The Future Of Less Work
00:00:00 · Nirit Cohen, Laurent Aufils
[00:00:00] S0: Welcome to The Future Of Less Work where we explore how work is transforming one conversation at a time. I'm Nirit Cohen. In today's episode, we'll look inside a large organization at what AI transformation really looks like. My guest today has a job that owns both sides of the HR... The the AI transformation. HR too, but the AI transformation. At the same time, redesigning the work through AI, but also taking care of the people who have to live through that change. And here's what makes it really interesting. As a person who owns scaling AI, he also insists we must protect some of the thinking from AI. Laurent Aufils is the chief AI and future work officer at The Orange Group, one of the world's largest telecom companies. What caught my attention was the tension inside Laurent's job. On the one hand, he's responsible for helping Orange transform with AI, but he also argues that the next competitive advantage won't be using AI everywhere, but also knowing when not to use it. And that the very effort of thinking is what builds judgment. Laurent, welcome to Future Of Less Work. [00:01:09] S1: Thanks, Nirit. I'm very glad to be with you today and spend some time on these exciting topics.
00:01:09 · Laurent Aufils, Nirit Cohen
[00:01:09] S1: Thanks, Nirit. I'm very glad to be with you today and spend some time on these exciting topics. [00:01:15] S0: It's absolutely exciting. I mean, anybody that has a feature of work in their title for me is exciting, but I'm biased that way. I actually wanna start with this two mandates inside your job job title. You know, the the AI road map is moving very fast, and we know that the people and organizational aspect of it, it doesn't move or can't even absorb that change so fast. So how do you manage that tension?
00:01:42 · Laurent Aufils
[00:01:42] S1: Well, that's actually what is at the core of my job today because, you know, transforming such a large organization as Orange requires to engage people into this transformation and as we are a tech company, the bias is usually to start with the tool, with the tech part, when actually the success lies more into how it's actually concretely sits in the jobs, the workflows, in the processes and how people engage into this transformation. So, you know, there is one rhythm for technology, it goes very very very fast and there are several rhythm and paces of adoption which are different across the organization, across people, because some people are more excited about it, some people are anxious about AI and the risk it might occur, you know, bring to their jobs. So that's really what is at the core of what I'm doing to make sure that we don't leave people on the side, that we actually have the right change management tools and also activities and actions to support everyone to go with this transformation and the transformation most of the time is not just adopting a tool and using a tool, but it goes much much deeper into the work processes, and that's why future of work is is really the, I think, the the key point to address this.
00:03:25 · Nirit Cohen
[00:03:25] S0: Yeah. So so actually, that's that's a good one. Let's talk about that, the process part. Right? Because I think in the first, you know, year or two, it was about getting people comfortable and familiar with the tools, and and some of it still is. But I think we understand now that we're actually redesigning the workflows. And we also see a lot of research that talks about organizational processes blocking the potential of what people can actually do. Right? I mean, you can go beyond beyond the boundaries of your regular job. And I've been wondering about how large, very large. I mean, Orange is a very large organization. How you can scale that?
00:04:10 · Laurent Aufils
[00:04:10] S1: Well, the the the scaling effect is is really at the, you know, at the heart of the strategy because first, I'm convinced that we are at a new stage when it comes to AI and AI adoption. The first stage was basically, you know, getting people familiar with AI, using it in their day to day job, but in some ways it was the augmented approach, you know, how, you know, AI can help you, you know, draft Yeah. Documents You know, do the same but faster. Yeah. And now we are reaching a new maturity level, at least at Orange at Orange Group is what is the next step? If we want AI to really change the way we do things, to really deliver the value through, you know, improving processes, but potentially doing other things, new things better, differently. We also have to think about now that people are familiar with AI, how do we make sure that we have the right approach in terms of value? Because again, sometimes people use AI to translate text when you have all the tools that are much more frugal and consuming less token and things like that, you know, can do the same job or even better job. And you also have this economic aspect which is very much present now, which is like, okay, if we really want to scale up the usage of AI and make it happen in the day to day job of everyone, not only at the surface, but really in their core activities, then we know that the consumption of tokens, we know that it will have an overall cost that will be much higher than what we have been experiencing so far, especially if the unit cost of tokens is going to increase. So that's really what is very, very important is how do we address this new maturity phase? How do we scale up with AI usage? And it means that there are I think two ways to cut a long story short. One is to just approach this through the tools and to have the injunctions to have like basically a top down order to use AI. Okay? But that's not the approach that we're taking. The other approach is really to decide where we feel that AI will deliver the most value because we cannot change everything at the same time, you know, in a short period of time. Yeah. And based on this, look at the work processes.
00:07:02 · Laurent Aufils, Nirit Cohen
So we really go down to the processes to analyze these processes and also to decide very quickly where actually AI, energetic AI most likely will deliver the value that we are expecting, but also what, you know, the human value will actually deliver in terms of differentiation and in terms of also engagement of our people, because it's very important to build a long term future and to have a sustainable model for the company. [00:07:36] S0: Yeah. I think you you touched on the heart of it, which I think is probably one of the most complicated things we're dealing with. Right? We take... We used to package work into jobs, and it was very clear we put people in jobs and we knew how to match them. Right? Now you're taking pieces of jobs and saying, AI can do this. Now you've introduced a really interesting economic question. Right? It's not just about AI can do this, but is that the most economic way to do it? Right? Maybe another tool that isn't AI and doesn't cost as much can do this. Right? But but also we have to start asking what is uniquely human in the job? And sometimes we've we've done the human things underneath the surface, you know, as we were doing the mechanics of it. But... So for example, you do the relationship building as you talk to customers. If you change talking to customers to tools, you lose the relationship building. Right? So how do you go about really understanding those nuances of what should humans retain, not just for training purposes, but also for the value of the work?