How to unlock IoT as an engine of human potential

Profile picture for user Nick Castellina By Nick Castellina February 18, 2019
Summary:
IoT will produce huge volumes of sensor data that, combined with AI, can drive human potential to new levels, predicts Infor's Nick Castellina

Engineer with tablet checks robot welding in intelligent manufacturing factory © PopTika - shutterstock

As information technology trends go, few if any have garnered the attention of IT leaders like the Internet of Things (IoT). But what of the human potential of this technology? On the one hand, IT executives foresee almost limitless potential to improve the customer experience, gained by leveraging tidal volumes of data from IoT sensors placed virtually anywhere and everywhere. On the other hand, business executives voice concerns about just how that data will be managed, and how all these new endpoints will be made secure.

There’s little doubt of the size of the mounting IoT wave. While IoT growth projections vary, virtually all of them foresee very rapid growth, roughly in the 25% per year range for the next several years, reaching a half trillion dollars globally within three years, according to global management consulting firm Bain & Company. Another report predicts the number of IoT devices will grow to more than 20 billion worldwide by 2020 – more than twice the human population of the globe.

IoT and human potential

There is, however, another benefit of the explosion in IoT that is emerging. That is the capability of networked IoT devices to boost human potential, provided organizations are open to modifying some business processes and operations as IoT proliferates.

For example, consider the case of a metals manufacturer that deployed IoT production sensors in an effort to address capacity constraints. Deploying the sensors and harvesting data proved easy. But factory floor employees weren’t using that data, much of which was too complex.

So, IT simplified some of the complex analytics output, enabling operators on the front lines to recognize when production bottlenecks were forming. These operators began engaging in daily production huddles with one another and were given greater freedom and latitude to make production line changes to avoid bottlenecks they foresaw. The result was a 50% increase in production equipment efficiency and downstream savings in capital expenditures – all owing to the IoT-driven increase in human potential of frontline workers.

However, billions of new Internet-connected endpoints are just one piece of the IoT equation. This is similar to the Internet revolution that started in the early 1990s. Without browsers and relatively low-cost networking and the consumerization of personal computing, the Internet would not have become much more than a global network designed chiefly for national defense purposes.

Mixing IoT and AI

And so it is with IoT. While sensors will produce and transmit heretofore unheard-of volumes of data, sophisticated platforms will then need to manage, analyze and ultimately create business insights from all the data. Said another way, such platforms will be needed to bring deeper context by blending IoT data with other sources such as transactional data and then model it into frameworks with consistent metrics. This can change the very nature of work in some cases, such as in the metals factory example above.

Increasingly too, these platforms will make use of Artificial Intelligence (AI), as advances in AI are bringing a convergence with IoT. In fact, experts believe AI soon will become indispensable to IoT solutions. The reason, according to PwC, is that major features of IoT, such as connectivity and sensor data, are leading to requirements for ‘dumb’ devices to morph into intelligent devices. As PwC sees it:

IoT needs smart machines. Hence the need for AI.

Consider a logistics/transportation use case for AI and IoT, say for fleet operations. Historically it has been difficult, if not impossible, for fleet managers to deeply understand the condition of an asset, like a semi-trailer truck. Managers would need to track far too much historical, current and future data. With big trucks, this means overlaying historical asset management, work management, reliability, and sustainability data with third-party data sources – operating statistics, climatic conditions, depreciation, on-board data, and so on.

AI platforms available today can aggregate data from these and many other seemingly disparate sensor data sources, exposing valuable insights to fleet managers and enabling them to track individual trucks or entire fleets. They are then better equipped to make independent decisions to improve operational efficiency, ensure compliance with safety regulations, and better coordinate fleet operations with sales and marketing efforts.

Better independent decisions with better data

Thus, the relationship of IoT and AI will amplify human potential in line managers and workers closest to the action. They will be empowered to make informed, business-critical decisions without having to seek permission or directives from higher up. Workers will do a better job of predictive analysis by determining when a major piece of machinery will fail, preventing that failure through proactive maintenance. They will adroitly handle more prescriptive tasks, as well. IoT sensors can yield data suggesting immediate action at the edges of operations – say at remote drilling rigs – and help avoid work stoppages or even disasters. And with AI working in concert with IoT and ‘learning’, as more data pours into sophisticated machine learning algorithms, AI-based IoT platforms can eventually learn to take many actions autonomously, freeing up workers for more vital tasks.

This AI-IoT wave is inevitable. The potential of these technologies to lift human potential in the workplace will depend upon the planning done today by IT and business teams, before the wave crashes ashore in full force.