure costs – especially in legacy systems.
Another common issue is the void between analytics and action. Plant managers may receive dashboards and alerts, but without revised maintenance procedures or staffing plans, daily routines remain unchanged. In those cases, the connected system produces information but not operational improvement.
Targeted projects still win support
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 in the maintenance system, and alerts led to action. Over time, downtime fell, spare-parts inventory was adjusted, and savings were made to the maintenance budget. Because the financial effect could be traced to specific equipment, the programme retained support while wider initiatives were paused.
Many firms now expect industrial IoT deployments to show a payback period in less than two years. Improvements in equipment effectiveness are being measured against historical performance and linked to interventions. Maintenance savings are validated against historic cost, and inventory reductions are tracked in working capital terms.
Narrower scope, clearer outcomes
Programmes framed around goals like “insight generation” are harder to justify than those linked to specific operating expenses or risk reduction. Capital allocation teams ask increasingly for conservative projections and ownership of operational changes tied to each deployment.
The next phase of industrial IoT adoption may depend on how tightly each connection links to measurable outcomes. Projects that reduce downtime, cut maintenance costs, or free up capital are progressed. Those that cannot show such a link are paused 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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