The first episode of The Wireless Noodle podcast is now available to download. The main focus is a book I wrote earlier this year called The Internet of Things Myth. For the podcast I have chosen a few selected topics from the book to illustrate the key point of it: why has IoT failed to live up to its promise?
The full transcript of the podcast is available below.
If we go back ten years there were promises that the Internet of Things would hit 50 billion devices by 2020, transforming businesses and lifestyles along the way. We’re not even close to that. Why not?
Welcome to this, the first in a series of weekly podcasts. I’m Matt Hatton, technology industry analyst with Transforma Insights.
Every week I will dig in to a topic or two from the world of Artificial Intelligence, Internet of Things, Blockchain or some other disruptive technology, and look at how they affect businesses.
Sometimes it’ll be based on a big event, or something that happened during the week. Sometimes it might be inspired by some research that I and the team at Transforma Insights has done. Or I may just pluck a random topic out of the ether.
The aim is always to provide insight to business people on what’s happening in the wonderful world of tech.
This week I’m going to be talking about a book I wrote earlier this year called the Internet of Things Myth. I would say it’s available in all good bookshops, but it’s not. It’s only available on Amazon. I’m not going to try to serialise it, but I think it’s worth pulling out a few areas from the book to discuss.
Before we start, I should set the scene for those who are, perhaps, less familiar with the Internet of Things. Really it’s an umbrella term for a lot of different technologies and an almost incalculable number of use cases but the central tenet is about connecting a remote device in order to either take data from it, control it, or both. The examples are varied, including for instance electricity smart metering which removes the necessity to send someone out to read the meter, remotely piloted drones, connected heart rate monitors, pet trackers, factory automation, and so on. The constituent parts are a device with some form of connectivity, a network to connect that device, and some kind of software to take the data off the end device and deliver it to the back-end application. There are a bunch of other functions too like device management and data analytics. But that, at it’s heart is what IoT is and does.
Back in 2011 some industry commentators were promising an IoT market of 50 billion connected devices by 2020. We’re in 2020 and we can safely predict that we won’t reach anywhere near that figure by the end of the year. Our latest prediction is between 8 and 9 billion. It would probably have been comfortably over 9bn but for the dreaded COVID-19.
As a little side-note COVID has probably bumped up demand for IoT-type monitoring services, for supply chain or automating manufacturing. And there are also a bunch of specific COVID-related use cases that can be addressed by IoT, like occupancy monitoring and even contact tracing. But the challenge has been one of supply. Manufacturing has been slowed down, global hardware supply chains have been problematic, and installation has been delayed. More on the impact of COVID in the next podcast.
Back to the task at hand. What happened to 50 billion connected devices? Why did we not hit that figure, or get anywhere near it? In ‘The Internet of Things Myth’, which I co-authored with fellow IoT veteran William Webb we tried to pull it apart, to lift the lid on the challenges and mis-steps that held back IoT adoption.
The first thing to note is that not every company was forecasting 50 billion connected devices. In late 2011, for instance, Machina Research, an analyst firm which I founded, was predicting just 12 billion devices by 2020. We were conservative, but we still overshot the market size by about 25%. It’s worth taking a moment to say that over a 10 year forecast of something growing that rapidly, only being 25% out is really not bad. However, the 50 billion figure grabbed the headlines, at least in part because it was one of the bigger estimates. That said I also remember seeing predictions of 1 trillion devices. At some point you start getting into questions of definitions. It’s best not to go down that path. But if you were counting every sensor in every car or smartphone or any other device, you may get a 1 trillion figure. However, based on the idea of a stand-alone connected device, 50 bn captured the headlines.
[If IoT Forecasts are your thing, take a look at the new Transforma Insights TAM Forecast database. Press release: 'Global IoT market will grow to 24.1 billion devices in 2030, generating $1.5 trillion annual revenue'. We also talk abot the headline figures on our blog. A summary report is available for anyone signing up as an 'Essential' subscriber.
It’s easy to see why some commentators got carried away. During the first half of the last decade there was a substantial reduction in barriers to entry to using IoT. In fact before 2010 we weren’t even talking about IoT we spoke of telematics or machine-to-machine. But a few things happened in parallel: cost of hardware fell, and connectivity costs plummeted. And there emerged a plethora of software platforms that made deployment and management of devices and data flows significantly easier. Between 2010 and 2015 the discussion shifted to ‘Internet of Things’ just as the technology became more democratised, more capable and cheaper.
In the second half of the decade, however, two things happened.
Firstly the technological progress dried up somewhat. I sometimes talk about the First IoT Winter. In AI they go in for ‘winters’ a lot. There have been two so far, in the 1970s and the mid-80s to mid-90s. They are the slow periods, following times of great technological innovation and the subsequent wave of progress that follows. For instance, the arrival of Deep Learning pulled AI out of its last winter. And we may be on the cusp of another in AI.
IoT during the latter half of the 2010s felt similar. The great technological developments of the first half of the decade that heralded the change from machine-to-machine to IoT (although the terminology itself doesn’t really matter) dried up somewhat.
That isn’t necessarily a bad thing. After such substantial progress, a period to allow the bedding in and adoption of a stable set of technologies is probably a good thing. Too much change can be unsettling for anyone adopting the techs. How do you know you have the right one? Will the suppliers disappear? Will I be left using an outdated technology for my IoT implementation which I will probably have to live with for 10 years. It’s good to have a little bit of stability in the technology environment.
The second thing that happened though was that enterprises and consumers generally struggled to adopt and deploy these new technologies. The big question was: why?
Let’s get one out of the way early on. There are some significant IoT use cases that are backed up by regulatory mandate. Things like connecting cash registers in the Czech Republic or fire alarms in the UAE. Even those applications that are driven by regulation have seen slower than expected growth, and some of them account for potentially billions of devices. As outlined in detail in the book, smart meter roll-outs in the EU alone were 200 million devices short in 2020 compared to initial mandates. The EU also had a mandate for connecting all cars to provide automatic emergency service response with something called eCall. This was similarly delayed, leading to a short fall of up to 100 million. Furthermore, much heralded government investment plans such as in China and India failed to create the anticipated boom in smart cities connections.
But the more pronounced shortfall was with discretionary IoT adoption.
The buzz around IoT perhaps fooled forecasters that the wacky ideas for connected products that graced the floors of CES were realistic: connected wine bottles, dental floss, toasters, and so forth. The reality was that the benefit to the end user was limited and certainly not worth the associated premium. Certainly not if the customer is paying. I should speak up at this point a little for the oft-derided connected fridge. There is value in connecting a fridge, it’s just not for telling you if you’ve run out of milk. It’s to provide information to the manufacturer, or maybe even to provide refrigeration-as-a-service. The problem with those business models is that they depend on the manufacturer picking up the additional bill-of-materials cost. Much better if the customer pays a premium for a connected version. But for the fridge, as with other products, the consumer does not seem keen to do so.
Even where the product was viable, many IoT customers will have been put off by records of poor customer care, such as the ‘bricking’ of devices like garage doors, lighting and music systems. We’ve seen this recently with the likes of Philips and Sonos. Lifecycle approaches more associated with smartphone apps were applied to physical products like lighting and music equipment that might otherwise has been expected to last for decades.
The lesson that Webscale dynamics don’t apply in the physical world is one that has seemingly taken a long time to learn. And, what’s more, the development process for IoT laid bare the inherent friction between the ‘internet’ and the ‘things’ worlds. The internet (or software) world is characterised by much greater tolerance of faults, less robust testing and faster iteration and time to market. The hardware industry, in contrast, comes from a heritage of organisations that are much more risk averse, understandably so because when hardware fails people die. Sometimes it feels more like the Internet vs Things than the Internet of Things.
While bricked products and poor user experience may be costly, frustrating and not conducive to encouraging adoption of IoT, security and privacy threats also have the potential to cause fundamental mistrust of all things connected. There have been numerous examples of security flaws, from the hacking of the Jeep Cherokee in 2015 to the Ring Santa Claus hack last year. These issues spill over into user reluctance to adopt. The same is true for enterprises, which have had equally challenging experiences. More on IoT security in New security rules and regulations for IoT.
We should also, at this point, highlight that the vendor community has demonstrated some serious flaws in how it addressed the IoT market. In part we can put this down to growing pains. For instance many have shown far more interest in moving into other parts of the value chain or chasing unicorn-style valuations than in delivering value. That’s a topic for another time though.
The conclusion is that the ecosystem has not been great in fostering a set of capabilities that are easy to adopt. Focusing in on enterprise IoT, there have also been big flaws in the approaches taken to implementation. Good ideas that have been rolled out badly, or for various reasons, not at all.
Early IoT implementations were generally appended to existing practices, simply offering a more efficient way of doing things. For instance, monitoring refrigerated shipping containers as a way to ensure the cargo had been cared for within acceptable parameters. Very valuable, but not likely to necessitate a fundamental change in how the shipping company operates. As the complexity of the application increases so too does the requirement to change business processes. Take for instance Anticimex, a pest control company, which introduced remote monitoring of its traps, an upgrade on the previous requirement to send a person to check them regularly. This relatively simple switch to a remote monitoring solution necessitates a new approach to customer care, workforce scheduling and more.
As further complexity is layered on, the implications for commercial and organisational change grow even further. One of the more extreme examples is any IoT solution predicated on providing an ‘x-as-a-service’ (xaaS) business model, such as paying for jet engines or agricultural equipment based on the number of miles flown, or amount of grain gathered. These types of approaches necessitate a substantial overhaul of the way in which the organisation operates. For instance, the shift from a capex-based model (where the client pays up-front for the hardware) to an opex-based model (based on ongoing payments) will transform the Finance department, with a recurring revenue model and a much greater loading of cost onto the balance sheet. That’s in addition to adapting to a completely different sales model, ongoing customer care requirements, demand for field service technicians and so forth.
The change implicit in embracing IoT, and particularly the most sophisticated models, within an organisation, ranges from disruptive to transformational. Adopters of IoT have typically not giving sufficient consideration to the commercial and operational implications of deployment. Literally everyone knows that the biggest challenges with deploying IoT are in the commercial and operational changes that need to be made, rather than in just deploying new technology.
One of the reasons why organisations adopting IoT find themselves in an seemingly interminable series of proofs-of-concept (PoC hell as I’ve termed it) is that they focus almost exclusively on the technology, rather than thinking about the wider business picture. This makes it almost impossible to progress the PoC to full implementation.
The fundamental issue is that it is harder to change organisational working practices and business models than it is to adopt new technologies.
In the book I examined eight major areas that are likely to be disrupted within an organisation if it is truly embracing the opportunities presented by IoT. These are:
Process – Starting point should be changing processes, so bare minimum this changes.
Business model –New models await. Maybe simple efficiency savings, or maybe it’s more transformational, e.g. aaS.
Finance – Many techs create or change revenue streams. IoT = recurring revenue streams. Or overhaul for aaS.
People – New technology necessitates new skills. Every company is becoming an IT company. You need skills to match.
Partners – New bunch of companies. Selecting and managing those new partners will be challenging.
Systems – Inevitably there will be new systems, and integration with existing systems like ERP and CRM.
Culture – perhaps the biggest one – DX can fundamentally change how an organisation operates for instance from being a traditional manufacturer to being a services or IT-led entity.
Also an 8th element: change management. Needs a structured and rigorous approach to the inevitable change.
Any meaningful adoption of IoT will involve changes to most, if not all, of those 8 areas. If you want to read more about those 7 areas of change, take a look at 'The 7 internal factors you need to consider to take advantage of IoT and other Digital Transformation technologies'
It is notable the extent to which adopters have ignored the more challenging transformational forms of IoT. IoT deployments have focused almost exclusively on ‘low-hanging fruit’ of simple efficiency saving based on streamlining internal processes. Done correctly IoT is transformational to many aspects of operations. It’s not possible to harness IoT without also being able to make the necessary changes to people and process.
In the book we offer a series of recommendations for enterprise adopters on how best to position themselves to make those people and process changes. That seems like a good way to tie up this first podcast.
Be bold. Some people will advise you to start small, and it’s true that the quickest ROI is usually from the simplest deployment. BUT most likely the small thing is not the thing that’s causing you problems. You won’t find a competitive differentiator in incremental change.
Adopt a ‘systems-first’ approach. Look at your internal processes and systems and work out what you want to change and how you will go about changing it to take advantage of IoT. Start from the business blockage and work back.
Put someone other than IT in charge. The IT department needs to focus on the day-to-day running of the IT systems, rather than the business transformation, which is implicit in IoT. A CTO would be great. And an evangelist at Board level can make a huge difference.
Make sure you’re planning commercial and operational changes as well as technological. It is a common mistake to focus on the technological aspect of testing and deploying IoT to the detriment of the commercial. The two strands need to be managed in parallel.
With approaches such as these, enterprises can make the necessary changes to embrace the transformational possibilities presented by IoT.
Finally this week, who the hell is this Matt Hatton guy and why should I listen to him?
For the majority of the last 25 years I’ve been a technology industry analyst, advising tech companies and adopters on new technology developments. My coverage areas have included cable TV, mobile and, for more than a decade, a focus on the Internet of Things. I founded a company called Machina Research back in 2011 which we rapidly established as the go-to experts on IoT. Over the years we found ourselves talking about all manner of new technology, including AI, blockchain, data exchanges, edge computing and so forth. All of these technologies were interlinked and for many it was impossible to envisage one without another. As a result, when I came to co-found another business late last year, it seemed obvious to me that our remit would be broader than just the Internet of Things but should include all disruptive technologies and the impact that they have, or will have, on businesses.
In future shows I’ll talk about horizon scanning. But essentially that’s what I do. Watch the technology horizon for the next big thing that might disrupt everyone’s business. And let’s face it there are a lot of things like that at the moment. And it’s seemingly accelerating.
In the next podcast I will look at the impact of COVID-19. I know, we’re all a little sick of hearing about it now. But it’s the key topic of the day and it’s impossible to ignore. Specifically I’ll be talking about attempts to use AI to solve the COVID-19 threat and, I’ll return to the IoT topic to delve in to the impact that COVID will have on the growth of the various elements of the Internet of Things.
Links to some of the research that I’ve refered to in this week’s show, as well as a transcript of the recording, will be available on the podcast website at WirelessNoodle.com
Thank you for listening to The Wireless Noodle. If you would like to learn more about the research that I do on IoT, AI and more, you can follow me on Twitter at MattyHatton and you can check out TransformaInsights.com.
Thanks for joining me. I’ve been Matt Hatton and you’ve been listening to the Wireless Noodle.