AI Disruption Index: Measuring AI’s Real Impact on Cloud, Software and Telecom Business Models

Episode highlights:

• Measuring qualitative business model changes
• Where companies are seeing the benefits of AI adoption
• The real impact of AI on headcount reduction

“We also wanted to add metrics [to our ©Human Intensity Reduction Index] that look at the unique nature of the cloud and software. So, it’s much more sales- and marketing-driven, and IP and development-driven. So, we already have a SMIRI, so sales and marketing efficiency index. And when you look at that, that’s a big area of cost. It’s also a big driver of the overall revenue performance for those vendors. It’s not consultative sales. It’s the programs and territories and a lot of expense going into travel and entertainment and marketing, all those things. Again, areas that a little change in the efficiency, leveraging AI and the capabilities that some of the vendors are promoting themselves to their clients, and you can get a pretty dramatic impact in terms of disconnecting headcount growth from revenue growth,” said Krans.

AI Disruption Index: Measuring AI’s Real Impact on Cloud, Software and Telecom Business Models

TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors. 

I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about how TBR’s new HIRI metric is applied in the cloud and telecom sectors with Allan Krans, Principal Analyst for TBR’s Cloud Practice, and Stephanie Long, Senior Analyst for TBR’s Telecom Practice. 

 

Background on Allan and Stephanie 

Stephanie and Allan, welcome back to TBR Talks. I’m excited for today because we’re going to talk about stuff I don’t know anything about. So, that doesn’t always happen on TBR Talks. So, this is going to be great for me. I’m going to have a ton of questions. But before we dive in, maybe you could each just give us a little bit of your background, a little bit of the practice that you’re running here at TBR. Allan, I’ll let you go first.

Allan Krans, TBR Principal Analyst: Okay, sounds great. So, I head up the Cloud and Software Practice. Both are still around. I’ve focused on software for a little bit longer than cloud, but really watched the evolution from one industry to the other. Tracked it pretty much since the beginning. I’ve looked a lot at the business model change between those two practices as it went both from the provider and the vendor side, which a lot of new providers came on the scene. And so, we’ve covered them as well as the rise of AI and all that new IP that the cloud providers that have been around now for over a decade are adjusting to and integrating into their overall position in the market. So, it’s been a continuing evolution, most recently with the onset of AI.

Patrick: Yeah, and how many years at TBR?

Allan: 20. 

Patrick: Wow.

Allan: And a little bit more. I was, fun fact, I was a TBR intern when I was a junior in college and then joined, left for a little bit, and I’ve been back for 20 years now.

Patrick: That’s fantastic. Wow, excellent. All right, Stephanie, slightly different story, but let’s hear the practice first and then the background.

Stephanie Long, TBR Senior Analyst: Sure. So, I’m in the Telecom practice, have been in the industry for about 11 years. And one of the things that’s interesting about the AI and it’s the evolution of AI and how it’s impacting tech and life, is that having been in the industry for a while, you see sort of these trends come in, you see the hype come with it, and then you see where the chess pieces fall as that hype sort of fizzles out and leaves the visibility into what the reality is going to be. And being able to see that unfold in real time, sort of living the history is something that I have always found interesting.

Patrick: Yeah. And so, 11 years in the industry, some time with TBR before, back to TBR now, and in between with Hitachi, right?

Stephanie: Yep.

 

How TBR is measuring AI impact: Comparing services to cloud

Patrick: Excellent. So, where we’re coming out right now with AI, so you mentioned the hype. And within the Services practice, the struggle that we ran into was there was no clear way to measure or evaluate what AI was doing to the companies that we covered. So, it was easy to say, what are they offering their clients? It was easy to say, these are the different capabilities they’ve developed, but they invested a billion, 2 billion, 3 billion in what? We didn’t know. Everyone was talking about what they were doing internally with AI, but there was no really good way to measure it. 

So given that uncertainty, the way we decided to look at it was to take the hypothesis of a theory that increasing adoption of AI within a services company would allow them to continue to make profitable revenues, increasingly profitable revenues with fewer people. That’s the whole eliminate the jobs part of AI that everybody talks about. So given that services is a people business, that seemed like a really important way to look at what was AI going to do with respect to disrupting the actual companies that we cover. 

So, we developed the Human Intensity Reduction Index, which is intentionally a scary sounding thing because it’s exactly what it is. You’re reducing the intensity of the humans in terms of their influence of how they’re driving increasing revenue growth. So that’s what we started with. We also developed a way to look at the business models within services to say, these are the different components of the services business model, commercial, delivery, operations, and partner model, and how are those models being disrupted as these companies adopt AI internally. Again, not what are they selling or what are they enabling their clients to do, but what is it doing internally to them? Because at the end of the day, we’re TBR, we look at these companies individually and we say, what’s their business model? What’s their strategy? What’s their performance? 

So that’s the approach we took in services, a very services, talent, people-centric look at the world. And that’s why we have the Human Intensity Reduction Index. But Allan, can you tell us how, when you think about AI disruption within the software and cloud space, what’s it going to look like for you? How are you guys going to roll out a measurement of that, a metric around it?

Allan: Sure. Yeah, I think overall, starting with HIRI, is it still a good framework? Because although the efficiencies and the revenue per employee and the types of operations that they’re involved in are different, people are still at the core of running the business of the cloud, and software, and software as a service providers. So, we’re starting with HIRI. And already when we look at how some of the early metrics are playing out, you can see the force multiplier effect of being in a cloud and software business. They’re able to get tremendous efficiencies by very little change in headcount and the efficiency of that headcount. So instead of single digit HIRI results, we’re seeing 10% to 20% HIRI results. So double digit force multipliers for the efficiency within those organizations. 

But we also wanted to add metrics that look at the unique nature of the cloud and software. So, it’s much more sales and marketing driven, and IP and development driven. So, we already have a SMIRI, so sales and marketing efficiency index. 

Patrick: Right.

 

Allan: And when you look at that, that’s a big area of cost. It’s also a big driver of the overall revenue performance for those vendors. It’s not the consultive sale, it’s programs and territories and a lot of expense going into travel and entertainment and marketing, all those things. Again, areas that a little change in the efficiency, leveraging AI and the capabilities that some of the vendors are promoting themselves to their clients, and you can get a pretty dramatic impact in terms of disconnecting headcount growth from revenue growth. And so really overall, HIRI is actually a bigger multiplier impact for the vendor results so far. Some of that goes to the hyperscalers have bigger levels of efficiency. So bigger operations, more automation. And so, they’re able to automate across the overall business and get some pretty dramatic results so far. Whereas the SaaS providers are leveraging more of the sales and marketing efficiencies and seeing bigger SMIRI results in terms of leveraging AI for their go-to-market activities and getting a lot of efficiency and again, disconnecting, seeing revenue growth disconnect from continued headcount growth. Head count is fairly stable overall on average actually across vendors, but the growth has continued. So, I think that’s part of the overall story is it’s not always replacing employees, but it is making the business more effective with stable or delayed or slowed growth in terms of the hiring.

Patrick: And are sales and marketing, is that typically one of the largest operational spends, the largest budget line items for the cloud and software companies?

Allan: It is absolutely. Sometimes it can be on par with R&D because obviously the innovation is very important. But yeah, anywhere from 15% to 30% of revenue for these firms can go to sales and marketing. So it does make a big impact on the bottom line margin and being able to reinvest back in infrastructure and continued innovation which is a big focus really for the hyperscalers in particular, as they look to build out the AI data center footprint and capitalize on the- there’s just not enough capability to meet the demand that’s there. 

Patrick: Right.

Allan: So, every dollar they can save to invest to grow that more quickly is something that they’re trying to achieve.

Patrick: Right. And that’s, to me, that’s really fascinating because you could apply that SMIRI, you could apply that approach to any company and say, what is the largest, your largest expense that’s related to people and how is adopting AI going to actually change that and what’s it going to do for your bottom line? So yeah, that’s pretty fascinating. I got to keep thinking about what the implications are for that. And especially because it ties then back to, you know, we’ve been talking for a while about how IT directors and CTOs and all that have been challenged by the increasing cost of cloud for years now, but now you’re going to add on AI on top of that. And we’re seeing- in the same time that we’re seeing less reduction in total headcount than we probably expected, we’re also seeing a greater anxiety around what the cloud and combined with the AI bills are going to be. So, if the cloud and software providers are able to continue to drive profitable growth with fewer people, then there might be a way that this starts to balance out for the CTOs and the ITOs.

Allan: Yeah, I mean, it’s all taking, you know, all these investments, whether it’s on the customer side or the vendor side, there is a theoretical fixed pool of money that they need to allocate in the best way that they can. So, it’s not always straight to the bottom line. It could be investing to capture growth.

Patrick: Right.

 

How TBR is measuring AI impact in the telecom space

Excellent. Stephanie, in the telecom space, very different, but the same kind of factors coming into play where adoption of AI is disrupting the business model. So how within the Telecom practice are you looking at what’s coming next?

Stephanie: So, I want to tie it into the cloud and hyperscale story initially, because something that’s interesting and is happening in telecom space is that AI is the catalyst for the network modernization, which is impacting how hyperscale customers are looking at and investing in their capabilities. The increase in data workflows coming from AI causing a fundamental shift in how the network traffic is behaving and flowing, and also the need for substantial uptick in low latency, high-capacity connectivity, especially for the hyperscale customers to meet the demands of their customers, so there’s this sort of flowing of needs through the whole technology space as a result of AI and how that cascades through the stack. 

From a telecom perspective, there’s sort of broader terms that are being used. AI is one of them, but we’re also seeing more broadly, automation is something that’s been happening since before GenAI came to the scene. And it’s sort of like shifting it into high gear now that we have AI layered on top of that. But it’s a trend that’s been happening for years in the telecom space as companies look to optimize things they’ve already been doing with maybe less manpower or having the same manpower do more things. They’re changing how the companies are presenting in this sort of modern era. AI being sort of the buzzword du jour that’s layered on top of a practice that’s been happening for years.

Patrick: You mentioned automation. I’m glad you brought that up because within the services space for us, HIRI represents not just AI, but all of the changes, all the analytics, automation, everything that’s gone into- really since the advent of ChatGPT in the fourth quarter of ‘22. So we’re looking at understanding that some of it is organizational change, some of it is greater adoption of automation, and some of it is AI with the idea that increasingly it will be AI-enabled solutions that are creating that ability to do. But you also said the same manpower to do more things. And that’s where I think it’s fascinating because Allan, you mentioned that you’re already seeing a HIRI in the double digits in the cloud and software space. In services, we haven’t seen anybody hit double digits yet. A lot of it is in the low single digits. And that’s because it’s being most frequently deployed to do more with the same people, not simply get rid of people and be able to serve those clients and generate that same kind of revenue. So, what’s the metric in the telecom space that you’re developing that’s a counterpart to or similar to HIRI?

Stephanie: So, we’re still aligning to a similar definition of HIRI as the Services practice is, but the story behind that number is coming at it from a different angle, talking about the things we just mentioned, like it’s more than just AI, there’s the automation component, there’s the modernization and transformation that’s been going on for years, and you’ve made those investments and then you see the fruits of that labor. It’s not an instant gratification, so to speak, on those investments. And also to sort of Allan’s point earlier about sales and marketing, the next level of that, the customer experience, there’s automation going into, operators serving their end customers, things like leveraging agentic AI to support customers, so you need fewer humans supporting those customers, many things they can do themselves, which customers appreciate the faster outcomes for their desires. It could be, they’re having a support struggle they need help with, or maybe they’re able to use some of these self-serve skills to tweak their subscriptions without having to involve humans in the process.

 

Measuring qualitative business model changes 

Patrick: Right. So, to move from, so the HIRI is a very, it’s the math is the math. And for most of the practices, I think, the math is the math because we have those numbers. A lot of the numbers are reported. We have a number of companies that don’t report any of their numbers, so we have to go find them ourselves. But still, we’re looking at reported figures and trying to put it into our own, both taxonomy, but our own approach and our own metric. But then when it comes to the business model disruption, are you seeing in the telecom space the same kind of disruption to the way that they are they’re doing pricing, so the commercial model is changing. Are you seeing the way they’re delivering connectivity? Is that changing because of AI? How much disruption are you seeing in the business model for the telcos that is the companies themselves being disrupted and how they do what they do, how they make money?

Stephanie: So, I’ll give an example of a use case. So, you have these towers all over the place. They could be in pretty remote locations. They could be in city centers. You could have them attached to sides of buildings. You could have the big towers out in the woods or the wilderness or wherever they end up being. And you’re responsible for maintaining those and ensuring they’re staying up. And people now more than ever want things as instantly as possible. So in order to do that, you could leverage some automation tools, something like a drone, provided you’re complying with all of the legality that goes with that to scan your physical infrastructure, look at it to see if anything looks off, and then you can use automation or AI capabilities to analyze those images and determine if you need to send a human being out there to fix something or if something is going to break and you can use those images to determine we should do X, Y, or Z before we have a problem. If there was a natural disaster, for example, you may be able to analyze the impact more quickly and therefore mitigate the problem faster by using some of these AI-centric and automatable technologies to arrive at that outcome.

Patrick: Allan, just to pivot from telco and what’s happening there to the cloud and software space, but specifically around the business model disruption, like how much are you seeing, how much is Microsoft or AWS’s business model been disrupted because of their own adoption of AI?

Allan: I think it’s been disrupted tremendously. If you look at starting with what they do at the core, it’s, you know, do big development projects and continuous innovation and maintenance. So coding was really one of the early use cases that stuck with AI and made sense and had a business model and an ROI that justified its continued use. And so, you do see not only an option, but encouragement to use as much AI as possible for these development projects to increase the pace of innovation, to improve the coding and the bug fix activities. So, it started there and then flowed into just like their customers, probably a little bit earlier than their customers, looking at customer service and sales and marketing and all of the different aspects of running cloud environments and developing software as a service solutions. So, it’s really flowed through very rapidly most elements of what they’re doing internally for these large companies. And that’s, as you know, it’s a challenge to harness and manage and control and measure. So that’s still ongoing. But certainly the experimentation phase is well over, and it’s been rolled out in production. And now it’s about optimizing and finding the right mix and levels. And it’s different for every company. 

 

Salesforce is a good example of a company that actually, if you look at the numbers, isn’t performing great on HIRI, isn’t performing great on SMIRI. And they even, Marc Benioff is talking about how they’ve tried a lot of, tried it everywhere, found out that for sales and marketing in particular, they’re not seeing a lot of benefit. They still need humans face to face to be able to close deals, build relationships. And so, if you look at their overall sales and marketing headcount, it’s actually trending up a bit. And as a result, their SMIRI score is actually in the negative, which is the only company for all that we cover, which showed that effect. But again, it was a balance of, roll it out broad, see what works, and then pull back in areas where you’re not seeing the benefit. And sales and marketing, at least for now, for Salesforce, seems to be one of those areas where they’re pulling back a little bit.

Where companies are seeing the benefits of AI adoption 

Patrick: And I think that we just said about, so rolling out, see where the benefits are, pull it back if you have to. I think we’re definitely in that sort of pull back phase right now where there’s a lot of companies and a lot of, in the same way that there’s a lot of hype about how great AI is, there’s now a lot of hype and angst about the negatives of AI. So maybe we can end with that. Just some thoughts on what are some really positive stories around AI? I’ve talked about Human Intensity Reduction Index. You’re talking about SMIRI. We’re talking about sort of all the downsides on people. But do you see, and Allan, go first, then Stephanie, come to you. Like, do you see, is there like a feel-good story around the way AI is actually helping the people who are in these companies that are getting disrupted?

Allan: Yeah, I’m not sure if I have one particular story, but I think for a lot of the repetitive administrative tasks, which really exist in most of the roles within large organizations, there’s definitely, and I think it’s been over the past six months where more times you go for some of these tasks or to do- to analyze a long document that would have taken, you know, a half day, maybe a full day in some cases, being able to get a result and an analysis back that helps employees move forward with confidence now after they’ve gone through and checked a couple times. I think the confidence for the quality of the models that have been released over the past six months has increased tremendously and is making people more effective. So that may not show up in the HIRI and the SMIRI for a couple quarters, but I think we’re seeing it ourselves as we use AI internally.

Patrick: Yeah, and we’re using it as well. So, Stephanie, a feel good story, a humans are not the problem story for AI.

Stephanie: Yeah, I have another perspective on AI and sort of a benefit to sort of life. You can use, in theory, an agent, agentic AI, to monitor things that need to be monitored 24/7 that you would have previously monitored with a human. You could have an agent do some of that monitoring while you’re, you know, living life. And it shifts the burden of that 24/7 away from the humans and enables the technology to sort of take over in some of the less intense moments of the monitoring. You could enable it to alert you when certain things are detected. You can pop back on and address problems as they come up, but it does enable you that flexibility to step away that you may not have had previously when everything had to be manual. And enabling that work-life balance for people improves life quality, provided you make it through the headcount cuts that are also related to AI.

Patrick: Right. And are you seeing, and in the telco space in particular, are you seeing any other applications of AI that are sort of benefiting, that are just a pure net positive? Because again, I feel like we get a lot of the disruption negative stories, but there’s some net positive stories in AI, in telco.

 

Stephanie: One of the things that I feel like gets lost in the loop because we talk a lot about AI taking people’s jobs and making things more difficult for people is with the example of drones assessing these towers and the physical infrastructure, you’re actually making things safer for some people because those people would otherwise have to physically go out there and climb a tower and assess something. That’s dangerous work. If you can do that with technology instead, it is a human benefit from a safety perspective to be able to do some of that stuff without needing to send a human into, you know, a place that was just ravaged by a natural disaster. If you can use some automated technologies to do that instead, you can make things a little bit safer for people in some ways. I know a lot of times when we talk about AI and we talk about people, it’s often a very negative perspective of AI taking the jobs, but there are these human benefits as well. 

And something that I think is a little bit more unique to telco than to cloud and hyperscale where there’s edge deployments and things like that in other technology areas, but telecom has a lot of those. And being able to monitor things that are in maybe less accessible environments using tools and AI enables people to not have to go to those places unless it’s absolutely necessary. And also if you had to do physical monitoring of physical sites, even if they’re not in a remote location, that’s still a person leaving their home base to go somewhere else, which is time away from the parts of your life that are the reason why you do the things you do for work, right? 

Patrick: Right.

Stephanie: Whether that’s your pet or your family or you just love hiking the mountains in your local area, being able to have more control over sort of the where and when in your life can be enabled using AI technologies.

Patrick: Yeah, that’s a really good point. And when you mentioned the agents too, it made me think like we’re, I don’t know how many years away from having an agent intensity reduction index. Like how soon do we see like the agents are great, but we need to retire them as well. 

Company callouts: Who will be talked about the most

Just want to wrap up with one thing, because so TBR, we’re always focused on individual companies. So not like winners and losers, but like if you think in a year from now, what’s the company over the course of this coming year that you think is going to be the most- I hate the word interesting. It’s the one that you know you’re going to spend a lot of time on because it’s the one that’s going to change the most, or it’s the one that’s going to force others to change the most. What’s the sort of the most dynamic, news making, change making company among the company sets that you cover? Stephanie, I’ll let you go first because you look like you’re completely unsure what you’re going to say.

Stephanie: Well, you assessed my body language correctly. 

Both: *laughs*

 

Stephanie: I’m thinking of it from this perspective. So, okay, now I’m going to come at this from left field because we have, so we’re talking about operators in the telecom space and there’s this concept of the AI phone that’s coming to the market. 

Patrick: Oh, yeah.

Stephanie: And so, I’m not super privy to the nitty gritties on that, but it’s a whole other level of AI that’s being brought to the average person. And I think that this whole fundamental shift of the shiny, complicated technology, but in the hand of just your average ordinary person and the multiplier of impact you can get from doing something like that. Like if you think back to the flip phone and then the iPhone comes to the stage, and it fundamentally changes how you exist as a person. And now if you, God forbid, misplace your phone for 5 minutes, you can like watch your heart rate rise on your Fitbit or other device that’s monitoring your heart rate because it’s now sort of an extension of who we are as people. And I think that now if we’re going to layer AI on top of that, it’s really going to fundamentally shift how we as people are going about life. 

Headcount reductions and how much are they actually impacted by AI

I have a question for you to spin it around.

Patrick: Uh-oh, this rarely happens, so let’s go.

Stephanie: Switch it up. So one of the things that I think about when I’m looking at the headcount reduction in AI is, in my opinion, many times, AI is the scapegoat and the easy thing to blame for a headcount reduction that was going to happen regardless. So, which is part of the value of HIRI, in my opinion, is that we are peeling back, sort of, the fluff that’s being layered on top of, oh, that was an AI reduction to was it really? So, I want your perspective on sort of that element of these headcount reductions.

Patrick: Yeah, three things. One, to make it worse, I think some of those headcount reductions are done not because they were necessary or because it was a business case for it, was more it was a marketing or a Wall Street kind of making the numbers case and sort of a following the leaders, so when one tech company is able to lay off 10% of its workforce. Every other tech company says, well, then we can lay off 10% of our workforce. And it’s not being thought through in terms of what is the long-term benefit of keeping the people we have and training them and all that. It’s just, well, we got to follow the crowd and do that. So, it’s worse than just blaming AI. 

The second thing we’re already seeing where companies that had talked about needing fewer people to serve the clients they already have and then increase their clients are actually adding people. Headcount, Allan, to your point earlier, headcount in some of these companies is actually going up. And it’s going up because they’re realizing that AI is more expensive, and agents are still more expensive than people. So as long as agents are more expensive than people, it’s better to hire people to do some of the tasks that need to get done in order to continue to drive revenue. So, it’s going up for that reason. 

 

The other reason it’s going up, which is fascinating, is this graduating class right now out of university, they were, what, freshmen, their freshman year, midway through their freshman year is when ChatGPT came out? So, for them- or maybe sophomore year. Anyway, they’re AI native in a way that nobody else has been before. So, this new set of fresh university graduates have a fundamental understanding and a comfortableness with AI, as well as a fear of where it’s going. And you hear that a lot from a lot of these recent graduates as well, but they need less time training. So, it’s easier to hire people now that have fluency with AI that will be immediately applicable. You don’t need to spend as much time training them as maybe you have the people that you already had in your company. So, I think we’re starting to see that trend in what I would say is the right direction, where the companies are realizing that it’s not a headcount reduction that’s actually going to improve their bottom line. It’s actually going to be taking the savings that they’re getting out of applying AI and being able to do, as you said earlier, do more with the people that you have and even add people to do an increasing amount of work. Does that answer the question?

Stephanie: It does.

Patrick: Okay. Is it a little scary too that these things are happening this fast? Yes.

Stephanie: I like the silver lining you spun though, because I know there’s a lot of talk in the news about, oh, I wouldn’t want to be graduating college this year. There’s going to be no jobs for the entry level person. They’re all being replaced with AI, and this really doom and gloom story, but you’ve put sort of the feel-good spin on it. And this is the first generation that is coming at AI with some organic knowledge, and it’s not being shoehorned into their life, they’re growing up with it.

Patrick: I think organic knowledge and, you know, sort of eyes wide open to the positives and negatives, because these are the same university students who got caught up in the early applications of AI when it was clunky, when it hallucinated a lot more than it does now, when students and professors both got caught up in, did you write this test using AI? Did you answer this test using AI? They’re so immersed in it that they’re both fluent with it, organic with it, but also fully understanding the dangers that are inherent in AI. 

Company callouts: Oracle

So, thank you for taking us off on that tangent. Really enjoyed that and made me completely forget the question that I had that I was supposed to ask Allan. Oh, the company, right? The company.

Allan: Yeah, for me, I think it’s Oracle for a couple reasons. I think they have more to gain from AI than the other hyperscalers. So, they were a distant kind of fourth in the market. AI has really given them an opportunity to grow and it shows in their backlog, secure a lot of big deals that could really change their position in the market. And then also they have just been ruthless throughout their entire history with operating expenses, very efficient, but still looking to drive further. And so, I think they’ll be the most ruthless with how AI is used in terms of increasing efficiency in an already efficient organization.

 

And so, they could be some of those examples of at the far end of the extreme, how far can you go in terms of reducing expense, reducing headcount, and leveraging some of these tools. Not everybody’s going to go to that degree. That’s kind of their position in the market, but it’ll be interesting to watch.

Patrick: Right. It’d be fascinating to see too who, as we see in the services side, companies becoming more tightly wedded to a specific set of preferred technology partners. So not exclusive, but preferred. Which of the companies that we cover make that kind of get married to and do that preferred relationship with Oracle and who picks somebody else and how much does Oracle’s, as you described, sort of ruthless ability to reduce costs and the way that they price, how much does that factor into what those alliances look like? 

TBR’s new coverage; AI Disruption Indexes

So, before we go, we are launching a new research stream, an entirely new research practice at Technology Business Research, AI Disruption Index, or the AI Disruption Research Area. So, I know in Services we’re going to have by peer set AIDI reports for every one of the companies that we cover. Putting in peer sets because you can’t really compare the HIRI and the business model disruption at McKinsey to what’s happening at Wipro. They’re wildly different business models and different companies. And so that will start rolling out very, very soon and be able to find it on the site. But Allan, for the Cloud and Software practice, what are the reports? Is there a name for the reports or what are they going to look like?

Allan: Yeah, it’s really two groups. So, the first is the Cloud Infrastructure AIDI and then the Cloud Software and Applications AIDI. And then within that, we’re going to have 9 vendor profiles across them, looking at the largest providers and going into more detail around their specific use of AI as well as monetization of AI.

Patrick: Yeah, that’s a good point. We’ll have vendor profiles as well. It’s a lot more than nine off the top of my head. I don’t know how many it is, but too many to manage. And then in the telco space, what’s the thinking? What’s coming next?

Stephanie: So, in the telco space, we’re going to have two sub-streams of the AIDI on the horizon coming out later in the summer. It’s going to be the enterprise networking set of companies and then the US telecom operator set.

Patrick: Okay, excellent. And all of that will be easy to find on Insight Center. It’s going to be its whole separate practice area. 

Final thoughts

So excellent. We’re going to have to come back in six months, revisit all of this, see where the SMIRI is, see what metrics that get developed in the telco space. Thank you both.

Allan: Thanks.

 

Stephanie: Thanks

Patrick: Tune in next week for the season 5 finale of TBR Talks. 

Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week. 

Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us and see you next week.

 

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