You look at the word AI, we can't do anything without talking about AI, so we're going to start with it right in the title to be front and center on that.But what is interesting that I'm sure you're all reading in the paper and seeing is that AI isn't isn't working. It is everywhere, but the results are definitely not showing up in the ways we would like to see them showing up. And whether it's MIT or IDC or Gartner, the numbers in terms of what's working and not continue to show that we're not getting the results we'd like to see for the investment we're making. What's interesting is when you double click on that, the messages are really that the challenges are around adoption. And so what I wanna talk about today is exactly how you drive and flip the script in terms of getting the results we're trying to get from the investment we're making.

I've been in this space for a long, long time. You heard from my introduction that I've been around the change, space. I led change management globally for Coca Cola Enterprises. And when I think about people, process, and technology, my litmus test is how do people's jobs change? So you look at technology, you wanna change the way you run your business. At the end of the day, how do people's jobs change or what gets you to the outcomes you're aiming for? And the thing is people now are not just people. People are agents and people are AI. And so that future of work I started the future of work community when I worked at SAP.

The future of work is not just where we work, but it's who's doing that work and how is it getting done. And the level of change that we're going through right now is unprecedented for sure. I created this equation probably almost twenty years ago now, looking at how to simplify all of the discipline of change management. When I first started working in this space, it wasn't a discipline. It wasn't something you could go to school for. Now there's many books and many classes and many accreditations, but I boil it down to expectations and accountability. If we're clear on what's going to happen and everyone knows where to step in and step up from the leadership on down to the individual contributor, then we can get to an outcome that we might not necessarily love or feel good about, but we can manage through that change deliberately and intentionally.

If there's a miss in expectation or surprises, then there's a miss in terms of what the outcomes are, and that's really where the stress and the tension and the friction comes from. So that old playbook in many of the the text scenarios that we know of where we have an expectation and we cascade on down is really becoming much more circular. I was at a a Gartner conference, two of them actually in December, where we were talking about how change typically had time, months, hours, days, weeks, years even to look at what can we expect to have happen. And now it has minutes, days, and hours, and at most weeks, and the volume of change is coming from the top and the bottom all at the same time. And so the challenge we have is really how do we engage and drive a different level of engagement and execution. So what is our point of view on this? I work for Celonis. I share that I work for SAP for a long, long time.

So thinking about systems of record and systems of engagement, they're all the systems that we play with. Celonis allows you to look at all of those systems to see what's that work really look like. You know, if you were to ask everyone how they think they do their job or if you were to say how you think the people who work for you do their job, you probably would be partially right, but not completely right. And what we do is create a picture, and you can see on the right, it looks kinda like the London subway map or any city subway map, that brings together all the information in systems of record and systems of engagement and shows you a map of what really happens. And at the top left hand corner, you can see it shows you the level of adherence to the standard process that you'd like to have. And like a subway station, you can drill down into each station and look at what's going on there, or you can look enterprise wide.

I'm not gonna give you a demo of the product because that's not the point of today. But my point being, when you think about change, we can really see now what's happening. We can compare what was happening to what's going to be happening. We can compare what's happening with machines and without, with AI and without. And so what now is with AI, we've gotta actually look at saying, what's the context? How do we get that work done? How do we manage through that change? Where do we put it? And how does it work with people and with the other systems that we have? So when I think about it, if I look at what AI is trying to achieve, which is getting to filling in those blind spots that people have had, we need that same context.

So the same context that comes with a human who can explain, here's what we did and why we did it, How do we get that same picture to fill in those blind spots that right now we know hallucinations show up for AI, just like we don't really know what everyone around us does all day long?

And so when I think about that context is how do we try to give the information and successfully give the information to AI to say, how do things fit together? How do systems and people and jobs and roles all come together? So when you can look at and pick, what might I wanna solve for? How do I solve for it? How do I see that? Because at the end of the day, frankly, it all comes down to people. And those people now might be a combination of man plus machine and AI, but that comes down to the people in the execution. So I wanna come back to where we started to say, what can you do now to really change how transformation is occurring and how adoption can grow? And And I'm gonna give a few customer examples as I move through this as well.

I've said for a long, long time that you really need to look at building shared ownership. A gentleman I worked for a long time ago used the phrase, people don't hurt what they own. So you protect your friends, you protect your job, you protect your space, you protect your customers. So what we wanna do now is not just say from top down, we wanna see results. We really wanna have everyone have some skin in the game and some opportunity to contribute and see how their ideas can come to life. So the democratization of AI is really where we start to see people who are closest to a process, closest to work, knowing the things that can change, and being able to identify and shape and form how that can shift.

So when I look at friction heavy workflows, start in the places that we know we have big problems. That's not really a mystery of where things are difficult, but giving people the chance to have the information and the specifics to build the trust between leadership and the worker really starts to allow for everyone to have the transparency you need to drive a different kind of change.

An example I'll give here, Constantino is a customer that we work with who's in building materials, and they had a lot of challenges around credit blocks. They had a lot of them. And so you can imagine credit blocks are a smart thing to have, but they also create a a stop or a pause in a process. And in reality, you don't need to do a credit check on every customer and every supplier vendor relationship all the time. So when they looked at putting in AI, they started with identifying what's the logic, what's the rationale, what's the reasoning behind having it. So as AI would identify, you could skip a credit block here. That reasoning was provided.

So if you look at the Gartner structure again of going from assisted to augmented to autonomous AI, they could go from being assisted and and augmented. And then really now what they do is people are only working on the exceptions. So people have the time to work with vendors to really say, why are we doing this? And that customer supplier vendor relationship is really where their time is being spent, not on just getting the right work done. So, again, when I look at AI, then you wanna look at rewarding experimentation and evolving. So, you know, look at look at successes and and celebrate them, but look at failures and celebrate them as well, and then evolve and change and shift.

And so what you wanna get to then is also saying, how do we measure adoption, not just deployment? Not we've got so many and we're looking at volume, but we've got so many and here's the traction that we're receiving. Here's the outcomes that we're getting to. Now I come back to change again, though, and the the multidirectional change is really part of what I alluded to earlier when you think of the cascading that we used to go through, and now it's coming like popcorn from all directions, is really how do you create localized change strategies.

So at a group level, at a team level, at a geography or or location level, How do you understand the expectations of how can we scale at a enterprise wide level, but how at an individual or a team level can we also manage and share and discuss? So the challenge I place back to a lot of the CIOs that we were speaking with in December and again a few weeks ago was really how do you start to give not only the access to everybody, but involve change people earlier, sooner, faster? And how do you also use AI to reinforce good behavior as people are learning new things? How do you tap them on the shoulder and use AI and workflow to say, hey, you're doing it right, or, hey, you made a mistake, and here's how you could be doing that differently? So, really, at the end of the day, and I wanna give a chance for q and a, is that AI transformation succeeds when organizations treat employees as architects of change and not recipients. And for all of us, we've got the opportunity to both contribute as well as lead how that happens.

So I'm gonna stop chatting because I know I've not got a lot of time and turn it back to the organizers to answer all the questions.