Live Podcast: Why We Still Hire in the Age of AI - and how AI Rewrites the M&A Deal Cycle transcript

Uh so yeah then uh I think we're going to start. It's 9:00 it's ringing in Bavaria. Uh so uh yeah welcome to Hobus not a strategy and Zasha to your podcast. We're doing a dual live stream podcast I would say. Uh it's the finance data and people podcast also from from radial. So my name is Christian Underwood. I'm founder and CEO of Strategy Frame AI and host of hope is not a strategy. And I'm welcoming um yeah two other AI founders or AI consulting founders I I should say.

Uh Dennis Schutz from Strategy Bridge AI and Dr. Zasha Hagamula. Welcome. >> Thank you very much Christian. We're very excited for this. >> Yes, absolutely. So it it was your your idea that we do that and you invited me. So thank you very much uh that that I I can now ask the questions but um just as a brief introduction I would say um can can both of you give me just an elevator pitch of what your company is doing uh just in a brief sense um and uh then then I will explain how I fit into that.

So and Dennis please uh please you you could start. >> Sure. So you could say we are a global database but with an analyst on top. So what we deliver is data any form of data on any given company on the world you could say. So starting from financials ownership data contact data whatever you need. And on top of that we are allowing users to do real research on markets. So generating long lists right market studies but also company studies I think that's what's uh standing out the most that on a given company you can generate benchmarking analysis multiple valuations whatever you need and in audit grade I think that's um the thing so we are targeting high quality with what we deliver so how high quality research not only data that's what strategy bridge is about >> yes thank you Sasha yeah thank you very much Dennis and and Christian.

Um I always say we are tech enabled um finance transactions and value creation services boutique. So um yes we do consulting. Yes we do consulting in the PE environment um and along the deal cycle of a private equity investor in any finance related topic. But our USP is that we bring together CPA backgrounds um transaction expertise and um a data engineering and AI side of things. Um and and that's pretty unique on the market um to have both yeah expertises sitting at one table and then really implementing projects and and solutions for our clients instead of just uh painting beautiful slides.

So that's a little bit the approach that radial follows. >> Yeah. Great. Yeah. And I I'm in between both of you. So uh Dennis is helping to acquire the companies. you're you're helping to sell uh and we're doing the strategy in between. No one really cares, but in the end should should care. So, uh >> I know a couple of clients from you, Christian, and they do absolutely care. I can tell. >> Yeah, absolutely. Um yes, we we have a topic today and it's a little bit provocative.

Um I'm not sure if the live stream on LinkedIn is already working, but uh anyway, we will put that on our um podcast streams. So um you will find it anyway. Um so we have the title of why we still hire in the age of AI or should say why you are both of you are still hiring. I'm not hiring at the moment and how AI is rewriting the M&A deal cycle. So um in every investor deck um that has an AI slide almost none has an AI result.

Why? That's the question. And yesterday um the uh AI company from Cologne uh announced a cut of I think 1/4 of all employees. So they're around thousands and they're cutting 250 to become an AI first company also in their operations. So the first questions goes to you Dennis. Um so you're still growing still growing in your team and shipping AI for M&A. So what does that tell us that you're still growing in team? >> So uh I mean speaking of deepel and all the other uh yeah news you see on the news floor where they now cut people because of AI.

The question is now do they really do it because of AI or did they overhire in the in the past and now see that as a good chance to get rid of people because I think the reason why we still hire is very simple two engineers with AI are still faster than one engineer with AI right that's the the very simple math we have and um what we are doing is not just uh punching cloth and whatever in the face of our clients and yeah for that I would definitely need not need not an engineering team but what we do is different right so AI is enabling us for sure is making us a lot faster without a doubt but the hard work is still for instance data engineering right it's very difficult still in this age to get high quality data without intersection of humans so our data engineering team is obviously growing we need to fulfill highest quality requirements there and then also maybe that's more a general topic of AI But uh the question we have to ask us is not only do we make our clients faster that's probably the simpler take but more important can we make them even better and also there so when we innovate when we deploy new features um there's still tons of things to be done outside the the uh typical AI bubble um generating new data points again what I mentioned in my in my first words on business analysis an audit grade right this is not something that would just simply come out of AI so we need to still work a lot with financial mathematics etc PP and as a growing company and most probably in the audience would agree we are still in a people's business right so also your sales team still needs to grow I think very difficult to uh to not work with outbound sales to not have firsterson contact with your clients >> um so I think these are the heavy hitters there's an obvious reasons why you would still hire as a growing company. >> Yeah.

And uh on LinkedIn I I I experienced that for outbound you can do everything now with AI but I think all of us are in a really trust uh environment. So trust first and there I think you need real people uh just just uh to get that done. Um but Dasha how does that look like with with your team on the let's say on the end uh on the other side of the deal cycle? Yeah, it's actually similar. I mean, if uh we would be a consulting firm that um would operate globally already and would have a research team with 2,000 employees, for example, then of course I would have an issue too.

Um but I think we are lucky enough that we founded this business four years ago and we are roughly 35 people now and still have a very very um low overhead um for the size of our team and now the question is rather okay, how can we increase the output per team member? It's the same what what Dennis basically mentioned, but um the output requirements are are growing way faster than I could hire people. Um and I still need to hire people and actually it's a similar issue if you if you go into a portfolio company of a PE for example after after the closing of the business um and it's all about um getting this AI strategy working um in finance or around controlling reporting and stuff like that.

The first question that comes up is where's the data for it? Yeah. Um and the the issue is that um many many of these companies have legacy systems. Um they have old systems. Um the trend was also in the past that yeah for every every problem that existed in a company um a new software was implemented from a very siloed perspective >> and then when you go towards value creation or or towards an exit um the issue is that all these data pools don't talk to each other.

Yeah. So you somewhere have a pricing table and at the same time you have procurement prices but at the end you want a contribution margin automatically but you these two data sources are not talking to each other >> and they cannot be be accessed automatically. So um a lot of stuff is happening around the data engineering as well as as Dennis mentioned. So you need the people that actually are able to bring the data from legacy systems into a central data warehouse. >> And nowadays it's still difficult to tell cloud, hey, please uh create a VPN connector into that system without talking to the IT department or potentially external um um suppliers for these things.

So 80 90% of actually getting an AI strategy going for a portfolio firm is still the data foundation. And this will also be the case for the next months and years from my perspective because um any regular medium-sized entity in Germany that you might be aware of um don't have a controlling or don't have sufficient data. So either the data needs to be generated or the data is somewhere and it needs to be accessed and only if that is um yeah taken care of then AI can reach its full potential because it actually needs the context of the data otherwise you always have to feed it again and and at the end you do the same mistake that you from a siloed perspective um um create your own AI tools and then 10 years later you have the same issue nowadays with with all these heterogeneous software solutions.

So, uh, companies are pretty much on the verge right now to make the same mistake again to be honest. >> Um, and I think it would be very good if they wouldn't this time. Um, and and and think it through a little bit more more in depth. >> Yeah. >> Yeah. Yeah. Absolutely. It it it always comes down to processes and data when you want to enable the AI systems or any other digital system. So and uh what what we are experiencing that because we are kind of a hybrid we have a hybrid solution for that.

So uh they're they're bringing in their data we're not because we're in a strategy process. So after doing that or we have on the one side the data from from Dennis and his team uh ready a hand for our corporate clients and they can use that we can also crawl market data that's also really helpful but answering really simple questions. where do you earn your money and what are your customer segments? So really simple questions uh yeah they're not prepared for that normally or what are your top five customers so therefore you are implementing then a system on a more sustainable way you have more time and I think therefore the the funnel from coming from uh from Dennis going through the strategy process and then end up uh in uh in in your system also uh is really helpful for the clients and and get get them more clarity.

I think that's uh pretty pretty important. But um >> yeah, >> maybe one uh funny note on that is um >> I think if you look at LinkedIn um Sasha is the face why we should actually do this. I think every month you're posting Sasha uh uh our new joiners and like five new faces showing up, right? So, uh I think yeah, just a side note, uh very typical for what we're talking about right now. >> Okay. Yeah. Crazy. Um yeah, I I think we're we're struggling a little bit with the with the live stream.

So, it's our first live stream. So, I I have some uh yeah different data information from the one side. They're writing it's not live. The other part is writing it is live. So, what is the truth? We will find out later on. Let's see. But we're learning on the way I would say that's uh that's the point. So um yeah but Dennis let me take me a little bit into sourcing. So what actually really changes when AI gets serious and not you know the hype cycle.

So and when I come to corporate clients they're telling me now oh we have now co-pilot and when we do that now for the CEO office we do the analysis part etc with co-pilot. So I think that like that's better than nothing, right? So for sure that's very nice. AI can help in in lots of ways. So if you want to instead of doing a Google research want to know the 10 most important companies in a segment, the quality you would get from any AI system will be quite nice.

I can tell that especially because we also started in the preAI era right uh in the beginning of 2020 and how we came up with this at that time was majorly centered around keyword search um which was okay um so you got something right but probably the so-called false positive rate so companies showing up that shouldn't show up was sometimes over 50% so you had to look at three companies to get >> uh more or less certainly a good hit and what changed since then I think are in technology wise two things.

So first of all we now have more power in structuring text data which is powerful. So what we essentially do is when we get a company in our database, we look at uh what is the product service of that business? What is the end client? Um what is the where in the value chain is the business? Is it a manufacturer? Is it a distributor or both? Um and also at the business model. So very important data which we can structure.

And then the second advantage is of course semantic search. So that we instead of looking at simple words we have the ability to really yeah understand even though they don't mention the same words they're still doing the same essentially and that is where the power gets in the problem why I would especially like users that do sourcing is very important. So getting a list of companies you want to buy or or you want to sell your company to um especially when you have such a situation like if you use an AI system you're still a strong slave to zo for instance right so you will find the companies that want to be found um that's a big problem so how we solve it is simple we are doing that same thing in our database of 50 million businesses so we have a completely democratized data set and can ensure that really every company that needs to show up really shows up.

Another problem is these AI systems are not built to generate long list. Right? Good luck of generating a list of couple of hundred clients. That's not what their calculation capacity works with >> and that is uh uh that will be also at least a midterm uh uh problem with these. So years ahead until you can get a highquality list of hundreds of companies again in a local environment in a dedicated setup where we can fine-tune calculation cost such that we can generate that uh with sensitive costs let's say sensible cost rather um I think that's where the power kicks in of still yeah niche solutions you could say like ours.

Yeah, great. And I I just hand over to Zasha because I always get the question is a AI ready to do some of let's say finance calculations. I had a talk yesterday that someone said ah AI cannot do that. So and there there's so many myth out there that they do not believe it can simply do oneplus one uh things. So and you're doing it in finance then it is doing really great calculations. So Zasha, your statement on that please. >> It definitely can.

I mean I I still stick to to what I've mentioned before that the that the database uh or the data context uh needs to be provided. Um so so this is definitely the truth and where I say where the real power lies. But if the data is provided in a proper way or if you for example need an integrated financial model or a business plan based on on on drivers or assumptions um and you nowadays in 2026 start uh to set up a manual Excel file um honestly you're like looking backwards.

Yeah. So uh no no um um financial modeling project uh needs to be done completely manually nowadays. Um if I look at clude um it it can create integrated financial models and I I did that like a couple of months ago already and then I was already laughing as a finance guy because in the first run the the the balance sheet uh didn't tie up. Um so I was like ah see um didn't manage but then it actually corrected itself.

Yeah. And said oh the balance sheet is not tying up um um and and and has the same amounts on each side. So we need to correct this. and then it goes through it and and and stuff like that. So especially within financial modeling and also due diligence um we already do a lot of things um AI based um that doesn't mean that um actually you you have tremendously less work around it but you can actually go deeper and actually create the insights that are more interesting than just paraphrasing um the annual reports and and um trying to to um or use the budget for manual data work. >> Mhm.

But instead you can actually work on the on the in-depth content um together with the management team and really finding out hey how is that business creating money? What are the verticals um that are worthwhile to consider um um can the business be be cut into specific verticals that the that the um specific company doesn't look at yet and stuff like that and um how is actually money flowing through the business. So also a financial due diligence can be can be created much more um commercially also that you really understand the business look look at the verticals look how the money flows through the business and what the what the value actually is that is created by this business that you want to buy.

So so it creates a lot of room to actually go even deeper what we already did before. So um I wouldn't definitely I would definitely say that that um AI can help uh in that regard. Of course you need to look uh into it in depth um from a human perspective from a finance experts uh perspective but not using it is like u um not an option anymore from my perspective. Yeah, >> I I I would say I would say it's stupid not using it, but also like it was in the past.

I would say not every Excel sheet was perfectly developed and without any mistakes. Huh. So there have been people doing yeah failures and wrong calculations and wrong code I would say in the past in the axle. So yeah that's that's the way things are happening and uh yeah that that's how it is. Exactly. And what addition on that? So >> I of course completely agree like the the modeling and everything that's that was never the the hard part though, right?

So what is getting commoditized now is I think again something that is speeding you up crazy. But um like building the model, populating it with the right data that was never the hard part. I think what is the hard part that is getting assumptions right? So very well thought through assumptions on how you calculate a beta let's say how you do the forecasting of the business which is not only a function of history which is also a function of your best take at the moment on the future on a given market and I think that is really where as Sasha explained where the human still kicks in um that you really get trust on your assumptions because that is a hard function and just getting a result by AI not knowing how exactly it came up with it and you will never know.

That's a problem. Yeah. >> Yeah. Maybe maybe adding in that regard, especially when you when you think about financial models and stuff like that. Um I see a I see a very very high danger. I think our generation has an advantage right now because we built these models from scratch. Yeah. Uh late at night um 100 times. >> So we know exactly how the result should look like and how an investor looks at it. um if I'm a very young employee now and I just use AI blindly so to say and not questioning what I'm actually creating that's a big mistake and I'm very much yeah concerned how the future education will look like um in consulting firms because actually people also need to learn what they actually want the AI to create at the end because otherwise you don't have a clue we'll see how that will show up but as you say Dennis is like really knowing what the what the goal should and the end result um is at the end driving your prompting uh style and your prompt actually drives the result.

Yeah. So um I think that's still important to really think about that. Also when we think about our data warehouses and we connect these with our with with AI um solutions and stuff like that probably half of the time goes into the prompt and really explaining the AI basically what do you find in which column in this huge database. Yeah. What does it describe? Um where are the issues? Where are gaps maybe and stuff like that.

So the technological part is not the issue but really prompting correctly and and and feeding the details into the solution will actually enable um a better result at the end. And this is maybe also where people struggle uh currently sometimes that the prompts are just not sufficient to enable the AI to do the job. >> Yeah. >> Yeah. Yeah. a totally different perspective from my side because we are shipping a solution that also goes directly to uneducated uh end customers I would say uh don't get me wrong they're really well educated business people uh on our client side but maybe they haven't done strategy uh in in their whole business life so therefore why we have real frameworks and it's it's pretty clear what happens with the data and our uh agents with the different tools tools uh are processing that and we have a a lead consultant agent for for the strategy that is assessing the the prompts that are coming and they are always not on the level you need them.

So we rewrite the prompting and redirect the user to the to the different tasks and help them and also showing them visually what the end result should look like and give them examples and the agents the data etc is directly presented in that format. So and and we do even do that directly now in the chat and you can then export that direct because we do not believe that uh people will be uh how to say it become the best prompters in the world.

Uh so and and and that's why we also im implemented the voice feature. Um so doing strategy with voice and but it totally changed the game for the people because they knew their sales, they knew their supply chain things. So they experts in really great fields and they just have to talk and when they talk they give enough context and then our tool should do the rest. So this is a little bit more of my perspective because we have some uh also consultants in there certified partners and also we are doing it but in the end uh in our perspective it should be done by the by the end customers but for sure h how to how to have a consultant in a future years but I think there will be a total different skill set um that they that they are focusing on and it will be different people also not that as I would say I was never the the right finance guy So I studied political science as a first study.

So uh nothing to do with numbers. So and then went into strategy which is not a real finding thing. It's just the let's say 20 out of the 80%. But uh I I would say that that makes it I I think there will be different people in consulting doing different things tomorrow. So and therefore I I see that's changing a little bit. But um >> your clients are running around now all saying make no mistakes right instead of writing it. >> Absolutely.

No but but when it comes to we have for example we are also doing for nonprofits and it's really funny we have that for for carers. So we we have nurses doing strategy doing OKRs doing project portfolio and therefore >> amazing >> uh it it it should work for for everyone uh who needs clarity. Um but let me come back to you Zasha um on every fund has now an AI officer. So where do they come from and isn't that a real progress? >> Yeah, that's a good point.

Um I mean you do you do strategy as well. So um strategy is always a broad term and uh I mean we have an AI officer as well but actually our AI officer is someone who can actually code and and understand LLMs and actually create workflows and agents um and is a hands-on uh person. Um I think what's what's currently the issue sometimes is um that there is a gap between uh creating um how should I say an AI strategy or an AI road map um on a slide uh in the ivory tower in Frankfurt um versus then actually implementing it somewhere in your medium-sized or small and medium-sized entity uh on the countryside where you have uh people 50 plus sitting in the finance department and and yeah coming around the corner and saying hey we are doing AI now um and then um following following the path to say okay now everyone gets a chat GPT or a cloud license and then you're suddenly an AI company which is complete BS yeah to be honest um we are actually currently conducting a study which is called winning the value creation game where we where we talk with approximately 100 or more than 100 private equity investors um about the whole value creation process. >> Mhm. >> Um and it's actually very interesting that um because we have one question that basically asks how how high do you assess the AI potential in different corporate functions.

Yeah. Along uh like basically the porter value chain. Yeah. Where you have finance and then IT and and HR and then production, procurement etc. Um and the potential is is assessed very high in every area. Um HR is a little bit lower quite interestingly because they say okay there's still a lot of human interaction and you need to create a certain vibe and stuff like that. >> But in every other department the potential is deemed to be very very high.

Um and then we do a question after this and say okay interesting but now let us know what the concrete use cases are that you are working on. And then it's very quiet in the room. Yeah. And I think this is where the discrepancy uh currently uh lies because again the data context is not existing. So um AI cannot be really deployed um and real usesa use cases cannot be really deployed if AI is not intertwined with every piece of data in the business.

Yeah. um and and just installing a few AI licenses here and there in their silos is is not changing anything. Of course, maybe a presentation is uh created a little bit quicker or an email is corrected or whatever, but this has nothing to do with AI readiness or an II enablement um in a business and and and therefore I think that the that the AI thesis is not written in the in the investor's office. um the value around AI is really created um onsite in the countryside where the business is and the where the changes where the change needs to happen and and stuff like that. >> And I also had a conversation actually a few weeks ago um with an investor um who said um we we want to do like or we want to create an AI first company with that one portfolio firm. >> Mhm.

So the first step we actually need is a new ERP system. Um because the current ERP system is not AI ready. Yeah. Okay. So the step would have been um to buy a new ERP system for probably 500,000 which would need two years to be implemented. >> Mh. >> Um and then you would have the same bad data and the same bad processes in a new environment. And then this ERP provider would say now you can use our AI or whatsoever. Yeah, it's really >> which is still not working. >> No, which is still not working.

So it's and and these like false um concepts around AI and which are also promoted by some AI specialists so to say >> they are wasting money. They are destroying value actually that you could really easily build with a central data warehouse. Connect your AI to that warehouse. It doesn't matter which systems there are. Try to gradually um improve the the quality of your data. Connect the AI to it and then build your agent and build your workflows instead of like relying on systems and and and stuff like that.

So there are really really >> wrong decisions made right now from my perspective that that destroy value rather than uh creating it. >> Yeah. Absolutely. And when it comes to uh let's say one function the sea level uh itself so uh what kind of solution do they really need and and coming also again to let's say uh M&A consulting boutique firms so Dennis you are doing a great job and your team on that with your solution because it's also u not just because here we have kind of a let's say plugandplay solution um so it's it's just out of the box so uh you do not need the data warehouse to build up because you're at the front end that makes it a little bit easier.

But can you please te tell us so how you're building that kind of data warehouse? What do you need and on Yeah. What are the Yeah. What are the main challenges for the client accessing that data with the AI? >> Yeah. Um the question goes to Dennis. Sorry. >> Oh, sorry. >> Yeah. I was also confused now for a second. Sasha, what did you build there? Why didn't you tell me? Um, so I think that's the big benefit of focus right on focus on the niche as you said the the M&A space.

So we know perfectly from a to the whole process uh um these experts need. So they need data and what usually data means is of course it means data on the single business. So the mandate let's say so we need to make it as easy as possible that they get that data into our platform with all the security necessities right so nobody else will see that data but essentially that is the only time they would actually yeah need to put something outside in into our platform >> and the rest is uh yeah our job I would say so when you give in that company name as as I said the when we started with the sourcing topic in a sellite uh case, we would find uh through our database all relevant buyers.

If we should miss one because you wouldn't know that they're doing something in that space, the client could easily add them. Um but usually what they would say is fantastic, all uh relevant buyers have been found by the platform. Um and then yeah they need the contact data need to know how to contact them and then of course generating the outputs uh the the marketing material let's say so from teaser to infomeo >> um historically what we have done is we generated the heavy hitter analysis parts of that right so what the users got was in their own template in their own design >> the powerpoint outputs they could just drag and drop it into their uh into their slide Right now with the next update, we will also take that over.

So generate the full-fledged PowerPoint report. So that's the I think big benefit of of what we're doing of f focusing that right that is um never possible if now I don't know another use case comes in um whoever needs a powerpoint presentation a sales guy from a random sales uh business make a pitch with us that will be difficult >> but as long as our clients focus are part of our ICPS M&A buy side uh audit they will get exactly the results they need more or less at the click of a few buttons Yeah, absolutely.

Great. Um, yeah, and then please Sasha, we we come to your playbook. So, please show us a little bit. So, how does it actually look like? Because I hear now, okay, uh, cleaning up the data, building data warehouse, and then in a in a in a portfolio company, it's like, oh my god. So, we we've never done that. So, what is your ma magic source that you can make it really happen? >> Yeah. Um I would say to make it simple behind every kind of system there's a database uh and the database is usually just a table right or a few tables multi-dimensions whatsoever.

So >> the simplest form is probably like a CRM system for example like Pipe Drive or whatsoever where you basically have an Excel file in a in a in a web uh context >> up to SAP for HANA or something where you have huge amounts of of different data sources and stuff like that. So at the end um every ERP is creating data um that that is uh sometimes manually sometimes automatically um um inserted into these systems and every kind of of insertion or or bookkeeping booking whatever is like like one one line or whatsoever in this in this database.

Um and at the end in order to create a warehouse um you basically just need to access this database somehow that the system creates and make sure that you pull the data automatically from that database. Yeah. Either you usually you mirror it into a separate database overnight that the main system is not struggling with with the request and so you feed a central data warehouse with that data. Yeah. If it's an on-remise system, you have like a VPN connection or something.

If it's a cloud system, it's sometimes easier. Some systems have an direct API and stuff like that. So, we usually set up that central data warehouse by basically pulling the data from these various systems. Okay. >> And then like a VLOOKUP trying to somehow intertwine them. Yeah. via I don't know a project number um where you basically connect for example then time recordings with project calculations or stuff like that or via a product number that you uh connect um the pricing table with with like the procurement costs from the warehousing system for example.

So you create a closed data warehouse that allows it at the end to um to connect data that hasn't been connected before. And this is actually the the most um complex and and and most um um or the task with the most effort required um and then you visualize that data that can happen via Excel that can happen with PowerBI whatsoever. We are very flexible in that regard and then you usually see the data issues or the data quality issues because as soon as it it gets transparent you see the gaps in the data.

Yeah. Are data sets missing? um are for example uh product cohorts not maintained um and so on and so forth and based on the transparency you can then walk backwards in the process and understand okay where's the data governance leaking yeah for example do I have a CRM system but sales never takes care of certain fields because they are not obligatory or they are not drop downs but free text fields for example >> and then we can think about the future data governance concept where we say okay these might be mandatory fields in the future.

This might rather be a drop down instead of a of a free text field and and stuff like that. Um, and so you basically already have a a central brain of the company with all data in there. Um, which is consolidated and which gets cleaner and cleaner over time which is updated once per night. So you also look look on on more or less real-time data and then you visualize that with with PowerBI or or Excel for example and then you have a view on your P&L life, your balance sheet, your cash flow statement, your working capital, your net debt uh whatsoever.

But on the other side also on operational KPIs where you can dive into contribution margins per product per client per um sales channel per region whatsoever and really make in-depth analysis and what we then do when this like central like data warehouse is created and and getting cleaner and cleaner >> we then connect an AI solution um where we are currently working with Langdoc which is GDPR compliant that gets connected to the data warehouse.

This is actually our main solution and then a controlling or reporting guy can basically chat with the data in natural language without using SQL statements or stuff like that. So the solution basically translates natural requests into SQL language gives back the database gives back SQL language which then is again translated into natural language and then you can ask them okay with which clients are we creating the highest losses in which regions are we selling the wrong products um um did we purchase goods and services which where prices got increased but we didn't forward them to the customer for example and stuff like that.

Um and then you really have an AI based reporting and controlling and also value creation engine um that can support you um um on on the way to create value and again >> all dependent on the database. Yeah. Connecting connecting the data warehouse with that AI solution and make it work is a matter of like two to four days to really make it work. by creating the data warehouse and really making sure that the database is correct is a matter of a couple of weeks rather sometimes months in order to make sure that this is is working and running. >> Yeah.

Okay. And I I found it really interesting. So that would have been my questions how long does it take you? So uh Dennis have an out ofthe-box solution with the own database that comes directly to the clients uh in the M&A field. So we are doing the hybrid mode crawling data but also using data from the customers and they're going to their systems and we say for strategy we just need that kind of data from your controlling and we connect it with the outside data and you can chat with everything over there also the projects and the OKRs and you are going deeper in let's say the data foundation building the lake uh data lakehouse and then adding AI on top of that uh to make it work in in a longer run I I would say also three really different use cases of AI but also usage of AI on top AI baked in AI as pure and here I think we see that how consulting is changing in that field and that there's not just one solution uh to the problem and to each problem there are different solutions and uh we're trying to find the best ways that are really uh possible to do so so Um yeah, looking to the uh looking to uh the time um I would like to ask now let's say um ju just one last question to both of you and then just one overall question.

Um we we had um for those not listening on LinkedIn now because they can't and they're maybe listening in our podcast. So we're sorry that it uh the the stream didn't start in the event. We don't know why but that is you know also in our companies today when you're working with AI when you're working with software there can always a buck it's not like a PowerPoint slide when you where you can control 100% of the typo and that that everything is right.

So you have to be a little bit more uh yeah um yeah careful of course but also some things happen and we're sorry for that but we will ship it uh over our uh podcast uh podcast streams anyway. So uh Dennis um overall uh being in that journey coming from the banking world where you originally started um where do you think is it going for you in your special field on the front end you're generate so you're optimizing the work for the people there they're getting faster and faster data quality is increasing but how far can you go >> so what is your vision I would say, so our vision is amongst many is we are of course preparing for a world where more or less most of our users will be agents. um that is a um very relevant scenario which we prepare for of course right so that we build our APIs um our MCP coverage in a way that uh whenever somebody doesn't want to do the hand work right get it perfectly visualize whatever they need but instead yeah do a a let an agent do whatever they want um that we still feed them with the right not only data but what we are creating on top of that is information right so a >> um >> valuation for instance, nobody cares about the just small number you get out of it, right?

You want to understand really how did they get to that number? What are uh transactions of similar businesses that happened? Whatever got you, what is like a size adjustment, a performance uh adjustment of that business. So to deliver information um uh really that's I think a world we are we are focusing on and there of course the solution survives that not only gives the easiest access to data that is of course important but also as you said the highest quality in data and this is just dirty work sometimes that needs to be done >> um in uh creating uh all these data sets these pieces of information such that you can get the best possible output of it.

But there is of course also another scenario. So I think uh also and this will sustain for a couple of uh years I'm sure is you still have the users that tell their clients look we are still humans doing that. you get full uh expertise there's no database that is capturing my 40 years of experience also still for them I think we are uh demanded to build the best possible user experience still for humans and ensure that they know each step what is happening they can intervene also that is a very important part still in the future right what if you're unsatisfied with data you get is there a simple way to intervene um a big important UX X topic to also deliver that so that somebody with decades of experience or years of experience is usually also something worth mentioning.

Um they can still get exactly the outputs they need just a lot faster just with access to information they wouldn't have otherwise. Mhm. >> Um so what we are seeing is going forward you have of course with the AI space um probably yeah incredibly speed up in what percentage of your process can be completely automatized with a generic tool that is uh heavily increasing. But then you still have the the parts where details matter where yeah everything belongs on the foundation as Sasha mentioned it minutes ago and that needs to be on point.

That means the best possible foundation you can get and this is where we yeah prepare for this is what we essentially stand for as a business to deliver highest possible quality and that's what we're doing. >> Yeah. Great. And Zasha from your side are you looking for let's say uh a fixed agent set when you're entering tomorrow to a company and there are collecting the data and building the data warehouse autonomously.

Is that kind of your vision or something else? >> I mean I mean you can think about every scenario to be honest. um uh we are we are on high high alert I would say to to um um yeah be on the forefront of all the developments out there. I mean I remember me saying half an hour ago currently >> um you still need data engineers in order to actually create the data um or or to actually connect um systems into the data warehouse.

Mhm. >> I mean, if that is still the case in a year, >> I don't know. Yeah. Maybe you prompt uh an agent and say, "Hey, um please connect me to the client's interface and pull out the data once per night. These are the the access uh this is the password and the username for it." Um yeah, I'm I'm sure this this might be this might be the case. Um I still think that there's a lot of groundwork to be done um in in the industry out there and and there will be still a lot of manual work because usually the businesses are not as far as as we are.

Um but but that is definitely a challenge we think about. And also um on the other hand like if you if you think about financial due diligence for example where you have a data room for example that is filled with with data by the the target business >> um and all the data room um um suppliers out there I'm sure they built their solutions uh for this because this might be a new way of a new way of doing business for them.

Yeah, to be honest, to actually creating an >> environment to do due diligence in the data room and all the due diligence might also be shifted into an earlier stage where I could imagine that a lot more due diligence is already done before the LOI instead of now after the LOI and all these things. >> Um, >> on the other hand, I have to say I'm I'm I'm an optimist by nature. I need to be as an entrepreneur. And I say um for a consulting firm um there is a like us like a boutique maybe not the large ones but for a boutique firm there's a huge um um option right now to actually increase margins on our businesses because every product at the end has a value for the customer.

So a financial due diligence that is required by the bank for example will always have a price. Yeah. Maybe in one or two years this price might have or or be a third of the current price. But then the only question I have to ask myself as an entrepreneur is how many of these do I do more? Yeah. And uh if I if I'm able to to provide um I don't know if if if due diligence costs like a third of a due diligence nowadays but I do six times as many in the future >> um then I increase my margin.

So for us that's also a huge lever or a huge opportunity to actually become more profitable in the future if we do it right. Yeah. So we are pushing towards everything. We try to change as quickly as possible. Um it's even challenging for us sometimes. It's also challenging for the team. I still have discussions where where people come and say hey we cannot write that many proposals at the same time. I say this is the wrong way of thinking.

The right way of thinking is >> how do we create an agent or a workflow or a process >> that makes us write proposals quicker or ideally that an AI automatically generates a 90% version of a proposal in the future. That's the way of thinking and that's the the change that needs to happen and um to round that up I think the biggest mistake right now for every company out there is to wait for the right solution that will emerge.

Yeah. because it might be smart to actually buy the tool in a year that works best but the main issue um that remains is that the change in the team doesn't happen and that the change in the way of thinking doesn't happen >> and this change is only happening when you try out stuff. Yeah. And this is sometimes against the nature of a of a German human being. But um you have to try it and when you try it and test it and and um also go into certain risks with it then you actually win at the end because then your team is ready to adopt the right tool in the moment it comes out.

So yeah, trying out being on the forefront of it, paying someone in our team who is an actual AI engineer, actual data engineer, maybe hiring more of them and and and trying to to to push that forward as quickly as possible. That's the way to go. But in reality and not in PowerPoint, that's uh what we are pushing towards. >> Yeah, absolutely. >> And then you also have the aspect of accountability, right? I think also that is something where everybody questions how will that evolve, right?

So we take full accountability for what we do consultant especially with the bank case you made right they are very interested to know when something doesn't work who's the one to be blamed um uh yeah I think also that is something um that will still persist and still needs yeah this uh or saves our ass right >> absolutely so so so to to to bring it to the point it's just do it with AI uh so I think that's really important important and let me please share just kind of our vision that we're that we're doing and for me is having both of you also here in the podcast but also kind of that solutions in our solution.

So we're talking about a strategic decision intelligence uh because that is in the most companies not there the strategic decision intelligence is one person with a gut feeling with biases etc. And when we want to make that better with better data with more clarity from outside in data uh the the data in the warehouse so uh so on on all that stages and bring it together in a simple form and we will launch that on the 1 of June uh on your mobile so that's basically the idea to ask all of that kind question um having dashboards for for for the for the burning KPIs of your business but most of them today don't know what are their most burning KPIs.

So, this is a way to go also for our clients, for all of our clients, I think. And we're on a journey now. We do not know where it will end, but um we're sure it's better to start now or yesterday than tomorrow. And uh therefore, one last sentence uh to maybe yeah, let's say PE partners watching here uh this this live remote podcast. Um, what's the move on Monday they should do, Dennis? >> Uh, yeah, reach out to us, of course, if they >> We put everything in the show notes for sure. >> No, but uh, seriously, I think, um, Sasha put it in the right phrase, right?

Waiting is the worst thing you can do. And >> usually if like time is money um and you have two options basically you can now try to start out what many do and I think for good reasons is the make decision right so you do make the issue and we've seen that now uh more of a dozens of times is when you start making you will know the actual result you will know the impact of what you were doing earliest months later right after weeks you never have something where you feel trustworthy and saying okay uh this isn't heading where we wanted to this has major flaws you will still continue to invest time right nobody psychologically we don't see some costs as humans right we continue to to optimize and there is the um advantage out there right there are many tools there are many tool providers that focus on a single niche they're handling which did that already successfully for hundreds of other clients where you immediately the second you by the next day you will have access.

You have a working solution that will work. And so what we actually tell to our clients is okay fine do your make please. Uh very happy we're not standing in your way. Take our solution as the benchmark. Uh take it that's right what the market delivers right now. Um take us and try to make us obsolete uh with your make efforts. And I think that is something I would challenge everybody. Um do that not waste your time. start with the working solution right now and then we are very happy to get into competition with uh make efforts I think that's the fairest you can get it right >> absolutely great uh Zasha what would you say >> um audit audit your portfolio company's data foundation I think that's the biggest step and um don't think about AI and an AI road map think about how you get the data together in order to enable AI at the end.

And >> please do not think about the idea that an ERP system might solve your problems. Yeah. Um it's it's really about the data >> and uh if you you can work with a with an IBM as 400 solution from 1989. >> If the bookkeeping is clean, you can still build an SQL database around it and digitalize the whole process and make it AI ready. Mhm. >> So, so, so don't believe the the usual pitfalls um um in in PE. Um really think about the data foundation and then take do it step by step and start implementing on Monday.

Yeah, that's uh that's it. >> That that's great. And just just my my last scent to it. So hope is not a strategy. And uh you need a strategy, but for strategy you need everything. You need the data, you need the processes, you need AI, but you need the people in your portfolio companies, but all also in your PE team. So therefore, I would say it's a yeah, bring them in harmony and just start tomorrow. So therefore, I would like to thank both of you, Dennis and Zasha, for this first live session we did and we just did it.

Uh so um we made some mistake, but we will be better the next time we do it. So, thank you very much for listening and we put everything that we discussed in the show notes and u you can reach out to to all of us if you have any questions on LinkedIn. We are always available and uh looking forward to that. Thank you very much guys. >> Thank you too. Sorry to the audience. See you. Bye-bye. >> Thank you Christian.