Inevitable Innovation

In the mobile industry, some developments are just inevitable. It’s only a question of time before the right mix of technologies, demand, and agents of change turn inevitable innovations into market reality. I was reminded of this while attending a recent webinar [1] organized by the Transport Data Initiative Forum. This featured stories about pilot projects in several UK regions to apply ‘IoT’ data to tackle a variety of what might be termed smart city applications. 

Several of these pilots involved applications built with mobile network operator data, provided by the UK’s O2. Pilot projects involved several municipalities. Each of them focused on a local need (e.g., road maintenance, environmental monitoring etc.) and each built applications using data from a range of their municipal sensors and data sources. In several cases, they combined their local data assets with mobile network operator data. 

I recall discussing this inevitability with a mobile operator in the UK, around 2017. The challenge in those days was to persuade different parties about the value of sharing data across organizational and commercial boundaries. A related challenge was the need to explore collaborative business models based on the equitable sharing of rewards. 

How Outsiders are Shaping the Telecommunications Market

A few weeks ago, the editorial panel at IoT Now invited me to give a short talk about innovation in the communications sector and the implications for digital transformation. This talk was part of a series of webinars [1] on 5G promoted by Fibocom, the global supplier of wireless communication modules and IoT solutions.

The key messages from this presentation are worth repeating in light of a recent technology licensing announcement [2] involving Huawei and Volkswagen (VW). This illustrates a reversal of the mobile industry’s approach to service adjacent markets. Now, organizations in adjacent markets are entering the mobile ecosystem through initiatives that will influence mobile operator strategies and business practices.

Hyperscalers and IoT Market Prospects

The analyst firm, Transforma Insights (TI), recently published a report comparing the cloud hyperscalers – Google, Microsoft, and Amazon Web Services – and evaluating their readiness for IoT and digital transformation solutions. The report is a timely reminder of the role of IoT in digital transformation. It is also a reflection of latent demand for IoT-enabling services and solutions.

Microsoft is marginally ahead of the pack, scoring just over 50 out of a possible total of 100 points in Transforma Insight’s evaluation framework. Each hyperscaler continues to deploy a range of capabilities and developer tools, which would correspond to a higher score, with the aim of maximizing their share of the market opportunity. However, issues about the role of platforms in on-line advertising and social media markets suggests that complications face the hyperscalers.

IoT Platforms and Digital Regulation

A couple of recent and seemingly unconnected publications provide food for strategic thought on the topic of IoT platforms. 

Platforms are an important topic for the following reasons. As businesses deploy Internet of Things applications, many will turn to the service provider market for affordable, feature-rich, and well-engineered platforms. Platforms also represent an important topic for the large Cloud-providers, such as Amazon Web Services, Google Cloud and Microsoft Azure, who understand the importance of platform strategies and data. 

The first publication that caught my attention is a short article on the IoT Agenda site. It outlines that issues of IoT technology fragmentation and discusses the trend towards concentration in the IoT platform market [1]. The second is a study by a group of economists with an expertise in platform economics and competition policy. They studied the EU’s Digital Markets Act (DMA) and its regulatory implications for large and dominant digital platforms [2]. 

2020 in Review: Corporates Adapt Their IoT Business Models

This review of 2020 corporate initiatives in the IoT market builds on a history of tracking strategic industry developments for over a decade. Two sets of corporate events that bookended the start and end of 2020 provide instructive examples of the roadmap and dead ends that characterize today’s IoT market. In the intervening months, organizations in different parts of the industry ecosystem bolstered their IoT strategies. Some developed complementary capabilities through M&A while others addressed go-to-market issues through business reorganization and product-innovation initiatives. For many organizations, however, there remain challenges in balancing short term imperatives with strategic positioning goals. There is a degree of comfort in embracing the familiar. The risk is that this leads to an under-investment in properly integrating new business approaches and complementary technologies.

Where the IoT Market is Heading

I delivered a presentation some weeks back at an online conference for the managed-services industry. My talk was about the implications of IoT for digital transformation [1]. To prepare for the presentation, I began by looking back over the past decade of market developments, joining a sequence of past and present developments to see into the future of IoT. This exercise provided useful insights into the evolving pattern of customer needs, consequences for where the market is heading and, implications for strategy and business innovation.

Opportunities to Apply AI and ML in IoT Systems

My last article [1] introduced a framework to explain the basic elements of an IoT system with the aim of highlighting where Artificial Intelligence (AI), Machine Learning (ML) and Digital Twin (DT) components are typically added. 

The aim of this article is to explore the longer-term opportunities for AI/ML technologies and how these will shape mobile operator and technology provider business strategies. There are two developments to consider in drawing out this roadmap. One is the tighter integration of IoT and AI/ML technologies vertically along the technology stack. Think of this as a way of improving how well different components interact to improve reliability and service quality. The second concerns a new set of requirements that users and regulatory agencies will expect from AI/ML systems. As an illustrative example, consider an AI application that issues an alarm that a machine is about to fail with some probabilistic context such as “greater than 75% chance of failure in the next month”. Is it enough to stop a production line based on this read out? In practice, there is likely to be a higher-level requirement that determines the trustworthiness of this alarm based on its performance over time. Like the boy who cried ‘wolf’, does a sequence of alarms point to a deteriorating piece of equipment or a faulty sensor? The judgement required here involves a different set of data and potentially the involvement of other, supervisory AI/ML sub-systems.