Technology Business Research, Inc.Defining Success in AI and Digital Transformations
In the Season 6 premiere of “TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms,” industry expert and thought leader Nav Thethi and TBR Senior Analyst Stephanie Long join host Patrick Heffernan for a discussion on measuring success in AI transformations.
“I am running my own podcast show, and I speak to executive leaders from mid to large companies all the time — all executive-level conversations — and these things keep coming up, that there is a lot of education gap with AI right now at all levels,” says Thethi. “IT or hard-core tech people might understand that better, but people who are driving the strategy, who are mandating their teams to adopt this change are not very well educated, okay? And that force [is] actually pushing teams to run faster because there is a FOMO in the industry to compete against competitors and industry standard to chase this AI transformation. And that’s the part I find very difficult for businesses and leaders to understand and to write about it.”
Episode highlights:
- The difference between transformation and adaptation
- The areas where AI is hindering adoption
- The definition of a successful transformation
This episode is now available on demand across all major streaming platforms. Listen and learn today!
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Defining Success in AI and Digital Transformations
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 digital transformation, the roadblocks to successful AI adoption, and measuring that success with Nav Thethi, industry expert and thought leader, as well as Stephanie Long, Senior Analyst for TBR’s Telecom Practice.
Nav Thethi’s background defining customer experience
All right, Nav, welcome to TBR Talks. I’m sitting here in the studio with my colleague Stephanie, and we’re excited to have you on today.
Nav Thethi, Industry Expert & Thought Leader: Thank you, Patrick. Thank you, Stephanie, and pleasure to be here.
Patrick: Yeah, maybe so we’re kicking off season six, and one of the things we want to try and talk about as much as possible is the bigger challenges around AI. A lot of people are kind of lost in the weeds of what AI can do for them directly. A lot of people want to talk about sort of scaling AI, the sort of the great big picture, but really we want to talk about what’s happening now, how to overcome some of the hurdles, what are the challenges that people are seeing day-to-day. And I know you’re in a great position to talk about that. Maybe you could start off by telling us, I know it sounds weird, but maybe tell us what did you do yesterday? Give us a sense of what you’re working on, kind of, every day right now. And then also maybe a little bit about, sort of, how you got to where you are right now.
Nav: Sure, that’s a great start. Okay, my journey has all along around customer experience. I have over 25 years experience in industry and my core expertise is digital experience. But on this digital experience, my focus is all about customers. Overall this journey, I have worked through big brands like Quark, Fidelity, Hitachi, still involved with leadership and defining the customer experience around all the digital customer facing touch points. I take care of back end and front end. Whatever touches and influences customer experience, I am involved. I am taking care of every single aspect.
Okay, but what I did yesterday was like how to create a robust back-end customer profiling that can deliver in the future post-AI era customer experience. How to understand those customer sentiments today when customers are very well educated, more educated than ever before, information is available at their handheld, and they come very well prepared. They know more than the sales team will sometimes know about the product. How to engage with them, how to motivate them and influence them to conversion path, and ultimately win business from them. So, I do this every day.
Patrick: And so that puts you in touch every day with leaders at companies that are struggling with how to get the most out of the investments they’ve made, not only in AI, but in any of their customer experience platforms and any of the sort of digital transformations that have happened in the last decade, right?
Nav: Absolutely. And you mentioned digital transformation, and transformation 20 years ago was different than today. Back then, just having a website and probably a database from the form fill is a digital transformation. Now, that was not even an awareness anymore. So, the transformation standards have changed. So does the guidelines and the growth path.
The difference between transformation and adaptation
Patrick: Let me ask you a quick question about that because we have always at TBR, we’ve always been a little bit persnickety I guess about the word transformation where what we want to see is not just what you just described, which is like, transformation could just be, you know, adopting a new piece of software. We’re always looking for transformation as a transformation in the business model, meaning a company is no longer pursuing the same business model they were pursuing prior to whatever technology, whatever changes, change management, whatever new strategy they took on. So, in your experience, how often does that kind of transformation take place and how often is it just sort of an adaptation or a transition to a new way of working?
Nav: Absolutely. Okay, so I call it strategic maturity of organizations, and that’s the maturity code that I have created, which has five stages. So, what happens is like, the very first stage is awareness. So, wherever the business is, whether it is a digital or strategic transformation, where first of all, there is an awareness phase, like there are some sort of processes in place. And then it comes to the first one is awareness and the second one is experimentation. Then you start playing around with this thing on the growth towards transformation. So, what I’m getting to is what transformation really means today versus 20-30 years back. So, at any given time with the growth, you are aware of something, you are aware of your current state and future state, and then you start experimenting things towards that growth. And then once you find success, for example, through experimentation, through pilots, through sample of your customers or data or employees on few things and you get success. And then you start integrating that to a larger scale or different teams within the organizations or larger number of processes. And then the fourth stage becomes optimization. You start settling it down, you start finding benefits from it. You start either saving cost, or increasing revenue from that. And then you start getting personalization with your audiences. And then finally, the transformation where you actually have highly mature business state. Could call it Six Sigma, could call it like, the stage five of maturity model from top-notch organizations or institutions that have defined the standard. So that is what the five stages of any maturity scale. That is what I defined a transformation means. For example, like 30 years ago, as I mentioned, having a database was a digital transformation because most websites were sending form fill to an e-mail address. Having a database was giving a lot of flexibility to analyze that data. Today, having a CRM is not even a digital transformation. Having a personalized customer data platform is not a digital transformation. How are you using it? Tools don’t define transformation anymore. It’s the maturity to utilize value from those tools and orchestrate that to the best potential is what defines digital transformation.
Defining successful transformation
Patrick: So, I want Stephanie to jump in here, but I got to ask you a quick question before she does. In your description there of those five stages, one of the key elements was success. And I think the challenge that we see right now for a lot of enterprises is defining success, measuring success, and then mapping that success to a return on the investment. So success, if it’s defined in an AI-enabled solution as a certain number of employees within an organization are using AI, or if it’s defined as a certain number of employees in an organization are building their own agents and having those agents take on part of their work and so they’re more productive, that’s all great, but that’s not necessarily one really success in terms of transformation, nor is it too necessarily a return on the investment, which was significant.
Nav: Yeah.
Patrick: So can you talk a little bit about how you help people define what success actually means in that framework that you laid out.
Nav: Yes. There is a study by McKinsey that says if we talk about today’s transformation, which is all about AI, 88% of all companies surveyed says they have AI transformation in place. They are scaling on top of that, and they are satisfied with the output. On the other side, PwC has published another report interviewing 1,000 CEOs from mid to large organizations, and only 55% of those CEOs claim to have either revenue growth or cost reduction in the process. So, what does it tell us? It tells us that almost just half of all AI investments are actually giving any value to their businesses. And the same study by PwC says only 12% of CEOs claim that they do have value. Okay. So, the majority are not seeing any value in the AI investments. Gartner have published another report, over 90% of AI investments are failing. And the reason is not technology, it’s leadership. Okay.
Patrick: Yeah.
Nav: And this means like, we are getting better on productivity. We are doing more than before. We have more options to work for us, to be more productive and be more efficient, but it is not really tuning into the balance sheet. It is not influencing the growth in any possible way. And we are stuck in transformation theater. Okay, but what success metric really is to your question, Patrick. So, the success here really is from all these studies, they’re pointing to either revenue growth or cost reduction. So, it means value improvement. So that is the success factor right now that industry is looking for in this transformation theater or productivity paradox of AI investments. How to define your success metric is totally based on businesses’ own preferences and internal structure, but it ultimately gets down to monetary value, cost reduction, or revenue growth.
Areas where AI adoption hinders organizations
Stephanie Long, TBR Senior Analyst: Right. So Nav, we’ve been talking about digital transformation, AI, and frequently when we talk about those topics, as it’s come up here, we talk about what are the advantages, the ROI, the success factors that you glean from those investments. But I kind of want to spin that around on you and ask, from your opinion and from your vantage point, how has the availability of AI actually made your job more difficult as it relates to sort of this customer experience, customer insights role?
Nav: What I am finding is there are blind spots in terms of data sharing to these large language models, to these frontier models. And there have been talks all around it. Like, are we training our competitors? Are we giving over business secrets to a public domain that might come back and bite us? There is a lot of gray area around it. I’m not claiming that there is a leak, but there are possibilities that we don’t have visibility where all that data is going, how it will actually impact us down the line in the future. So that’s one thing, but it doesn’t mean we should not use it.
How to address that. That is why industry is moving towards small language models, SLMs and local models. So have your own model, train it, and then transform around it to achieve that AI sovereignty. This is something that businesses have already started looking at. This is the future. Having small language models, any single business doesn’t need billions of parameters to run their processes around AI. Some do, but not everyone. Whatever is required for LLMs level can go public, public as in like frontier models. And by the time that industry will also mature to the point that it is safer. But I would recommend definitely and as early as possible, businesses should start exploring SLMs and start working towards it to have that control over their own data and privacy factors. What I’m hearing is even like mid leadership giving business information, strategic documentation to LLMs to be prepared for next leadership update meetings. Okay.
I am running my own podcast show and I speak to executive leaders from mid to large companies all the time, all executive level conversations and these things keep coming up, that there is a lot of education gap with AI right now at all levels. IT or hard-core tech people might understand that better, but people who are driving the strategy, who are mandating their teams to adopt this change are not very well educated, okay? And that force actually pushing teams to run faster because there is a FOMO in the industry to compete against competitors and industry standard to chase this AI transformation. And that’s the part I find very difficult for businesses and leaders to understand and to write about it.
Patrick: So, I want to build on that a little bit because as you were talking, I was sort of writing a couple notes down, thinking about the different, and Stephanie, I’m glad you pivoted to the like, what is going wrong with AI and what are the challenges? I think it’s so easy to get caught up in, you know, sort of all the magic of it. And Nav, you brought up some really good points about the challenges. And I sort of, initially I had three buckets and the more you talked, I kept adding buckets. But I feel like the challenges to adopting AI at scale in an enterprise and what I’d love to hear you- I’d love to hear Nav, your reaction to these five buckets, and also which of these is the one when you talk to leaders on your podcast, which is the one that comes up the most? So, there’s, you mentioned knowledge gaps, so like the lack of, or a lack of talent, and that falls into the kind of the knowledge gap. There’s lousy data. You talked about small language models, even large language models. At the end of the day, it’s data, and sometimes it’s just lousy data that causes AI to be inefficient and not worth the time and the investment. There’s also the cost. I mean, we see the stories all the time now about how much more expensive having an agent is than hiring a person at the moment.
Nav: Correct.
Patrick: But cost is there. And then the last two, there’s, and you mentioned this as well, there’s risk. There’s definitely a governance, risk, and compliance. Now you could call that a cost, but I think it’s independently more of a risk in that you can have a spread of AI throughout an enterprise that’s unchecked. You can have your data being spread to places where it shouldn’t go. And then I would say the last one would be kind of a distraction element where AI and trying to adopt AI distracts time that leaders should be spending on other parts of running their organization that are more vital than just, are we adopting the best new AI solution? So out of distraction, governance, risk and compliance, cost, lousy data and knowledge, which of those five do you think is the one that really is primarily getting in the way for a lot of the leaders that you talk to?
Nav: Okay, that’s a great point. Thanks for asking. And that reminds me another acronym that I go by is I’m calling it Fear Factor. Okay, and this is F as in FOMO, of course. Leaders are curious to adopt AI fast and having that system in place. There is a push from the board also to be AI enabled end to end. And then E is an education gap where at all levels, there is something that people don’t understand about AI. And from strategic perspective, what AI capabilities are and their limitations are, they don’t understand. Some leaders do understand AI just as another automation tool and not the whole ecosystem. And A is an alignment gap. Whatever programs businesses are running with AI are experiments or just to explore what it can do, not really aligned with the actual business or customer related outcome. That is why we have been hearing pilot purgatory more often and keep coming back in different conversations that AI is just have been another pilot purgatory or transformation theater because there is an alignment gap. And that’s been coming over and over in conversation with different leaders that I sit with and speak for very long conversations. And last one (R) is roadmap void. What this means is businesses do know what they want. They have money, they have strategy, they have decisions, they have teams. But how to get to the finish line is something is not clear because of that education gap, alignment gap, and FOMO. Companies are just moving forward, just trying to do what the best they can do and keep chasing it.
From those five buckets that you had defined, Patrick, with all this, I would pick the leadership is the main thing to start with because that accountability needs to be there. Okay, just making a mandate and pushing teams to bring in results is not enough. Leaders today, especially in today’s time with AI, when the horizon is changing, the way customers are researching for purchasing products or even researching about businesses to deal with, they are educating themselves. Why not leaders educate themselves to the point that they understand this AI technology and how to enable their teams, equip them with the right tools and services to be enabled. So, leadership is number one thing. And education, again, having leaders self-educate, get themselves trained. They don’t have to know like how to develop an agent and how to deploy LLMs or SLMs. No, not every level of team member leaders need to understand that, but at least to the point they understand what are the challenges that the next layer or next level of team might face and be ready to support that.
And then data. I am running a survey right now on the strategic maturity and I have a handful of responses. But whatever the sample I have received so far, it determines the strategic maturity of businesses on five pillars: technology, data, customer experience, leadership, and culture. Out of that, what I have found so far, talent and culture. That has been like the most, like the highest standard in the organization. Like all companies, majority of them do have strong talent and culture in place already. Where they are lacking to get higher on strategic maturity. Number one, leadership. Number two, data. So, these two are the weakest links right now, that I have studied so far in my research. And I kind of agree with that based on my conversations with other senior leaders also on my podcast, even otherwise on different settings.
The AI yield crisis and what to do about it
Stephanie: When I was listening to you talk about those things, it brought up a couple of things in my mind. You know, you talked about things like pilot purgatory and SLMs and FOMO and the leadership gaps and all these things together sort of send up the warning bells in my head about the silos and how that can actually reduce the efficiency and productivity of some of these investments that companies are making. And to your point before, data and leadership could be solutions to that challenge if done correctly.
Nav: Correct. Correct. What I have analyzed just recently, I mean, it’s like three, four months now. Gartner published an estimated prediction for AI investments will exceed two and a half trillion dollars by the end of 2026. And RAND Company published other report that 42% of all AI investments, all AI experiments are being halted because they’re not possible to scale or they’re not successful. So, all these experiments are failing. And out of that $2.5 trillion estimated spend on AI investments, nearly $1 trillion is just about AI solutions, software and service investments. The rest is infrastructure and logistics.
Okay, so based on this, I have analyzed there are some other numbers in the calculation, but what I have analyzed is $383 billion is being spent in AI is already stuck and it is predicted by end of 2026 to be at $383 billion in AI yield crisis. And Stephanie, to your point is this AI investment, $383 billion is going to get stuck in AI investments by end of 2026, and that’s global. Okay, so what does it mean? Because pilot purgatory, because transformation theater, because alignment gap, knowledge gap, roadmap void, and all these things are supporting that AI yield crisis that is set to grow at $383 billion. How to get out of it is where I’m spending my time right now. What are those key reasons that this investment either made incorrectly, or it is impossible to yield or reclaim this? And as we discussed, leadership and data are the two top reasons based on my findings and I’m also seeing that a pattern from other reports and interviews as well. Measurement is very important. What we don’t do is that we don’t define benchmarks.
And Patrick, to your point, like what success metric should be is something that is missing as well. Okay, we are relying on those traditional metrics to determine our success. For example, if we speak about websites, we see website as recently, of course, it has become a demand gen tool and we do see websites as our awareness property. How many people are visiting to the website has been a very successful metric for a very, very long time. Okay. For example, 50,000 visitors in June and now in July, we have already exceeded 50,000 and we still have more than a week’s time and we are set to generate over 60,000 unique visitors to our website this month. This metric doesn’t matter anymore because the market has shifted. Right now, customers are researching about businesses on GenAI. They are finding context, they are looking at vendors, looking at their solutions to their problems on GenAI. A lot of traffic is directly going to GenAI. They’re not going to search engines and that keep’s growing. And visitors coming to the website has reduced over the last couple of years. Most businesses are still looking at visitors’ traffic to their website, but that is not a highly relevant metric anymore. Now it has been observed that whatever traffic is coming to the website is highly intent. They are very likely to convert because they are already coming educated. They’re already aware of your product, your services, your other customers and business model. Means you just have to divert them to the right person as fast as possible. I come back to the point, measure, defining your benchmarks. So those benchmarks have to be revised right now. I started with that AI yield crisis in billion dollars. If you have a $10,000 investment on AI program and six months later you kill it because you don’t see value or you don’t see scalability on that program means you are also contributing to that $383 billion of AI yield crisis this year. You can only save it if you define your benchmark first, where the current state of your business is, what your goals are, what your success metric looks like, and then adopt technology later. Okay, so it starts with the leadership. It starts with a vision and define your goals. Again, same practice as we used to have like many, many years ago. It’s just that we stopped doing it. And now the framework has improved a little bit with the technology, with the industry norms, but baseline is still the same. Define your benchmark, define your goal, and then have your roadmap in place and then start working on it. And that’s how we can save from growing that AI yield crisis number to multi-billion dollars.
Pivoting operations to embrace the future
Patrick: I want to use the example that you gave just a couple of minutes ago about the sort of search engines and search and GenAI. And I want to look at it through two different frames, sort of the today right now frame and the year from now frame. Stephanie, like, did you want to sort of frame it up as what’s happening now? Like, sort of the- what maybe, because everything you just said, I think I understand, but I also am not sure that most people who aren’t where you are seeing that. Are you- Stephanie, do you know what I’m saying?
Stephanie: Yeah, so like for example, Nav, to your point before about like web traffic and people visiting your website and now we’re pivoting toward GenAI, how or do we measure at all, do we bother to measure, for example, bot traffic and how that might correlate to some sort of a productive metric toward an AI investment or maybe it doesn’t? Or maybe a website needs to be redesigned for the bot traffic instead of the human traffic. How do we sort of pivot from where we are today to embrace that near-term futuristic state where GenAI is, and subsequent agents are in some ways the users of these traditional business websites?
Nav: Websites don’t need revamps with this change. It’s just that, like, the customer intent has shifted. They are shifted from in context to intent based search. Okay. When customers go on GenAI, they ask long questions and they keep interacting with that. And then AI, GenAI based on their indexed data across the internet generates the result. We all know that. We all do that every day, every hour. Okay. So, what we have to do now is to make sure that we have those intent awareness, what customers are looking for, and have our website answering those questions in one way or the other. It could be either from FAQs, which has been already used for even Google-based search engine optimization process. But that has to improve now. The content- the content structure is needed to change now in order to be more GenAI friendly. This is important. And second thing you mentioned about bot friendly website, I don’t think that is necessary right off the bat. It addresses some use cases and we have a protocol MCP for that, which is Model Context Protocol, totally for bots. That is a different scenario. But having just the content update to be more GenAI friendly is low hanging fruit. It’s just, like, answer questions, answer intent-based questions on the website is important. And even writing page structure from top to bottom in the story based on intent signal, based on intent-driven search terms. And your other aspect of the question is, I’m trying to remember, and that is about how to get there, right?
Patrick: Yeah, how much does that transition need to happen now? How much of it are you seeing because of your actual physical location in the valley, but also sort of your place in the ecosystem? And how do you think folks that are not as immersed in AI or maybe not as fully aware, what is really happening and at what speed?
Nav: Go to data. Go check analytics. Go check analytics. One thing I would suggest, another area to look at is whatever visitors are coming to your website, we used to have a traditional metric called bounce rate. Okay, if you have 50,000 visitors to the website and your bounce rate is 25%, has that been the same with reduced number of visitors or it’s been increased or whatever? So, if your- since the data has proven that whatever visitors today are coming to the website are high intent means you should have lesser bounce rates.
Patrick: Right.
Nav: You should have more people converting ultimately to the goal.
Patrick: Right.
Nav: If that’s not happening. you already see where the gaps are on the digital properties. I mean, that customer journey needs to be connected all the way from the first touch point coming from GenAI to conversion. That path should be shorter now because customers are already aware and highly intent.
Patrick: Right.
Nav: Okay. So, data. Understanding your data is very, very important. And again, it comes back to the same point you mentioned earlier, Patrick, the success metric. The success metrics need to be redefined now based on the current circumstances and customer education.
Patrick: Yeah, and I’m glad you said data because of course we’re Technology Business Research, so we sit on top of 30 years worth of data. So, it’s not just the data that people generate themselves, it’s the data that’s out there. As you’ve mentioned a bunch of times so far with some of the studies.
I want to wrap Nav with just a question about the future. And so, a year from now in the summer of 2027, when it comes to AI, technology, leadership, enterprises, adoption, scale, all of those sort of things that are constantly top of mind today, what of those are going to be top of mind in 2027? And is there something else when you sort of look into your crystal ball, when you look ahead and you say, this is going to be the topic of 2027 in the summer, what comes to mind for you?
Nav: Okay. We started this race in 2022 when ChatGPT was first announced in 2022. And it’s been four years and next year, five years. And I’m really anticipating right now that we’ll be quite settled by next year. We’ll be very much clear about what AI can and is doing for us. And we will start actually implementing stable projects with this technology, and we’ll be productive. We will be productive and we’ll have something new to chase. It’s been five years.
Patrick: Yeah. And five years is that- it’s a lot of time for any technology trend.
Hear more from Nav
Nav, what’s the, you’ve mentioned a couple things that you’re working on. What’s the easiest way for people who are interested in hearing more about what you’re doing and your thoughts and your podcast? What’s the easiest way for people to reach you?
Nav: Sure, yeah, my website, navthethi.com is my bio and it has pretty much everything that I do every day and my podcasts, my articles, my knowledge base, I keep it updated. Otherwise, LinkedIn, I will share my LinkedIn URL also and I’ll be happy to connect. I’ll be happy to share my knowledge, like recorded, non-recorded, either way and like, yeah, I do speak and I can give free advice too, if anyone wants to learn from my experience, not just my experience, like I have inherited a lot of leadership experience from my podcast and talking to different leaders. I’d be more than happy to exchange knowledge and experiences.
Final thoughts
Patrick: Yeah, and I got to say, we’ve had a few conversations I’ve really enjoyed, every time we’re done talking, I’ve got something new in my notebook that I go back to again and again. So, it’s been really, really insightful, really appreciate it. Stephanie, thank you for helping out. And as anyone who’s ever listened to this podcast before knows, I’ve always got one last question, even after I say last question. So, here is truly the last question. And I’m going to be asking this all through season six. So, we’ll see what kind of answers we get. But if there’s, and here’s where you got to be, Nav, you got to be really creative. If there is one thing that you wish AI could do just for you, what would that one thing be? And actually, you know what, Nav, I’m going to give you a break. I’m going to let you marinate on that for a second. Stephanie, you got to share what that one thing is.
Stephanie: You know, I was thinking if you could program AI to do household tasks, but exactly how you like them done, that would be a game changer because we all have our own particular ways we like to do certain things around our own houses and to have that uniquely just for you programmed something to do it for you would be really nice.
Patrick: See, the only thing that would challenge me there is like, is mixing a cocktail a household task because, so I like to do it a very specific way. And I actually might be afraid if AI came in and did it for me, then I would lose that sort of Zen that comes with that. But that’s not a household task, you’re talking about folding laundry, right?
Stephanie: Folding laundry, yeah. Things like that, which we all have our own particular way of doing.
Patrick: So Nav, what is the one thing, one thing you wish AI could do just for you?
Nav: At this point, if I want to use AI for the very first thing, I wanted to make decisions on my behalf for my podcast workflow.
Patrick: Oh, okay.
Nav: What does it mean all the way from guest research, post-production and publishing to social media based on the cadence, analyze and research. Again, I am a big supporter for human-in-the-loop aspect. But it does do- it should do the due diligence for me and make everything and bring in front of me just for approval and does everything else.
Patrick: Right, Yeah, that would free up a lot of your time and a lot of your mental space as well, no doubt.
Nav: Yeah. And at scale, if I want to talk from corporate perspective, yeah, I would, I would leave AI to understand customer sentiments to the very one-on-one personalized basis. It can do that, but there are hallucinations. There are customer frustrations involved with AI. You talk to a chatbot and even if it is AI enabled, it keeps sending you in loops. If you call a bot answered phone line, oh, you still have to talk to a human today. And from a corporate perspective, I think, that is the element I would look for my very first preference that customer experience can be optimized and it should address that customer sentimental element as soon as possible.
Patrick: Yeah, that’s a great one. That’s a great one. So, Nav, thank you so much for coming on the podcast. Stephanie, thank you for hosting with me and we will definitely chat soon.
Nav: Thank you. Thank you. It was a pleasure to talk to you.
Patrick: 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.
Technology Business Research, Inc.