AI Consulting: How Strategy as a Product Is Transforming AI Adoption | Alibek Dostiyarov | S1E7

Speaker A: Foreign so for decades consulting scaled through people. Today it is beginning to scale through software. Uh, welcome to Alt Consulting Conversation. This series brings together people who are shaping this transformation. My guest today is someone who has seen the shift up and close and is now building one of the most interesting examples of strategy as a product, which is one of the archetypes captured in the book Alt Consulting. I'm speaking today with Alibek Dostiyarov, Co founder and CEO of Perceptus AI. Before starting Perceptus AI, he spent years at McKinsey and saw firsthand how the firm was evolving into a data and digital powerhouse. He supported various fortune finder companies and government on custom model building, data strategy and advanced analytics. Along with his co founder, they are reimagining AI first tooling designed specifically for professional service firms. It's great to have you here. Uh, let me start with a basic question, uh, that everyone would want to know. Could you please tell a backstory about yourself and how Perceptus came to life?

Speaker B: Yeah, absolutely. Thank you so much for having me here. You know, we connected uh, before we talked about the amazing insights that you share in your book and so uh, very happy then to uh, you know, share uh, a bit more in this setting. So my story, I guess, um, you can think of it as a path of an engineer who later on became uh, a management consultant who then kind of tries to combine uh, both backgrounds of mine. So I studied electrical engineering, computer science and uh, right out of college I was working as a software engineer at Amazon and then later at Google and I also had a brief stint at connected vehicle startup. Um, so that's kind of where I have my training and that's where I um, you know, learn to appreciate um, the um, technological opportunities that you have. Right, to solve real life problems. Um, but then back in 2015 I came back to my home country, Kazakhstan and um, I uh, kind of looked around. I was looking for a job and I was looking around and um, you know, McKinsey, uh, crossed my path. Uh, back then I didn't even know what. McKinsey is a shame on me. But I still got very interested, uh, in the company. I read a book, uh, the firm by Duff McDonald, uh, that shared uh, you know, a lot of interesting insights about uh, how much of an outsized influence a firm like McKinsey has had, uh, on the world, on governments, on, you know, large companies, et cetera. And I became very much interested because um, I always was uh, technologically inclined, if you wish. But at the Same time I was uh, interested to start shaping what this technology does, um, in a way that's kind of higher level than just an individual contributor. Uh, and so that's when I uh, joined McKinsey as a business analyst. Um, I was thinking technically a generalist consultant back then. Uh, and in the very first project that we had, um, we basically needed to figure out how citizens of a particular city are reacting to some of the initiatives that were driven by the local municipality. Um, and our manager basically was saying, okay guys, you're gonna have a bunch of surveys. We're going to send consultants on the street and we're going to ask them to collect that data from the citizens. Just stopping people and talking to them. And I said, well hold on a second. A lot of this information is already contained in the web online discussion forums, uh, on Facebook and a bunch of other social media channels. Why don't we just uh, scrape all of this data and apply some aspect labeling and sentiment analysis type algorithms and basically we'll have our answer. Uh, and back then the pushback was alabec, don't do your Google thing here. It's not what we are doing as management consultants. So I kind of, back then, you know, to me it sounded a little bit like as if I came from one world into a completely different world. But it also was a very interesting moment because the year was 2015 and I believe exactly that year actually uh, McKinsey acquired Quantum Black, uh, which is now known as AI by McKinsey, uh, has grown tremendously. There's like, I don't know, thousands of people now that are part of Quantum black within McKinsey. Uh, and I think that's what truly jump started the huge transformation within the firm into the world of data analytics and digital implementation and even to some extent AI, uh, and machine learning. Um, and I was lucky to be at the firm throughout that part, serving either tech customers on mostly non technical questions or non technical customers clients, uh, on technical questions such as, you know, cloud migration, implementation of custom AI tools and a bunch of other topics.

Speaker A: Right. We do have some common things to share. Engineers, generalist consultants, coming from a different part of the world into sort of a, uh, different market and having spent some time in consulting firms solving uh, very interesting problems for clients. So you know, from there, having seen what Google of Google of working can bring to the table and being more creative, you started Perceptus AI. Now how does this actually work for someone who wants to understand what Perceptus is about?

Speaker B: Yeah, absolutely. In One sentence we say we are an AI first consulting operating system or professional services operating system. Uh, and it's hard to grasp exactly what it means. But to maybe connect the dots, we started with a very simple question, which is, can a consulting company, um, boutique consulting company for that matter, serve, you know, 3x4x5x as many clients without exploding their headcount by the same amount? As we know in professional services the top line growth, the revenue right is very much tied to the headcount and headcount of course drives the costs. And so we asked ourselves what can we do to make consulting business truly scalable? And our very first principles answer to that is yes, you can do it if you can significantly streamline, which means either automate or at least significantly augment the most tedious uh, consulting work streams, workflows. Um, myself, I found myself way too many times aligning slides on a presentation at 1am or something like that. Something that no educated, highly skilled person uh, should ever do in my opinion. Very menial task. And so when we started working on Perceptis, we said okay, what are the most mind numbing, soul sucking consulting workflows that we can optimize? Uh, and of course we were starting, uh, we actually incorporated in April of 2024. Uh, by then the generative AI wave was in the full swing and to us of course it was the biggest enabler. Uh, if you ask like why now? That's the answer because uh, for a long time, uh, Even while at McKinsey I was working on reports that basically would say okay, these are uh, different industries and how automatable they are, how tech forward they are, etc. And a lot of them were like construction and banking, etc. And a lot of the white collar work, um, like it was very hard to automate it. And then now we do have this amazing tool and that's what Perceptys does. And I can go into the specific use cases but uh, you let me know.

Speaker A: Yeah, so in terms of uh, if you look at a, ah, consulting firm which is now uh, taken in Perceptus, what are major changes in ways of working when it comes to proposal development or content creation or sort of day to day knowledge reuse, what is different?

Speaker B: So I mean anybody who is using ChatGPT would know how different the world is today, how much faster you can accomplish something with these smart uh, tools. The biggest problem in my view is that the generic genai tools, general purpose genai tools are uh, not a perfect fit for the world of consulting and I think the most unfortunately picturesque in a negative way, ah, example of that was what, um, unfolded in Australia in October of this year, 2025, when we've heard that Australian government was receiving some kind of report from Deloitte, if I'm not mistaken, uh, and that report contained um, a ton of hallucinations. It was referring to regulations that don't exist, it was referring to sources that don't exist, etc. And then ultimately Deloitte had to reimburse close to 4,100,000 Australian dollars back to the government. And I think that's exactly the perfect example of where the generic tools fall short. Um, and so if you think about Perceptus on the other hand, the kind of five big value propositions that we see is that we are giving people an AI that they can finally trust, we wage war on hallucinations. And um, that's a very important thing. The second thing is that you don't really want to sit in front of ChatGPT and wrestle with the prompt until you get that perfect email or perfect executive summary or something like that. You actually want to say, okay, uh, this is the piece of work I need to be done. I need a proposal to be written, I need a white paper to be written, or I need to create a slide that's sitting in my head and I want to create it in the most efficient way and it needs to be in PowerPoint, it needs to be editable and all that stuff. Right? So understanding that at Perceptives we automate workflows, not just kind of give you a chat, but we'd rather say you need a document, there you go. You need a slide, there you go. And then there are a couple of other considerations such as security is of paramount importance when you use a $20 per month ChatGPT subscription. Uh, that one doesn't have enterprise guarantees, for example, which means it might be training its models on your data. Um, data retention that they apply to your data are not, you know, is not that favorite, uh, favorable. So you know, basically what we're saying then is we take that tooling and we turn it into a very context appropriate. Um,

Speaker A: but then, you know, uh, uh, a consultant would ask like if I'm using Perceptis and my competitor is using Perceptis and you're having uh, pre built prompts or the knowledge layer of if I have to generate a proposal versus I have to generate a template, how my template or my white paper would be different from someone else. So how do you sort of bring in that differentiation. Or if I want to borrow, uh, slides from consulting firms, we talk about our uh, unique value propositions and how are we different. So how you bring that uh, aspect into the content which you generate?

Speaker B: Yeah, absolutely. So here I think if you take for example, a tool like Gamma App, which produces slides from very short prompts, it's able to give you a, uh, comprehensive presentation. When you see a presentation that was created in Gamma, you can tell right away, okay, that was created by Gamma, because they have a pretty good library of different types of visualizations, etc. And yet that library is very repetitive. You see it again and again and so on. Um, the beauty of using perceptives actually is exactly the opposite of that. The very first thing that we do when we bring a new account on board is we ask them to upload a bunch of what we call training data, which is the historical documents, historical proposals, case studies, methodologies, resumes of their experts and partners, their style guide and brand book. We actually take all of that data and we actually disappear for about three business days. Uh, what we do is we run a semi automated process on, um, digesting all of this data, sanitizing that data, um, labeling all of that data and so on. And then when we reemerge on the other end of the three business days, we actually come back and we say, look, the suite of custom developed models for this particular account is now ready. And then what happens from then on is whenever, uh, a consultant from that account, from that firm is requesting a white paper, requesting a proposal, it's actually generated, highly inspired by the historical, you know, slides, by your tone of voice, by the facts and uh, methodologies that you are actually practicing within this consulting company. And that's what the new document is going to look like. Our goal, right, is that a client that saw your document a year ago and that they see today, they're not going to be able to tell what's kind of, you know, what's the difference style wise, that it was written by the same person.

Speaker A: So this is very interesting. And that sort of makes me ask the next question that, fine, I have now a template, uh, which is created by Perceptus AI based on my data, which I'm sharing for training. But a lot of boutique consulting firms are also in this fix of, they don't have a standardized library of 200 slides that they use. And often you see on LinkedIn and other places where people say, okay, you know, like my post, or, you know, put something and I'll send you a deck with 200 beautiful slides of consulting firms. So do you sort of also have your own repository for those who might be like, yes, we have a white paper or a proposal format, but please tell me something more because, because I'm constrained with my current thinking, uh, anything sort of on that and if any client has actually asked you for that kind of support.

Speaker B: Yeah, absolutely. And again, we understand, right, that this, uh, data ingestion process is the biggest friction point. Basically, if you want to sign up for Perceptus, you have to suspend your disbelief, uh, for three business days, share a bunch of data, pay, and then hope that the document that you're going to order is actually going to be pretty good. Which by the way, is what happens all the time. However, to your point, there would be some companies that either don't have the legacy of like a ton of documents, but they at least would have like a website, they would have their marketing deck, they would have something, and we can absolutely start with something. But you, uh, know, even though if you don't have a ton of data or some actually companies that come to us and they say we do have a ton of historical data, but we actually want to be more McKinsey esque or more BCG esque, you know, in our communication, in our slides, like the fidelity is just not there. And so what we do for them is indeed Perceptis actually is sitting on a training data of 10,000, uh, pages from, uh, the world's best consulting companies. There's a bunch of sources essentially, is what I'm saying. Um, and so what we can do is and we have a toggle, right, and we can crank it up and down, essentially saying all the documents need to look like what you have shared with us versus all the documents need to be inspired by other sources. Um, and so you can get the best of the two worlds, right?

Speaker A: And you know, uh, it might sound absolutely compelling for a company to use such a tool. But then when you have conversations with prospects and clients, what's the hesitation or barrier or kind of questions you hear from them, which is stopping them from dropping that suspicion they have about using AI tool and any sort of key, uh, themes which you have seen across your conversations.

Speaker B: Yeah, absolutely. The biggest questions that keep on coming up are exactly actually what you were asking. Like, hey, if I have my competitor using Perceptis, our two proposals and we are like competing in the same rfp, are our bits going to look exactly the same? And my answer to that is no, because we built individualized models based on your firm's profile. So all the data, all the strength that you have within your firm is actually just make it in the best way possible. That's all it is. Um, another question is data security. Um, and I often ask them, do you use ChatGPT or something like that already? And they usually say yes. And I'm like, well, guess what, our security guarantees are going to be even better because of the enterprise agreements that we have with the, uh, model providers. Um, another question that we often have, um, is like, well, but kind of, I'm already pretty fast. It used to take me 30 hours to put together a single proposal, but like with ChatGPT, it actually takes me about eight today. And I'm like, well, you are copying a ton of text from ChatGPT into PowerPoint, right? Even once you get the right paragraph, et cetera. And the answer is yes. And I'm like, well, can we take it down from eight hours to one hour of your time? Um, and then, uh, I guess the last thing is they also ask, well, okay, I can already some of the more sophisticated users say, well, I already connected my historical documents with ChatGPT, for example, right? And I can, you know, ask questions and I can find the right references, write case studies and so on. Um, and all of that is great. That's the rag, simple RAG functionality. Um, again, the truth is that RAG is still a generic or general purpose tool. I always say RAG answers the question of does the data exist, but it doesn't answer the question of how that data can be used. An example of that would be, let's say, as a consulting company, you served at&T last year and you have somewhere, you know, a proposal that might be a little bit sensitive somewhere in your documents. And now you are going into ChatGPT and you're asking a question like, what was the methodology in Telco? Because you are looking to serve T Mobile. Uh, and you know, unless there is some smart mechanism in your system that says, yeah, there is something that we have, but we need to highly sanitize anything, uh, before we actually can reference that data, you're going to be screwed. Those are, um, again, some examples of, uh, where people come and ask their questions, but I think we have very decent answers to all of them, right?

Speaker A: And based on what you described at the very beginning, when I asked you what is Perceptus and you said it's an operating system, and that's a very interesting and powerful analogy. So you might have started from helping boutique consulting Firms develop proposals faster. But the way you are explaining Perceptus now and the uh, potential of the tool, it seems like, uh, if I can use, draw that analogy like a Sage or Dexter or Lilly, but for a boutique consulting firm, it is customized for your needs. So how are you seeing the next version of Perceptus, which direction it's headed, what seems like the next interesting problem to solve.

Speaker B: So for us, uh, maybe just to share a little bit, uh, how the evolution has been of the use cases. Right. Within Perceptis, we started on the client development side of the house. And the main reason for that is it serves the partner use cases, uh, in the best way possible. And partners are the one who make decision about paying for the operating system. So specifically, proposal writing was the first use case, which we nailed down pretty well. Um, but then another thing is a lot of times actually consulting, uh, firms are looking to check in with their historical clients or maybe clients that they have connections with, even though they might have not ever been able to convert them. So what we do is we put a bunch of AI listeners on the website of, uh, those clients on their social media, on the websites of their competitors, uh, industry news, SEC filings, etc. And so at the end of the day what we are trying to do is we are trying to identify signals, um, uh, in the public domain that we can then forward to the consulting company and say, hey, it might be the right time to check in with this client because they just made a senior hire that looks interesting, or because they actually declared a hiring freeze, which probably means they don't have enough hands internally to run their initiatives and so on. Um, so that's kind of our, uh, you know, our intent there is of course to help these businesses grow. But then even if you truly make the difference and if you enable that growth fueled by AI, what happens is you actually are pushing the bottleneck of productivity down the, down the, down the line because now you have a bunch of projects that have converted, but you don't have enough people. So now you need to go and maybe that's where you know, uh, your area of expertise is going to shine. But now you have, we don't have enough hands. Now you need to go for this 1099 contractors find, you know, unproven people to put on those projects, etc. And so this is where we now give you productivity tooling to actually your analysts and associates. So we give um, slide building capabilities, we give knowledge management capabilities where you can converse with your historical doctors, documents and so on. Um, so that's kind of what Perceptis does today. To come back to your question of where Perceptis is heading. Ah, well, what we do is from those use cases, we actually go into the adjacent workflows and we, uh, you know, we conquer them one by one. A lot of our customers have been asking us, hey, guys, when I'm going to start using perceptives for building, you know, PMO update deck, board of director update deck for my final deliverables and working documents and so on. Um, and that's going to be big because when you go from proposals and white papers, when you go into the world of, oh, you're going to be able to create just any consulting deck you can ever imagine. Uh, that's a very big technological challenge and, uh, we're pretty close to releasing that to the world.

Speaker A: Nice. That's great to hear. So when you're sort of looking in this space, you are definitely a, uh, you're taking a product approach to the market and saying, I'll create a AI product to help consulting firms and clients in future. What other sort of archetypes are you seeing? Uh, some new challenges which are trying to, uh, you know, take on the traditional established consulting business model through a unique, uh, offering. Are you seeing any other kind of archetypes emerging apart from product companies?

Speaker B: Yeah, I think. Uh, so. Actually, on that front, we recently released a, uh, white paper, which I'm happy to share with you, tzav, but essentially what we did is we went and we talked to a bunch of decision makers in the industry who, uh, have been procuring consulting services. And we asked them questions like, well, what are your considerations? You know, for how long are you running your. When do you run RFP process versus you do sole sourcing? Uh, what are the key things that you pay attention to and so on, like what shifts you're seeing? Uh, there are a couple of things, but I think the biggest trend that we are seeing is that boutiques are getting a lot of interest. You know, like, it almost like if you propagate what's happening right now and, uh, you assume that this trend is going to continue, it feels that boutiques are, uh, standing to, you know, take away about 20 additional percentage points of the market in, in the next ten years. Um, a lot of that is driven by the fact that boutiques are seen as more specialized and they are seen as more cost efficient, uh, as compared to the, you know, very established big, uh, consulting firms and especially. Right. Like the historically the difference was that boutiques just don't have the army of people to throw at a problem. They might not have the very well established in house back office processes and so on. But again with the AI tooling I do feel that that's going to be the major enabler, was going to basically put a smaller boutique and a larger firm on the same playing field. And now you know, they're going to go head to head for those same types uh, of clients. So that's one thing that I'm seeing. And when you talk about you know, the emergent um, boutiques or maybe even like smaller mom and pop shops, we do see a lot of new entrants who are either like a former BCG partner for example, going on on their own. We see um, academics at the top of their field who are running consultancies on the site. And again, historically they would be able to serve only one client at a time. Now enabled by all this tooling, they can actually expand their revenue to 3x just because they are more efficient in doing things. In the background, uh, we see uh, other types of a ton of fractional CFOs, uh, this type of uh, talent that are also kind of providing this consulting like services. So there is a lot of interesting things that are emerging and happening. But um, that's at least what I'm seeing from my vantage point.

Speaker A: I think some of the points really resonate. I'm sort of also running a um, company which is specialized in some aspect and then again uh, working with clients on some of the topics which you covered earlier. Like help me with creating a strategic narrative for my board meeting. Now there's a bunch of information. I don't have time and bandwidth to hire a big consulting firm. It's much faster to engage a smaller boutique firm and just go with them. So yeah, those are very interesting trends and um, let's see what sticks um, and what stays permanent uh, amidst this uh, interesting space we are in. So my final question is, you have read the book and thank you so much for taking time to skim through it. Which ideas from the book resonated most with you?

Speaker B: I mean the book itself. Right. I think uh, what I like about what you're doing is that you are basically foreseeing a major tectonic shift that's going to be taking the industry uh, as a storm. And I like how you pinpoint the fact that AI is going to be the major factor. But I also appreciate uh, that unlike you know, a lot of other visionaries who are uh, sharing their perspectives on the topic. You are not going to an extreme to say that AI is going to be a panacea or, uh, you know, that it's going to kill consulting in any way, or that the big consulting firms are going to go away tomorrow because of that and so on. You are very pragmatic in saying that. Yes, it's going to be a major enabler. It's definitely going to help shuffling things around in the industry. There's going to be new winners, there might be losers, losers are going to be dying a slow end, uh, but they have a ton of time to try and adjust and marry the new world. So I guess the pragmatism that you maintain, uh, in your book is definitely resonating with me a ton, which is why I'm very much excited to be in this conversation with you.

Speaker A: Thank you so much, uh, for the kind words. And in these conversations which I'm having, there's a lot of learning in terms of people coming with their own perspective. They are doing their own research. You talk about your white paper, which we'll share, but, you know, the, the uh, one thing which I learned was it's not the typical disruption of other industries because here the incumbents are not sleeping. In fact, they are heavy users of all the big tools. They have deep partnerships with them. All the consultants are trained on it. It's the question of how far they can go. Like they are reimagining their business model to an extent. They are pushing the boundary, but would they be able to start from a blank slate and say, okay, let's rebuild the whole model? That's where sort of the model breaks because you still need that leverage to make money. And the entire business model relies on that. So that's something which is interesting to see and in some spaces where that model is still very relevant, where you need to go and do leadership alignment in a boardroom or you know, do an ops consulting going and doing in time and time, emotion study. But for other pieces of work where you could get it done via AI quite efficiently, I think the answer might be different, but great. I think, you know, these are very interesting inputs and uh, I thank you so much for taking our time and providing this interesting perspective from what you are seeing in the market. So percept is, uh, is an important, uh, part of the shift taking place in consulting with this whole, uh, strategy as a product model. And we'll see how this next generation of tools actually evolve in the future, uh, as standalone or you might have a product plus service layer on top of it to make it more interesting and easy for clients to use. But than thank you so much for being here and sharing your insights. Really appreciate it.

Speaker B: Thank you to again, really, uh, really excited about what you're doing. Keep on being this, uh, kind of filter for, you know, between the noise and what's actually, um, a signal in these disruptive times. Uh, yeah. And let's stay in touch.

Speaker A: Yeah, my pleasure. Thanks.