What can we expect from the IoT world in 2022? If the past two years taught us anything, it is that we cannot prepare for everything. But some trends and technologies can help guide our way. Consider how the rise of AI has pointed to more intelligent IoT solutions—making the tools easier for everyone to use. This, in turn, could result in stronger regulations or efforts for trustworthy AI.
Or think about how the move to a remote workforce as well as increased virtual care services point to a broader use of 5G to support home broadband and ensure connectivity going forward.
And there’s still so much more to look forward to. In this podcast, we talk about lessons learned in 2021, IoT technology trends to pay attention to in 2022, and how the IoT landscape will continue to evolve beyond next year.
Our Guest: CCS Insight
Our guest this episode is Martin Garner, COO and Head of IoT research for CCS Insight, where he focuses on the commercial and industrial side of IoT. Martin joined CCS Insight in 2009 with the desire to work with a smaller, independent firm focused both on quality and clients. Every year, CCS Insight publishes predictions on network technology, telecoms, and the enterprise. This is the 15th year that CCS Insight is publishing its predictions.
Martin answers our questions about:
- (3:01) CCS Insight predictions in 2021: What went wrong and what went right
- (8:06) Technology trends and predictions for 2022
- (14:57) How the role of cloud players will evolve moving forward
- (17:16) Where cloud-like experiences in on-premises infrastructure will fit into the landscape
- (21:08) Where AI, machine learning, and computer vision are going in the future
- (26:16) Efforts and impacts of democratizing AI
- (28:01) How to address AI concerns
- (30:32) Ongoing transformation of the healthcare industry
- (34:36) The future of IoT and the intelligence of things
To learn more about the future of IoT, read Forecasting the Future of IoT with CCS Insight. For the latest innovations from CCS Insight, follow them on Twitter at @ccsinsight and on LinkedIn at CCS-Insight.
This podcast was edited by Christina Cardoza, Senior Editor for insight.tech.
Kenton Williston: Welcome to the IoT Chat, where we explore the trends that matter for consultants, systems integrators, and enterprises. I’m Kenton Williston, the Editor-in-Chief of insight.tech. Every episode, we talk to a leading expert about the latest developments in the Internet of Things. Today, our guest is Martin Garner, the COO and Head of IOT research at the analyst firm CCS Insight.
They’ve just put out their predictions for 2022 and it is a fantastic read. You can actually go check it out for yourself on insight.tech right now. I am really looking forward to getting into the details of these predictions. So, Martin I would like to welcome you to the podcast.
Martin Garner: Thank you very much.
Kenton Williston: Tell me about your role at CCS Insights and what brought you to the firm?
Martin Garner: Sure, well I have two roles at CCS Insights. One is that I’m Head of IoT research where I focus mostly on the commercial and industrial side of IoT for that. I’m also COO here and I joined CCS Insights in 2009 after Ovin was sold to Informa Group and later became Omnia. I was chief of research there and the attraction of coming to CCS Insights was that it’s a smaller firm, but very quality and client focused and independent, and obviously being smaller, had very good growth opportunities. And I’m happy to say those are all still true 12 years later.
Kenton Williston: Excellent, so on that note, I’d like to know a little bit more about CCS itself, CCS Insights and its annual prediction. So what is this beast?
Martin Garner: So well CCS Insights is a medium sized analyst firm covering quite a lot on the consumer side, very strong on the mobile technologies and devices, quite a lot on the telecoms side itself, the networks and the network technologies, and also strong on the enterprise side, how they use a lot of the technologies ranging from what happens in the workplace through to digital transformation of operations in the industrial world. And the predictions is something that we do each year. Last year in 2021, that was our 14th run of predictions. Now several analyst firms do these. What makes ours a little bit different is that we deliberately do it as a complete cross-company thing across all topic areas. And also all staff contribute to prediction. Some of our best ones historically have come from people who aren’t analysts at all. The other thing is that we carefully track what we get right and what we get wrong and we publish some of that each year. And the aim is to be quite transparent about that and to improve what we’re doing.
Kenton Williston: So one of the things I really like in what you just said is going back and revisiting your prior years’ predictions to see how things played out. That’s really great. The times we’re in have been very difficult to predict. I don’t think there’s any doubt about that. So very curious how the predictions for 2021 played out and what went right, what went wrong?
Martin Garner: Yeah, you’re right. That was a particularly interesting year because it was the first year we were in pandemic conditions. Lots to think about, lots to speculate about. We got a few that we were quite pleased we got right. One was that COVID would accelerate adoption of robots, automation, and IoT across sectors. Now it didn’t initially look like that. There was a pause in investment, but it did then accelerate as people realized they needed this stuff to keep their operations going. Another one was that 2021 would be the year of vertical clouds. And we have since then seen big launches from all of the major players here and that plays into what we’re doing in IoT. And another one was that security and privacy in AI and machine learning would become much stronger areas of concern. I think it’s now widely understood that machine learning is quite a big attack surface and it could be really hard to detect a hack, at least initially.
Now we did get a few wrong that year as well. So we did predict that somebody would buy Nokia and no one did. We also predicted that the regulation of the big tech players would slow down and countries would take more time. Actually in China it’s grown much stronger much more quickly. And that’s being echoed to some extent, both in the US and in Europe. So actually that’s moving faster than we expected. And then there’s a few that we’re waiting on, which were longer-term predictions. So for example, a big cloud player will offer the full range of mobile network solutions by 2025. Now we have seen some big moves in 5G from AWS, from Microsoft, and from Google, but nothing yet on quite that scale. Another one was that tiny AI would move up to 20% of all AI workloads. Now this is mostly an IoT thing where small edge devices really need small AI. There is a lot going on, especially in IoT and the role is growing, but we’re not at that level yet.
Kenton Williston: So one thing you mentioned there I’d love to get a little clarification on is what do you mean by vertical clouds?
Martin Garner: Sure, this is a cloud service. Many cloud services have been offered as a purely horizontal infrastructure thing, like data storage, which everybody has a need for, but actually each sector stores different types of data with different labels, different metadata, different language used even, and they measure things in different ways across sectors, even down to things like the impact of carbon footprints within the sector and so on. And what a number of the offerings from the cloud players are now doing is packaging those up in a way that’s suitable for manufacturing or for automotive or for retail or for healthcare, those kind of things, and deliberately fixing them in the right language, the right constructs, the right metadata and so on, so that they can be more easily adopted directly into specific verticals. It’s one thing I think to launch those services, it’s something else to get them all adopted across those sectors. That’s just a long road to get a big share of that going around the world. And we’re in that stage now.
Kenton Williston: Fascinating, and the funny thing is I think there’s been a lot of really interesting activity both on the cloud side, everything you’re describing about very industry-specific use case, specific activity happening in the cloud, and also just a tremendous amount of activity happening at the edge over the last year, and I think it will be pretty important going forward. So as we’re recording this, for example, just yesterday Intel® announced its latest core processors, which some of the things that are notable there are they’re offering a tremendous upgrade in performance for the edge as well as considerable advances in power efficiency and quite a bit of addition of AI capabilities, graphics, just all kinds of things that are happening. And you mentioned, for example, some of the things that we’re waiting on so to speak are AI at the edge, and there’s just so, so much of that happening at the edge. So it’s I think a really exciting transitional time right now.
And this is probably a good opportunity for me to mention, since I said something about Intel and its fabulous 12th generation core processors, that the insight.tech program and this podcast itself are Intel productions. So full disclosure there, but that leads me to looking forward into this next year with all this tremendous change that is happening in the technology space. What is on your mind for 2022?
Martin Garner: I think overall we have 99 predictions for 2022 and beyond. And we obviously can’t go through all of those here. What we did for this podcast is we did a cut of those that are relevant in some way for the IoT community, and we’ve packaged that up in a report which is available as a download from insight.tech. And I’ll just highlight a few that caught my attention, if that’s okay. So there were a few around the follow on from COVID, and a couple were that by 2025 there’ll be somewhat less use of office space in the developed world. We reckon it’ll be down about 25% by then. Also as a sort of balancing factor, there’ll be much more use of 5G as an additional home broadband for home working. We think maybe 10% of households will have that. I think we’ve all had the experience where you’re trying to do a Zoom call or a Teams call or a podcast and your broadband goes off, and it’s really, really frustrating.
So more backup there. We also saw, coming out of last year, much higher attention on sustainability, and we really think that clean cloud is going to be something of a battlefield this year, partly in cloud services. We also think that IoT can really benefit from using sustainability in its marketing. IoT is great news for sustainability, generally speaking, and we’re not mostly making enough use of that. We also think sustainability will be built into the specifications for 6G, when we get there. And then there’s quite a lot around IoT itself. So, much greater focus on software, machine learning, shift towards higher intelligence of things. Much greater linkage between smart grid and wide area networking. We actually expect to see a pan-utility, where one company is both an energy provider and a network provider doing both by 2025, because those two networks are becoming remarkably similar.
And then there’s also the arrival of antitrust cases in IoT, as a lot of IoT suppliers really like to lock down their maintenance contracts, and that’s attracting antitrust attention. And we think that people will need to move to an as-a-service–type business model in order to avoid antitrust attention. And then as you mentioned, lots and lots on edge computing and mobility. We think the two are going to cause quite a big change in terms of which suppliers do what things across enterprise, telecoms, computing, and internet services. We expect to see all the boundaries changing over the next few years, new players taking different roles and so on. So we think there’s a lot of change, a lot to look forward to, and of course some threats in there for traditional suppliers, but super interesting few years.
Kenton Williston: Yeah, for sure. So some of the things that stand out to me, and boy, there’s a lot to chew on here. I think you’re right about sustainability being a really big deal going forward. And I totally agree that we’ll see it everywhere. Myself, for example, recently taking a stroll down a street here in Oakland where I live, and I noticed that the lights were brightening as I was taking my evening stroll as I walked past them. Even just these little simple things can make a huge difference in energy consumption, and of course there’s much more sophisticated use cases beyond that.
Martin Garner: What we find is that with IoT, you’re often monitoring things that have never really been monitored before, like streetlights. And so the savings you can make by doing more intelligent things with them are just enormous.
Kenton Williston: Yeah, absolutely. One of the things that stands out to me is this idea of linking the smart grid with networking. And we actually did a podcast recently with ABB talking about this very idea. We need to have so many intelligent end points in the 5G network ,and presumably going forward in the 6G networks to support all of these small cells and private networks.
And it’s really similar for the smart grid where you need to push intelligence out to the edge to achieve sustainability and resilience. And of course, both applications need a combination of power and communication. So why not put the two together?
Martin Garner: I think that’s right. It’s the decentralization which is the big commonality, plus the kind of cloud architecture that they’re building in. So in the energy grid, you’ve got now lots and lots of smaller energy generators through solar and wind farms and so on at the edge, and they’re pushing energy into what used to be a very centralized system. And it’s an exact parallel with IoT. We’re generating so much data at the edge thanks to IoT, and we’re pushing that into the network, where we used to depend mostly on things like YouTube being streamed from the middle outward. And so it’s a big shift in both cases, and they’re very similar architecturally and topologically and we expect much more convergence across those two.
Kenton Williston: And I think that speaks also very much to the point you made about big changes are happening now in the who does what. So again, just thinking about some of the recent conversations we’ve had in our podcast series, we’ve had a conversation with Cisco, which I believe we’ll publish after this podcast, where they were talking about their efforts in the rail space with national rail transport there in the UK, and how the complexity of what needs to be done and the speed at which things need to be delivered has led them to work very closely with companies who in the very recent past they would’ve considered their competition.
Martin Garner: Right, and we also think that as we get a cloud architecture in a 5G network, then where is the boundary between the cloud where the data lives, and the cloud where you’re now generating the data which is part of the 5G network? I think it’s going to become a really fuzzy boundary, and that creates opportunities for specialist players who might only do edge cloud things and feed that into a telecoms network, or the other way around. We just think the whole who does what, and where are the boundaries, is going to become a much more sophisticated picture than we’ve had before.
Kenton Williston: Yes, for sure, and that leads me to a question that I’d like to dig into a little bit more deeply, about the role of the existing cloud players. We’ve got industry leaders like Amazon and Google and Microsoft, and they have undoubtedly greatly benefited from all the activity that’s been happening in our last couple of years, and I’d love to know a little bit more about how you see their role evolving as we move forward.
Martin Garner: It’s a great question. And we’ve already talked a little bit about the verticals, and one area where they’re all pushing very hard, one vertical is telecoms networks, and we’ve mentioned already that they’re doing more in the 5G world, especially as 5G moves from its current consumer phase more into an industrial phase. But I think one example that illustrates it very nicely is that if you are, say, a global automotive manufacturer and you want a 5G private network in all of your manufacturing sites across the globe, who is best placed to provide that? Well I don’t think it’s the local telco, because they’re not global enough. So it’s more likely to be your big cloud provider, and we think they’re going to become a really key distribution channel for some of the telecom products, even if they don’t offer them themselves on their own behalf. And I think this is a good example of where the domains between what the cloud providers do and what the telecom guys do are going to blur quite a lot over the coming years.
Kenton Williston: Yeah, no, that’s all very interesting. And I think your point about 5G is very well said. And of course we just talked recently to your colleague Richard about a CCS Insights prediction in the 5G space, and I think the evolution of that space is going to be incredibly important, both for the role of the cloud provider, and to your point there’s this whole new concept of a private cellular network that has come along with 5G that I think will be very, very important as we move forward. And much in that same vein, as we talked a little bit in that conversation, I’d love to hear more from your perspective how companies like HPE and Dell are starting to offer cloud-like experiences in the on-prem infrastructure, and where that will fit into the landscape going forward.
Martin Garner: Yeah, absolutely. And the cloud guys really have had a good run at this as far as we can tell, and we’re not expecting that to change much, but we do expect a bit of a shift going on, and now I know that some people think that the market anyway has a fashion swing between what’s centralized and what’s decentralized; what’s cloud, what’s on-prem. And what we’re now seeing is Dell, HP, and other computing providers, that they’re offering cloud-like experiences and they’re offering, this is really important, as a service-business model for on-premises computing so you don’t have to have the big capital costs in order to get started with quite a major computing program. You can do it all on OpEx. Now we’re all reinforcing that. We’re also seeing the big cloud providers offering local cloud containers in on-premises devices, AWS green grass, Azure stack, and so on, and they’re offering as-a-service hardware.
So that whole area is being fueled, and our expectation is that on-premises will, if anything, make a bit of a comeback and that will tend to slow the growth of public cloud, but definitely not stop it. And that’s a trend that’s not going away. Now we also think that IoT is a really, really big part of this because of the strength of edge computing, the fact that we’re generating such a lot of data in industrial IoT systems, and the fact that we need often to act on that data really quickly in, say, a process-control plant or something like that. We can’t do everything just in the cloud, we need the on-premises side, and as IoT grows and grows and grows, we think that will enhance that trend back towards a stronger on-premises suite.
Kenton Williston: Yeah, and I think one of the things that’s interesting there too, is, like you said, there definitely does tend to be a constant pendulum going back really basically to the earliest days of computing as to whether things were centralized or distributed. But I think one of the things a little bit different about our current situation is that the concepts of cloud architecture are showing up everywhere. So of course it’s in the public cloud, but also on-prem systems are starting to look very much like the cloud in terms of things like containers, but so are edge systems. And in fact, I think one of the most important things that’s happening right now from an architectural perspective is moving all of the software that you’re doing to the containerized, as-a-service cloud model so that you can, as these things continue to evolve and the workloads move from one place to another, have the flexibility to deploy these workloads in the public cloud, in a private cloud, on-prem, at the edge, wherever it makes the most sense for whatever you happen to be doing at the moment.
Martin Garner: And you can then manage them centrally. You can do things like optimization across computing stacks. And so it gives you a lot more flexibility.
Kenton Williston: Yes, yes, absolutely. And I think there’s some really good examples of this that are happening in, for example, the machine learning and AI space, where people are doing things like developing the models in the cloud and then bringing down the inference engines, which actually execute the work, into a more local environment, perhaps into an even very lightweight environment at the edge. And I think that’s a good place for me to ask you about where you see those technologies of AI, machine learning, and computer vision going in the future.
Martin Garner: Yeah, and another great question, and this links back to our idea that there’ll be a huge focus on the intelligence rather than the IoT itself. And what we see at the moment is that there’s a very strong focus on the tools for machine learning and AI, making it easier for ordinary engineers in ordinary companies around the world to choose algorithms and to set them up for use, and to build them into your development and your DevOps and things, and have a whole life cycle for your machine learning, just like you do with your other software and so on. But still, I think one of the things we’re seeing is that the machine learning and AI world is full of componenttechnologies. It’s very much similar to the IoT world the way it was a few years ago.
And so it’s actually really challenging for ordinary people to choose and use systems in that area. So we’re also expecting a lot more focus on providing finished systems for machine learning and AI, quite similar to the way Intel did market-ready solutions for IoT. We may even see some of the finished AI bundled into things like market-ready solutions increasingly. Now Intel’s not the only one. Others have made a start on this as well. For example, AWS has Panorama video analytics appliances, which you can just buy on Amazon and plug in, and they come with the algorithms and you can get going really very quickly. They do something similar for predictive maintenance with their monitron system. We also are expecting the role of smaller and specialist systems integrators to grow a lot here so that they can take on a lot of the training and configuration for you, because it’s still true that the widgets that you make in your factory are not the same as other people use.
And so you need to train the models on images of what you are doing. And there’s just a little caveat here, which is that it’s a large task to get thousands and thousands of specialist systems integrators who maybe they originally trained as installers for surveillance systems. They may not be very skilled in machine learning, but we have to get them up to speed in this area. We have to get them comfortable and competent in training on machine learning, because it’s going to be a big part of their role going forward. And then just one thing that follows on. You talked about AI at the edge and so on. One of our other predictions left over from a couple of years ago is that we will move over time toward much more distributed training rather than centralized training.
Kenton Williston: Yeah, so I think it’s all very interesting points. And I think one of the things that really strikes me here, it kind of goes back to the who is doing what, and the fact that we’re seeing technologies just become so pervasive everywhere you look. And people have been talking about, for example, this idea of digital transformation for some number of years, to the point that I think it’s kind of worn out its welcome to a certain extent, but it’s true that everything’s being digitized, and especially I think this year and going forward, people are looking at just increasingly everything’s connected, distributed intelligence everywhere. But this certainly does introduce a lot of complexity in who’s actually going to do this work of adding this intelligence everywhere, how do these systems all talk to one another. You mentioned, I think quite rightly, the challenges of when you start talking about AI, for example, you’ve got a lot of different point solutions, and how do you get these things all to work with each other?
And we had, for example, a very, very interesting conversation with a company called Plainsight. It’s one of our most recent podcasts here, talking about this very challenge that you’re not just going to have data scientists sitting about in every part of an organization, and in fact many organizations won’t have them at all. So how in the world do you go about actually deploying all these great AI capabilities that are out there right now? And so I agree that having trusted partners that enterprises can rely on like systems integrators will be very important going forward, and I think it will be, to your point, very important for folks who have been doing a lot of the physical installation of things and specialize in those sort of areas to team up with partners who really understand this technology in a deep way so they can go to their enterprise customers and do these very complex installations and integrations where you’re bringing a lot of different things together.
Martin Garner: Yeah, that’s right. And then having done that, you then need to trust it enough to run your operations off it. And that’s a different question, isn’t it?
Kenton Williston: Yes, absolutely is. And on that point, there are a lot of efforts happening right now to make especially the AI trustworthy and democratized so that it is more accessible and so that enterprises can put their trust in these systems, and I know that there are significant efforts happening from like IBM and Microsoft, AWS, and Google in these areas. Can you speak a little bit to where you see these efforts going and what kind impact they will have?
Martin Garner: I think this is one of the most fascinating areas in the whole tech sector at the moment. And for sure those players have been leading the technical development of AI and the tools around it, and things like TensorFlow and PyTorch and so on have had a huge impact in making all of this technology much more available and accessible to people who maybe aren’t fully schooled in the technology behind the scenes. And that really has helped the democratization. But I want to sound just a little bit of a warning here, because we think AI is a special category of technology where small assumptions or biases introduced by a designer or an engineer at the design stage can cause huge difficulties in society. We need more layers of support and regulation in place before we can all be comfortable that it’s being used appropriately and properly and we’re all technically competent and so on.
Kenton Williston: Yeah, for sure. And there’s good examples of that even in just our daily lives. There’s lots and lots of firsthand experiences we’re starting to have of AI not behaving the way we expect it. So I think you’re absolutely right that there is going to need to be a lot of work done to ensure that these systems are being used appropriately by good actors and are doing things that we expect them to do. That’s a pretty tough challenge.
Martin Garner: Yeah, and I think we can start to see what those need to be. And there are already quite a few initiatives across some of these areas. So one key aspect is the formation of ethics groups that are not tied to specific companies. I think we need to take away the commercial-profit focus, and focus purely on the ethics before we can really trust totally in that. It’s also clear that to build strong user trust, we’re going to need a mix of other things like external regulation. When you think about cars and traffic, there’s an awful lot of government regulation that goes with that. But we also need then industry best practices and standards, and we need sector-level certification of AI systems. A bit like crash testing of cars. We’re going to need something like that for AI systems.
Then we need to certify the practitioners. There have got to be professional qualifications for people who develop AI algorithms. Maybe we need a Hippocratic oath and things like that. There are all these layers that we’re going to need. They’re being developed and they’re being introduced, but we’re just not there yet. So one prediction in this area that we have is that 80% of large enterprises will formalize human oversight of their AI systems by 2024. In other words, we’re not just going to leave the AI to get on with it. We’re going to need AI compliance officers, we’re going to need QA departments. It’s going to be a whole layer of quality control that we put in place with human oversight before we let it loose.
Kenton Williston: Yeah, for sure. And one industry that comes to mind in particular here when we’re thinking about needing to take extra care to make sure our technology is doing what we want it to do is the healthcare industry. First of all, kudos to all the folks who’ve been working incredibly hard in the healthcare sector, not just the technologists, but the care providers. This has been such a difficult time. And I really cannot express enough gratitude for all the folks who have really just put everything on the line there. Really, really commendable, and a big part of that from the technology side is things like telehealth and telemedicine and virtual care in general have incredibly quickly accelerated, and I think it’s just an amazing accomplishment by everyone who’s been working on that space. But I think there’s a lot left to do still. And I think there are definitely questions in my mind about how do we keep pushing this forward in a way that’s going to be truly beneficial to everyone, patients and care providers alike.
Martin Garner: Yeah, exactly. And I echo your thanks to the healthcare systems in various countries around the world. The effort they’ve put in, the changes they’ve made, and the support they’ve given are unbelievable, and we owe them a huge debt of gratitude. But just coming back to the technology, there are a few things I think which stand out in terms of IoT and the adoption of machine learning and things like that, which we’re coming onto. So one is that healthcare, it’s very easy to talk about healthcare as if it was one thing, but it’s really not. It’s enormous and diverse. And it’s many, many different areas perhaps with different compliance requirements themselves. Also, I think as you mentioned, it’s been historically a bit slow to change, but COVID has really kick-started the adoption of a lot of new ways of doing things. And so we have made a lot of progress over the last two years, but my sense is there’s still a long, big shopping list of opportunities which are enabled by IoT or machine learning or AI that we haven’t really got going on in a big way yet.
And just one example I’ve come across is tracking machines in the hospital. Trying to find machines in the hospital can waste a lot of valuable time for doctors and nurses. And so hospitals often over provision: they put one machine per ward, when actually the usage doesn’t really support that. And it’s just wasteful. So if the machines could be tagged and geolocated within the whole hospital, then they become easy to find. We’ve seen examples where that generates capital savings of 10% to 20% on that type of machine, and that can be really significant amounts of money coming through. So we think there’s a lot more to come in this area, and the great news is that hospitals and the healthcare system is now in a place with change that is much more ready to adopt new systems.
Kenton Williston: Yeah, for sure. It’s interesting, I think. That point you made about the ability to even locate these devices is huge. Even beyond that, we’re seeing some of the stuff we’ve written about on insight.tech, things that are autonomous. I think healthcare settings are an extraordinarily good application for autonomous vehicles. Not in the sense of course like a car, but just self-guided nurse carts and drug-delivery systems and things like this so that you can, rather than have the providers go find these things, have them just directly come to the providers. It’s I think a really incredible opportunity there.
Martin Garner: Absolutely, along with some interesting challenges: how do they use the lift of the elevator that takes them up to the fourth floor? None of that comes easy, but it’s a great opportunity. You’re right.
Kenton Williston: Absolutely, and I should mention here too that I mentioned a couple of our earlier podcasts and forthcoming podcasts, and of course our listeners are very strongly encouraged to subscribe to this podcast series so they can keep up with all that. But I would also very strongly encourage our listeners to go check out insight.tech. There’s just a tremendous amount of very in-depth content on all these things we’ve been talking about, not least of which is the report that you yourself have created with these predictions for the coming year. Definitely worth taking a read of that for sure.
Martin Garner: Hope so.
Kenton Williston: I certainly think so. So on that point, I think a good place to wrap our conversation would be talking a little bit about the bigger picture of where you see things trending, and something that caught my attention was the idea that the Internet of Things will become more of an Intelligence of Things. So can you explain what that means to you, and why think this is happening?
Martin Garner: It’s interesting, isn’t it? I’ve always thought that the label Internet of Things, or IoT, is a bit of a rubbish label, because it really doesn’t describe the full complexity of what’s going on underneath. I think now though, there’s quite a good understanding that IoT is part of digital transformation. You mentioned that’s maybe an overused phrase, but we kind of know what it means. And it’s a big thing that’s going on. IoT is part of it, but actually very few people buy IoT. What they do is they buy a solution to a business issue. And somewhere inside that is IoT used as a technology to make it work. And the real value of IoT is not in the connection that we’ve created with the things, but it’s in how you use the data that you now have access to. And I think if you, if you think about a smart city, for example, with intelligent traffic management or air quality monitoring, then it’s quite obvious that you are more worried about the data than the connection.
And that’s where the value is. And it’s equally true with smaller systems like computer vision on a production line. You don’t care much about the camera, you do care about what it’s telling you, and that’s the distinction. The trouble is we are now generating so much of this data that we increasingly need lots of machine learning and AI to analyze it, and we have to do it at the edge to do it really quickly and so on. So getting the maximum value out of those systems is going to become all about the intelligence you can apply to the data. Probably a lot of that will be at the edge. Now we think there are going to be three main areas for this: obviously monitoring something is useful, but we still need good analytics to help us focus on the right data and not get distracted.
Controlling something is more useful with suitable intelligence, as we said, about streetlights and things like that, we can make huge savings by controlling these things better, but actually optimizing is even more useful. And again, with suitable intelligence, we can now optimize a machine, a system, or a whole supply chain, maybe in ways we never could before. So we think that the Internet of Things, we now understand pretty much what that is and how you go about it and there’s a lot of opportunity, but we understand it. We think that’s going to fade away as a term, and there’ll be much more focus on the intelligence, the way you use it, and the value you get out of exploiting the data you’ve got. Now when we think about specific sectors, like manufacturing or retail or healthcare, there are a few things that jump out. So it’s quite easy to get caught up in the detail of getting all of these things connected. Should we use Wi-Fi, should we use 5G, wired connections?
Of course that’s important, but only up to the point where it’s working, and then you can move on. We will need suitable systems for aggregating and analyzing the data, data lakes analytics, digital twins, machine learning, AI, and so on. And many, many companies are already well down this path, but actually there’s a lot to learn. Each of those areas is quite big and complicated, and you’ll need new technologies, new skills to get really good at those. But then the other bit is that, even assuming you get all of that done, really a lot of the value you get comes from then applying it across the organization and having it all adopted in the various systems that you use. And that’s a people issue more than a technology issue. And we’re back then to one of the truisms of digital transformation, which is that success depends on taking people with you more than on the technology that you’re using to make it all work. And I think that for me, that’s a really interesting point. It’s ultimately a people issue.
Kenton Williston: Yeah, I couldn’t agree more. And I think, to return to an earlier point, you were talking about the who does what, and I think it’s going to be incredibly important as we move forward into this increasingly complex world to have an ecosystem of players who you can count on, who understand the kind of challenges that your organization is facing, where the technology is heading, how to deploy these things. And one of the things we’ve talked an awful lot about on the insight.tech site is how to work with folks who’ve traditionally been thought of as merely distributors of technology.
I’m thinking of the Arrows and CENXs and Tech Datas of the world. Their role has changed a lot, to where they’re gaining an incredible amount of internal expertise on their customer needs. They’re able to provide these more complete Intel market-ready, solution-type solutions you mentioned, and are partnering very actively with the sort of systems integrators who are doing the physical installation and have those relationships with the enterprises. And I think it’s just going to be very important for all of these players to come together in a very collaborative way to really unleash all these possibilities we’ve been talking about today.
Martin Garner: I absolutely agree. And I think the ecosystem angle is a really important theme to bring out here. Very few companies can do this on their own, and most depend on working successfully with others. There’s also an interesting organizational point I think for a lot of IoT suppliers. From what I can tell, and I haven’t done a big survey on this yet, but from what I can tell, most IoT suppliers are 80% engineers working on the product and 20% other, which includes HR, marketing, sales, and so on and so on. I kind of think it needs to be the other way around. They need to have a big customer engagement group in there, where if you’re in healthcare, you employ ex-nurses and ex-doctors and what have you, who really understand what’s going on within the customer organizations and who feed that back into the product. And I think most IoT suppliers haven’t really got to that yet, but it’s something we see coming before too long.
Kenton Williston: Absolutely. So with that, Martin, I really want to thank you for your time and your insights today. This has been a really fascinating conversation.
Martin Garner: Well, and thank you too. And thank you to Intel for hosting this and for having me along. It’s always a pleasure dealing with you guys, and I hope it’s been an interesting session.
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