In Episode 4 of the Wireless Noodle I provide a summary of some of the fascinating work that we at Transforma Insights have done analysing hundreds of real world deployments of AI, IoT, blockchain, RPA and other disruptive technologies. Armed with this we provided advice in a recent webinar on seven strategies for ensuring that any enterprise is properly harnessing disruptive technologies.
You can access it here, or via Google or Apple.
The full transcript of the podcast is available below.
What are the best ways for enterprises to harness some the emerging disruptive new technologies such as AI, blockchain and IoT in order to gain competitive advantage?
Based on the Digital Transformation Investment forecasts done earlier in the year by Transforma Insights, companies will be investing over $5 trillion in digital transformation initiatives between now and 2030. Against that background, every company must be tech-savvy to an extent that has never been seen before. The unfortunate thing is that businesses have generally been quite bad at adopting new technology. In an earlier podcast I talked about my book the Internet of Things Myth which highlighted some of the failings of implementation.
One way to get better at adopting new technology is to learn the lessons of others who have run similar projects. Use lessons from what other companies had done badly or well in the past to educate yourself about how best to address the future opportunity. That was the thinking behind a set of research that we have been doing at Transforma Insights.
Our team has been analysing case studies of AI, IoT, Robotic Process Automation, Product Lifecycle Management, Distributed Ledger and so on, looking at hundreds of different deployment parameters like time to deploy, complexity, vendor choice, and all sorts of technology specific parameters like type of algorithms used in AI. This data can be used either individually or in aggregate to analyse more macro-level trends.
Based on that research, on the 30th July we ran a webinar entitled ‘7 ways to harness AI, IoT and other disruptive technologies for competitive advantage’. Replay is still available.
I want to share with you some of the findings, firstly looking at the overall trends and then digging into some (well, 7) examples of best practice that come through from the specific case studies.
Analysing the aggregated information across all the case studies brought out some fascinating results. Firstly companies are largely focusing on low-hanging fruit. Most organisations are hesitant to extend beyond relatively safe deployments focused solely or predominantly on simple efficiency savings.
In our case study assessments we looked at impact based on three areas: internal process efficiency, external products and services and overall market disruption. Specifically we gauge the extent to which the project has a significant or transformational impact on process efficiency (i.e. internal processes), value proposition (i.e. external products) or the industry as a whole.
Unsurprisingly, perhaps, all have their prime impact on internal processes. Over 70% of projects can be considered to have a significant impact on process efficiency. Cost cutting is always the easiest way to realise a benefit from a new technology, and justify investment. That is particularly true of the applications focused on repetitive processes, like RPA, PLM and autonomous robotic systems.
In contrast less than 30% have significant impact on the organisation’s products and services. The 3D printing and additive manufacturing category and the AI category all show much greater proportions of impact on products. 3DP because it’s literally about making products, how, where, and degree of customisation. AI because to date it has mostly fed into things like chatbots and supplementing products like video analytics.
Barely 10% are potentially disruptive to the industry as a whole. The applications seen as most disruptive to the market as a whole are Human Machine Interface, largely AR/VR, and RPA again. This reflects the more mature use cases for these technologies. Going beyond just the low hanging fruit.
Further supporting the idea that enterprises are playing it safe, only 24% of projects can be categorised as ‘mission critical’. Again RPA is the outlier, demonstrating what the other technologies might be capable of given a year or two more maturing. It has a relatively simple progression from discussion to trial or proof of concept to deployment. We think the other technologies can prove almost as simple if you, as a deploying company, approach them in the correct way. This would encourage a lot more mission critical use cases.
Given the focus on easy wins, it is unsurprising that the emphasis is generally on deployments that are quick to deploy and pay back. On average projects take 12 months to deploy, with payback in 20 months, although there are some outliers. RPA is substantially slower to deploy, reflecting the fact that its is being used more comprehensively, and PLM slower to generate a payback.
The study also examined three measures of the complexity of implementations: functional (i.e. how complex the project is), stakeholder (i.e. the number of internal and external stakeholders in a project), and geographical scale (i.e. how many countries it is deployed in). While some technologies do score high on some ratings of complexity, no single technology family ranked as being complex in more than one category. This implies that complexity in one area limits the capacity to accept complexity in any other.
It is also noteworthy that there is a close relationship between complexity and impact. Impact of the adoption of these technologies is generally low (with an honourable mention to Robotic Process Automation) reflecting the fact that these are simple deployments focused on incremental change to existing processes. The complexity of rolling out the new technology is putting them off opting to engage in more transformational projects.
Of course, the lion’s share of the responsibility for driving out the complexity sits with the vendor community. They’ve largely come on in leaps and bounds in the last few years, but there’s still a lot to do. In the webinar we identified seven key things that companies can do to mitigate the complexity that still persists and help adopters get to a productive use of disruptive technologies faster than their peers. In the webinar, for each of these areas we use real-world examples as diverse as a Norwegian dairy farmer using AI to a Turkish construction company’s adoption of drones to illustrate the points. Here is a brief summary:
Here are the key points:
Have a thorough and systematic approach to horizon scanning. Technical, commercial and regulatory disruption is creating challenges for every sector and you need to be aware of what’s coming, so you know what’s available to harness, and what might cause competitive issues. It’s critical to not only consider the direct impact of a new technology but also give mind to the secondary effects. Don’t focus exclusively on the narrow set of technologies you might use, but think about impact on the wider ecosystem and adjacent spaces. Having a good understanding of what’s coming and your competitor’s plans will help your decision-making process about what to do with your own product lines. And if you can’t do this well yourself, find a partner.
Have a prioritised list of projects which is constantly updated. You need a structured approach to selecting and prioritising which of the many potential digital transformation projects you may pursue. First filter based on viability and then rank based on attractiveness and fit for the business. Next look at timelines and dependencies. Last, and critically, you need to feed back what you learn into the planning process. It’s a constantly iterating process. As an example, you may have chosen to implement a particular software platform which might make certain projects more appropriate to pursue.
Your team structure needs to match the project. Just as projects are diverse, so are the skills needed to implement them. In the webinar we looked at the difference between a smart farming use case and a smart meter solution. They need radically different skills. In some cases it’s an completely novel and customised deployment and there’s a substantial discovery and ideation phase. In others it’s just a functional roll-out of a fully formed pre-existing solution. Pick your project team according to the project.
Be flexible as you implement. The key characteristic here is ‘agility’. During deployment of the specific project you should constantly feed back into the overarching planning process and make any changes so they are keeping with that overall strategic plan. You do not want project teams to solve their own problems because it risks creating silos of technological development.
Begin with the operational blockage. Most companies when trialling and deploying new technology focus predominantly on the technical issues. This leads to repeated proofs-of-concept. More important is to focus on the process you want to change. Also be sure to consider technical and commercial elements in parallel.
Keep security at the front of you mind. Security is a problem that is referred to again and again when deploying digital transformation projects. There are many high profile examples of failures. You need to consider security from the start of your project and constantly review and iterate. A good example, as quoted in the webinar, was Kepco, the South Korean energy company which contracted with ARM for an end-to-end secure system for its smart grid upgrade, including hardware, OS, device management and associated consulting. You also need to ensure you aren’t just thinking of security in isolation but considering the trade off with commercial factors such as profitability or churn, and with other aspects of ‘Trustworthiness’ such as reliability or privacy.
You will need to change many aspects of what you do. Slow technology adoption is often due to a lack of consideration of the requirements to make substantial internal changes. Any sufficiently important digital transformation implementation should require you to make changes to business process, business model, finance, people, partners, systems and culture. It also needs a robust approach to change management.
There is a tremendous opportunity associated with adoption of technologies such as AI, IoT, RPA, distributed ledger, additive manufacturing, AR/VR and digital twins. But it requires a co-ordinated approach to identifying technology trends, selecting projects, managing implementation and adopting the internal changes required.
I now want to have a little rant. OK, maybe not a rant, but a good-natured and passionate statement of view. There are certain phrases that are used by technology market commentators that are spectacularly banal. You know the type of thing. “People buy solutions not technologies”. The popular one at the moment is: “The real challenge with deploying <insert name of technology> is in the commercial and operational changes, not in the technology”. Literally everyone knows this. If you are ever faced with anyone who says any of these things and then looks like they think they’ve imparted a pearl of wisdom you should run a mile. Anyone thinking for a moment about using IoT or AI will be immediately struck that it’s much harder to push through internal operational issues than to buy another box.
But that’s not my bugbear of the day. It’s the ever popular statement “think big, start small, move fast”. Nonsense. “Think big” is OK, but rather redundant. Anyone can do strategizing, that’s not where the battle is won. Ideas are easy, delivery is hard, as anyone who has ever tried to write a novel will tell you. Move fast is also rather pointless. What would the alternative be? Move slow?
Often when I’m writing recommendations for reports I try to spin it around and think what the opposite recommendation might be. If there effectively is no meaningful opposite then don’t use it. For instance, a recommendation to “consider x” is generally worthless because the opposite is “don’t consider x”. Who advises people not to consider things? It's worth noting that I’ve probably broken my own rules many times in reports.
My problem isn’t with thinking big or moving fast, it’s with starting small. If your house was on fire would you start small by putting out the dog kennel, or throwing a couple of pints of water on first to see what happens. Of course not. Put another way, that advice to start small specifically says to consider size, or lack of it, as a positive gating factor for selecting the projects to pursue. Surely the things to consider are things like strategic fit, impact, criticality and immediacy of need. Those should be the key factors. The advice to start small risks organisations specifically pursuing a sub-optimal project simply because it meets the criteria of being small. Of course it may be that the quick win projects are small ones. If so, great. But they shouldn’t be selected just because they’re small.
The other big issue with starting small is that you will likely lose a competitive advantage to a rival which has decided to start big. All the more reason to do your horizon scanning effectively, as outlined earlier. That will tell you what you need to do, whereas the logic behind starting small is to do what you can, rather than necessarily what you need to do.
In the webinar we have some quite substantial advice about project prioritisation and implementation. Simplifying it as ‘think big, start small, move fast’ does a disservice to the complexity of decision-making that should prevail.
Rant over.
The webinar is available on replay (details here) and the slides are available to download for anyone signing up as a subscriber to Transforma Insights’ free ‘Essential’ subscription.
A final thought.
As we were putting together our webinar we did wonder whether in these days of lockdown whether webinar-fatigue had set in. There have been weeks where I could spend every day on virtual conferences and webinars of some sort. I long for the return of real, live, in-person events. But I have to say it’s not looking good. I don’t anticipate travelling for conferences this year and the first big even of next year, CES, has already been called off.
Virtual events are fine, but they have a scarcity problem. In fact several scarcity problems, but not perhaps in the way you might immediately think. These are down to basic economic issues of low barriers to entry and minimal incremental costs. From a host’s standpoint there’s minimal cost associated with running an event and inviting everyone, so there are a plethora of events demanding attention. This isn’t so bad as attending the events is much easier. I save on travel time, can pick and choose sessions and so forth. That’s fine.
The second issue is that attendance being easy removes events as a filter to being a serious participant in a market. For us as analysts that’s quite a big deal. I’ve had numerous conversations with clients about how they knew we were present in the IoT because we attended all the events. It was a significant cost, but it was worth it, both to be better informed and connected but also to show face.
Finally the scarcity issue is most prominently illustrated by networking. At a real life event if someone chooses to speak to another attendee they are doing so to the exclusion of others. They have prioritised each other. With virtual events and virtual networking, it’s easy to ‘swipe right’ on every attendee to see who bites. There is little or no time limit and therefore no indication that they actually value an interaction. At a recent virtual conference I attended I received lots of networking requests. Many of those that explicitly stated why they wanted to chat had clearly copied and pasted a message to every attendee. As a result I’ve ignored it all. If someone really wants to contact me I’m not hard to find on our website or LinkedIn. Sometimes you need to introduce a little friction to the system, like travel or needing to do more than just hit send.
In next week’s podcast I will be talking about autonomous robotic systems, including factory robots, drones and a few other types. It’s a rapidly evolving and fascinating field. As part of that I’ll share some of the specific analysis of case studies in that area, as I’ve done today across a range of technologies. I hope you can join me.
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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