From Pilots to Programs: Making AI Stick (Ivo Strohhammer)

Welcome back for another episode of What's the BUZZ? where leaders share how they have turned AI hype into business outcomes. Today, we'll talk about how to go from pilots to programs and to make AI stick, and who better to talk about it than Ivo who's doing that and who's been doing that for a long time. Ivo, thank you so much for joining.

Thanks for having me, Andreas. It's a pleasure. Fantastic. Ivo.

When I published the AI Leadership Handbook in the fall of 2024, you were the first person to, to comment on LinkedIn and share, say, Hey, I see so many great examples there. I've been I've been voraciously reading it and so many parallels to what I've seen in from that post. Actually really good connection and friendship I would say has evolved over the last year and a half roughly. So I'm super excited to, to have you on and to learn more about what you are seeing, what you have seen.

But maybe for those of our listeners and you who are not quite familiar with you yet, maybe share a little bit about who you are and what you. Of course. Thanks. So yeah, my name is er as you can hear Swiss native living in Switzerland working for Siemens driving the AI adoption in that regard.

We started two and a half years ago with programs, and that was basically, we were desperately looking for some help or some guidance because it was an uncharted territory for everyone. So then. Your book came around and that was really a helper for us then to, to make the next steps. And now meanwhile doing this for years I decided to move on to to, to one of the next steps because yeah, let's face it, the big corporations, they have.

Addressed it. They have solved it to a certain degree. Now, the next step is really, I want to bring that to the smaller enterprises, to the community. So we are building an ecosystem here, inuk, locally in souk for souk to really drive this adoption.

To really make it, yeah, more from hype to value at the end of the day. I love it. I think it's so important. I'm seeing similar things here in, in the US as well.

And when I visited you in souk just south of Zurich in, in the summer, it was amazing to, to see the vibrant ecosystem that already exists there across so many different industries. Certainly financial services being one of the prominent ones but many others as well. So excited to see what you're building up there. And obviously know that it's a it's very supportive area and community.

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Good old fashion on What's the BUZZ?, should we play a little game to kick things off? Yes. Yes.

Wonderful. So let's do this. So when I hit the buzzer, the wheels will start spinning and when they'll stop, you see a sentence. I'd love for you to answer with the first thing that comes to mind and why, in your own words.

You have 60 seconds for your answer to make it a little more interesting. So are you ready for What's the BUZZ? Yes. So here we go.

If AI were a color, what would it be and why? 60 seconds on the clock. Here we go. It's mean when everything is colored blue in the background, right?

The first thing that comes to my mind is. Do you know that meme were shit, shit out. And now with AI, everything is a unicorn and rainbowy and shiny and colorful. So that's, I'm thinking about in, in, in rainbows at the moment, right?

So having it multicolored everything now becomes more colorful. AI can be everything. AI can at the moment. It helps us in bots, in, on, on a screen, in variables, in machines, everything, right?

So it's it's colors rather than a color. I like that. And sticking with the rainbow theme, ideally at the end of the rainbow, there's a big part of gold that everybody is hoping for. But let's see.

I haven't found the pot of gold yet. At least not at the end of the rainbow. Has someone. Yes.

Sometimes, that's maybe also the things that that organizations are chasing here. The the proverbial rainbow. Alright. Awesome.

I love your answer. We said well within time and definitely very colorful, but you still need to know what you're actually building for it to have value. Looking at the last two years in, in AI, last year we obviously talked a lot about agentic AI. The year before it was generative AI.

And even before that we talked about machine learning. But what I feel has stayed the same in all of this, is this balance, sometimes imbalance between hype. On one hand, we need to do AI. AI is awesome.

We can do all these things with all the doom and gloom and the big promises. And on the other hand, what is the actual impact? So I'm curious, what have you seen there? Where are the challenges and what works when we compare hype versus impact?

Yeah. And I guess that's similar for everyone when we talk to leaders about a new technology. And that has been the same in the last transformations probably as well. Everyone wants to be the first one.

Everyone wants to have this magic box where you just put that red button and it solves all your problems. Exactly. So the big expectations in regards of impact, but most of the time you forget the people. Why should we do something?

How should we do something? And the first thing is at least in our ecosystem, it was we were looking desperately for use cases. So where do you find these use cases? Just by asking someone, what's your use case?

What can you do? Without knowing that they don't really have a clue about AI as well. It's then it's just a random list of ideas. Top down, we see that, and as especially that, that was growing in the last year, especially of, we need productivity, we need impact with AI.

But on the general employee level, we are still trying to find our ways. So what can I do with it? How can it help me from an everyday perspective into a process perspective, into new products? And that's a transformation, which we really had to be careful with in regards of change management and that whole transformation support.

So we, we started. With a program called AI by Design, which is basically nothing less than a huge community to foster enablement, to create awareness in there, but also having all the experts from a legal perspective, from a compliance perspective, from cybersecurity and sustainability to answer certain questions together, to get a better understanding of what can we do with the new technology? Where does it really help us? We tend.

Just sprinkle new technologies, AI over our process and hope that it gets us better results, but in certain areas, we have to drastically rethink how this process should look like, right? To really harvest this big gains. So many great parallels to what I've experienced in many projects as, as well especially in larger organizations, right? It's the, we need to go on a fishing expedition, we need to collect use cases.

One of my team members back then called it Innovation Theater. So you say, give me your best use cases. And like you said, most of the employees don't even know what to look for. What can the technology actually do?

What are the opportunities, what are the challenges? What are the limitations? So they come with, or they pour their heart out with the things that they've always wanted to have fixed. But most of the things are not even solvable with AI.

The other part isn't solvable with the data you have. And then you end up with very few that, that actually make a meaningful impact. And to your point on the other hand the community is helpful to drive that awareness so you can have these conversations about where does it add value, what should we actually do? Or even worse, right?

So it's on one side. We have to change our mindset that we are faster, that we experience experiment a lot, but meanwhile, our experiments become the next feature of the next AI solution, right? Then that challenge comes in, why do we spend money for experiments when that becomes the feature in half a year, right? So it's that.

We want speed, but not too fast because we don't want to reinvent something which comes for free then somewhere soon. Reminds me of the Chat with your PDF example, right? The early examples of startups and companies building something that then everybody else built in into their product natively. You talked about this bottom up approach, you talked about building a community through AI by design, it sounds like it's working really well.

But I could imagine, again, knowing how large enterprises work, that there are probably also some challenges in the approach. So in, in hindsight, looking back looking back at two and a half years of your journey, what would you say were some of the challenges that you didn't expect going into this, and how would you do that differently nowadays? So the best approach was, we just did, right? So it's something new.

No one really had the answers. There were tons of books out, but no real handbook how we should approach this transformation. So we just started exactly. One year later that came around the corner and that really helped.

So what we did, we started with a flexible, agile approach. Gathering a community together, gathering the champions network that we know who is working on AI. And then really an empowered community. An open community where we ask for feedback, where we gave feedback, early access to tools so that we can learn and on a yearly basis, we.

Reinvented our transformation program means all the core team with members from all different functions came together for a day and we said, okay, how should the next year look? Because in these fast turning world, it's impossible to really drive something or define something for the next five years, right? So one year cycles at the beginning it was all fine and great and yeah, awesome community. And after one and a half years, the pressure for impact was bigger and bigger, and it was all about productivity, right?

And we know how hard it is from a, from an idea to a productive use case. All the struggles, experiments look fancy and good, but the deployment is still harder than we think. And the pressure was then really high. So that there, there were multiple programs coming up, top down programs bottom up programs, different regions were running with different speeds.

So it's keeping this whole hurt together, fostering the transparency of. What are we doing? And that mix between let people do, let people experiment, but also trying to standardize certain topics to not reinvent the wheel over and over again to finding this balance. I think this is something that probably many of you in the audience either have experienced as well or are likely to experience soon too, depending on your tenure and the role, I've seen this myself, right?

You get about probably a year to, to build things up. And to establish processes and governance and your community. But then probably at the year or 18 months, mark, the question comes, what have you delivered? Yeah.

And then it's already super important. If you're early on, on that journey from the very beginning, focus on business impact, high value scenarios. Yes, there have to be some quick wins so you can show some momentum and you build some momentum. But I think also you need to be very clear about the resources you have, the resources you need, the budgets to be able to deliver something meaningful by the one or one and a half year mark so you can continue.

With with that momentum and the continue on, on, on that trajectory. Otherwise, like you said expectations are high not only to the AI leader, but those who are asking you, what are you doing? What are you delivering? They have even higher expectations raised to, to towards them as well.

So not always easy to keep them in balance. So you said it's about building the community. It's about sharing what's possible. It's also about hearing and listening.

What are they doing with it? How well is it working? What else was a good approach there to get people en engaged? There's so much out there in terms of content, in terms of in, in information.

At least by now. How do you get people to. To actually use it and do something with it and not just consume and absorb the information. Say, okay, fine, now go about my business the way I always have.

And there were two components which were really crucial doing that. We have within AI design, we have a work stream called People Enablement. And that has two directions. On one side it's.

Telling the people why we need AI, use it, giving them some guidance, how to use it and in a safe way. And the other thing is early access to playgrounds, to secure tools to experience it. You can read a lot about it. You can watch readers about it, but you have to learn, you have to test it, to, to experience that on your own, and that sparks then the next level of idea, okay, when I can drive, when I can write an email, how good is it with my documents when I can then write a document?

How good is it when I combine this and this, but that's the only way how we get to a certain quality level of use cases. We and we have a, my learning platform, right? If you type in AI at the moment, it gives you 6,000 results. So that obvious Netflix problem that you search one hour for the right training and then you don't have the time for the training.

So it's, we were curating lists of preferred trainings. Our cohort, our community have that we call the role AI champions. It comes with four levels. So first level is just be part of the community, get access to the community material, be part of it, have access to it, to everything which is around there, and have the chance to communicate.

Second level is theoretical knowledge, entry level trainings to really get people onboarded. So we said that these are our proposals of trainings, but if you have some, if you have done something else, come to us. We don't wanna spend ridiculous time of doing it over and over again, but a certain baseline of knowledge, theoretically about AI, but also about fostering AI use cases. So the ideation point, the third level of AI champions send a practical application.

So that's okay. I either. Worked on use cases, or I worked on ideation workshops, or I did trainings. I facilitated trainings to grow that AI champion score.

And the fourth level, that's basically in connection with a p and o talent community called the AI captain. That's a single point of contact for a division business unit reach. So that means we have a pyramid. With then sub communities in the us, sub communities in Germany, sub-community for a particular business unit.

All really hands-on experience as fast as possible that we can get the word out, that we can share the experiences and build on each other. I really like the idea of the tiered or pyramid system. And also what you shared, like basic access for anyone, anybody can read and can consume the information, but then based on the additional qualifications and skills you gain, you can advance, to, to, to us become a trainer for others. I think that's great.

Especially the part about getting hands-on. Typically when I give trainings I'm always reminded of the bell curve, right? You have some that are super advanced. You have the vast majority somewhere in the middle saying, yeah, I've done a few things.

I know how to write an email with AI. You don't need to tell me about that. But. There are other things about prompting advanced techniques.

I didn't know it, it could do that. I didn't know it could compare documents or something like that. Vast majority. And then towards the end of the bell curve, it's, oh I'm not really sure how to use this.

What happens with my data? What kind of data I can put in there? And so on. So I think it's really important to meet people where they are, but also give them an opportunity to grow in their learning journey.

And Andreas to give them a secure opportunity to grow because people are using it. It doesn't matter if you offer a secure copilot or not. People are using tools and as soon as we don't offer a secure way to the technology, they use it what they find out on the internet, and we all know how secure that can be. So that's then that governance nightmare of.

The enthusiasts, early adopters, they're running with the technology. And the technology is so approachable for everyone that we have to be fast as well as a company or as a larger network to offer a secure and a safe version to that. I think that's super important that you. That you need to do something, whether you want to or not, because your team members, your employees, they're going to do it.

They're going to find ways to use it. They're not being bogged down by rules or by clamping down access to most of the tools. They're saying we'll take you, wait and see approach. They're running with it, like you said.

So it's inevitable in that sense. As, as much as I don't like that the narrative of it's here and it's inevitable, but you see this in the usage statistics. You see this in, in, in many reports as well. Now there's one point I wanted to come back to.

You said right now a lot of focus is on, on productivity and I see that too. It's the. How do we get people off the public version of chat g PT onto something that is governed, something that we either have licensed or we have more control over Large enterprises. Lots of them host their own LLM playground or model garden or some way to experiment in a sandbox.

But a lot of the focus I see right now is on personal productivity. When I wrote the book, there was a lot more focus on operational efficiencies, strategic differentiation these kinds of aspects. How do we use AI in our business function to do more with less, to get better insights, to engage differently with our customers. But I feel nowadays, so much of the conversation is about how do I write emails, reports, how do I summarize stuff, transcribe, meeting minutes and what have you.

Where are you seeing this right now and where are you seeing this going in 2026? For example, we segregated AI into three buckets. One is everyday AI, so that's a personal use of everyone. The second one is process AI.

So the big processes end to end standardized. And the third one is new AI. In 2025, the focus was on that whole process AI part. How do we really make it better, faster productivity on that level?

Because let's face it, the personal productivity is really a difficult thing to measure. How are you as an employee awarded today when you are faster with your work? Normally with, okay, let's do this as well. Or more work.

More work. When I found as manager that you're more efficient, I give you more work because you have more time ex exactly right. So people are using it, but don't really tell it. Yes.

How do you measure that adoption? But that's, for me personally, I think the key driver for the whole success part. Because on the process AI side, as I mentioned before, sprinkling AI over a customer service process. Does it really make that big leap?

No. It gives us a couple percent point of productivity. We have to drastically rethink how customer service could look like Now with a new technology on board to then completely reshape these processes. It's a big international company ready for that.

From a risk awareness perspective, probably not. But the personal productivity, that's the key for everything. If people are motivated, if people are driven by that intrinsic drive to to become something. And if we then are able to.

Avoid these people as well with Well done. Spend your day at the beach if you are finished your work at Friday morning. For example. Because that's, then that's fostering everything else, that's fostering better ideas for the processes that keeps us open for the creativity, for new products, for new services.

So I hope that shift goes a little bit more to the individual, to that personal usage, to that personal productivity. So now you've obviously seen this in and out in large enterprises. I know you, you you said you've been a consultant prior to that. So you've seen a lot.

How are you now bringing this to small, medium sized businesses, and what do they need to do? How do they need to think about this too? Not repeat the same mistakes that we've all made in, in, in larger enterprises, but benefit from those learnings in invest the limited capital and resources that they have effectively to get some good output. So what's the net for small, medium sized businesses?

Yeah, It's a great question because they are lagging behind. Timewise. So as a, that's a, that's the big advantage I have when I send an email to one of the tech companies with a Siemens email address. You get a meeting the next day if you're coming with your email address from your small or medium enterprise.

Yeah. Best case you get a sales rep. On a call in a couple of weeks. So what we said, especially now in our area, we said, okay, let's create a physical lab with open doors for everyone.

And we work on a triple A framework. We want to create awareness, we want to foster ability, and we want to really drive adoption of use cases. Know what we or the leaders from smaller companies know what they can do with the technology and also more important what they can't do because it's not that magic box so far. The second thing, then empowering the workforce to use it right over and over again.

And the third one is really harvesting than the productivity gains, right? And smaller companies. They don't have the luxury of having a known IT department, AI specialists, and so on. So as a community driven approach, we want and also help each other.

Supporting with standards, supporting with industry wide solutions, supporting with out of the box trainings that we can make the best out of that. Limited time, limited capacity, limited budgets, not reinventing approaches over and over again. But learning from each other. So awareness, what's AI?

How does it work? What do you need to know? Ability, being able to build it and then actually build it. Apply it.

Yes. I think that's, that. That's awesome. I love things in threes awareness, ability, application.

Makes perfect sense to me. Now, vo we're coming close to the end of the show and I was wondering if you can summarize the key three takeaways for our audience today when it comes to moving from pilots to programs and how to make AI stick. First of all, don't forget your employees. That's the most powerful resource we have, and it's the biggest democratization of technology and capabilities we have ever seen, right?

So use their employees, train them, teach them, give them the possibilities. Second, find a way between creating productivity. Also fostering that experimentation. Let's face it, experimentation will create a lot of similar use cases, but that's needed to learn, right?

So don't just focus on the high productive, use productivity use cases, find a balance of between. Let them do something probably in parallel, and then find the right way to standardize. And the third one, keep an open eye, right? It's so fast turning.

We don't know what's tomorrow, what's in a month, what's in a year. So it's, I'm far away of defining strategies right now. It's really having that agile mindset of being open to whatever comes, test everything. If it sticks fine, roll it out, right?

If not, fail fast, move on. I love that. I think that's so important when things are moving so quickly, but there's so much already there to learn about, to experiment with and to apply. So Ivo, thank you so much for sharing your experience and your expertise with us.

It was great having you on the show finally. Thanks for all your support. Welcome. And best of luck with the new Endeavor and the new Venture.

Thank you. It was a pleasure. And yeah, let's stay tuned and let's continue that great exchange.