In this week's episode Matt looks at the approaches taken by technology services companies such as Accenture and IBM to replicating projects, the efficiency savings this brings, and how it represents the key to unlocking huge amounts of market value. He also digs into some Transforma Insights research on how Artificial Intelligence is being deployed in the real world today and what lessons that gives us for the future.
The full transcript of the podcast is available below.
Welcome to this week’s Wireless Noodle. I’ve spoken a lot about IoT the last couple of episodes, so I’m going to give that a rest this time, although there’s a very interesting blog post I wrote about connectivity platforms for IoT based on a recent report, so I recommend having a look at that. But no, this week I want to delve into some work that we’ve been doing at Transforma Insight on real-world AI deployment. And before that I want to talk a little bit about vendors, specialisation and replicability of solutions.
One of the main things we do at Transforma Insights is to look at the vendor landscape, i.e. who is good at delivering particular products and services to customers (almost always enterprise customers). In some cases we’re assessing the capabilities of a set of products, as with our recent reports on Communications Service Providers or Application Enablement Platforms in IoT. I said I wouldn’t talk IoT this episode didn’t I? So much for that. In other cases we’re looking at the companies that provide professional services and systems integration. The likes of IBM, Accenture, T-Systems, Tech Mahindra and many more. The interesting thing about these types of companies is that they cut across most of the technology areas that we cover. IoT (did it again), AI, blockchain and so on.
For these professional services companies it’s generally not about selling products, but about selling consulting hours. Which is what I’m interested in. In particular in unpicking the market landscape. Who’s best at doing what. With products it’s comparatively easy, but with these types of services companies it’s harder to pin down who is good at what.
Obviously some of them have particular technical capabilities which differentiate them. T-Systems is good at networking, for instance, IBM is hot on AI. What is perhaps more interesting is the extent that they are better or worse at addressing particular verticals. The key thing is that there SHOULD be some variation there. There should be efficiency savings from taking one solution delivered for one company and doing more or less the same thing for someone else. That efficiency saving of only having to customise, say, 50% of a project is huge. Whichever company establishes early dominance should be able to continue to build on it until it is unassailably more efficient at developing, say, AI solutions for retail. That’s assuming they’re good at replicating solutions. Some are, some aren’t, in my experience. Those that aren’t seem to have the mistaken belief that it’s better for a project to take longer and be more expensive because that’s more billable hours and more revenue. Pretty short-sighted.
So, technology services companies should become increasingly vertically specialised the more they win projects. But can a company do something to short-cut that. The answer is yes, of course. They can buy into a particular vertical by accepting less revenue on a few projects until the capability is established. Or they could acquire the relevant capabilities.
What’s most important to the success of any given services company is that it recognises that replicating solutions from one client to another is essential, and it implements a robust system for doing it.
This is a rich seam of research, understanding this service provider landscape, particularly as they cut so much across all of technology areas that we look at at Transforma Insights. We will publish more reports on this in the near future.
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. (Link to blogpost looking at this topic: The 7 internal factors you need to consider to take advantage of IoT and other Digital Transformation technologies, link to previous podcast on the topic: Wireless Noodle Episode 1: The Internet of Things Myth. 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 Mobile Private Networks, and I should have completed a report about how it’s better to buy specialist capabilities rather than building your own and I’ll aim to share something of that too.
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.