This week's Wireless Noodle delves into Mobile Private Networks, Device Management and real-world AI deployments. All three are evolving quickly bringing changing market dynamics, new opportunities and some sticky challenges.
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The full transcript of the podcast is available below.
Welcome to this week’s Wireless Noodle. It’s been a little while since the last one, so apologies for that but I thought it was time for a little break. Things got pretty busy at the end of 2020, in a good way. And it’s been lovely taking a break over Christmas and New Year, notwithstanding the Zoom cocktail evening on new years eve which took a while to recover from.
And it means I have a whole bunch of things to talk with you about in these next few podcast episodes. Today I want to share with you some highlights from a series of reports I wrote recently about mobile private networks. I also want to talk a bit about device management, i.e. making sure that remote devices are secure and working properly and therefore able to support the apps that run on them.
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The topic of mobile private networks (MPNs) has been high on the technology agenda during 2020. In particular, it is seen as one of the most immediate opportunities associated with 5G, although 4G MPNs have already been deployed in manufacturing, transportation, energy and many other sectors. The last twelve months has also seen an increasing availability of spectrum that will be useful for MPNs.
Transforma Insights today published a series of reports looking at the opportunity associated with MPNs. The first, 'Mobile Private Networks for IoT: 5G capabilities and new dedicated spectrum will drive demand', looks at the reasons why demand for MPNs is rising. These include factors such as the benefits compared to using other alternative technologies, most prominently improvements in reliability and security, the increasing focus from mobile network operators and telecommunications infrastructure vendors, and the recent spectrum allocations, most notably the CBRS auctions in the US, which have created a stimulus to adoption.
There is a global addressable market for around 42,000 Mobile Private Networks for IoT (excluding agriculture), as discussed in the second report ‘Mobile Private Networks for IoT: Which sectors will see the biggest adoption?' and by 2030 Transforma Insights estimates that there will be 150 million cellular IoT devices connected to private networks. The chart below shows Transforma Insights view on which verticals might have the biggest addressable market.
One of the main conclusions of the reports is that the maximization of the value delivered by MPNs is dependent on the emergence of a global market. Today there is a lot of variation between countries, particularly in respect of where spectrum is available, and who might hold it. This means that the MPN market is inhibited and more localized than might be optimum. A global market for MPNs will allow for more specialization amongst service providers, for instance focused on specific verticals. More specialization will lead to more innovation in services offered.
Over time we expect MPN adoption to grow relatively rapidly, and to see more homogeneity in the deployment environment, e.g. with more countries making dedicated private spectrum available. This almost inevitably goes hand-in-hand with growing productization of offerings, while at the same time the contract size will decline. These factors are closely linked, with average price being a key determinant of adoption, and productization being required to reduce price. At the same time, and perhaps counter-intuitively, we also expect that there will be increasing requirements for value-add on top of the pure network offering.
The verticals in which MPNs are adopted have very different requirements, particularly for IoT. What a port needs is very different from a factory, mall or hospital. This means that once the market becomes big enough, ideally globalised, the market will start to fracture, with vendors inevitably layering on vertical-specific capabilities as a way to differentiate.
The final report in the series, ‘Mobile Private Networks for IoT: Who will be the winners?’ examines all of the various participants in providing MPNs and how their internal dynamics and interrelationships are likely to evolve in the next 5-10 years. It proposes Transforma Insights’ view on who will be the likely winners in terms of realizing the benefit from the market.
Ultimately pure-play Mobile Private Networks as a stand-alone market will not last. The offerings are very useful but they will ultimately need to be subsumed into one of several bigger portfolios of offerings. MNOs will sell MPNs as part of a wider suite of enterprise services, enterprise infrastructure vendors can integrate them quite neatly into a wider networking portfolio, systems integrators will harness them for customized projects covering a broader range of functionality, and an emerging set of specialist service providers will do a similar thing for more vertically-focused productized offerings. Telecoms infrastructure vendors will need to find a way to add additional value to their vanilla MPN offerings.
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The changing face of IoT connectivity is driving increased requirements for device management. This article examines three of them: increasing security needs, the rise of LPWA technologies and growing complexity of application implementation.
The first trend that complicates device management is the increase in security requirements for IoT devices. In a way IoT is a victim of its own success. Just ten years ago there was barely a billion devices that could be described as IoT. Today that figure is around 9 billion, and by the end of 2030 it will have risen to 25 billion. With growing numbers comes an exponential increase in interest from hackers.
There is also a growing raft of regulations including in Australia, the EU, Japan and the US, relating to IoT security. While most of them are positioned as voluntary, the likelihood is that meeting regulated security requirements will become increasingly important, either because of risk of sanctions against the manufacturer or by virtue of increasing user demand for compliant solutions.
Security demands also needs to be balanced with the need for products to be user-friendly, profitable and quick to market. Add in the need to maximise battery life in some cases (see below) and you have a very complex set of interrelated demands. Layering on security protocols which will be good for the lifetime of the device will be increasingly challenging.
The second trend is the growth of Low Power Wide Area devices. The sheer volume of connected devices clearly creates an elevated scale of requirement for device management, but more important are the characteristics of these technologies and the applications they will connect.
LPWA devices can support only limited downloads, necessitating a rethink of over-the-air device management. There is a lot of variation between the technologies, but all of them share the same principle: to extend battery life by reducing the amount of traffic being sent and received. For Sigfox the effective data transfer capacity is around 1KB per day, making firmware updates nigh on impossible. But even for higher functioning technologies such as NB-IoT and LTE-M there is still a big incentive to reduce the amount of traffic being sent and received so as to maintain long battery life.
Furthermore, LPWA technologies are overwhelming used for applications where human intervention is unlikely. Most IoT devices today are accessible by a human that can, if necessary, stage a manual intervention to correct the device. Major legacy applications, from fleet management to industrial SCADA systems, are higher value and usually relatively easily accessible. This makes them less painful to have to manually manage than billions of remote environmental sensors which individually don’t justify a truck roll to reset and are almost always in highly distributed and hard-to-reach locations.
The final trend is slightly more nebulous and relates to the changing nature of application implementation. One aspect of this is the requirement for more efficient provisioning. There is an increasing requirement for applications to be deployed into the field and work without the need for manual intervention. This low- (or zero-) touch provisioning is essential for many IoT applications to be cost-effective. It’s a large part of the reason why there will be significant demand for eSIM – not having to mess around with swapping out physical SIM cards. However, localisation is not simply about SIM cards. It also relates to numerous other functions of the application and the device. Different countries have different regulatory environments relating to data sovereignty, for instance, necessitating different approaches. Also, data delivery (e.g. APNs) needs to be configured. All of these things can be done manually, but IoT applications will increasingly rely on zero-touch provisioning, meaning that efficient OTA device management is a must.
The other big challenge related to application implementation is edge computing. There has been a noticeably trend in the last few years of more movement of application processing, including quite sophisticated elements such as machine vision, to the edge device. IoT devices are becoming much smarter. To quote Bob Swan, CEO of Intel: “increasingly everything looks like a computer”. There is a lot of truth in that, but unfortunately unlike computers, most IoT devices don’t have a convenient human to reboot them when they go wrong, or implement patches. Putting more smarts on unmanned edge devices creates a greater device management headache.
This article has covered just a few things that will drive additional device management requirements. Learn more about the changing nature of device management in IoT, and specifically smart cities, by joining the ‘Cellular IoT for Smart City: An Evolving Landscape’ webinar on 19th January at 5pm CET. Matt will join representatives of 1NCE, IoTerop and Itron to discuss the use of cellular technologies for smart cities and the impact of device management and LwM2M in IoT. Register HERE.
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During 2020, Transforma Insights has been analysing hundreds of examples of Digital Transformation implementations. Many of the most cutting edge relate to the use of Artificial Intelligence, a subject that continues to attract a huge amount of interest from enterprises. There is a lot of talk about the abstract concept of Artificial Intelligence, but what really matters to businesses is how it can be used in real-life, for instance to streamline business processes or open up new revenue opportunities. This article looks at some of the key learnings from the Transforma Insights analysis of real-world deployments, including the motivations for, best practice in, and experiences of deploying AI in the real-world.
The first finding is that AI deployments are typically limited to being deployed in relatively mundane applications but are usually strongly embraced where they are used. The top use cases are for customer behaviour analysis, smart customer support and personalised marketing. Other widely adopted applications include recommendation engines, chatbots and repetitive process automation. All of those can be considered to be relatively simple uses for AI. Of the top applications, only risk analysis and workflow optimisation could be considered to be really sophisticated.
This implies that AI is focusing on relatively low-hanging fruit, an idea that is further supported by looking at the level of risk that we at Transforma Insights perceive from the deployment. We rate only 11% of deployments as having a high level of operational risk. Around half have low risk or are actually being deployed as a way of de-risking operations, i.e. not only do they not themselves present a risk, but they reduce other operational risks to the organisation.
We get a similar picture when we examine what aspect of the business is affected by the use of AI. Over 85% of implementations have a significant impact on internal process efficiency. Around half that have an impact on the company’s value proposition, i.e. what they sell externally, and less than 20% can be expected to have a truly disruptive impact on the industry in which they are deployed. AI is being fantastically impactful on internal processes, but much less so in other areas of business. The focus on internal cost savings is understandable: any dollars saved come straight off the bottom line, so are inevitably the first focus.
In contrast with the relatively low degree of sophistication of the use case, the level of autonomy is quite impressive. Almost one-third of deployments are used in order to fully automate a decision-making process, i.e. to remove a human element from a business process. This implies that where it is adopted, AI is being used in a quite aggressive way, fundamentally transforming the process into which it is being adopted. Where adopted AI is not just an add-on, but is fundamentally changing, or replacing, the legacy process.
AI is also demonstrating a relatively quick time to deploy and pay back. Over 70% take less than one year to deploy, and almost 75% pay back within two years. It’s somewhat inevitable that when enterprises shift to more transformational uses of AI that the deployment and payback times will be longer. Nevertheless, those short time-frames bode well for future use.
Another positive indicator of future widespread adoption is the degree to which deployments are based on productised solutions. Over 70% of AI implementations are based on fully productised solutions, and less than 10% are one-offs. Replicability is going to be a key factor for the widespread use of AI. At Transforma Insights we expect the requirement for customisation to increase substantially the more sophisticated the use case. Nevertheless, it is starting from a very high base line of productization and therefore replicability.
AI as it’s deployed today is also surprisingly lacking in complexity in terms of key deployment parameters. Transforma Insights looks at three areas of complexity: functional (i.e. how complex the project is), stakeholder (i.e. the number of internal an external stakeholders in a project) and geographical scale (i.e. how many countries is it deployed in). Perhaps unsurprisingly AI projects are amongst the most functionally complex that we look at. There is little doubting that AI in all its incarnations, no matter how mundane, is cutting edge stuff. However, stakeholder and geographical complexity rate relatively low. Some of the barriers to deploying other technologies often don’t manifest themselves with regard to rolling out AI. At least not for current deployments.
The message from examining real-world AI deployments is fairly clear. Today we have barely scratched the surface of the possible enterprise applications. The early adoption is understandably happening in those use cases that represent quick wins for the enterprise deploying them. As such it’s no different from the adoption of more or less any other new technology you choose to name. Quick wins tend to involve internal efficiency savings as they have the most immediate impact on the organisation. They also tend to be the easiest projects to implement because of a lack of internal barriers; saving money tends not to need a lot of approvals or necessitate a fundamental transformation in how the company runs. Once you move out of cutting costs and into changing products, the number of interested stakeholders tends to grow exponentially. At that point the complexity levels rise and the time to deploy gets longer. As is the case with the adoption of almost all new technologies, it is the internal process, business model, system and culture changes that are the real challenge. AI is poised to move into this space and anyone serious about adopting it will need to look very carefully first at whether it has made the necessary internal process changes to do so.
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Next week I want to talk a bit about Amazon’s re:Invent virtual event which I tuned in for during December. I will also cover the topic of using different technologies for connecting IoT devices, and a summary of an interview I did with an exec from Sierra Wireless recently.
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.
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