Within the enterprise technology landscape, the Internet of Things (IoT) has long established itself as a chieftain. There is, however, one small snag that remains with it since its inception – the uncertainty and complexity of the ROI.

The reproach is quite contrary to the notion that IoT has driven a range of initiatives across functions, enabling firms to bring down error rates, reduce risk, and improve monitoring and control and all of these leading to higher efficiency and productivity. Despite this, a majority of the companies are still in the early stages of IoT implementation. This slow adoption is probably attributed to the belief, “Is the return worth the investment?”

Last year, while publishing the fifth edition of a report titled, “,” the research vice president of Gartner, Alfonso Velosa, made an interesting comment. He highlighted that IoT will definitely foster digital transformations but will take five to 10 years to gain mainstream adoption.

This insipid rate of IoT adoption may very well be attributed to the absence of viable use cases, beyond basic proof of concepts. Companies continue to remain at the peak of bloated expectations when it comes to deriving value from IoT-based solutions. In fact, aren’t even sure whether the new technology will pay off or not. Yet, the appeal of IoT is steadily increasing.

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To derive consistent ROI from IoT-based technologies and solutions, the enterprise infrastructure and IT capabilities will need to be scaled up in line with its evolving manufacturing operations. Insights from edge devices will also play a crucial role in this regard.

Currently, only a very small proportion of all the data being aggregated from edge devices is actually used for meaningful decisions. A supports this assertion. The consulting firm suggests that an oil rig with almost 30,000 sensors uses only 1% of the overall data generated. The data is used for tasks like machine control and anomaly detection, and not for preventive maintenance, process optimization, and prediction aspects that can deliver real business value. This shortfall arises as most business leaders do not have a concrete plan when it comes to turning data derived from ‘things’ to actual conceivable revenue.

While IoT holds the potential to generate in the coming years, enterprises will have to capitalize on this opportunity by creating new revenue streams that specifically leverage edge data. And, this will just be the start. Next in pipeline is the task to utilize the processed data, generate actionable machine analytics, and rapidly scale up diagnostic, predictive, and prescriptive capabilities. These may not seem like revenue-generating activities but will actually serve as a baseline for extrapolating ROI through extended asset life.

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The key to solve the IoT data monetization dilemma lies in selecting the right technologies and partners. Organizations also need to find out where in its value-delivery process analytics can be leveraged to curb the costs of noncore functions.

For instance, a major power company developed a to monitor its power generation plants with steam turbines embedded with sensors. One of the company’s steam turbines started vibrating after regular maintenance procedure and the predictive asset analytics application connected to the main monitoring unit triggered an early alert that the unit would soon require blade separation. This quick identification and action saved almost $4.1 million for the company by averting serious damage to the equipment and loss of power generation.

This type of preventive maintenance implemented through systematic processing of IoT data can help enterprises save almost . It’s evident that unscheduled maintenance also disrupts employee productivity. When something goes wrong in a machine, employees will spend valuable work time to locate and solve the issue. For a bigger company, time drain issues will naturally amplify and start affecting the budget. This is exactly where the ROI of IoT lies – not just creating new value but also squeezing out the most from every dollar already invested in heavy assets.

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Predictive maintenance can not only help enterprises enhance productivity and curb costs, but also enable them to explore a new dimension of value-added services (VAS). For example, a leading truck manufacturer teamed up with a renowned fleet management and vehicle tracking service provider to integrate a remote diagnostic solution for all its legacy vehicles.

The procedure included the installation of an which would connect the vehicle with uptime experts of a separate center. At the center, agents offered 24x7 monitoring services to the customers based on critical vehicle codes. Once the agents detected an issue, they assessed the severity of the case and helped the vehicle owner decide whether to keep operating the truck or take it for immediate service. This, in turn, reduces service cycles and preempts expensive equipment failure – reducing the cost of ownership and keeping the fleet running like clockwork. None of this would have been possible without the power of IoT.

Developing IoT solutions and systems as separate operational islands and vertical silos impede the ability to drive collective ROI. Strategic synthesis of IoT data across a scalable and connected technology foundation removes barriers and helps in integrating separate processes into a single unit which is easy to maintain, operate, and expand.

It is clear that in spite of the ROI reservations against IoT, the technology is delivering on its promise of generating value but not how most would expect. The debate is, however, likely to continue unless industry leaders begin assessing IoT for what it is – a business enabler and accelerator.