How AI, IoT, and blockchain together unleash the business value of data

This article was co-authored by Nick Dingemans, partner at Watson Farley & Williams LLP.

There have been many discussions around AI, IoT, and blockchain, and many claim these technologies to be revolutionary.

The reality, of course, is that while there is industry-wide acknowledgement of the potential of these three “vectors” and a general understanding of the underlying concepts behind them, businesses are still grappling with how to capitalize on each technology.

Introducing these in the first place is already a major headache for businesses. But perhaps the biggest challenge is that these technologies tend to be treated in isolation, instead of as part of a whole. So, what if we were to connect the dots and bring the three together?

IoT will fuel digital transformation

With 55,000 new IoT devices poised to be added to the network every minute by 2022, you’d be hard pressed to find an industry which hasn’t implemented some form of IoT technology.

These IoT devices are gathering data at a colossal rate, giving businesses near-infinite possibilities to improve their operational effectiveness or even broker proprietary data as a product.

However, we’re just at the tip of the iceberg, as only a tiny fraction of data generated in the world today is being analyzed. Current IoT conversations largely revolve around infrastructure (i.e. 5G networks and connected devices) when we should really be focusing our attention on how data can be processed into actionable insights.

This is where AI comes into the picture.

If IoT data is paint, AI is Picasso

In one of our studies at Ecosystm, we surveyed 1,115 respondents (at the time of writing) and found that less than half of organizations understand the capabilities and limitations of syncing up IoT with AI. This is a key knowledge gap that, if bridged, could be the key to enabling IoT to reach its highest potential.

Photo credit: Ecosystm Global IoT Study 2018

AI is able to make sense of what raw data tells us (i.e. the trends), while machine learning takes those insights and puts them in practice (i.e. actionable insights) at the device or sensor location itself. This then forms a cycle where a device gathers data, AI analyzes this data for insights, machine learning takes these to improve the device, and the device gathers more nuanced data.

For example, a healthcare device that monitors an elderly patient’s condition gathers data. AI then translates this data to understand that perhaps the patient hasn’t taken their medication on time. Machine learning then alerts the system to send more notifications and reminders or escalates the case to a care worker.

This also makes brokering business information on an exchange possible. Data from the healthcare industry, for instance, would be of interest to the insurance and sports lifestyle industries. However, this brings security into question.

Enter blockchain.

Blockchain to quell security anxieties

Envisioning a union

The way forward