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

1 - > SPEAKER_00: I would I always tend to say it's an 8020 rule 2 - > and that's 8020 changed. 3 - > It's not applying to any case that we are working on at the 4 - > moment, but it gives you a very good idea of what we are talking 5 - > about. 6 - > So back in the days, 80% has been tech, 20% has been 7 - > organization around it, and today tech has become very easy. 8 - > 20% is tech, 80% is organization.

9 - > And organization is exactly the part of understanding the 10 - > workflows before. 11 - > It doesn't start after it has been implemented. 12 - > The before is really, really relevant to be able to find the 13 - > perfect solution. 14 - > I always love to say, fall in love with your problems and not 15 - > with your solutions.

16 - > SPEAKER_01: Welcome to the Leaders in Consulting Podcast. 17 - > I'm Sammy, your host and founder of the community. 18 - > In this show, I speak to CEOs, partners and MDs of consulting 19 - > firms about their best practices and hard-worn insights. 20 - > If you're keen to join the conversation visit 21 - > leadersinconsulting.

com to find out how you can connect with 22 - > peers at our summits, peer coaching sessions, and monthly 23 - > confidential forums in your city. 24 - > And now, let's get started with the show. 25 - > Today I am very happy to welcome Dr. 26 - > Anja Kohnhäuser, co-founder and partner at Omex to our show.

27 - > Welcome Anja. 28 - > SPEAKER_00: Hi, hi Sammy, good to see you. 29 - > SPEAKER_01: Yeah, nice to have you with us. 30 - > And uh the very interesting topic that you brought, which is 31 - > basically AI and how to implement it at your clients, 32 - > um, which is a little bit controversial to what most um 33 - > yeah C Devel executives at companies maybe think about it, 34 - > or maybe even consulting leaders.

35 - > But before we dive into that, tell me a little bit more about 36 - > OMAX and what you're doing. 37 - > SPEAKER_00: I am co-founder and partner at Omax. 38 - > Uh, we are supporting a lot of clients either on transaction or 39 - > on digital transformation. 40 - > And we are their partner in the area of tech, data, and AI.

41 - > We do both. 42 - > We strategize and we implement. 43 - > And uh we are talking today a bit more about our AI work and 44 - > what it means to be an external partner for a lot of 45 - > entrepreneurs and for a lot of mid-sized and mid-sized 46 - > companies, large companies and corporates, and helping them 47 - > navigating through the AI noise in a very dynamic world that we 48 - > are facing ourselves in at the moment. 49 - > Being their sparing partner, being their implementation 50 - > partner, being a shoulder to hold on and uh giving an ear, 51 - > listening uh to their worries and thoughts and dreams, uh, 52 - > what they are having in their mind.

53 - > This is what we do with our Max. 54 - > SPEAKER_01: Very cool. 55 - > And I still remember one sentence that you said well, 56 - > creating the AI solution is easy, implementing it is hard. 57 - > Um, and we'll definitely some consulting leaders who are 58 - > listening are still struggling with creating the AI solution, 59 - > but there's still a long way to go once you have the capability 60 - > inside of your own company.

61 - > Um When did you start, Omax? 62 - > Which year? 63 - > SPEAKER_00: We are now 15 years old. 64 - > SPEAKER_01: And how many employees do you have now?

65 - > SPEAKER_00: We have 300 employees today, and um we split 66 - > them a bit into uh tech data AI experts and uh also experts that 67 - > are supporting on the market side digitalization and internal 68 - > digitalizations. 69 - > SPEAKER_01: And you have a very interesting background because 70 - > after our pre-interview, I asked, well, how did you come up 71 - > with your cool uh way of implementing and and you wrote 72 - > your doctor thesis um about it? 73 - > What was the title of your um thesis?

74 - > SPEAKER_00: So the title was Dealing with Innovation from a 75 - > Sales Management Point of View, and it was about innovation 76 - > adoption, and I think the word adoption will accompany us in 77 - > the next minutes quite a lot. 78 - > And I never thought that there would be a second spring for 79 - > this thesis, but there it is. 80 - > So adoption is um more relevant than ever, and stuff that I have 81 - > already investigated in my PhD is um helping me to implement, 82 - > in this case, AI.

83 - > Um, back in the days I was focusing on innovation, on 84 - > product innovation mainly. 85 - > But also AI is a relevant innovation of our time, and this 86 - > is now yeah, more relevant than ever to make sure that people 87 - > accept, understand, and adopt what is happening there. 88 - > SPEAKER_01: Yeah, and we are right now recording it on the 89 - > 13th of March 26th, and um I think most of the big AI LM 90 - > companies are starting to partner very strongly with 91 - > consulting companies.

92 - > So it's not like AI is killing consulting, AI needs consulting. 93 - > SPEAKER_00: Yes, exactly. 94 - > And also the the companies, I have the feeling, Sami, they 95 - > realize that there's so much going on in the market, and it's 96 - > also very hard, even if they built up their own AI data tech 97 - > entities and strengthened their team in the past years. 98 - > Now it is a very good momentum for consulting companies that 99 - > walk the talk and that really deliver measurable results.

100 - > And we also come to the measurement uh topic later on as 101 - > well. 102 - > And um, this is for us a big chance in the market. 103 - > SPEAKER_01: Yeah, and I like this positivity because I had so 104 - > many conversations that are not so positive, but I also think 105 - > that you have to have the right look at the market and the 106 - > opportunities that there are due to new technology, and that's 107 - > why I really look forward to our conversation now. 108 - > So um, you you are an expert with your company in in AI and 109 - > AI implementation.

110 - > Um and especially what we singled out in our pre-interview 111 - > is that AI adoption is basically one of the keys. 112 - > So, what are the key barriers that you see at the clients that 113 - > you you help uh develop and implement AI solutions that um 114 - > that hinders them from basically having the impact that they want 115 - > to have? 116 - > SPEAKER_00: So when we speak, it depends always on certain uh 117 - > environments and companies have always specific situations they 118 - > are facing themselves in.

119 - > Um, one very big observation obviously is now at the moment 120 - > when we are talking with them about AI, that firstly, to set 121 - > the scene, we as a consultancy company always have to take the 122 - > time on our end to understand the specific situation our 123 - > client company is in. 124 - > May it be how the market is, how the clients are, how the 125 - > technology standards are, if there's regulation in place, if 126 - > there's any dependency, whatever it is.

127 - > So it's even more important today on a consultant side to 128 - > understand the peculiaries and the specifics of the company on 129 - > the client side. 130 - > That being said, if we then said, if we then look on the 131 - > client side, we see that if they struggle with measuring the 132 - > impact, some of them looked at an AI concept as a pure 133 - > technical concept, for example. 134 - > So they approached it with a very technical task force coming 135 - > from the IT, and thinking that it's a technology-only operation 136 - > that they are now starting, forgetting about the big 137 - > influence something like that might have on routines, on 138 - > workforces, on workflows, also even on roles, and that makes 139 - > them stumble throughout the process because they forgot 140 - > about the human part, the people part, and also the process part.

141 - > So whenever there is um certain hesitation or a delay, something 142 - > in the triangle of people process technology got lost 143 - > along the way. 144 - > And those triangles is super crucial to be successful today. 145 - > SPEAKER_01: So um it seems like technology, that's what um many 146 - > of your clients tick off and say, yeah, yeah, we can figure 147 - > that out or we figured it out, but they uh they are what are 148 - > why are leadership teams neglecting the people side then?

149 - > SPEAKER_00: I don't think that they neglect it actively or 150 - > underestimated. 151 - > They underestimate it completely because the past experiences 152 - > have been that tech projects have been guided and monitored 153 - > by IT teams for certain sub-departments or certain 154 - > infrastructures, and then there have been trainings, there have 155 - > been a hyper care phase, and the tech infrastructure has been 156 - > implemented. 157 - > So the history of tech projects has nothing to do with what we 158 - > see and do now in AI projects.

159 - > And if you then make the mistake of parking it as a tech project, 160 - > you just adopt the wrong expectations and the wrong 161 - > mechanism and the wrong rollout plan because it is something 162 - > different than a tech project. 163 - > And I don't think that they are neglecting it actively and say 164 - > we don't need it. 165 - > They just um most of the times didn't expect it uh that they 166 - > would stumble over something like that later on. 167 - > SPEAKER_01: And why is it different now?

168 - > SPEAKER_00: It is different now because if you really deal with 169 - > AI solutions, this has a tremendous impact on how you do 170 - > your business today. 171 - > So the tech involvement and also the tech influence and what 172 - > those, I mean, you even experience it yourself, what 173 - > those solutions now come up with is much bigger and much intense 174 - > and broader as you would have expected it at the beginning. 175 - > When you use yourself even privately LLMs to support you 176 - > and work with those, what you can get out of those LLMs is 177 - > really significant and remarkable.

178 - > And I think this applies very well also to the professional 179 - > area. 180 - > What those AI solutions can do within companies and the depths 181 - > and the breadth of the analysis or whatever the use case is, um, 182 - > is so tremendous and changing to and not comparable to anything, 183 - > and changing infrastructure as it is thought of and built up 184 - > today causes simply that people didn't expect it to be that wide 185 - > when it comes to the influence on different areas in their 186 - > companies.

187 - > SPEAKER_01: Yeah, I can imagine. 188 - > I mean, I see it with our very small company, and we have some 189 - > roles that suddenly use AI and LLMs and workflows on a daily 190 - > basis, and they output basically 20-fold more output now, where 191 - > people really suddenly had the fear of losing their job by 192 - > Sammy. 193 - > Do we need as many people? 194 - > And do I have to be afraid?

195 - > And at least for us, it's no. 196 - > And now they can do cooler stuff where we didn't have the 197 - > resources to do before. 198 - > But I all like, and we are like a company of young people who 199 - > are willing and able to learn and are are open in our culture 200 - > to speak up. 201 - > So I can imagine in a bigger company, it these fears are 202 - > there and these hesitants are there, and um that might do you 203 - > see something similar where um this basically leads to a shift 204 - > to this is not just tech, this is a lot about people.

205 - > SPEAKER_00: Absolutely. 206 - > It's actually two things that you were uh touching upon. 207 - > One thing being as long as the company hasn't made the 208 - > experience, what is really possible with the new AI 209 - > solution, it is very hard for them to imagine and to further 210 - > develop the existing workflow, the existing workforce, the 211 - > existing routines. 212 - > It is the same as with us with the LLMs.

213 - > As long as you haven't had a touch point with this, with it, 214 - > you cannot imagine what you can do with it and what you can get 215 - > out of it, and how it can influence your daily life and 216 - > how it can influence your routine. 217 - > And by using it, you explore more. 218 - > This same logic applies to a certain extent also to 219 - > professional setups because the teams have to get familiar, have 220 - > to get familiar with what is possible with the AI solution.

221 - > Well, how can it help? 222 - > How can it support me, etc. 223 - > etc. 224 - > This is one part.

225 - > And in our world, coming now again from an analogy with tech 226 - > projects, you did your in the old world, you did your 227 - > analysis, you did your vendor selection, you selected you need 228 - > a tech solution to solve A, B, C challenges. 229 - > You selected the best vendor, you implemented it, you rolled 230 - > it out, and it sold A, B, C of your solutions as it was 231 - > planned. 232 - > Now, with AI coming in, there are so many solutions, so many 233 - > challenges, challenges that it can solve, which brings to an 234 - > existing complexity in the companies, another layer of 235 - > complexity, which is how can the teams now work best with what 236 - > they have been now implemented and what they have been provided 237 - > with, and how can they integrate it in their workforce?

238 - > So we see this being happening that just after everything has 239 - > been implemented and set up, the teams really start to get 240 - > familiar and develop their own logics and workforce and 241 - > routines. 242 - > And yes, the second dimension that you mentioned is fear of 243 - > being obsolete. 244 - > So the FOBO, um, coming from the FOMO analogy. 245 - > Fear of being obsolete means people are afraid that they are 246 - > not needed anymore.

247 - > So I had a situation last week. 248 - > There was one employee responsible for the shift 249 - > planning of the production plant, doing this um one week 250 - > per month, always at the end in the last week of the month, and 251 - > not taking holidays in that specific week for the past plus 252 - > 10 years, because this is her task to do. 253 - > So fully committed, she's uh prioritizing this, even 254 - > sometimes over her private uh stuff that she has, just being 255 - > there because this is now her relevant workflow and where 256 - > she's responsible.

257 - > Understanding now that this is not needed anymore in an era of 258 - > AI, that shift planning is done within milliseconds, and to kind 259 - > of make it a bit extreme from an um comparison point of view, and 260 - > it's been done in milliseconds, and she doesn't need to be there 261 - > anymore. 262 - > And of course, this causes questions, worries, and also 263 - > without giving them an answer, what do you do in your last week 264 - > of the months, or what can you then instead kind of fill your 265 - > your time, is one also at the same time of the most seen 266 - > mistakes that we see, that we focus too much on the AI 267 - > solution and too much too much on what is happening until we 268 - > have the solution, and we neglect the post-AI integration 269 - > phase, which we called back in the days hypercare.

270 - > But hypercare today really means sometimes hyperpersonalized 271 - > care, because every single member of the team that is now 272 - > working with the new AI solution has to find his or her new 273 - > routine and new way of realizing the potential that he or she has 274 - > in her uh business hours. 275 - > SPEAKER_01: So, Anya, if you take basically 100% of the 276 - > budget that if a company buys you as a consulting company and 277 - > um and compare it how the budget was allocated between technology 278 - > creation and implementation and also taking the people along 279 - > back in the day with traditional technology and now with AI, how 280 - > did it shift?

281 - > SPEAKER_00: I would I always tend to say it's an 8020 rule 282 - > and that 8020 changed. 283 - > It's not applying to any case that we are working on at the at 284 - > the moment, but it gives you a very good idea of what we are 285 - > talking about. 286 - > So back in the days, 80% has been tech, 20% has been 287 - > organization around it. 288 - > And today, tech has become very easy.

289 - > 20% is tech, 80% is organization. 290 - > And organization is exactly the part of understanding the 291 - > workflows before it doesn't start after it has been 292 - > implemented. 293 - > The before is really, really relevant to be able to find the 294 - > perfect solution. 295 - > I always love to say fall in love with your problems and not 296 - > with your solutions, because it always sounds so inviting that 297 - > if you just implement this and that um solution, you would just 298 - > add that module that of your already existing infrastructure.

299 - > You can do these 10 use cases, and this will make your lives 300 - > easier. 301 - > A lot of solutions did a very good job when it comes to their 302 - > marketing and sales approach. 303 - > A lot of companies underestimate the relevance of really taking 304 - > the time double-clicking in their existing infrastructure 305 - > and uh team workflows setup to understand where the real 306 - > problems and pain points are. 307 - > And that should then be the focus of the AI solution.

308 - > SPEAKER_01: That's uh I mean that's a big shift, but that's 309 - > also the hope for the consulting industry, so to say, because 310 - > then um it's still a people job to help people um go all the way 311 - > to also um leveraging the new technology and maybe, or not 312 - > maybe, for sure, accomplish much more than before. 313 - > SPEAKER_00: Yes, exactly. 314 - > And as I said earlier, we had we really have seen that companies 315 - > are welcoming external sparing partner that are entrepreneurs 316 - > themselves, like we are, that are seeing a lot of different 317 - > situations.

318 - > This is why I mentioned that we are doing transaction advisory 319 - > at the beginning. 320 - > Within that tech due diligences, AI due diligences, by the way, 321 - > is also a very popular topic at the moment because investors or 322 - > corporates that are buying other companies, they want to know how 323 - > can AI push, how can I grow, can I grow with AI and what is the 324 - > risk that is coming, what is disruption risk coming from the 325 - > AI side. 326 - > Um and this gives us a very uh big knowledge base where we see 327 - > different companies from the inside and we can support as a 328 - > really educated sparring partner and help them navigate through 329 - > that storm.

330 - > Um, and this is like the first part, and then the second part 331 - > is being emotionally intelligent enough to understand what the 332 - > different teams and how they set up and how we can make it work 333 - > based on an AI tech layer. 334 - > SPEAKER_01: And one thing that really stuck with me is that you 335 - > before a project starts, you even analyze uh the employees 336 - > and categorize them into different personas, which then 337 - > um helps you basically also in later phases when you start to 338 - > implement and roll out the solutions.

339 - > Can you take us on that journey and what that looks like? 340 - > SPEAKER_00: Yeah, sure, sure. 341 - > So, and this is coming a bit also out of my uh HD, PhD, 342 - > sorry, it is coming also out of my PhD thesis that I wrote a 343 - > couple of years ago. 344 - > Um where we realize that not everyone is obviously starting 345 - > his or her AI journey on the same level.

346 - > It is even, it is so hard for me to do an AI speaker engagement 347 - > on um whatever event, because you never know the level of 348 - > knowledge people are having in the audience. 349 - > And it's so hard with that buzzword to give something, 350 - > bring something to the table that is really able that 351 - > everyone understands it. 352 - > And that's similar when we are entering new companies. 353 - > So when we enter new companies, we have to understand firstly, 354 - > on the top level management leadership team.

355 - > How do they think about AI? 356 - > What is the history they have with AI? 357 - > Did they have already some experience, touch points, 358 - > pilots, or whatever? 359 - > Then how is their setup internally?

360 - > Then how is their setup internally? 361 - > Do they have um already a Very good tech basis and 362 - > understanding and very good tech and data management. 363 - > These are kind of different layers which give us already 364 - > where we can draw a picture of the company's status quo. 365 - > And the last element you are talking about is that we have to 366 - > understand, depending on in which teams we start with our AI 367 - > engagement, how those teams are thinking about the topic of AI.

368 - > And it cannot be, as we did it in the past, that there's then 369 - > the team layer and all fits one kind of logic. 370 - > It's more that we split the team into four different groups. 371 - > The first group is the enthusiastic group. 372 - > So they are super interested, they are open, they tried it out 373 - > themselves.

374 - > They are, they can't wait for us to get started. 375 - > And we have to utilize them as well as positive influencers and 376 - > uh understand how they can support our initiatives that we 377 - > want to uh drive within the company. 378 - > And we make them kind of co-designers of the work 379 - > streams, so not just testers, but even owners, so to say. 380 - > So we have the enthusiasts.

381 - > The second group is skeptical. 382 - > Skeptical um is often at the moment, I would say, the largest 383 - > group, especially even in regulated sectors, as you can 384 - > imagine. 385 - > And um they are not anti-technology, they are just 386 - > risk sensitive. 387 - > So um they are more coming from the perspective if something 388 - > goes wrong, am I accountable?

389 - > They kind of um they need some some guidance, they don't really 390 - > understand what is uh going on, but they need to be convinced to 391 - > a certain extent. 392 - > SPEAKER_01: Sorry, I didn't want to interrupt you. 393 - > SPEAKER_00: No, please, are these? 394 - > SPEAKER_01: Are these the ones who are uh afraid of that job?

395 - > The skeptics? 396 - > SPEAKER_00: Yes, as well. 397 - > As well. 398 - > They just don't know what is going on and uh why it is 399 - > happening, and they heard some uh nightmare stories about AI, 400 - > they can repeat them uh like in the meetings, and they what they 401 - > need is basically transparency, a human in the loop, clarity, 402 - > clear boundaries, trust, and um also training to a certain 403 - > extent helps to shift them between the different kinds of 404 - > uh not everyone has to become an enthusiast.

405 - > I I will touch upon this uh also uh uh at the at the end, but you 406 - > get the the framing around skepticism, uh skepticism. 407 - > The third one are the ones that are completely overwhelmed. 408 - > And this is different because this group is underestimated. 409 - > And um, if someone is overwhelmed, they are a bit more 410 - > like they are either already overloaded, they are afraid of 411 - > not being able to use the tool, to utilize the tool, maybe even 412 - > of a different interface they have to work with.

413 - > And AI feels to them. 414 - > They are also coming from a very technical standpoint. 415 - > They are really coming from not one more tool, I have to learn, 416 - > I have to use, I'm even overwhelmed with the tools that 417 - > I have today. 418 - > This is a bit the overwhelmed um group.

419 - > And the fourth group are the rejectors. 420 - > So you always have in organizations a certain 421 - > percentage of people who openly resist and say um they have a 422 - > fear of the whole AI itself, uh, if this isn't bad in general for 423 - > the whole society. 424 - > So they question the whole um movement that is going on, and 425 - > they are very strict about not being willing to participate in 426 - > the projects, and they also openly, in most of the cases, 427 - > address that they don't believe in AI and they don't want to use 428 - > AI because the company has been working successfully in the past 429 - > years, or um, they have been working successfully in the past 430 - > years on their job and they don't need it.

431 - > So this is the uh the fourth group. 432 - > SPEAKER_01: In a in a normal setting, and I know that can 433 - > vary by industry or company size, or even by company, what 434 - > what is what distributions do you most of the time uh find in 435 - > companies between these four personas or in teams? 436 - > SPEAKER_00: So you have usually when companies work with us, 437 - > there's a certain bias already. 438 - > So this may be being um put uh as at first.

439 - > If companies work with an external party, there is already 440 - > a certain uh openness towards AI support. 441 - > This might highly influence how the structure is. 442 - > But what we see is um a good portion of enthusiasts, and I 443 - > also want to highlight that we try to identify them across 444 - > teams. 445 - > Because even if we start with an AI initiative in a certain team, 446 - > that doesn't mean that not other enthusiasts from other teams 447 - > that are willing to participate and support this initiative 448 - > might be relevant to push in general the AI journey of the 449 - > company in a certain direction, right?

450 - > So even if we now start in the HR department, for example, uh, 451 - > or in production, then you would still try to uh identify 452 - > together with the leadership team and the sponsor of the 453 - > project, maybe other enthusiasts in the team. 454 - > So um, but I would say usually the smallest, the smallest group 455 - > is uh is the rejectors. 456 - > The smallest group is the rejectors, I would say. 457 - > Then we have um followed by the enthusiasts, and then we have 458 - > the largest group with the skeptical ones, and then a 459 - > certain degree of overwhelmed ones.

460 - > This correlates as well with the age of the um team members. 461 - > Yeah, this is how I would frame it a bit. 462 - > SPEAKER_01: And do you see also cultural differences? 463 - > Because I know that you you don't only work in the 464 - > German-speaking um area, but also in other countries.

465 - > SPEAKER_00: Yes, we do. 466 - > We do have in certain European countries a larger portion of 467 - > enthusiasts. 468 - > And this also, I would say, um, highly correlates with how the 469 - > whole country is dealing with the AI infrastructure. 470 - > So we see that France is very open when it comes to AI 471 - > solutions.

472 - > Interesting. 473 - > Yeah, we see that there is more openness, and in um in the UK as 474 - > well, we have certain skeptical um movements that we see, but 475 - > also the openness is is higher. 476 - > SPEAKER_01: Do you also have touch points with the US? 477 - > SPEAKER_00: Yes, we also have touch points with the US.

478 - > In the US, I mean the US frames AI as the next big thing. 479 - > And a lot of AI companies move to the US because they are they 480 - > have a much bigger playground and a much they have much more 481 - > tailwind when it comes to the whole AI infrastructure. 482 - > And yes, this can also be seen when it comes to decision 483 - > cycles, much shorter in the US, also um willingness to kind of 484 - > risk a pilot. 485 - > So you don't know if it really works out, and the openness is 486 - > much larger in the US, I would say.

487 - > And the people are more open uh towards that uh development that 488 - > we see there. 489 - > SPEAKER_01: Very interesting. 490 - > Um now you know when you start a project, um, who in the teams 491 - > were you started with um falls into which of these four 492 - > categories. 493 - > What do you do with this information?

494 - > How do you use it to basically then um develop or also roll out 495 - > um the AI solution for that specific company, Anja? 496 - > SPEAKER_00: So um we do not necessarily know it for every 497 - > single team member. 498 - > It also depends if companies in Germany tend to have a 499 - > Betriebsrat. 500 - > If they have that, uh obviously it gets more complex in um 501 - > defining that on a personal level.

502 - > And I actually don't necessarily need to know it on a person 503 - > level. 504 - > I more need to understand what are the percentages per group, 505 - > because depending on if we have more skeptic people than 506 - > rejecting people, we would adapt the rollout. 507 - > So what we do is once we understood how, and we measure 508 - > more than just those four kind of groups, we also have a look, 509 - > as I said, on how is data management generally handled? 510 - > How trusted is the leadership team when implementing changes 511 - > in workflows?

512 - > So you also have to kind of measure and get an understanding 513 - > of is the team actually following the leadership team 514 - > and the management on that journey? 515 - > Is the leadership team or the management able to stand for an 516 - > AI journey in that company? 517 - > Or do we firstly have to work on the reputation and on the 518 - > positioning of the management when it comes to AI initiatives? 519 - > Because it also needs a lot of trust.

520 - > And um, if you have a management that is not trusted when it 521 - > comes to AI initiatives, is not utilizing it themselves, is not 522 - > um capable of explaining what an LLM is, or never had built an 523 - > agent themselves and wants the whole team to do it tomorrow, 524 - > this kind of doesn't get together, right? 525 - > So you can only support those companies when you have 526 - > different dimensions of um, yeah, different dimensions in 527 - > place of influence factors that make the project successful.

528 - > And the personas are one part, but there are some others that 529 - > are really, really, really relevant to make sure that the 530 - > team also understands we are tackling it holistically, 531 - > end-to-end. 532 - > It is nothing that we do now once, and everyone gets like an 533 - > LLM app on his or her smartphone, and then that's it. 534 - > When you really want to drive it into an AI journey, you usually 535 - > have to redesign the workforce, have to redesign sometimes 536 - > leadership structures, also redefine roles to make sure that 537 - > the company is um successful in the mid and long run.

538 - > SPEAKER_01: Understood. 539 - > Um but coming back basically to the simplified question of um 540 - > assuming everything else is equal and you you just know that 541 - > a team is more leaning onto the enthusiastic side on the 542 - > skeptical side, or which maybe you don't even see, and then we 543 - > don't even have to talk about it more, they reject us. 544 - > SPEAKER_00: If we see that, then kind of the rollout is planned 545 - > exactly on those persona percentage groups.

546 - > So if there are people that are more skeptical, the topic of 547 - > training plays a more important role. 548 - > If we see we have more enthusiasts, we let them build 549 - > something. 550 - > The skeptical ones, they are not so much into directly starting 551 - > to build something. 552 - > The skeptical ones, they need trust.

553 - > The skeptical ones, they need trust and information first. 554 - > The enthusiastic ones, they can start building already like the 555 - > next day because they can't wait for the stuff to start. 556 - > And exactly those little examples gives you maybe a 557 - > better understanding how we can, and also who is training them is 558 - > also a very important component. 559 - > So if you have skeptical ones, it is not always ideal if we are 560 - > only training them from the outside as an external service 561 - > provider.

562 - > So if they are skeptical, they need someone from their like 563 - > company community and their crowd. 564 - > So you also have to think of who's the best counterpart to 565 - > train that skeptical group who is already trusted because we 566 - > are potentially not, they don't know us. 567 - > We are an external service provider. 568 - > But who within the team can teach them to make sure they 569 - > listen and trust?

570 - > And we just emphasize, maybe also with benchmarks and show 571 - > them, and this also is generating trust. 572 - > Look, other companies that have your size, your market access, 573 - > your client base, are doing it in this kind of way and are 574 - > utilizing AI on in those situations, and that makes them 575 - > relate and understand what is possible, and uh they lose the 576 - > skepticism um faster. 577 - > SPEAKER_01: Okay, very good. 578 - > What do you do?

579 - > Like, do you even see more overwhelmed teams as well? 580 - > This is happening. 581 - > SPEAKER_00: It depends on um on the industry. 582 - > Yes, we also see overwhelmed teams, and overwhelming is then 583 - > also um very important because what we then stumble over is 584 - > maybe not ideal um tech integrations of the past.

585 - > So they have some legacy on their desks. 586 - > Tools, technology that never have been properly implemented, 587 - > and they need workarounds and they need to do something very 588 - > difficult because certain things haven't been solved in the past 589 - > years, and they just don't want to get another tech tool on 590 - > their desks. 591 - > Yeah. 592 - > And those overwhelmed teams, what you usually do there, you 593 - > go two steps back, you understand the infrastructure 594 - > that they have today, why they are overwhelmed, if you can 595 - > maybe even fix something of their daily work already up 596 - > front because they are overwhelmed because something 597 - > runs is not running ideally today in their uh daily work.

598 - > And then you go one step closer to, and if we have now fixed 599 - > this, uh you can you can now work with the AI. 600 - > But here, especially training and training made easy and being 601 - > easily implemented in their daily work is super, super 602 - > relevant because taking them out a full day won't also move the 603 - > needle. 604 - > They need on a continuous basis uh being accompanied through 605 - > their own AI journey and learning how they can work with 606 - > us and learning workflows.

607 - > SPEAKER_01: And now, and but as you said, that rarely happens. 608 - > Uh, the majority of people where you have rejectors. 609 - > SPEAKER_00: Yeah. 610 - > SPEAKER_01: So you don't have to have a playbook for those 611 - > because it's always a minority.

612 - > Or do you have a playbook for those? 613 - > SPEAKER_00: I mean, we do have a playbook where rejection comes 614 - > from. 615 - > We need to understand why they reject. 616 - > Um whatever the reason is, the reasons can can vary depending 617 - > on the teams or whatever.

618 - > And um we then actually, especially when it comes to the 619 - > rejectors, speak closely with the leadership teams and the 620 - > management in how far we should turn them around, if we can turn 621 - > them around, and if we kind of um can align it with certain 622 - > initiatives that maybe are already running within the 623 - > company to not bring even more topics on their table if they 624 - > are rejecting it. 625 - > But this is a very difficult group. 626 - > But you have to measure it, you have to get an understanding how 627 - > big they are, because they might act as negative influences.

628 - > And uh this is why you have to have them on your agenda. 629 - > And I think the I mean, it's a people business then at the end 630 - > of the day. 631 - > Understanding why they reject is actually most of the times the 632 - > key, but you can also never, never turn around anyone. 633 - > SPEAKER_01: Yeah.

634 - > Yeah, it seems like a lot of work that you have to do to get 635 - > the whole team up and running. 636 - > Um, but in the end, like putting on the head of the CEO, I 637 - > definitely would like everyone in the team to embrace it and to 638 - > be enabled. 639 - > So I understand that it's not just a solution, it's like uh 640 - > getting everybody up to speed in the company. 641 - > And I mean, the upside is crazy.

642 - > SPEAKER_00: The upside is crazy, and what I also always tend to 643 - > say with an eye on the rejectors, I'm I strongly 644 - > believe that it is not only the task of the company to train 645 - > their people when it comes to AI solutions and what AI can do and 646 - > how AI can support you. 647 - > I also think that everyone, given that tremendous shift that 648 - > we are in, finding ourselves, if you, I mean, I have three kids, 649 - > if you have kids, etc., you are living in a world where AI gets 650 - > more and more relevant and you are exposed every day to a lot 651 - > of agents everywhere without recognizing it.

652 - > There's a lot of stuff going on, you cannot ignore it. 653 - > And it cannot only be the task of the company and the employer 654 - > to give this knowledge to the people. 655 - > I also strongly believe that people themselves have to 656 - > undertake responsibility living in that AI era to understand 657 - > what is going on. 658 - > And if they decide not to do that, they might then also not 659 - > want to work in a company that embraces AI.

660 - > SPEAKER_01: Yeah. 661 - > Yeah, you touch a point where I have to touch um my own nose. 662 - > So I basically uh wipe coded for the first time a tool, um, a 663 - > financial Ebit forecast planning tool for ourselves, just because 664 - > I wanted to learn how wipe coding works, never did it 665 - > before. 666 - > And um and then I did it two weeks ago.

667 - > It took me like a day um of time with all the iterations, but now 668 - > it's it's working, it's really cool. 669 - > And um, and I even used all these tools that our CTO uses 670 - > that I never heard before, like Superbase Vercel. 671 - > Um, all this it's like uh now I understand what it means and 672 - > what you can do with this, but that also showed me that um our 673 - > leadership team, um everyone should have done that, not just 674 - > me. 675 - > Yeah, and why am I the only one who has done it up to now?

676 - > And we started to have a conversation around that, and 677 - > and um and if we are not the ones, like you said before, who 678 - > are embracing it and and seeing the value and just building 679 - > something for ourselves just to see how it works, how can we 680 - > expect it from our employees to go all the way? 681 - > SPEAKER_00: Yeah, yeah. 682 - > So the the energy the top management is putting into the 683 - > AI is uh very relevant when it comes to that shift within the 684 - > companies.

685 - > Yeah, and it has to be visible to the employees as well. 686 - > So it's really relevant, and this is why it's not only a tech 687 - > project anymore. 688 - > Uh there should be people and culture sitting on the table, 689 - > there should be also internal communication sitting on the 690 - > table, because if you really want to turn around that um AI 691 - > mindset and develop an AI mindset in the company, it 692 - > should be reflected uh in the in, as I said, in the roles, in 693 - > the performance talks.

694 - > And um then also it should be visible for the team what the 695 - > top management is doing when it comes to AI. 696 - > SPEAKER_01: Yeah. 697 - > Um now shifting basically to um yeah, how it looks like in 698 - > practice when you have a client. 699 - > Um, I assume often it is the CEO says, yeah, I mean, we have to 700 - > embrace this technology, we have to do something.

701 - > Um, let's get started. 702 - > Um what happens then? 703 - > They they call you, so they know they need outside help. 704 - > SPEAKER_00: Yeah, they know they need outside help.

705 - > Then uh, as I said, we understand uh what their status 706 - > quo today is. 707 - > Do they have did they have experience? 708 - > How's their data management? 709 - > How's their tech management?

710 - > How is uh how open is the workforce? 711 - > Have there been already touch points in the AI infrastructure? 712 - > And we better understand uh what they want to achieve because 713 - > this is one of the first uh stumbling blocks. 714 - > If they call us, they are not so sure.

715 - > They cannot break it down to a KPI what they really want to 716 - > achieve with it. 717 - > They have a bit of an idea where it can lead to, but getting that 718 - > in kind of in information and definition at the beginning is 719 - > super relevant because then we speak about the same thing and 720 - > we define the same thing as being successful or not. 721 - > And if it is successful, it has to be measured. 722 - > SPEAKER_01: Um but as you said, um most CEOs or or leadership 723 - > teams don't know exactly where what they want to uh use this 724 - > cool new opportunity that is like we talk we talk about AI, 725 - > but there's like a myriad of solutions out there, and you 726 - > could do a lot of things.

727 - > And and um how do you help them like in these First steps, how 728 - > does it look like until you find a topic or say, ah, or maybe you 729 - > don't find one topic and all your tests? 730 - > So, how how does it look like in practice when you do that? 731 - > SPEAKER_00: So there's the case companies come to us and say, I 732 - > want to have more AI in my HR department. 733 - > Then it's quite clear, then we go into the HR departments.

734 - > Another example is that we meet CEOs and they say, AI, I have 735 - > the feeling and I'm convinced AI can help me to do my business 736 - > different, um, more efficient, more modern in the future. 737 - > Where do I start? 738 - > And this, where do I start is for us then a signal that we 739 - > really have to go through the different departments, speak 740 - > with the different departments, understand how they work today, 741 - > and define those use cases. 742 - > So, and this is also an exercise a lot of companies have already 743 - > done in the past weeks and months.

744 - > So, this is my perception. 745 - > This was different 12 months ago, but now companies do have 746 - > an understanding of where potential lies. 747 - > If not, then we start really going step by step through the 748 - > different departments, understanding also their 749 - > relevance, their size. 750 - > Um, also, again, here looking at the um more intuitive things and 751 - > things where we expect a lot of potential, looking in those 752 - > first, understanding the openness, understanding how the 753 - > team reacts, understanding also the investment chances and the 754 - > investment strengths that company has to invest in AI in a 755 - > certain department, and how this turns out and uh can be like 756 - > turned into a positive ROI in which amount of time.

757 - > So, what we basically always do is not only the use case 758 - > definition for the most relevant five to ten use cases of every 759 - > area, we directly set up the business cases to make it very 760 - > easy for the management to decide where they want to start. 761 - > And this is also for the board meetings, et cetera, then uh the 762 - > document where they then decide together, okay, let's do this 763 - > because this has the highest impact, or you have different 764 - > kinds of uh KPIs that you that you look at.

765 - > And it also depends semi on the situation the company is in at 766 - > the moment. 767 - > Do they want to save costs? 768 - > Do they want to scale further? 769 - > Are they limited in growth?

770 - > Are they uh about to enter a new market and do not want to build 771 - > up a complete team there? 772 - > Do we have a green field to a certain extent to scale the 773 - > company further? 774 - > So very different use cases, but you always have to like come 775 - > from where the company is today, understand the setup, and 776 - > evaluate with an entrepreneurial mindset what is the next best 777 - > action, where should they focus on, and what is the solution 778 - > that fits best to this um to this exercise and to this 779 - > ambition that we define together.

780 - > And here you have most of the most of the times you have a 781 - > certain legacy, may it be IT infrastructure, may it be data 782 - > that is somewhere unstructured. 783 - > And this is then um something that we need to understand very 784 - > fast in combination with how the people are kind of behaving when 785 - > it comes to AI, and we make with those different uh kind of 786 - > components our plan, how we can support the company, who is 787 - > leading the project on the client side.

788 - > As I as I said earlier, do we need internal communication? 789 - > Do we need people in culture? 790 - > We most of the cases need IT, we need I we need security, we need 791 - > the data uh component, and of course the top man management 792 - > also being in line. 793 - > We even offer Sami that we do AI masterclasses for the leadership 794 - > team and the management before we start.

795 - > SPEAKER_01: Cool. 796 - > SPEAKER_00: Like coming back to the point I mentioned earlier, 797 - > the top management is then the one who has to stand before the 798 - > team and stand up for the topic of AI and uh confidently discuss 799 - > those topics together with the team and guide them in through 800 - > this through this vision, define that vision together with the 801 - > team. 802 - > And this is why it's super crucial that they themselves own 803 - > the topic.

804 - > Yeah. 805 - > SPEAKER_01: Understand. 806 - > It's super interesting. 807 - > Um on the one hand, um, yeah, I understand how you work with 808 - > bigger companies.

809 - > On the other hand, I always think about my own company and 810 - > think what we can learn from what you just say. 811 - > And I already took a lot of things with me, so that's super, 812 - > super cool. 813 - > Um, now this is the Leaders in Consulting Podcast. 814 - > So everything you set up to know was how like your product, so to 815 - > say.

816 - > So, how do you enable your clients to win and how do you 817 - > win projects through this? 818 - > But um, it also changed the work, how you were um basically 819 - > helping your client. 820 - > So um the two-folded question is basically how did this whole um 821 - > AI product I call it? 822 - > Yeah, it's a lot of things, but um, let's call it AI product, 823 - > change how you operate, and does it have an impact on the type of 824 - > people you employ and the basically um historical um 825 - > pyramid that you have as a consulting company, where you 826 - > have a few people on top and many juniors, and the juniors 827 - > tickle up over time, become fewer, and so that builds the 828 - > leadership of the future.

829 - > SPEAKER_00: Yes, so we do see that, especially for those AI 830 - > projects, and it's actually applying to any project I just 831 - > referred to, need a very, very good and profound senior 832 - > steering from our end. 833 - > So you really need people that have experience from the 834 - > industry, that have a very good understanding of tech, of data, 835 - > that are understanding what is going on on the AI front, can 836 - > answer any kind of question. 837 - > So you go away, and I mean at Omarx, we have always had this 838 - > focus on tech and data.

839 - > So for us, it is not so super new. 840 - > But if you speak of traditional consulting roles, I see that 841 - > especially the senior teams, they need now to understand. 842 - > We all have to go back to that kind of university and learn how 843 - > AI is going, what AI is enabling us, how can we um how can we 844 - > implement AI? 845 - > What are the use cases?

846 - > So I see for us as consultants, it is important to be on top of 847 - > things, to understand what is going on. 848 - > And I also see that you need, for certain cases, a lot more 849 - > senior leaders, because it's not as you said it, as you said it 850 - > right, traditionally you had one senior partner steering the 851 - > project, and then there was the middle management, and then 852 - > there was like the junior supporting. 853 - > Now that AI adoption work, that AI implementation work needs to 854 - > be done by people that really know what they do and they can 855 - > relate with the people on the client side, and they are 856 - > usually not juniors, right?

857 - > So we do see the shift that when it comes to the staffing of the 858 - > projects, that we need more seniors there to deliver it. 859 - > And this highly influences also our hiring strategy that we have 860 - > um more seniors that we want to win, because this is this is 861 - > also where you build up the client relationship. 862 - > And this maybe again, what does it change for the consultants? 863 - > The consultants now need to be also much broader, not only the 864 - > tech part, not only the data part, but also the people part, 865 - > the whole adoption part.

866 - > Um you always say it and read it like in your Instagram feed, 867 - > emotional intelligence is so important, but it really is now, 868 - > because if you cannot put yourself in the shoes of the 869 - > people that are working there in that company and what their 870 - > fears are and what their um questions are, it will be very 871 - > hard for us as consultants to guide them through that journey. 872 - > And this makes our, at the same time, our senior profile 873 - > expectation much more complex.

874 - > SPEAKER_01: That sounds like a lot of change that you went 875 - > through in your own organization and used to go through. 876 - > SPEAKER_00: And in addition, what we also have is we have a 877 - > team that is dedicatedly working with the newest AI tools, um, 878 - > getting familiar with the hot stuff out there, um, because no 879 - > one has done this in the past 20 years up and down, has 880 - > experienced it in the different situations that you find in the 881 - > client infrastructure.

882 - > So you need kind of a lab, or yeah, you need an you need a 883 - > team, an internal, like an RD department, if you want. 884 - > So that enables us and gives us knowledge and gives us the 885 - > chance to try out things. 886 - > And this is not how you usually imagine a consulting company to 887 - > be set up, right? 888 - > SPEAKER_01: Not originally.

889 - > No, it was the old pyramid, and the partner is hunting and then 890 - > coming flying in from time to time. 891 - > That's how I know it when I was a consultant, and that's 892 - > basically it. 893 - > But that is changing apparently, and has a big impact on the 894 - > consulting industry. 895 - > SPEAKER_00: Yes, and also we have to make sure that our team 896 - > is trained.

897 - > So making sure our team is really trained on all levels is 898 - > also something that changed a lot. 899 - > It's not the usual training that you had in the past where you're 900 - > like doing it while you were driving somewhere. 901 - > No, this is a real training where you really have to 902 - > generate knowledge because you will fall back if you if you 903 - > don't generate that knowledge. 904 - > This also changed.

905 - > SPEAKER_01: But that's a good like guiding post for other 906 - > consulting leaders to say, okay, we don't have it yet, but I see 907 - > it in big organizations. 908 - > So I I talk to this kind of department or people within this 909 - > department and Capgemini invent. 910 - > And they are um at this forefront that you're just 911 - > describing, um, like they are super enthusiastic. 912 - > So I mean it's easy, you pick the enthusiasts to do that, I 913 - > assume.

914 - > Um and and they are amazed by what's possible, and they bring 915 - > this change then uh with workshops and and whatever into 916 - > the whole organization with thousands of employees. 917 - > SPEAKER_00: Yeah. 918 - > unknown: Yeah. 919 - > SPEAKER_01: So it's working in a in a very, very big setting and 920 - > it's working in a boutique setting.

921 - > Um so there's no reason not to do it yourself now if you're a 922 - > consulting leader. 923 - > No. 924 - > If you um now could like imagine um a senior consulting leader 925 - > who's maybe even owning his own boutique consulting company, um, 926 - > who says, Yeah, we are not that far on the change towards that. 927 - > We didn't even implement any AI project.

928 - > I roughly have an idea what that could be, but I never really I 929 - > use ChatGPT and that's it, so to say. 930 - > Um what would be the high-level steps that you would advise 931 - > those leaders to take? 932 - > SPEAKER_00: So I think that status quo analysis is something 933 - > that should be intuitively doable for any leader in 934 - > consulting. 935 - > So understand your own workflows and identify those areas where 936 - > you say this is where AI can really make a difference and 937 - > support me.

938 - > And uh, by the way, I uh what I, for example, use a lot is the I 939 - > have an uh AI integration into my Excel and into my PowerPoint. 940 - > So find those little, maybe even AI solutions when you say it's a 941 - > rather smaller company and they haven't started. 942 - > This makes a big difference already. 943 - > So if you have a smart interface, AI-based interface in 944 - > your daily routine that you can easily integrate, I think the 945 - > most important thing, and this is again highly correlating with 946 - > the adoption piece, it has to be easy accessible and easily 947 - > usable in your daily workflow in Excel and in PowerPoint.

948 - > And also, what I do a lot is um when it comes to big uh PDFs, 949 - > big market studies, whatever, I transform them into podcasts and 950 - > uh listen to them because it's easier to consume and we are 951 - > getting less used to read a lot of texts all the time and like 952 - > focus our concentration on long texts is not always very easy. 953 - > So it helps as well to um uh listen to them. 954 - > Uh something like that is already helping a lot. 955 - > SPEAKER_01: Okay, very good, Tanja.

956 - > So we are already at the end of our conversation. 957 - > I have a couple of rapid fire questions left for you. 958 - > Um so consulting is still uh a tough sport, and you even have a 959 - > family on top with kids. 960 - > How do you keep body and mind fit and sharp?

961 - > SPEAKER_00: I defend Sundays. 962 - > I think this is super relevant for me as a working mom, that 963 - > Sunday is non-negotiable uh for the for the kids. 964 - > And if it's not the Sunday, because I have to go to a um 965 - > client meeting already Sunday evening, then it's the Saturday. 966 - > I um do uh breastwork that helps me personally, uh no matter 967 - > where.

968 - > Uh ideally when no one recognizes, standing in a queue, 969 - > for example, so no need to get half an hour in your calendar 970 - > won't work. 971 - > I don't have that half an hour. 972 - > SPEAKER_01: Yeah. 973 - > And uh, can you give us one one simple routine that everybody 974 - > could adapt right away?

975 - > SPEAKER_00: If you if you stand in the in the queue and wait at 976 - > DM for paying your your stuff, you breathe in four seconds, you 977 - > hold it four seconds, you breathe out four seconds. 978 - > You do this three times, and it gives you a bit of a centering 979 - > and a bit of a relaxation. 980 - > And I really believe for me personally, it works. 981 - > It sounds a bit um um esoteric, but uh I really get uh fresh 982 - > power from that.

983 - > And you can wire your brain to use that little moment for 984 - > relaxation. 985 - > And maybe one thing which I also do, and I can only um recommend 986 - > before I dial in in a call, I take 10 seconds. 987 - > What is my role in the call? 988 - > What do I want to get out of the call?

989 - > What points do I want to bring up in that call? 990 - > So, what is the content and what is the next best action? 991 - > So, my role, what do I expect? 992 - > What is the content and what is the next best action?

993 - > Doesn't necessarily take 10 seconds, gives you the chance to 994 - > be sharp in that meeting and not stumble from one meeting to the 995 - > next and forget what you actually want and who you are 996 - > and how many. 997 - > SPEAKER_01: Very cool, and that is all actionable. 998 - > I love it. 999 - > Thanks a lot again.

1000 - > Um do you have a favorite book right now? 1001 - > It doesn't have to be a business book, it can. 1002 - > SPEAKER_00: A favorite book, I love you are the placebo. 1003 - > It's a very nice book.

1004 - > And I also read Um Why We Sleep at the Moment from Walker, which 1005 - > is also a nice book too. 1006 - > I like those neuroscience kind of stuff, and those two are um 1007 - > good to read, I would say. 1008 - > SPEAKER_01: Why the first one? 1009 - > SPEAKER_00: You are the placebo gives you um resilience.

1010 - > SPEAKER_01: Okay, very good. 1011 - > We put it into the show notes. 1012 - > Um, what is the biggest challenge of being a consulting 1013 - > leader that nobody talks about, Tanya? 1014 - > SPEAKER_00: Too little sleep.

1015 - > So we manage somehow, but I think the uh this is why I'm 1016 - > reading. 1017 - > Why are we sleeping? 1018 - > Why do we sleep? 1019 - > Um from Walker, because uh this is uh really a challenge at the 1020 - > moment.

1021 - > SPEAKER_01: Yeah, yeah, I can imagine uh on top being your 1022 - > mom, um that that is definitely not making it easier. 1023 - > SPEAKER_00: Yeah. 1024 - > SPEAKER_01: Um who should be um our next podcast guest for our 1025 - > leaders and consulting show, who should we try to win? 1026 - > SPEAKER_00: You should try to win uh Remy from Singulier.

1027 - > Remy CEO of Singulier. 1028 - > SPEAKER_01: I don't even know Singulier. 1029 - > What is it? 1030 - > SPEAKER_00: It's a consulting company.

1031 - > SPEAKER_01: Okay. 1032 - > It sounds French. 1033 - > SPEAKER_00: Yes, it's French. 1034 - > They have an office in Munich and an office in the UK, and um 1035 - > I think it's interesting to speak with him.

1036 - > SPEAKER_01: And why? 1037 - > SPEAKER_00: Because it's an interesting character, and he's 1038 - > bringing in that French perspective, which you tackled 1039 - > as well from an international point of view. 1040 - > And I was thinking of Finn when you asked that question, and it 1041 - > he could bring in a bit more of how they do it in France and 1042 - > what we can maybe learn as well in the German setup. 1043 - > SPEAKER_01: Um that would be interesting.

1044 - > Because I'm pretty sure uh from Germany, looking at France, if 1045 - > you don't have touch points, you see them differently than they 1046 - > are in fact. 1047 - > Yes. 1048 - > And you saying they're leading something, I don't think many 1049 - > Germans are thinking France is leading in AI or in adoption. 1050 - > So that could be super interesting.

1051 - > I try to um to get them on the show. 1052 - > Um, and now it's time for you to tell us what you need from us. 1053 - > So a lot of consulting leaders, partners MDs of small and big 1054 - > companies listening. 1055 - > Is there anything we can help you with, Anya?

1056 - > SPEAKER_00: No, I just uh for me it's always important to embrace 1057 - > AI that I have people that understand that AI is the 1058 - > future, it won't get away anymore. 1059 - > We have to control it, embrace it, and make the best out of it, 1060 - > no matter if it's with an external service provider or 1061 - > not. 1062 - > But we should not fall back and ignore it. 1063 - > So every person that is open to AI tries it out, experiments 1064 - > with it.

1065 - > This is kind of my mission to uh giving an impulse and influence 1066 - > them to try it out. 1067 - > SPEAKER_01: And how could people best get in touch with you? 1068 - > SPEAKER_00: Through LinkedIn. 1069 - > SPEAKER_01: Okay, we also put your LinkedIn profile into the 1070 - > show notes.

1071 - > So thanks a lot for taking us on this journey. 1072 - > I I really like the the the whole angle that you you showed 1073 - > us and that um, well, it's not just about having the solution. 1074 - > Solution is a little part of implementing the whole the whole 1075 - > package to make a client successful. 1076 - > So thanks a lot, Dania.

1077 - > SPEAKER_00: Thank you, Sami, was a pleasure. 1078 - > SPEAKER_01: Thank you for listening to the Leaders in 1079 - > Consulting podcast. 1080 - > To learn from and go with fellow consulting leaders in person, 1081 - > apply to join our monthly confidential forums and peer 1082 - > coaching in your city. 1083 - > Visit leadersinconsulting.

com for details. 1084 - > See you soon.