Enterprise AI Adoption: Operating System for Real Results

Hey, Utsav, thanks for having me. Very excited to be part of this conversation.

Yeah, I I think that's a great question. And I'm sure there are lots of views on this topic. And each one of them correct from the vantage point of the person who has the view. ⁓ I've seen two worlds, the world that you just described I came from, which are the world of consulting, working with some of the largest enterprises in the world on some of their most mission critical initiatives. And the other vantage point, which is of trmeric where we again are partnering with some of the largest enterprises in the world, but we're building a product which is AI native or intelligence native, so to say.

And the way I would look at how strategy consulting or consulting for that matter would evolve is I would say the bar for what represents good consulting output is definitely going to go up. ⁓ Because intelligent systems will begin to organize a lot of the learnings and the experiences of every organization into some sort of a knowledge fabric, which means that organizations which traditionally lost knowledge to attrition or to tribal ways of working ⁓ will see a systemic approach or a systemic intervention where that knowledge will not exactly be lost. So ⁓ I think the role of consulting, strategy consulting, will need to evolve. It will still be pretty pertinent, I would say. But ⁓ I would say the days of consulting which is not genuinely value adding are kind of on their taper down.

So I would say it will raise the bar a lot for the consulting stream in general. And the guys who get on the right side of it, the guys who truly reimagine consulting and output of consulting with AI will do get disproportionate benefit from it.

Sure, absolutely. So, know, Trmeric essentially, we call ourselves as an enterprise transformation OS. And if I have to unpack what that means, if you look at most enterprises, and especially the technology functions in the enterprise, they have two primary roles. One is to keep the business running, keep the systems running, keep your applications, infrastructure, all of that.

And the second part is to help that enterprise transform consistently, know, continuously. Earlier it was cloud, then it was data, before that was ERP, now it's Gen AI. Every enterprise needs to stay, for them to stay competitively, strong, needs to, is embracing technology to kind of stay ahead. We are that for all things change, right?

So we bring the flow of work of change. the resources associated to change, and the outcomes that change produces all in one platform. The other way to look at it is we kind of collapse this gap between business strategy and tech execution. And you've been in the strategy world long enough, Utsav to know that the best of strategy eventually gets into some sort of a PPT. That PPT then gets into some documents to get fleshed out into digital initiatives.

And then it gets somewhere into some execution mode. So the distance between what was thought to be done and what is actually getting done is very wide. It's very fragmented. And we're using AI to kind of collapse that gap so that they live on the same plane.

That's an awesome question. Well, I feel that there are a host of considerations that enterprises have when they buy any software, and specifically in our case. I'd say the first one is the ability to embrace change. Not every enterprise is at a point in time where they want to go through a a change like this.

This is a fairly significant change in kind of ways of working. So enterprises are always sizing up in terms of are we ready? ⁓ We believe they're ready. Every enterprise is ready, but they have to be in the right frame of mind, the right point in time to feel like that. And I'm talking about the concerns that they typically have.

I would say the second concern that is often ⁓ cited is Who within my organization is going to own this ⁓ ⁓ if they don't find the right owner who was going to be able to dedicate the right time and effort, then they will kind of push it out to a point. This, again, because it's change, you don't want to just for it to ⁓ lost in translation. I would say those are the two. I mean, I can go on, but I would say those are the two reasons.

There is always an inherent concern about AI and what it will do to But when people see our product and the way we built it out, it amplifies human potential. So I think that fear goes away rather very quickly. So I would say those are the top two issues which remain concerns in terms of ⁓ getting started on trmeric. What excites them on the other side ⁓ the, and I think you, you we ⁓ started conversation, you were talking about this embedding AI into workflows.

Which means it's his native AI, not just superimposed AI. So they like the fact how we've reimagined ⁓ their day in life, the processes they execute day in, day out. For example, if you're planning a strategic initiative, typically that initiative planning took many conversations, took forums, took PPTs, spreadsheets, what have you. And what we've been able to do is to reimagine that whole mishmash of experience into a very conversational, agentic, knowledge-rich, memory-rich conversation that many people can come together on.

And AI acts like one of the participants to the process and say, hey, listen, if you're looking to do this, here are some considerations you want to keep in mind. Here's what success would look like. Here are some past challenges you faced in similar programs. Here's the time and effort that it's likely to take.

Now they like the fact that somebody is bringing rich insights to this and facilitating the process and not trying to eliminate the guy who's trying to conceive the whole initiative. So I think they get very excited by the very practical embedding of AI in the right places for the right insights, the right actions. I think people are really troubled by wasted time. There's just zero to one reinvention happens at every level.

Is just a massive waste of time. And I think AI cuts that out in a very, very material way. I would say that excites them. ⁓ And the fact that today, one of our customers said that, and she's a chief transformation officer, she said, everybody in my team finds a higher sense of purpose because now they can see which business strategy are they moving the needle on. So I think finding that purpose and not being lost in a technology project is a pretty cool thing.

So I would say those are the three things that excite

Yeah, I feel that different stakeholders respond to different vectors of value, if I may say so. And maybe I'll share the top two or three. And maybe there is an overlap with the previous point as well. ⁓ But first one would be. This whole combination of being practically be able to use AI and not use AI because everybody's going to say use AI, but so practically use AI and be able to do their day job better.

And I just gave that planning example, I can give more, but it is real. It's not like I'm just trying to write a better email. I am prioritizing the right project and deprioritizing a project that doesn't have ROI is a very material implication transaction that you can do with AI and that would do with trmeric. So this combination of, I am AI aware and AI user and using it in a very material way truly appeals to ⁓ The second theme that comes up is, I had said that there's a lot of waste of time.

Incredible amount of waste of time, that intelligent system can eliminate reports, ⁓ hoc requests, ⁓ for information. That's like 30 to 50 % of a day of a manager, right? And you can say you just don't need to do all this and you can stay on top of it. So this ⁓ prospect doing more with their time and doing things that they really think will add more value is very appealing.

To our users. ⁓ And I'd say third is this, it makes my team, my organization look good. Get ahead, get ahead, and stay more competitive. Because again, transformation is a space which is like this, strategy to execution thing, where I can go in front of lot of our customers, our technology people. Now they are far more business aware because that's how we build the product.

So I can have an equal conversation with my functional business partners and say, hey, this is what you should be doing. This is what you may not be doing. Makes you and your team look good. So I think those are some massively attractive points. ⁓ That's what I would say is it eventually gets personal as well in a lot of these conversations.

Yeah, feel that ⁓ I genuinely feel that, and I can speak more for Trmeric, I'm not generalizing AI, ⁓ because people have different versions of what AI should do. People talk about of jobs. ⁓ We're not into that gig. ⁓ I feel the role humans is ⁓ two or three folds. Firstly, I think to truly understand how AI can augment and amplify their role. Many a times, that understanding remains very superficial.

And it's like any other change, right? If you just don't understand a technology theme, something new, and you kind of operate on the surface, you may not get the best value from it. So I feel the one big thing is to genuinely understand that, hey, here's what's in front of me, but what can I really do with it? Because that power of a knowledge fabric, the power of telemetry of connected views of the world, the power of what agents can do is immense.

And it's only as good as what humans put it to use for in many ways. So I think that's one big thing. And the role of humans, that was your question. I think the other piece that becomes very important is now the connected tissue of the organization is just not a Slack channel.

Right? A system is learning from the behavior of everybody using the system. So I think the humans can, what they can do is kind of amplify the experiences they are having with an intelligent system so that everybody else can have a similar good experience or everybody else can prevent a not so good experience. So I think getting together and saying here, here's what. ⁓ So I would say that's the second piece. in third piece is really I feel human discretion, human experience, human decision making is still quite amazing, right?

And yes, AI can dish out a whole bunch of options for you, but you can constantly be raising the bar. How do we look at a product? A product is getting constantly more and more intelligent. The reason it's getting more and more intelligent is because we're pushing our brains and saying, OK, what more can we do?

What more can we do? And similarly, the users of AI technologies can really do a lot more with their work, a lot more with what the organizations are expecting them to do. So it's to constantly reimagine what more can they do with their job. So I would say that's the trifecta of know what AI can do well. ⁓ Spread that knowledge, spread that experience because as an organization then everybody's benefiting from it.

And thirdly is spend the mind to think if a lot of my work is getting automated, what do I do with my time? What more can I do? And then that's a very cool thing to think about.

That is exactly right.

Wow, that's a tough question.

Yeah, maybe two, that's a great question again. As always, you ask very good questions. ⁓ I'd say the first thing is. You're not solving an AI problem. You're solving a customer problem with AI.

Many times you can get very confused about it. A lot of this narrative is about ROI from AI. It's not ROI from AI, it's ROI from the use case that you used AI for. And there two different things.

When people, and I think your example was pretty cool, zoom in on real problems to solve for. And you will realize that AI is only one component of solving that problem. And so I think that's one part that look at the problem, look at it holistically, and make sure you're not over-indexing on AI just because you want to be an AI company. ⁓ Chances are that that journey is going to be short ⁓ because it's not appealing to the customer. The customer really wants to solve the problem.

I would say that's one part. ⁓ I think the second part is, at least I can speak for myself, is just be ready for all sorts of surprises, ready for unexpected responses, be ready for unexpected failures and unexpected successes. And when your expectation is very hard cast, then the disappointment is much higher. And you don't want to be. And so it's a more entrepreneurial journey kind of a thing that Stay very nimble, very agile, very flexible in your expectations because chances are there will be surprises out there.

So I'd say just because you're doing an AI product, there's no guarantee for success at all. And I'd say the third piece that we've learned is, besides the obvious ones of team and other thing, is not, is the fact that and maybe it's connected to the first point is that AI can be one component, but your moat so to say, your differentiation that you bring to the table, you got to look at, you have to apply systems thinking to it. You have to think about it holistically because that magic of connecting three or four layers of competitive advantage, for example, our product builds, autonomously builds a knowledge fabric. But knowledge fabric without the right workflows is not fun.

If I don't embed the insight at the right point, what do I do with that fabric which is sitting somewhere else, right? I'm in planning mode and I'm not getting anything from it. So we combine that with workflows. Now workflows are great, but I have a lot of work.

So how do I do so many times the, yeah, it's a great workflow, but can I automate some of this stuff? So do you bring in agents? Okay, the agent will go do some of that stuff. layer these things in so that you can, so my point simply is that think holistically about your problem and apply that systems thinking approach and then build that whole total solution. This is the era of building total solutions and not piecemeal ones.

So I'd say those three.

Yeah, I'd say that... My first suggestion is don't be really look around for opportunities. Try very hard to look for opportunities. This narrative of an industry which is slowing down and an industry which is like you're not going to need engineers and all that.

I am not a big subscriber of that. ⁓ Yes, engineers will produce more work than they used to. But there will be more work being produced in general, because now a lot more innovation is possible. So don't subscribe. First, my suggestion is don't subscribe to this downbeat theme.

Traditional avenues might be shrinking the typical IT services companies, and not hiring as many and all of that. But there's so many more companies coming up. I'd say the second part is, and a lot of our engineering team, which is not very large, by the way, you don't need too many. people these days, most of them are very young. Some of them are right after college.

And they have demonstrated incredible hunger to learn. And by the way, this field is only three years old, isn't it? Nobody can say I'm 15 years experienced in this. So a newbie's experience is only three years less than the most experienced guy in the world, so to say.

So you're not that far behind from a very experienced guy. So I'd say that it's a great moment to be an engineer. Give it your best shot. Learn.

Push the boundaries of what you think you're capable of doing. And you will do so much more than what you ever thought you'd do in the first year of your job. Right? So engineering is very powerful function.

So I'd say that is the second piece. And the third is look for an environment which is empowering, which is ⁓ not finger pointing. It's not easy to find it and who knows what's the best method, but conversations give it away ⁓ So you also need an environment where you're allowed to try things out, where you're given the responsibility and not told you're one rookie and this is all you're going to do. So I would say, firstly, feel positive about the time you're in.

Secondly, and work very hard to get the right scout for the opportunities. Secondly, when you get one, give it 155 % because there's just so much you will be able to do. And the third thing is, and maybe that should have been the second thing, look for an environment that enables and empowers. ⁓ Because we've seen stunning success with our team. Couldn't have myself imagined this.

You know I was at a large VC today and they saw what we've done and said, really? With this engineering team and that size? And I said, yeah. I would say phenomenal time.

Yeah, I mean, think that you have taken almost a first step of saying that consulting as we know it is, well, I won't use dire words, but legacy, is to me the biggest theme there. And then what you've done with the book is you kind of unpeeled that onion and explored every aspect of what traditional consulting was and what it's going to become. So you kind of almost put a framework around the future of, I would say, strategic advisory. I won't use the word strategic advisory because I think consulting has this slightly interesting notion of people come and go.

Consultant will come, give some gyan, and like, you know. They're off to the next engagement. A strategic advisor, I say this because you're not going to have in the future, you're not going to give the same advice to the same guy three times over. That happened in the past.

With intelligent systems, and I would say folks such as yourselves have to build those systems so that the customer feels I'm compounding the value from the same strategic advisor. And I'm not asking them for the same, old shit, now six months ago I had the same thing, two years ago I had the same thing, that can't happen. So I feel that what you've done is you broached a very critical topic that is everybody's mind. You put a framework to it ⁓ say this is how the future looks like. ⁓ ⁓ have anecdotes, you have examples to kind of, you've sprung it all in.

So I just feel that ⁓ like a charter for the future. ⁓ That's what I really like about what you've done. And you've, in many ways, shared your personal experiences, not theoretical. So it comes from a position of real life experiences, which makes it all that more powerful.

Yeah, thanks for having me and all the best wishes for continuing to do the amazing stuff you're doing Utsav So all the very best. Thank you. Bye-bye.