The Framework for Successful AI Adoption Within Enterprise

 
“I think a lot of senior business executives are clearly thinking in terms of productivity, which is the flip side of the efficiency coin, of course. They’re thinking more broadly in terms of, if we can get AI to do the boring work, right? If we could get AI to do the things that are a distraction, then maybe my employees would have additional capacity to grow my business, right? So in general, the language seems to be one around growth and speed and innovation,” said Flower.
 

  

The Framework for Successful AI Adoption Within Enterprise

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 artificial intelligence, alliances, and the long view of technology with Alan Flower, Global Head, AI Labs at HCLTech.

The inflection point of AI 

Alan, welcome to TBR Talks. This is season five, and I’m really excited that you’re with us today. I wish I was sitting in your office in London having this chat with you live, but we do what we can these days. And this is always the challenge, but also the blessing of having this kind of technology.

Alan Flower, Executive Vice President – CTO & Global Head, AI & Cloud Native Labs at HCLTech: Yeah, well, Patrick, I’m delighted that we meet again, albeit virtually, of course. I really enjoyed the last time we met in person in London. And of course, hasn’t our world changed so much since we last met?

Patrick: Yes.

Alan: So, I’m looking forward to discussing what we’re actually seeing out there.

Patrick: I think our world has changed since we last exchanged emails last week, but let’s dive into it. The two things I really want to talk about today, Alan, we’re- this season on TBR Talks, we’re focused on the longitudinal view of technology. So I wanted to talk to people who could put in perspective what we’re seeing changing with respect to AI, with respect to technology overall, with respect to enterprises and how they’re run differently now than they were when all of us, and I’ll put us in the same kind of age category, when we all started in business, when we all started our working lives, how much technology has changed. And the reason I want to focus on the longitudinal view is because we spent the last couple of years very much in an AI hype cycle. And so maybe we come out the other side of it and we think, that was just like all the other hype cycles, or maybe things have really completely changed. But only having that longitudinal view, only having that longer perspective can tell us what we might be looking forward to in the next phase as we go through the next evolution of the age of AI.

And I also want to talk about alliances. It’s an area that we do a lot of research on. I know you’re deeply involved in, and it’s another area that has changed a lot over the last five years in the way that companies behave in the ecosystem and the way that they partner is different than it was a few years ago. So, let’s start with the longitudinal view, the longitudinal view of technology and what you think of where we are with AI. Is it- are we ending a hype cycle and going into a real change or are we ending a hype cycle and going back into something else?

Alan: That’s a really good question, you know, Patrick, right? And I’ll give you my view and you delicately brought attention to the fact that maybe you and I have been in this industry for rather a long time, right, Patrick? And I would say, when I look at my career, right, my career started writing software for the original IBM PC.

Patrick: Wow.

Alan: Or indeed the PC that came before the IBM PC, right? So, my professional career, right, as a creator of solutions extends over 40 years. Now, during my career, let me just be really kind of straightforward, I think I’ve only seen four, maybe five genuine inflection points. An inflection point being basically a point in time after which nothing is the same ever again, right? So just think about those just briefly, right? The introduction of the original PC, that was just explosive, right, in terms of its impact, right?

Patrick: Right.

Alan: Think about the emergence of the internet followed by the World Wide Web. That was a massive one, you know, wasn’t it, right? Then we had the smartphone, right? Think about the iPhone, for example, that transformed computing for everyone, right? What came next, right? Maybe we could say the cloud, right? The cloud came next, right? And now we are in this fifth inflection point, which is AI. And when I look at those five inflection points in my career, the really big ones, think about it, right? The really big ones, the emergence of the internet and the World Wide Web, the smartphone, and now AI, right?

AI might be the biggest one of all, you know, Patrick. And so why do I say that, right? Why do I say to you that I don’t think this is a bubble, all right? Now, I’m kind of somewhat lucky, right? Fortunate. I lead our global AI labs here in HCLTech. You know us really well, you know, Patrick, right? But I’ve got six AI labs around the world. I’m actually speaking to you today from our AI lab in London, right, which is one of our busiest right now. We’ve delivered pretty much nearly 1,000 advanced AI engagements from these labs with clients around the world. So, this is GenAI and agentic AI. We’ve had that advantage of doing around about 1000 engagements with over 500 of the world’s biggest companies, right? And the remarkable thing is we’ve gained a huge amount of insight into not just what works and what doesn’t work, right? But we’ve gained a huge amount of insight into what the world’s leading businesses intend to do next.

Now, whilst I lead our AI labs, you would be kind of forgiven, right, for thinking that the people that come into an AI lab are probably technologists, they want to kick the tires, ask us our opinion of the latest models or whatever, Patrick, right? And that was certainly the case, right? Maybe three years ago, when the GenAI era started, we saw a lot of that technical exploration, right? But here, I think, is probably the most compelling change that I’ve seen, you know, Patrick, and certainly over the last year, but in particular the last six months, right? The people that are coming into our AI labs are the world’s most senior business leaders, chief executives, chief operating officers, CFOs, as well, of course, as maybe the more traditional CIO audience as well. So, when a chief executive rocks up inside an AI lab, they tend to have a huge amount of vision in terms of how they expect AI to impact their business. And I would say to you, Patrick, the reason for, I guess, my confidence around the genuine nature of this inflection point is it’s really clear when you speak to the world’s business leaders, they’re going to re-engineer their entire company around this premise. That AI is going to significantly augment the way that work- that way that work is done. And whilst we’ve got this combination of technology leaders and business leaders coming into the labs, there’s one really important change that I’d love to share with you really, Patrick. And that is, I think for most of the companies that we’ve supported over this kind of this recent period, for most of them, the experimentation has stopped, right? They got to a level of confidence probably around about the middle of last year in many cases, they got to a level of confidence where they believe that the impact is genuine and significant.

So, what this really means from my perspective is most of the work that we’re doing in our AI labs at HCLTech now, it’s not experimentation, right? These are clients saying to us, I want to deploy autonomous agents into production. I want to use AI to transform the way that my key value streams operate in my company. And I just share this with you because we’ve got this ringside seat, right, into what the world’s businesses seem to be doing. And I think just in summary, to answer your question, I think the evidence really in terms of what we’re seeing, the things that we’re building today, the things that we see running in production, I think there’s no doubt in my mind that the impact of AI is genuine and will be sustained, right?

The only thing, I guess, final comment I would make on this, you know, Patrick, and I’m sure you’ve got a strong view on this as well. From my vantage point, this journey is going far faster than we ever expected, far quicker, far, far quicker, right? So, the level of confidence that we see in business leaders in particular, far exceeds what I expected at this point in time in the journey. And along with that confidence, I think comes, you know, quite a concrete instruction to deploy this where it’s viable, you know, to do so, right? So, it’s moving more quickly. I think it’s absolutely a solid inflection point that’s here to change. And as I said, I think we see real evidence that clients want to deploy this where it’s appropriate to do so.

Cost cutting, optimization, productivity and efficiency

Patrick: So that, Alan, that opens up so many questions. Let me start with this one. We have been thinking about and hearing about the primary benefit of AI being about cost cutting, being about optimization, being about how many, to be very direct, how many people can you get rid of because people are expensive. When you meet with, especially when you meet with CEOs that come into the AI labs, are they talking just about cost cutting, or are you seeing a true business model reinvention, a real true transformation where there’s growth opportunities associated with their implementation of AI-enabled solutions?

Alan: Yeah, another good question. See, I think, right, I think it would be particularly short-sighted of a chief executive to be only thinking in those extreme terms that you shared, right, in terms of efficiency and cost cutting. And what I will say is I probably see the opposite, quite honestly, you know, Patrick. I think a lot of senior business executives are clearly thinking in terms of productivity, which is the flip side of the efficiency coin, of course. They’re thinking more broadly in terms of, if we could get AI to do the boring work, right? If we could get AI to do the things that are a distraction, then maybe my employees would have additional capacity to grow my business, right? So, in general, the language seems to be one around growth and speed and innovation. I think we should take a huge amount of reassurance from that, right? That the world’s business leaders seem to be looking at this in a more positive mindset. And if I think, and we’ve got so many examples, Patrick, of where modern AI, agentic AI in particular now is being deployed into the heart of these businesses. There are a number of themes that kind of spring to mind. I’ve mentioned productivity to you, simplification, right? The world’s businesses have an awful lot of complexity, complex processes in particular. So many examples now of where we’ve deployed advanced AI into those complex business processes to bring simplification. Humans love simplification, Patrick, quite frankly so this is a key area, anything to do with improving the customer or the client experience. That’s an obvious one where you would expect AI to be bringing impact today. And, you know, in general, this expectation that we will all be given the opportunity to delegate work to AI agents in particular. You know, this kind of assumption, I think, from a lot of businesses now that to improve the productivity of my most valued kind of employees, I want them to have the opportunity to delegate work to AI agents to just remove some of the load on them, right? So, I think overall, you know, the overwhelming message that we hear from clients is they just see this as a terrific opportunity, right, to drive growth, improve the customer experience. Clearly, it’s going to bring efficiency as well, you know, Patrick, that’s pretty obvious. But I’m generally encouraged by the overwhelming kind of positive theme of the conversations we’re having.

Patrick: I love the idea of framing the AI helps with the efficiency and the productivity, which frees up the humans to do the growth part of it. Because the productivity is very easy to understand. How much can we simplify? Oh, I should say, I think simplification might be the word for 2026. I spent a few days in Australia with a Big Four firm recently and every single client used the word simplify or simplification during those sessions. So clearly you guys have tapped into exactly the right thing. Simplification is going to be the catchphrase for 2026. But I love the idea of the humans bring the growth and the AI brings the efficiency and the productivity.

The framework for successful AI adoption within enterprise

I’m curious too, I want to run something by you. This idea that adoption within an enterprise- that you can have, you can have the best sort of business case for it, but what you need enterprise-wide is you need leadership buy-in, you need the masses to buy-in. That is, you need lots of people within your enterprise who are willing and open, maybe see AI as a little bit scary, but are willing to try and try it out and experiment with it. And then you need the lab, literally a lab like you guys have, or at least within an enterprise, you need a dedicated group of people who are working on AI, not only full-time, but working on it as a way of bringing it to scale within the enterprise. You have to have all three elements, the leaders, the masses, and the lab for AI adoption at scale within an enterprise to actually be successful. Is that framework work for you? Is that what you’re seeing when you talk to clients now?

Alan: Yeah, I like the way you framed it, Patrick. I do, and I’m going to give you a really good example of where that has worked, you know, in practice, right? And we’ve got so many of these examples, right? But I think time’s going to limit the number I can share with you. But let me give you one from the States, right?

So, we are in the process in the US. We are deploying an AI clinical advisor. This is AI in the consultation room. It sits alongside your clinician, your doctor, right? And it’s being deployed- it’s being deployed to 20,000 clinicians in the States as we speak. And it’s been, you know, the deployment’s been going on for quite some time. But the really interesting thing about why this particular project has been so successful was it started off as like an innovation idea, right, from someone inside the client’s clinical kind of workforce. And, you know, our role as the AI lab inside HCLTech, the client came to us to say, we’ve got a great idea, would it work, right? And we spend a lot of time helping clients understand maybe the art of the possible with AI. But anyway, we built an early-stage AI advisor. And guess what, Patrick, right? We handed it over to our client and it was quickly picked up by the chief nursing officer for one of the US’s largest healthcare providers, right? And this individual saw value, started sharing it with a few clinicians. Guess what? They started using it in the consultation room, right? And so, the remarkable thing was the frontline staff, right? The nursing staff, the doctors, the clinicians. The reason why they saw so much value in this AI clinical advisor, Patrick, was because it is reducing clinician burnout.

You can imagine if you’re a doctor today, whether you work in the States or with me in the UK, there’s overwhelming demand, right? And clinicians are getting burnt out. And this was an example where modern AI can start to reduce the burden on the clinician, right? It’s doing the obvious things like transcribing medical notes, but it’s ordering medicines from the pharmacy, for example, it’s updating medical records, but most importantly, it’s given the clinician access to the world’s leading medical research, right? It’s helping the doctor make better decisions. You go in to see your doctor with some sort of ailment, you know, Patrick, you hope, right? You hope that your doctor is going to prescribe, you know, the best medication for you and fully understands the impact on your health and so on, right? So, our AI clinical advisor is tapping into all of this research, right?

Now, overwhelmingly, right, the users, the beneficiaries of this keep asking for more, right? Patrick, you made this point, right? Having a user base that are basically screaming, demanding the benefits of this has been really helpful. Now, think about this from the business’s perspective, the stakeholder perspective, because you refer to this kind of, you know, the need to have this kind of top-down approach, right? This AI clinical advisor, typically, it’s giving back three-minutes for each consultation. Now, when I say to you, AI is giving a doctor three-minutes, it doesn’t sound like much, right? But here in the UK, a typical consultation with your doctor in the local surgery, you’ll be lucky to get more than 10 minutes, Patrick, with a GP in some countries, right? So, giving back three-minutes is- that’s absolutely a massive dividend, right? Now, in the case of our particular client, that three-minute bonus translates to a minimal annual saving of $200 million, right?

Patrick: Wow.

Alan: This AI clinical advisor is giving back $200 million worth of productivity, right? So, there’s the example, right? Where you have, let me call it a frontline workforce who are looking at AI as reducing the burden on them, AI doing the less interesting work. You’ve then got the senior leadership looking at this in commercial terms. And then of course, our role at HCLTech in the AI lab, helping the client rapidly build this solution. That’s a good example, Patrick, right, of where those three elements that you described come together quite well.

Patrick: Yeah, that’s an impressive number, no doubt, and that’s a great example.

The changing of roles and AI proficiency

I’m curious too, because you could start to think about how that use case will evolve. And at some point, the masses become so adept at the technology and adept at using AI, it just sort of becomes, you know, it becomes, well, it’s already embedded in e-mail, but it becomes just sort of part of the everyday. And that’s where I wonder sort of long-term, do- well, short-term, do companies, are the enterprise, the clients you’re working with now, short-term, right now, do they have the talent on hand that can implement and scale and then manage, and decommission agents as needed? You know, every agent’s going to need some support and maintenance. So, do they have the talent on hand now? And do you see a future where you don’t even think about AI talent as something special or unique, sort of every human in the organization has their own, proficiency with AI to a degree that, I don’t know if they build their own agents and all that. We’ll get to that later. But do you sort of see a future where, and I guess I’m asking a really hard question because it’s, do you see a future where your firm’s role changes a lot because you’re no longer providing that expertise that you’re providing now? You’re providing something else because everybody’s got that expertise.

Alan: It’s a good question, right, Patrick? And I, you know, there’s several elements for that question, but the first part, of course, was around this kind of belief that everyone will need to be AI native, right, in terms of their skills. And I think, yeah, that’s absolutely the case, right? There is, you see evidence today, quite frankly, Patrick, of almost a two-tier workforce emerging in some sectors where you see those earlier doctors maybe with a growth mindset, for example, they latch on. They latch on to the power that these tools can provide them. They latch on to the competitive advantage that these tools can offer. And they kind of supercharge their journey, right? And they’ll often come to us at HCLTech to guide that adoption of tools, right? But then you see that second group of people who haven’t quite invested in the journey yet at scale, right? And what we’ve got to, I guess, bring focus to is to get everyone up to the same level, you know, Patrick, so they’re able to fully utilize it. And this is no different, by the way, if you were employing someone to join your team, Patrick, as an example. You almost take it for granted that they know how to use Office Suites, for example. You just assume that everyone knows how to use PowerPoint or Excel, right? But it’s the case even with Office Suites today that most of us don’t really tap into the full depth of capability, right? We can survive with Excel.

Patrick: Right.

Alan: And every once in a while, every once in a while, you’ll come across someone who knows how to really drive Excel an example, right?

Patrick: Right.

Alan: Now, in this realm of AI, you know, I think really, each of us- there is an incumbent expectation on each of us that we learn how to really, really drive the power that these tools can offer us. And again, when I look around, you know, organizations today, you see these rock stars emerging, Patrick, people who are super productive, whether it’s the 10x developer or the salesperson that just seems to be able to issue more proposals than anyone else. There is really good evidence that people are starting to dig deep into the capability, but I will draw your attention. There was this recent report from one of the AI companies, Anthropic, where they’ve looked at, so what are people using Claude for, right? Who are they, what sort of work do they do, what are they asking Claude to do, but in summary, you know, they’ve kind of concluded that the average user of Anthropic’s products is barely tapping, you know, barely tapping into the surface, right, of the power. In other words, if you’ve got a Claude subscription, most people with a Claude subscription might only be using 10% of its capability, right? So, there’s an assumption, I think, Patrick, that over time, we all become a lot more conversant, use of these tools becomes a lot more habitual. And then that sort of significant uptick in productivity is going to be more widespread than maybe it is today.

Patrick: Yeah, and that’s really encouraging because when you think about it, if we’re only using 10% of this capability, that when we see it, as you mentioned, sort of those superstars, when you see AI used in its, even if it’s not full, even in its 50% or 75% capacity, what it can really do, it is absolutely astonishing. And I guess we’re all just heading in that direction. It’s just going to be at very different paces, of course.

Alan: Yeah, it is. And you asked the question about the impact on companies like HCLTech, right?

Patrick: Yeah.

Alan: And you know as well as I do, Patrick, that under the surface AI is a ferociously complex and sophisticated environment, right? You know this, right?

Patrick: Oh, yeah.

Alan: There is so much complexity there, right? And what we’re seeing, of course, right, is, you know, clients that come into us to ask us to, you know, design and build these capabilities. They need our help to implement and govern, of course, the consumption of, you know, modern AI across their organizations. It is, you know, it is revealing, right, significant additional demand for the services that HCLTech offers.

How HCLTech is partnering differently

Patrick: Yeah, and so let’s actually, let’s use that and think about the complexity. And I do want to, as I mentioned at the beginning, talk about the ecosystem and alliances. And just sort of very quickly, can you reflect a little bit on how much you’ve seen change in the last five years and sort of how HCLTech is partnering differently than maybe they did a few years ago, especially not just around cloud, but also around AI?

Alan: Yeah, it’s another kind of interesting question, Patrick, to reflect on. I think one of the great things about this company, right, HCLTech, is partnering and the, you know, the curation of an ecosystem of collaborative partners has always been at the heart of everything that we do, right? So as a company, we tend to partner quite naturally. And I think, you know, if there’s one thing I’ve learned, Patrick, during my career, if you want to move quickly and tap into new growth markets, partner is the way to do it, of course, right? Combine your strengths with that of a partner to go after the bigger opportunity.

And I think back in the past, right, before this AI kind of era, right? I think sometimes partners may have looked at a company like HCLTech as a channel to market. In other words, we want you to resell. We want you to resell our product, right? Now, our clients, of course, they want a more strategic relationship with a company like HCLTech. They see us as their transformation partner. They see us as that partner that will support, right, the complete reinvention of their business with AI. And that again is then reflected, I think, in the maturity and the evolution of the ecosystem relationships that we have. And if you look across what I would call the ecosystem of AI partners, companies like OpenAI, for example, and others, right, where there is great demand, just as an example, right, is whilst many of us clearly can see the benefit in using a product like ChatGPT, right? The benefits are obvious. Under the surface, if you want to integrate ChatGPT with your enterprise systems, right? Whether it’s single sign-on or more importantly, access to your data sources and other systems inside your company, right? There is a huge amount of work to be done, Patrick, right? To get that smooth integration between a product like ChatGPT Enterprise and a typical kind of complex kind of corporate IT environment, right? And that is a really good example of where, you know, our relationship with OpenAI as an example, brings this kind of two-way kind of benefit to both companies, right? In terms of, you know, we help the world’s largest enterprises integrate modern AI, not just OpenAI, of course, but a broad range of offerings. We help modern enterprises integrate advanced AI with their kind of corporate infrastructure. And then the flip side of that, again, is for the technology company, the AI provider, many of those companies don’t really have the ability to integrate their products with these complex legacy IT estates. So, it’s a mutually beneficial relationship, right? A company like OpenAI has the benefit of knowing that HCLTech, with all of our great kind of expertise and reach, we can help clients obtain maximum value from their use of a product like OpenAI.

Patrick: Right. And you said it exactly right at the beginning. Everybody needs a partner in order to grow. Nobody does end to end. Nobody serves every single client need. Everyone needs a partner in an ecosystem.

Career aspirations at 22 years old

Alan, this has been great. I want to wrap up with one quick question I’ve been asking everybody this season, and I’m reflecting on two things. One, my own- my daughter’s about to finish university. So, she’s, you know, 22 and the world is in front of her. And also that longitudinal view of technology that we were talking about before. So, I’m just curious. So, the 22-year-old Alan, what is it you wanted to be? And did you ever expect you’d be sitting in an AI lab when you were 22? But most importantly, when you were 22, what was it you looked at the world and thought, okay, the world’s in front of me. This is what I want to do.

Alan: What a really good question that I didn’t expect, Patrick, but it’s a really good question and I’m going to answer it, right? So, you know, when I was younger, right, when I was a teenager, when I was in my early 20s, there were only two things I wanted to do. I wanted to be an entrepreneur, right? I came from a family of businesspeople, right? So, I knew that I was going to create my own career, right? I’ll go and start a business was my belief. But the second thing, I knew that I wanted to work with computers, but software in particular. I knew as a teenager, that I’d figure out a way to run a business that involved software, right? Now, I will say that when I was 22, I can tell you what I was- I can tell you exactly, Patrick, what I was doing as a 22-year-old, right? I went back into the office unpaid every Saturday and Sunday for a year. In fact, it was longer than a year. Every weekend, I went back to the office unpaid. And you might ask, why on earth did you go back to the office as a 20-year-old?

Patrick: Yeah.

Alan: I went back to the office as a 20-year-old to teach myself then, new capabilities that would make me more productive. So as an example, this is probably showing my age, right? I taught myself the C language every weekend for a year, right? I used to write software products in Assembly Language, right? That’s going back quite some time, Patrick, right? But I realized that if I could master the C language, and of course, subsequent languages since then, I realized that I could actually create products far quicker than I was, right? And that investment of time as a 22-year-old enabled me to launch my first business, right? I was soon running a software company, for example, right? Now, I share that example with you, Patrick, right? Because, you know, my advice to your daughter or any other, you know, kind of ambitious young person is, it’s never been easier.

Patrick: Ha

Alan: It’s never been easier to start a business or launch a product or convert a good idea. And I met- this is my final story for you, Patrick, but we’ve got so many. I met a medical student from New Zealand, right? And she was probably the age of your daughter, right? And during her eight-week experience in India, she and the other students were asked to create an app, okay? And so, whilst I was there, I was at the New Zealand High Commission at the time, she showed me the application that she had created, right? She is a trainee surgeon. She knows nothing about technology, but she had built this app that basically knew pretty much everything around resolving common medical issues. If you injured yourself, was out hiking, this app would take a video of your injury and recommend treatment and then, you know, talked to various medical services, right? And I called over the New Zealand, I called over the New Zealand Minister for AI and I said, you’ve got to look at this, right? This is a medical student from New Zealand and just look at this app that she’s built, right? Now the remarkable thing, right? I just told you about me having to invest a year, right, just to learn a new programming language back when I was that age. This student built this product in two days.

Patrick: Oh my God.

Alan: In two days, right?

Patrick: Yeah.

Alan: So, if I was a 22-year-old today, Patrick, I would be innovating like crazy, right? The ability now to convert all of your good ideas into products and services that you can monetize and take to market, it’s never been easier, right? So, I think any young person, Patrick, now, they should be encouraged, right? Look with a positive view at all of the capability that’s now been made available to you. And if you’ve got the ambition to do so, leverage these tools, launch a business, launch a product. It’s never been easier.

Final thoughts

Patrick: Alan, I got to tell you, I’ve had a lot of conversations about AI. I can’t think of one that’s been more positive overall. You’ve just got, you’ve got just a great outlook on how these tools are- a great outlook based on experience on how AI and these tools are just so great for us. So, I really, I appreciate that. It is really refreshing to hear that kind of perspective. So, thank you.

Alan: Well, thank you, Patrick. It’s always nice to spend time with you. Thank you very much.

Patrick: Great. And we’ll be in person as soon as we can be. I will see you soon, Alan. Thank you so much for coming on the podcast.

Alan: Yeah, take care. Bye.

Patrick: Great. Take care, Alan. Bye-bye.

Tune in next week for another episode 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.