The failed promise of the IoT

The must-have good design practice to get the most out of AI and IIoT by ensuring the technology can deliver business value.?

Like a bolt from the blue, the IoT lit up the technology skies when it was coined back in 1999 by Kevin Ashton for a sensor project he was working on. Since then, the IoT has been behind significant innovations driving smart cities, medical devices, industrial processes, etc.

The IoT promise offered improved efficiencies, deep insights, and better productivity. Over the last twenty years, the IoT has become synonymous with everything, from a fridge to the driving force behind Industry 4.0.

However, in recent months, the IoT has fallen from grace. According to an exclusive report from?The Register, December 2023, IBM will sunset Watson IoT and has no plans for a replacement. IBM is not the only company to drop the IoT baton;?Google announced?in August 2022 that it would deprecate its Cloud IoT Core Service on August 16 2023.

These decisions seem odd in light of predictions from?analysts that IoT?will continue to grow globally, year-on-year, with a predicted spend in 2027 of 483 billion USD.

So, are the decisions by major companies such as IBM and Google a portent of ‘things’ to come? Or can the IoT morph into something even more critical for the industry with the right approach?

The failed promise of the IoT: is the tail wagging the IoT dog?

The Industrial Internet of Things (IIoT) has oft been hailed as a transformative force that would revolutionise the industrial landscape. IIoT promised to increase operational efficiency, optimise production processes, and enable remote monitoring of industrial facilities. However, despite the hype and optimism, the IIoT still needs to live up to expectations.

The current IIoT landscape faces tough challenges, failing to deliver its promises. Failure has prevented the IIoT from reaching its full potential; two fundamental reasons link?value and indicate?why the IoT, specifically the Industrial IoT (IIoT), is failing to deliver:

#1 Lack of alignment of IIoT to business value

This reason is the most fundamental and most impactful. Without understanding what the business wants and needs from IIoT projects, a program initiative can spiral out of control. One of the most critical parts of ensuring success with IIoT is to evaluate the use cases during IIoT definition and develop a comprehensive set of project requirements. This is not always done.

#2 Inability to extract value from sensor data

The IIoT requires massive amounts of data from multiple sensor points. Extracting the total value of these data is a challenge. Technologies that are developed to take account of this, and design to this remit, are more likely to be able to extract value from sensor data.

Because these core issues are not always addressed, the future of the IIoT platforms may look bleak. However, help is at hand. Instead of the tail wagging the dog and technology driving business, what businesses want, i.e., value, must be imbued into the design process; AI and IIoT technologies must work symbiotically and transparently to achieve business goals.

Ultimate value: alignment of IIoT and AI to business value

"IIoT platforms have failed to deliver on their promise because of the lack of clear business value."

IIoT solutions are often viewed as technology in search of a problem; many industrial facilities need to be convinced of the value of IIoT solutions and can become hesitant to invest in them without evidence of this value.

Despite technological advancements, numerous companies still offer Industrial Internet of Things (IIoT) devices with sensors that display data to factory operators. However, these companies need help to extract value from their platform or solutions. In today's world, technology must be transparent, and platforms must utilise technology to emphasise delivering business value rather than merely offering a plug-and-play IoT solution.

Remember, data is there to do a job. However, when designing a solution, keep in mind these two core elements, which must be used within a dedicated AI-driven platform to ensure data is insightful:

#1 Remove data silos: improve interoperability

Often, IIoT platforms fail because they are proprietary and need to work better with other platforms. This creates data silos that limit the usefulness of IIoT data. By improving interoperability between different systems and devices, organizations can break down these silos and enable seamless data sharing and collaboration. This can lead to improved efficiency, productivity, and decision-making capabilities, ultimately driving business growth and success.

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#2 AI and IoT data: the whole is greater than the sum of the parts

Exploring alternative ways that AI can use data generated using IoT sensors is vital. An example is when applied to?visual inspection and insights; sensors?are used to capture digital or analogue electrical signals from equipment. The?data?includes variables such as vibration, temperature, and workload.?Digital AI-enabled cameras?provide a view of floor locations where workers are essential to the production process.?Using?computer vision and AI,?images from the factory floor are captured in real-time. Data can even be captured where no electrical signals are present; images are interpreted using computer vision and AI, resulting in real-time oversight of human processes—quantitative, real-time data for operations that previously could only be observed by walking the factory floor.

How to ensure AI and IIoT work in symbiosis

“AI technology is key for unlocking the potential of IIoT and enabling digital transformation.” – Industry IoT Consortium paper 2022

This exploration of AI, together with IIoT, bsed on the JTBD approach, takes the above statement a little further. The way forward is to deliver business value from technology, including from the symbiotic match of AI+IIoT; but how is this achieved?

Design with business value in mind

You cannot just take two technologies, even powerful and disruptive technologies such as AI and IIoT, and expect to build products that magically fit a business need. As Steve Jobs once said, "Start with the Customer Experience, then work back to the technology." Jobs wasn't talking about the IIoT or AI then, but the principle remains. Solutions are just that; they solve a problem, in this case, a manufacturing problem; technologies are the tools, but the business need informs how those technologies are used.?

Delivering business value must begin at the design stage. The?Jobs-to-be-Done (JTBD) framework,?was invented by?Tony Ulwick. JTBD centres on the audience, i.e., design for the product's audience. This approach uses?goals,?situations,?and?motivations?as design remits.

AI and IoT are enabling technologies, but they cannot meet the question, "how does a solution help get the job done?"?

In truth, a customer does not want IoT platforms, AI-enabled technology, or "tech for tech's sake;" They want to achieve better efficiencies out of manufacturing processes.

Source: Alan Klement on Medium

AI is vital for the future of the IIoT, but it must be designed for business

“95% of our customers doesn’t even know we are leveraging AI and IoT as technology to deliver value to them” – Statistics from ThingTrax Manufacturing Performance Platform

AI and IIoT offer massive potential for manufacturing, but industry wobbles, such as the deprecation of IBM Watson IoT, may be causing concern. Now is the time to take stock and realise the potential of these valuable technological innovations. A rethink about how solutions using AI and IoT needs to happen to take full advantage of the technology's capabilities. AI is a technology that can be used to drive business value. But the IoT and AI must be hidden or transparent, allowing the product to focus on the business value it gives to its customers. IIoT and AI will continue to be a component of the overall narrative rather than a prospective. Successful platforms will keep focusing on "Jobs to Be Done" and "Ultimate Value" to create value for their customers and Investors.

This JTBD approach is the philosophy here at?ThingTrax; our Manufacturing Performance Platform utilises AI and IIoT to deliver customer value, but our customers are unaware that both AI and IoT power the platform as they are entirely transparent to our customers. In addition, the ThingTrax platform uses a highly intuitive integration layer around the platform, which will help by ingesting outside data and exporting data at a click.

ThingTrax customers want tremendous business value, which is what #ThingTrax gives them.