Ever since the Internet of Things emerged as a concept in the early noughties and grew to impact our daily lives, the narrative has been almost single-handedly wrapped around connectivity.
Inevitably, the promise of innovation and opportunity has run in tandem with the challenge to tackle latency issues and optimise bandwidth; the subtext being that when it comes to the IoT, seamless connectivity equals mission accomplished.
Of course, the ease with which the two sensors ultimately communicate remains a fundamental concern, but as artificial intelligence (AI) is added to the mix, the focus is broadening out and the bar has been raised.
For astute business leaders, having connected and interoperable systems alone, is only part of the equation. With far more now demanded of real-time data streams, harnessing the power of AI-driven analytics has become another essential step as the Internet of Things (IoT) evolves to the Internet of Intelligent Things.
In essence, embedding analytical intelligence into 'things' sees more quality insight derived from the data, which can now be aggregated from multiple devices. In practice, this sees sensors monitoring machines on the factory floor bringing improved predictive capabilities around asset maintenance as potential issues are pinpointed before they bring operations to a halt.
Comparisons can be made more easily, resulting in the slicker detection of anomalies around temperature or energy consumption to, for example, optimize core processes.
The control and understanding that comes from having an enhanced view of device activity, particularly in a rapidly changing situation, translates to a significant business advantage. Furthermore, visibility becomes a powerful weapon in the drive to improve security, an area that has long been compromised when enterprises struggle to keep tabs on what is happening within their IoT ecosystems.
No better is this brought to life than in the context of industrial settings where data must flow between the myriad sensors, devices, assets and machinery in the field, often in unstructured, challenging and remote conditions. Giving sensors sufficient processing power to make mission-critical decisions locally, at the edge of the network, avoids the delays and opportunity for corruption that can arise when data needs to be transmitted to a central cloud.
Perhaps most significantly, the convergence of IoT and AI reminds us that when it comes to data, it really is a case of quality over quantity, with mountains of information not guaranteeing actionable intelligence. Take for example the modern oil platform which typically comprises some 30,000 connected sensors but uses less than one percent of the data generated for decision-making.
Indeed, it is far more about what you do with it and intelligent analytics which extract the core insight, without the excess fluff, which has never been more critical. Especially as increasingly imaginative applications demand more efficient ways of processing and transmitting the volumes of data generated.
Research from the McKinsey Global Institute sets out the potential value to be captured when this more discerning approach is taken. The think tank predicts a total economic impact of $3.9 trillion to $11.1 trillion to be made per year in 2025, based on more than 150 specific IoT applications that exist today or could be in widespread use within the next decade.
Healthcare is an area ripe for this innovation. With our consumption of digital services in this field set to rise exponentially, data-rich personalized analysis of our health will become the norm, with tailored strategies driven specifically by technologies that track and manage health through devices and mobile apps.
Furthermore, machine learning will be able to cut through the swathes of medical data, drawing key insights from patient information to identify the best treatment option far quicker than a human could, cutting the time and cost involved in many clinical processes.
Then there are the possibilities around how we drive and the repercussions for broader traffic management issues. The driverless car, with its promise of replicating the human driver without the human mistakes, remains the most-hyped and oft-cited example for obvious reasons. But for those who prefer to still physically drive their cars, there will still be expectations of a vehicle that is better connected with its surroundings, through technology that provides real-time updates on traffic conditions and weather information, and may also enable us to share information with other cars.
…all of which barely scratches the surface. This is why industry commentators are describing the combination of IoT and AI, two of the most disruptive technologies of our age, as a seminal moment. The savviest operators will embrace the potential of this dynamic duo as soon as possible.