Ep. 134 – The Broken Mindset Behind Most AI Engagements (Why Clients Bear the Cost and How to Fix It) – Dr. Anja Konhaeuser

AI projects fail because leadership assumes deploying the tool is the finish line.

In this episode, Dr. Anja Konhaeuser, Co-Founder and Partner at OMMAX, explains why AI transformation cannot be treated like a traditional technology rollout.

The real challenge starts once the solution is live: employees need to trust outputs, workflows need to change, roles need to be redefined, and leadership needs to actively model the new behavior. Drawing from OMMAX’s work with clients across AI, data, and digital transformation, Anja shows why the old 80/20 logic has flipped: AI may now be 20% technology and 80% organization. For consulting leaders, this episode offers a clear view of where AI projects really succeed or stall: not in the technical build, but in whether people, processes, and leadership behavior change after implementation.

You’ll learn:

1. Why AI adoption requires more than technical deployment
2. How leaders misread AI when they treat it like an IT rollout
3. What employee groups need different adoption strategies
4. Why visible leadership behavior determines trust in AI
5. How consulting delivery changes when adoption becomes the real work

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Dr. Anja Konhaeuser is open to connecting about AI transformation, adoption, and organizational change. If you would like to exchange perspectives, reach out on LinkedIn: https://www.linkedin.com/in/dr-anja-konh%C3%A4user-28282297/

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Anja’s recommendations:
You Are the Placebo | https://www.amazon.de/-/en/You-Are-Placebo-Making-Matter/dp/1781802572/r

Why We Sleep | https://www.amazon.de/-/en/Why-We-Sleep-Unlocking-Dreams/dp/1501144324/

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Sammy and SAWOO offer a B2B community-building service that establishes you as a thought leader in your industry and helps you build genuine, human-to-human relationships with your dream clients. Get in touch with Sammy Gebele on LinkedIn: https://www.linkedin.com/in/sammygebele/

For more perspectives on how leaders are navigating topics like this, follow the LEADERS IN CONSULTING Community on LinkedIn: https://www.linkedin.com/company/leadersinconsulting-en 

Transcript

SPEAKER_00 0:02

I would I always tend to say it's an 8020 rule and that's 8020 changed. It's not applying to any case that we are working on at the moment, but it gives you a very good idea of what we are talking about. So back in the days, 80% has been tech, 20% has been organization around it, and today tech has become very easy. 20% is tech, 80% is organization. And organization is exactly the part of understanding the workflows before. It doesn't start after it has been implemented. The before is really, really relevant to be able to find the perfect solution. I always love to say, fall in love with your problems and not with your solutions.

SPEAKER_01 0:51

Welcome to the Leaders in Consulting Podcast. I'm Sammy, your host and founder of the community. In this show, I speak to CEOs, partners and MDs of consulting firms about their best practices and hard-worn insights. If you're keen to join the conversation visit leadersinconsulting.com to find out how you can connect with peers at our summits, peer coaching sessions, and monthly confidential forums in your city. And now, let's get started with the show. Today I am very happy to welcome Dr. Anja Kohnhäuser, co-founder and partner at Omex to our show. Welcome Anja.

SPEAKER_00 1:30

Hi, hi Sammy, good to see you.

SPEAKER_01 1:32

Yeah, nice to have you with us. And uh the very interesting topic that you brought, which is basically AI and how to implement it at your clients, um, which is a little bit controversial to what most um yeah C Devel executives at companies maybe think about it, or maybe even consulting leaders. But before we dive into that, tell me a little bit more about OMAX and what you're doing.

SPEAKER_00 1:59

I am co-founder and partner at Omax. Uh, we are supporting a lot of clients either on transaction or on digital transformation. And we are their partner in the area of tech, data, and AI. We do both. We strategize and we implement. And uh we are talking today a bit more about our AI work and what it means to be an external partner for a lot of entrepreneurs and for a lot of mid-sized and mid-sized companies, large companies and corporates, and helping them navigating through the AI noise in a very dynamic world that we are facing ourselves in at the moment. Being their sparing partner, being their implementation partner, being a shoulder to hold on and uh giving an ear, listening uh to their worries and thoughts and dreams, uh, what they are having in their mind. This is what we do with our Max.

SPEAKER_01 3:04

Very cool. And I still remember one sentence that you said well, creating the AI solution is easy, implementing it is hard. Um, and we'll definitely some consulting leaders who are listening are still struggling with creating the AI solution, but there's still a long way to go once you have the capability inside of your own company. Um When did you start, Omax? Which year?

SPEAKER_00 3:28

We are now 15 years old.

SPEAKER_01 3:30

And how many employees do you have now?

SPEAKER_00 3:33

We have 300 employees today, and um we split them a bit into uh tech data AI experts and uh also experts that are supporting on the market side digitalization and internal digitalizations.

SPEAKER_01 3:50

And you have a very interesting background because after our pre-interview, I asked, well, how did you come up with your cool uh way of implementing and and you wrote your doctor thesis um about it? What was the title of your um thesis?

SPEAKER_00 4:06

So the title was Dealing with Innovation from a Sales Management Point of View, and it was about innovation adoption, and I think the word adoption will accompany us in the next minutes quite a lot. And I never thought that there would be a second spring for this thesis, but there it is. So adoption is um more relevant than ever, and stuff that I have already investigated in my PhD is um helping me to implement, in this case, AI. Um, back in the days I was focusing on innovation, on product innovation mainly. But also AI is a relevant innovation of our time, and this is now yeah, more relevant than ever to make sure that people accept, understand, and adopt what is happening there.

SPEAKER_01 5:02

Yeah, and we are right now recording it on the 13th of March 26th, and um I think most of the big AI LM companies are starting to partner very strongly with consulting companies. So it's not like AI is killing consulting, AI needs consulting.

SPEAKER_00 5:21

Yes, exactly. And also the the companies, I have the feeling, Sami, they realize that there's so much going on in the market, and it's also very hard, even if they built up their own AI data tech entities and strengthened their team in the past years. Now it is a very good momentum for consulting companies that walk the talk and that really deliver measurable results. And we also come to the measurement uh topic later on as well. And um, this is for us a big chance in the market.

SPEAKER_01 5:56

Yeah, and I like this positivity because I had so many conversations that are not so positive, but I also think that you have to have the right look at the market and the opportunities that there are due to new technology, and that's why I really look forward to our conversation now. So um, you you are an expert with your company in in AI and AI implementation. Um and especially what we singled out in our pre-interview is that AI adoption is basically one of the keys. So, what are the key barriers that you see at the clients that you you help uh develop and implement AI solutions that um that hinders them from basically having the impact that they want to have?

SPEAKER_00 6:43

So when we speak, it depends always on certain uh environments and companies have always specific situations they are facing themselves in. Um, one very big observation obviously is now at the moment when we are talking with them about AI, that firstly, to set the scene, we as a consultancy company always have to take the time on our end to understand the specific situation our client company is in. May it be how the market is, how the clients are, how the technology standards are, if there's regulation in place, if there's any dependency, whatever it is. So it's even more important today on a consultant side to understand the peculiaries and the specifics of the company on the client side. That being said, if we then said, if we then look on the client side, we see that if they struggle with measuring the impact, some of them looked at an AI concept as a pure technical concept, for example. So they approached it with a very technical task force coming from the IT, and thinking that it's a technology-only operation that they are now starting, forgetting about the big influence something like that might have on routines, on workforces, on workflows, also even on roles, and that makes them stumble throughout the process because they forgot about the human part, the people part, and also the process part. So whenever there is um certain hesitation or a delay, something in the triangle of people process technology got lost along the way. And those triangles is super crucial to be successful today.

SPEAKER_01 8:51

So um it seems like technology, that's what um many of your clients tick off and say, yeah, yeah, we can figure that out or we figured it out, but they uh they are what are why are leadership teams neglecting the people side then?

SPEAKER_00 9:06

I don't think that they neglect it actively or underestimated. They underestimate it completely because the past experiences have been that tech projects have been guided and monitored by IT teams for certain sub-departments or certain infrastructures, and then there have been trainings, there have been a hyper care phase, and the tech infrastructure has been implemented. So the history of tech projects has nothing to do with what we see and do now in AI projects. And if you then make the mistake of parking it as a tech project, you just adopt the wrong expectations and the wrong mechanism and the wrong rollout plan because it is something different than a tech project. And I don't think that they are neglecting it actively and say we don't need it. They just um most of the times didn't expect it uh that they would stumble over something like that later on.

SPEAKER_01 10:16

And why is it different now?

SPEAKER_00 10:18

It is different now because if you really deal with AI solutions, this has a tremendous impact on how you do your business today. So the tech involvement and also the tech influence and what those, I mean, you even experience it yourself, what those solutions now come up with is much bigger and much intense and broader as you would have expected it at the beginning. When you use yourself even privately LLMs to support you and work with those, what you can get out of those LLMs is really significant and remarkable. And I think this applies very well also to the professional area. What those AI solutions can do within companies and the depths and the breadth of the analysis or whatever the use case is, um, is so tremendous and changing to and not comparable to anything, and changing infrastructure as it is thought of and built up today causes simply that people didn't expect it to be that wide when it comes to the influence on different areas in their companies.

SPEAKER_01 11:37

Yeah, I can imagine. I mean, I see it with our very small company, and we have some roles that suddenly use AI and LLMs and workflows on a daily basis, and they output basically 20-fold more output now, where people really suddenly had the fear of losing their job by Sammy. Do we need as many people? And do I have to be afraid? And at least for us, it's no. And now they can do cooler stuff where we didn't have the resources to do before. But I all like, and we are like a company of young people who are willing and able to learn and are are open in our culture to speak up. So I can imagine in a bigger company, it these fears are there and these hesitants are there, and um that might do you see something similar where um this basically leads to a shift to this is not just tech, this is a lot about people.

SPEAKER_00 12:30

Absolutely. It's actually two things that you were uh touching upon. One thing being as long as the company hasn't made the experience, what is really possible with the new AI solution, it is very hard for them to imagine and to further develop the existing workflow, the existing workforce, the existing routines. It is the same as with us with the LLMs. As long as you haven't had a touch point with this, with it, you cannot imagine what you can do with it and what you can get out of it, and how it can influence your daily life and how it can influence your routine. And by using it, you explore more. This same logic applies to a certain extent also to professional setups because the teams have to get familiar, have to get familiar with what is possible with the AI solution. Well, how can it help? How can it support me, etc. etc. This is one part. And in our world, coming now again from an analogy with tech projects, you did your in the old world, you did your analysis, you did your vendor selection, you selected you need a tech solution to solve A, B, C challenges. You selected the best vendor, you implemented it, you rolled it out, and it sold A, B, C of your solutions as it was planned. Now, with AI coming in, there are so many solutions, so many challenges, challenges that it can solve, which brings to an existing complexity in the companies, another layer of complexity, which is how can the teams now work best with what they have been now implemented and what they have been provided with, and how can they integrate it in their workforce? So we see this being happening that just after everything has been implemented and set up, the teams really start to get familiar and develop their own logics and workforce and routines. And yes, the second dimension that you mentioned is fear of being obsolete. So the FOBO, um, coming from the FOMO analogy. Fear of being obsolete means people are afraid that they are not needed anymore. So I had a situation last week. There was one employee responsible for the shift planning of the production plant, doing this um one week per month, always at the end in the last week of the month, and not taking holidays in that specific week for the past plus 10 years, because this is her task to do. So fully committed, she's uh prioritizing this, even sometimes over her private uh stuff that she has, just being there because this is now her relevant workflow and where she's responsible. Understanding now that this is not needed anymore in an era of AI, that shift planning is done within milliseconds, and to kind of make it a bit extreme from an um comparison point of view, and it's been done in milliseconds, and she doesn't need to be there anymore. And of course, this causes questions, worries, and also without giving them an answer, what do you do in your last week of the months, or what can you then instead kind of fill your your time, is one also at the same time of the most seen mistakes that we see, that we focus too much on the AI solution and too much too much on what is happening until we have the solution, and we neglect the post-AI integration phase, which we called back in the days hypercare. But hypercare today really means sometimes hyperpersonalized care, because every single member of the team that is now working with the new AI solution has to find his or her new routine and new way of realizing the potential that he or she has in her uh business hours.

SPEAKER_01 16:50

So, Anya, if you take basically 100% of the budget that if a company buys you as a consulting company and um and compare it how the budget was allocated between technology creation and implementation and also taking the people along back in the day with traditional technology and now with AI, how did it shift?

SPEAKER_00 17:14

I would I always tend to say it's an 8020 rule and that 8020 changed. It's not applying to any case that we are working on at the at the moment, but it gives you a very good idea of what we are talking about. So back in the days, 80% has been tech, 20% has been organization around it. And today, tech has become very easy. 20% is tech, 80% is organization. And organization is exactly the part of understanding the workflows before it doesn't start after it has been implemented. The before is really, really relevant to be able to find the perfect solution. I always love to say fall in love with your problems and not with your solutions, because it always sounds so inviting that if you just implement this and that um solution, you would just add that module that of your already existing infrastructure. You can do these 10 use cases, and this will make your lives easier. A lot of solutions did a very good job when it comes to their marketing and sales approach. A lot of companies underestimate the relevance of really taking the time double-clicking in their existing infrastructure and uh team workflows setup to understand where the real problems and pain points are. And that should then be the focus of the AI solution.

SPEAKER_01 18:49

That's uh I mean that's a big shift, but that's also the hope for the consulting industry, so to say, because then um it's still a people job to help people um go all the way to also um leveraging the new technology and maybe, or not maybe, for sure, accomplish much more than before.

SPEAKER_00 19:08

Yes, exactly. And as I said earlier, we had we really have seen that companies are welcoming external sparing partner that are entrepreneurs themselves, like we are, that are seeing a lot of different situations. This is why I mentioned that we are doing transaction advisory at the beginning. Within that tech due diligences, AI due diligences, by the way, is also a very popular topic at the moment because investors or corporates that are buying other companies, they want to know how can AI push, how can I grow, can I grow with AI and what is the risk that is coming, what is disruption risk coming from the AI side. Um and this gives us a very uh big knowledge base where we see different companies from the inside and we can support as a really educated sparring partner and help them navigate through that storm. Um, and this is like the first part, and then the second part is being emotionally intelligent enough to understand what the different teams and how they set up and how we can make it work based on an AI tech layer.

SPEAKER_01 20:23

And one thing that really stuck with me is that you before a project starts, you even analyze uh the employees and categorize them into different personas, which then um helps you basically also in later phases when you start to implement and roll out the solutions. Can you take us on that journey and what that looks like?

SPEAKER_00 20:45

Yeah, sure, sure. So, and this is coming a bit also out of my uh HD, PhD, sorry, it is coming also out of my PhD thesis that I wrote a couple of years ago. Um where we realize that not everyone is obviously starting his or her AI journey on the same level. It is even, it is so hard for me to do an AI speaker engagement on um whatever event, because you never know the level of knowledge people are having in the audience. And it's so hard with that buzzword to give something, bring something to the table that is really able that everyone understands it. And that's similar when we are entering new companies. So when we enter new companies, we have to understand firstly, on the top level management leadership team. How do they think about AI? What is the history they have with AI? Did they have already some experience, touch points, pilots, or whatever? Then how is their setup internally? Then how is their setup internally? Do they have um already a Very good tech basis and understanding and very good tech and data management. These are kind of different layers which give us already where we can draw a picture of the company's status quo. And the last element you are talking about is that we have to understand, depending on in which teams we start with our AI engagement, how those teams are thinking about the topic of AI. And it cannot be, as we did it in the past, that there's then the team layer and all fits one kind of logic. It's more that we split the team into four different groups. The first group is the enthusiastic group. So they are super interested, they are open, they tried it out themselves. They are, they can't wait for us to get started. And we have to utilize them as well as positive influencers and uh understand how they can support our initiatives that we want to uh drive within the company. And we make them kind of co-designers of the work streams, so not just testers, but even owners, so to say. So we have the enthusiasts. The second group is skeptical. Skeptical um is often at the moment, I would say, the largest group, especially even in regulated sectors, as you can imagine. And um they are not anti-technology, they are just risk sensitive. So um they are more coming from the perspective if something goes wrong, am I accountable? They kind of um they need some some guidance, they don't really understand what is uh going on, but they need to be convinced to a certain extent.

SPEAKER_01 23:52

Sorry, I didn't want to interrupt you.

SPEAKER_00 23:54

No, please, are these?

SPEAKER_01 23:55

Are these the ones who are uh afraid of that job? The skeptics?

SPEAKER_00 24:00

Yes, as well. As well. They just don't know what is going on and uh why it is happening, and they heard some uh nightmare stories about AI, they can repeat them uh like in the meetings, and they what they need is basically transparency, a human in the loop, clarity, clear boundaries, trust, and um also training to a certain extent helps to shift them between the different kinds of uh not everyone has to become an enthusiast. I I will touch upon this uh also uh uh at the at the end, but you get the the framing around skepticism, uh skepticism. The third one are the ones that are completely overwhelmed. And this is different because this group is underestimated. And um, if someone is overwhelmed, they are a bit more like they are either already overloaded, they are afraid of not being able to use the tool, to utilize the tool, maybe even of a different interface they have to work with. And AI feels to them. They are also coming from a very technical standpoint. They are really coming from not one more tool, I have to learn, I have to use, I'm even overwhelmed with the tools that I have today. This is a bit the overwhelmed um group. And the fourth group are the rejectors. So you always have in organizations a certain percentage of people who openly resist and say um they have a fear of the whole AI itself, uh, if this isn't bad in general for the whole society. So they question the whole um movement that is going on, and they are very strict about not being willing to participate in the projects, and they also openly, in most of the cases, address that they don't believe in AI and they don't want to use AI because the company has been working successfully in the past years, or um, they have been working successfully in the past years on their job and they don't need it. So this is the uh the fourth group.

SPEAKER_01 26:18

In a in a normal setting, and I know that can vary by industry or company size, or even by company, what what is what distributions do you most of the time uh find in companies between these four personas or in teams?

SPEAKER_00 26:32

So you have usually when companies work with us, there's a certain bias already. So this may be being um put uh as at first. If companies work with an external party, there is already a certain uh openness towards AI support. This might highly influence how the structure is. But what we see is um a good portion of enthusiasts, and I also want to highlight that we try to identify them across teams. Because even if we start with an AI initiative in a certain team, that doesn't mean that not other enthusiasts from other teams that are willing to participate and support this initiative might be relevant to push in general the AI journey of the company in a certain direction, right? So even if we now start in the HR department, for example, uh, or in production, then you would still try to uh identify together with the leadership team and the sponsor of the project, maybe other enthusiasts in the team. So um, but I would say usually the smallest, the smallest group is uh is the rejectors. The smallest group is the rejectors, I would say. Then we have um followed by the enthusiasts, and then we have the largest group with the skeptical ones, and then a certain degree of overwhelmed ones. This correlates as well with the age of the um team members. Yeah, this is how I would frame it a bit.

SPEAKER_01 28:18

And do you see also cultural differences? Because I know that you you don't only work in the German-speaking um area, but also in other countries.

SPEAKER_00 28:28

Yes, we do. We do have in certain European countries a larger portion of enthusiasts. And this also, I would say, um, highly correlates with how the whole country is dealing with the AI infrastructure. So we see that France is very open when it comes to AI solutions. Interesting. Yeah, we see that there is more openness, and in um in the UK as well, we have certain skeptical um movements that we see, but also the openness is is higher.

SPEAKER_01 29:08

Do you also have touch points with the US?

SPEAKER_00 29:10

Yes, we also have touch points with the US. In the US, I mean the US frames AI as the next big thing. And a lot of AI companies move to the US because they are they have a much bigger playground and a much they have much more tailwind when it comes to the whole AI infrastructure. And yes, this can also be seen when it comes to decision cycles, much shorter in the US, also um willingness to kind of risk a pilot. So you don't know if it really works out, and the openness is much larger in the US, I would say. And the people are more open uh towards that uh development that we see there.

SPEAKER_01 29:57

Very interesting. Um now you know when you start a project, um, who in the teams were you started with um falls into which of these four categories. What do you do with this information? How do you use it to basically then um develop or also roll out um the AI solution for that specific company, Anja?

SPEAKER_00 30:22

So um we do not necessarily know it for every single team member. It also depends if companies in Germany tend to have a Betriebsrat. If they have that, uh obviously it gets more complex in um defining that on a personal level. And I actually don't necessarily need to know it on a person level. I more need to understand what are the percentages per group, because depending on if we have more skeptic people than rejecting people, we would adapt the rollout. So what we do is once we understood how, and we measure more than just those four kind of groups, we also have a look, as I said, on how is data management generally handled? How trusted is the leadership team when implementing changes in workflows? So you also have to kind of measure and get an understanding of is the team actually following the leadership team and the management on that journey? Is the leadership team or the management able to stand for an AI journey in that company? Or do we firstly have to work on the reputation and on the positioning of the management when it comes to AI initiatives? Because it also needs a lot of trust. And um, if you have a management that is not trusted when it comes to AI initiatives, is not utilizing it themselves, is not um capable of explaining what an LLM is, or never had built an agent themselves and wants the whole team to do it tomorrow, this kind of doesn't get together, right? So you can only support those companies when you have different dimensions of um, yeah, different dimensions in place of influence factors that make the project successful. And the personas are one part, but there are some others that are really, really, really relevant to make sure that the team also understands we are tackling it holistically, end-to-end. It is nothing that we do now once, and everyone gets like an LLM app on his or her smartphone, and then that's it. When you really want to drive it into an AI journey, you usually have to redesign the workforce, have to redesign sometimes leadership structures, also redefine roles to make sure that the company is um successful in the mid and long run.

SPEAKER_01 33:08

Understood. Um but coming back basically to the simplified question of um assuming everything else is equal and you you just know that a team is more leaning onto the enthusiastic side on the skeptical side, or which maybe you don't even see, and then we don't even have to talk about it more, they reject us.

SPEAKER_00 33:30

If we see that, then kind of the rollout is planned exactly on those persona percentage groups. So if there are people that are more skeptical, the topic of training plays a more important role. If we see we have more enthusiasts, we let them build something. The skeptical ones, they are not so much into directly starting to build something. The skeptical ones, they need trust. The skeptical ones, they need trust and information first. The enthusiastic ones, they can start building already like the next day because they can't wait for the stuff to start. And exactly those little examples gives you maybe a better understanding how we can, and also who is training them is also a very important component. So if you have skeptical ones, it is not always ideal if we are only training them from the outside as an external service provider. So if they are skeptical, they need someone from their like company community and their crowd. So you also have to think of who's the best counterpart to train that skeptical group who is already trusted because we are potentially not, they don't know us. We are an external service provider. But who within the team can teach them to make sure they listen and trust? And we just emphasize, maybe also with benchmarks and show them, and this also is generating trust. Look, other companies that have your size, your market access, your client base, are doing it in this kind of way and are utilizing AI on in those situations, and that makes them relate and understand what is possible, and uh they lose the skepticism um faster.

SPEAKER_01 35:21

Okay, very good. What do you do? Like, do you even see more overwhelmed teams as well? This is happening.

SPEAKER_00 35:28

It depends on um on the industry. Yes, we also see overwhelmed teams, and overwhelming is then also um very important because what we then stumble over is maybe not ideal um tech integrations of the past. So they have some legacy on their desks. Tools, technology that never have been properly implemented, and they need workarounds and they need to do something very difficult because certain things haven't been solved in the past years, and they just don't want to get another tech tool on their desks. Yeah. And those overwhelmed teams, what you usually do there, you go two steps back, you understand the infrastructure that they have today, why they are overwhelmed, if you can maybe even fix something of their daily work already up front because they are overwhelmed because something runs is not running ideally today in their uh daily work. And then you go one step closer to, and if we have now fixed this, uh you can you can now work with the AI. But here, especially training and training made easy and being easily implemented in their daily work is super, super relevant because taking them out a full day won't also move the needle. They need on a continuous basis uh being accompanied through their own AI journey and learning how they can work with us and learning workflows.

SPEAKER_01 37:12

And now, and but as you said, that rarely happens. Uh, the majority of people where you have rejectors.

SPEAKER_00 37:20

Yeah.

SPEAKER_01 37:21

So you don't have to have a playbook for those because it's always a minority. Or do you have a playbook for those?

SPEAKER_00 37:28

I mean, we do have a playbook where rejection comes from. We need to understand why they reject. Um whatever the reason is, the reasons can can vary depending on the teams or whatever. And um we then actually, especially when it comes to the rejectors, speak closely with the leadership teams and the management in how far we should turn them around, if we can turn them around, and if we kind of um can align it with certain initiatives that maybe are already running within the company to not bring even more topics on their table if they are rejecting it. But this is a very difficult group. But you have to measure it, you have to get an understanding how big they are, because they might act as negative influences. And uh this is why you have to have them on your agenda. And I think the I mean, it's a people business then at the end of the day. Understanding why they reject is actually most of the times the key, but you can also never, never turn around anyone.

SPEAKER_01 38:41

Yeah. Yeah, it seems like a lot of work that you have to do to get the whole team up and running. Um, but in the end, like putting on the head of the CEO, I definitely would like everyone in the team to embrace it and to be enabled. So I understand that it's not just a solution, it's like uh getting everybody up to speed in the company. And I mean, the upside is crazy.

SPEAKER_00 39:05

The upside is crazy, and what I also always tend to say with an eye on the rejectors, I'm I strongly believe that it is not only the task of the company to train their people when it comes to AI solutions and what AI can do and how AI can support you. I also think that everyone, given that tremendous shift that we are in, finding ourselves, if you, I mean, I have three kids, if you have kids, etc., you are living in a world where AI gets more and more relevant and you are exposed every day to a lot of agents everywhere without recognizing it. There's a lot of stuff going on, you cannot ignore it. And it cannot only be the task of the company and the employer to give this knowledge to the people. I also strongly believe that people themselves have to undertake responsibility living in that AI era to understand what is going on. And if they decide not to do that, they might then also not want to work in a company that embraces AI.

SPEAKER_01 40:18

Yeah. Yeah, you touch a point where I have to touch um my own nose. So I basically uh wipe coded for the first time a tool, um, a financial Ebit forecast planning tool for ourselves, just because I wanted to learn how wipe coding works, never did it before. And um and then I did it two weeks ago. It took me like a day um of time with all the iterations, but now it's it's working, it's really cool. And um, and I even used all these tools that our CTO uses that I never heard before, like Superbase Vercel. Um, all this it's like uh now I understand what it means and what you can do with this, but that also showed me that um our leadership team, um everyone should have done that, not just me. Yeah, and why am I the only one who has done it up to now? And we started to have a conversation around that, and and um and if we are not the ones, like you said before, who are embracing it and and seeing the value and just building something for ourselves just to see how it works, how can we expect it from our employees to go all the way?

SPEAKER_00 41:28

Yeah, yeah. So the the energy the top management is putting into the AI is uh very relevant when it comes to that shift within the companies. Yeah, and it has to be visible to the employees as well. So it's really relevant, and this is why it's not only a tech project anymore. Uh there should be people and culture sitting on the table, there should be also internal communication sitting on the table, because if you really want to turn around that um AI mindset and develop an AI mindset in the company, it should be reflected uh in the in, as I said, in the roles, in the performance talks. And um then also it should be visible for the team what the top management is doing when it comes to AI.

SPEAKER_01 42:17

Yeah. Um now shifting basically to um yeah, how it looks like in practice when you have a client. Um, I assume often it is the CEO says, yeah, I mean, we have to embrace this technology, we have to do something. Um, let's get started. Um what happens then? They they call you, so they know they need outside help.

SPEAKER_00 42:42

Yeah, they know they need outside help. Then uh, as I said, we understand uh what their status quo today is. Do they have did they have experience? How's their data management? How's their tech management? How is uh how open is the workforce? Have there been already touch points in the AI infrastructure? And we better understand uh what they want to achieve because this is one of the first uh stumbling blocks. If they call us, they are not so sure. They cannot break it down to a KPI what they really want to achieve with it. They have a bit of an idea where it can lead to, but getting that in kind of in information and definition at the beginning is super relevant because then we speak about the same thing and we define the same thing as being successful or not. And if it is successful, it has to be measured.

SPEAKER_01 43:37

Um but as you said, um most CEOs or or leadership teams don't know exactly where what they want to uh use this cool new opportunity that is like we talk we talk about AI, but there's like a myriad of solutions out there, and you could do a lot of things. And and um how do you help them like in these First steps, how does it look like until you find a topic or say, ah, or maybe you don't find one topic and all your tests? So, how how does it look like in practice when you do that?

SPEAKER_00 44:11

So there's the case companies come to us and say, I want to have more AI in my HR department. Then it's quite clear, then we go into the HR departments. Another example is that we meet CEOs and they say, AI, I have the feeling and I'm convinced AI can help me to do my business different, um, more efficient, more modern in the future. Where do I start? And this, where do I start is for us then a signal that we really have to go through the different departments, speak with the different departments, understand how they work today, and define those use cases. So, and this is also an exercise a lot of companies have already done in the past weeks and months. So, this is my perception. This was different 12 months ago, but now companies do have an understanding of where potential lies. If not, then we start really going step by step through the different departments, understanding also their relevance, their size. Um, also, again, here looking at the um more intuitive things and things where we expect a lot of potential, looking in those first, understanding the openness, understanding how the team reacts, understanding also the investment chances and the investment strengths that company has to invest in AI in a certain department, and how this turns out and uh can be like turned into a positive ROI in which amount of time. So, what we basically always do is not only the use case definition for the most relevant five to ten use cases of every area, we directly set up the business cases to make it very easy for the management to decide where they want to start. And this is also for the board meetings, et cetera, then uh the document where they then decide together, okay, let's do this because this has the highest impact, or you have different kinds of uh KPIs that you that you look at. And it also depends semi on the situation the company is in at the moment. Do they want to save costs? Do they want to scale further? Are they limited in growth? Are they uh about to enter a new market and do not want to build up a complete team there? Do we have a green field to a certain extent to scale the company further? So very different use cases, but you always have to like come from where the company is today, understand the setup, and evaluate with an entrepreneurial mindset what is the next best action, where should they focus on, and what is the solution that fits best to this um to this exercise and to this ambition that we define together. And here you have most of the most of the times you have a certain legacy, may it be IT infrastructure, may it be data that is somewhere unstructured. And this is then um something that we need to understand very fast in combination with how the people are kind of behaving when it comes to AI, and we make with those different uh kind of components our plan, how we can support the company, who is leading the project on the client side. As I as I said earlier, do we need internal communication? Do we need people in culture? We most of the cases need IT, we need I we need security, we need the data uh component, and of course the top man management also being in line. We even offer Sami that we do AI masterclasses for the leadership team and the management before we start.

SPEAKER_01 48:13

Cool.

SPEAKER_00 48:14

Like coming back to the point I mentioned earlier, the top management is then the one who has to stand before the team and stand up for the topic of AI and uh confidently discuss those topics together with the team and guide them in through this through this vision, define that vision together with the team. And this is why it's super crucial that they themselves own the topic. Yeah.

SPEAKER_01 48:44

Understand. It's super interesting. Um on the one hand, um, yeah, I understand how you work with bigger companies. On the other hand, I always think about my own company and think what we can learn from what you just say. And I already took a lot of things with me, so that's super, super cool. Um, now this is the Leaders in Consulting Podcast. So everything you set up to know was how like your product, so to say. So, how do you enable your clients to win and how do you win projects through this? But um, it also changed the work, how you were um basically helping your client. So um the two-folded question is basically how did this whole um AI product I call it? Yeah, it's a lot of things, but um, let's call it AI product, change how you operate, and does it have an impact on the type of people you employ and the basically um historical um pyramid that you have as a consulting company, where you have a few people on top and many juniors, and the juniors tickle up over time, become fewer, and so that builds the leadership of the future.

SPEAKER_00 49:56

Yes, so we do see that, especially for those AI projects, and it's actually applying to any project I just referred to, need a very, very good and profound senior steering from our end. So you really need people that have experience from the industry, that have a very good understanding of tech, of data, that are understanding what is going on on the AI front, can answer any kind of question. So you go away, and I mean at Omarx, we have always had this focus on tech and data. So for us, it is not so super new. But if you speak of traditional consulting roles, I see that especially the senior teams, they need now to understand. We all have to go back to that kind of university and learn how AI is going, what AI is enabling us, how can we um how can we implement AI? What are the use cases? So I see for us as consultants, it is important to be on top of things, to understand what is going on. And I also see that you need, for certain cases, a lot more senior leaders, because it's not as you said it, as you said it right, traditionally you had one senior partner steering the project, and then there was the middle management, and then there was like the junior supporting. Now that AI adoption work, that AI implementation work needs to be done by people that really know what they do and they can relate with the people on the client side, and they are usually not juniors, right? So we do see the shift that when it comes to the staffing of the projects, that we need more seniors there to deliver it. And this highly influences also our hiring strategy that we have um more seniors that we want to win, because this is this is also where you build up the client relationship. And this maybe again, what does it change for the consultants? The consultants now need to be also much broader, not only the tech part, not only the data part, but also the people part, the whole adoption part. Um you always say it and read it like in your Instagram feed, emotional intelligence is so important, but it really is now, because if you cannot put yourself in the shoes of the people that are working there in that company and what their fears are and what their um questions are, it will be very hard for us as consultants to guide them through that journey. And this makes our, at the same time, our senior profile expectation much more complex.

SPEAKER_01 52:57

That sounds like a lot of change that you went through in your own organization and used to go through.

SPEAKER_00 53:05

And in addition, what we also have is we have a team that is dedicatedly working with the newest AI tools, um, getting familiar with the hot stuff out there, um, because no one has done this in the past 20 years up and down, has experienced it in the different situations that you find in the client infrastructure. So you need kind of a lab, or yeah, you need an you need a team, an internal, like an RD department, if you want. So that enables us and gives us knowledge and gives us the chance to try out things. And this is not how you usually imagine a consulting company to be set up, right?

SPEAKER_01 53:54

Not originally. No, it was the old pyramid, and the partner is hunting and then coming flying in from time to time. That's how I know it when I was a consultant, and that's basically it. But that is changing apparently, and has a big impact on the consulting industry.

SPEAKER_00 54:09

Yes, and also we have to make sure that our team is trained. So making sure our team is really trained on all levels is also something that changed a lot. It's not the usual training that you had in the past where you're like doing it while you were driving somewhere. No, this is a real training where you really have to generate knowledge because you will fall back if you if you don't generate that knowledge. This also changed.

SPEAKER_01 54:36

But that's a good like guiding post for other consulting leaders to say, okay, we don't have it yet, but I see it in big organizations. So I I talk to this kind of department or people within this department and Capgemini invent. And they are um at this forefront that you're just describing, um, like they are super enthusiastic. So I mean it's easy, you pick the enthusiasts to do that, I assume. Um and and they are amazed by what's possible, and they bring this change then uh with workshops and and whatever into the whole organization with thousands of employees.

SPEAKER_00 55:13

Yeah.

unknown 55:14

Yeah.

SPEAKER_01 55:14

So it's working in a in a very, very big setting and it's working in a boutique setting. Um so there's no reason not to do it yourself now if you're a consulting leader. No. If you um now could like imagine um a senior consulting leader who's maybe even owning his own boutique consulting company, um, who says, Yeah, we are not that far on the change towards that. We didn't even implement any AI project. I roughly have an idea what that could be, but I never really I use ChatGPT and that's it, so to say. Um what would be the high-level steps that you would advise those leaders to take?

SPEAKER_00 55:59

So I think that status quo analysis is something that should be intuitively doable for any leader in consulting. So understand your own workflows and identify those areas where you say this is where AI can really make a difference and support me. And uh, by the way, I uh what I, for example, use a lot is the I have an uh AI integration into my Excel and into my PowerPoint. So find those little, maybe even AI solutions when you say it's a rather smaller company and they haven't started. This makes a big difference already. So if you have a smart interface, AI-based interface in your daily routine that you can easily integrate, I think the most important thing, and this is again highly correlating with the adoption piece, it has to be easy accessible and easily usable in your daily workflow in Excel and in PowerPoint. And also, what I do a lot is um when it comes to big uh PDFs, big market studies, whatever, I transform them into podcasts and uh listen to them because it's easier to consume and we are getting less used to read a lot of texts all the time and like focus our concentration on long texts is not always very easy. So it helps as well to um uh listen to them. Uh something like that is already helping a lot.

SPEAKER_01 57:36

Okay, very good, Tanja. So we are already at the end of our conversation. I have a couple of rapid fire questions left for you. Um so consulting is still uh a tough sport, and you even have a family on top with kids. How do you keep body and mind fit and sharp?

SPEAKER_00 57:56

I defend Sundays. I think this is super relevant for me as a working mom, that Sunday is non-negotiable uh for the for the kids. And if it's not the Sunday, because I have to go to a um client meeting already Sunday evening, then it's the Saturday. I um do uh breastwork that helps me personally, uh no matter where. Uh ideally when no one recognizes, standing in a queue, for example, so no need to get half an hour in your calendar won't work. I don't have that half an hour.

SPEAKER_01 58:31

Yeah. And uh, can you give us one one simple routine that everybody could adapt right away?

SPEAKER_00 58:36

If you if you stand in the in the queue and wait at DM for paying your your stuff, you breathe in four seconds, you hold it four seconds, you breathe out four seconds. You do this three times, and it gives you a bit of a centering and a bit of a relaxation. And I really believe for me personally, it works. It sounds a bit um um esoteric, but uh I really get uh fresh power from that. And you can wire your brain to use that little moment for relaxation. And maybe one thing which I also do, and I can only um recommend before I dial in in a call, I take 10 seconds. What is my role in the call? What do I want to get out of the call? What points do I want to bring up in that call? So, what is the content and what is the next best action? So, my role, what do I expect? What is the content and what is the next best action? Doesn't necessarily take 10 seconds, gives you the chance to be sharp in that meeting and not stumble from one meeting to the next and forget what you actually want and who you are and how many.

SPEAKER_01 59:48

Very cool, and that is all actionable. I love it. Thanks a lot again. Um do you have a favorite book right now? It doesn't have to be a business book, it can.

SPEAKER_00 59:58

A favorite book, I love you are the placebo. It's a very nice book. And I also read Um Why We Sleep at the Moment from Walker, which is also a nice book too. I like those neuroscience kind of stuff, and those two are um good to read, I would say.

SPEAKER_01 1:00:21

Why the first one?

SPEAKER_00 1:00:23

You are the placebo gives you um resilience.

SPEAKER_01 1:00:27

Okay, very good. We put it into the show notes. Um, what is the biggest challenge of being a consulting leader that nobody talks about, Tanya?

SPEAKER_00 1:00:36

Too little sleep. So we manage somehow, but I think the uh this is why I'm reading. Why are we sleeping? Why do we sleep? Um from Walker, because uh this is uh really a challenge at the moment.

SPEAKER_01 1:00:55

Yeah, yeah, I can imagine uh on top being your mom, um that that is definitely not making it easier.

SPEAKER_00 1:01:01

Yeah.

SPEAKER_01 1:01:02

Um who should be um our next podcast guest for our leaders and consulting show, who should we try to win?

SPEAKER_00 1:01:13

You should try to win uh Remy from Singulier. Remy CEO of Singulier.

SPEAKER_01 1:01:24

I don't even know Singulier. What is it?

SPEAKER_00 1:01:28

It's a consulting company.

SPEAKER_01 1:01:29

Okay. It sounds French.

SPEAKER_00 1:01:31

Yes, it's French. They have an office in Munich and an office in the UK, and um I think it's interesting to speak with him.

SPEAKER_01 1:01:39

And why?

SPEAKER_00 1:01:41

Because it's an interesting character, and he's bringing in that French perspective, which you tackled as well from an international point of view. And I was thinking of Finn when you asked that question, and it he could bring in a bit more of how they do it in France and what we can maybe learn as well in the German setup.

SPEAKER_01 1:01:59

Um that would be interesting. Because I'm pretty sure uh from Germany, looking at France, if you don't have touch points, you see them differently than they are in fact. Yes. And you saying they're leading something, I don't think many Germans are thinking France is leading in AI or in adoption. So that could be super interesting. I try to um to get them on the show. Um, and now it's time for you to tell us what you need from us. So a lot of consulting leaders, partners MDs of small and big companies listening. Is there anything we can help you with, Anya?

SPEAKER_00 1:02:33

No, I just uh for me it's always important to embrace AI that I have people that understand that AI is the future, it won't get away anymore. We have to control it, embrace it, and make the best out of it, no matter if it's with an external service provider or not. But we should not fall back and ignore it. So every person that is open to AI tries it out, experiments with it. This is kind of my mission to uh giving an impulse and influence them to try it out.

SPEAKER_01 1:03:09

And how could people best get in touch with you?

SPEAKER_00 1:03:13

Through LinkedIn.

SPEAKER_01 1:03:14

Okay, we also put your LinkedIn profile into the show notes. So thanks a lot for taking us on this journey. I I really like the the the whole angle that you you showed us and that um, well, it's not just about having the solution. Solution is a little part of implementing the whole the whole package to make a client successful. So thanks a lot, Dania.

SPEAKER_00 1:03:35

Thank you, Sami, was a pleasure.

SPEAKER_01 1:03:37

Thank you for listening to the Leaders in Consulting podcast. To learn from and go with fellow consulting leaders in person, apply to join our monthly confidential forums and peer coaching in your city. Visit leadersinconsulting.com for details. See you soon.