Lightly edited for readability. Speaker 1: So welcome to this edition of The Business of Marketing, recorded live here at the Cannes Lions 2026. I'm delighted to welcome Ali Alkhafaji, who is the chief executive officer of Apply Digital. Welcome to Business of Marketing.
Speaker 2: Thank you. Thank you so much for having me. It's a pleasure.
Speaker 1: So look, this is gonna be a... I have a sense this is gonna be a conversation about sort of what AI does to the service business in our, in our sector, once it stops being a pitch deck topic, and you really start to kind of get into the weeds of delivery, pricing, talent, client expectations. You- you've, you've done both sides. You've built and sold digital services companies. You're building AI at enterprise scale inside Omnicom, and now you've kind of got the job of defining kind of what's next at Apply. I feel this is gonna be a good conversation. So let's get into it. Let's talk first of all about the shift from digital services to AI-first services. You're running a large digital business. You've, you- you- you've kind of really understand kind of AI at scale. What does an AI-first services company look like in practice?
Speaker 2: Yeah, so for us, we, we call ourselves a agentic customer experience partner, and for us, that's delivering content, marketing, data, commerce solutions, leveraging agentic AI workflows. And the, the opportunity is, uh, deliver what we always delivered, which is digital experiences, but to do it agentically. And for us to, to be able to do that at scale is the challenge, and no one has done this before. Uh, right now, we always- we feel like there's a few companies who are at our heels, uh, and then a lot of companies are just waiting to see where the market goes. But it's, it's very inspiring to be in the lead, but it's also terrifying because there's no blueprint, and you kind of have to pivot very, very frequently and, you know, try different things and see if they work.
Speaker 1: And for someone who's kind of been there and done that during the other kind of paradigm shifts we've gone through, what's the same and what's different?
Speaker 2: So the same about AI or about the service industry?
Speaker 1: I guess the sa- the same about sort of the transformation and disruption that we're going through and the process of that, and then what's different about this particular time in the world.
Speaker 2: Yeah. A- a lot of people look at AI as just another change and shift, which is true. You know, it's another shift. We've seen it with mobile. We've seen it with social. We've seen it with digital. Uh, but it is much more than that because AI does change the business of services. You know, historically, services have been about the people, and, you know, you have large, uh, service, uh, uh, SIs who are just staffing accounts with hundreds and hundreds of people, and their jobs are just billing time and material. For us, AI has changed that because now it's about outcomes, now it's about outputs. Now it's about delivering business value. So it really democratized the size because a company like Apply with 700 people, now we can go neck to neck with the very largest size because we can deliver those solutions at scale.
Speaker 1: And when you talk about sort of outcomes and outputs, kind of how do you, you know, see, uh, um, the, the, the kind of... What are the metrics? How do you measure that? You know, what, what tends to count as a sort of real impact in the room with a client?
Speaker 2: So business impact. At the end of the day, anything we measure will be rudimentary if the client doesn't buy into it and doesn't tie directly to their goals and their KPIs. The challenge is oftentimes the client doesn't have that information.
Speaker 1: Mm-hmm.
Speaker 2: So our challenge most- more often than not is sitting down and trying to figure out, how do we help truly move the business and focus on those KPIs? And once you have that, you c- you're able to really engineer the engagement around those KPIs.
Speaker 1: And where are you finding kind of clients are on that maturity curve, right, in terms of their ability to be able to kind of... 'Cause they probably wanna move to that model, but maybe there are some structural challenges or educative issues that they need to kind of, you know, resolve before they can.
Speaker 2: Yeah. So when you do this, you have to do it with fixed price, and fixed price is scary for both the agencies and the clients. Um, fortunately, you have to do it now, and when the client sees the, the value and the savings they get when we engage with them in a fixed price engagement that is outcome and output based, it's, it's crazy not to go with that approach.
Speaker 1: Give me some examples of, of, of kind of the way that you're kind of selling those outcomes in. What- You know, make it real for me.
Speaker 2: So for us, what we mean by ACX, uh, uh, agentic customer experience, is twofold. One is our content, commerce, marketing, data solutions are delivered with agentic workflows, meaning they're dynamic. They're not static. They're not-
Speaker 1: Mm-hmm
Speaker 2: ... uh, set into stone. And then the other side of the coin is our delivery teams are a matrix of humans and agents working together, where humans set the agenda, set the direction, and agents are able to scale and, and execute.
Speaker 1: You've been on the record talking about sort of the gap between kind of AI leaders and laggards, right? In your experience, kind of where does that gap really come from?
Speaker 2: Yeah, I mean, most people think it's a technology gap, but the, the reality is we all have access to the same models and the same solutions. Everybody can afford it now, despite the talking costs. Uh, but at the end of the day, it is governance. It is training, enablement, and governance. Those are the things that we've seen the laggards versus the leaders really, really start seeing themselves apart. You know, the ability to train your staff and enable them with the latest technology and then put the right governance around it, so you're not putting any- your brand at risk. Those are the areas that separate the, the leaders from the followers.
Speaker 1: And what are you doing as Apply, and what does the industry need to do more of to kind of close those specific gaps?
Speaker 2: So the first thing we've done is we built our own custom Apply training program for our people, and the content comes from our partners like Anthropic and Google, but the framework is ours, the framework that establishes the different streams, the- The different, uh, training tracks, uh, for different roles. And we built a badging system, and we expect everyone to achieve a certain level because that's the way we work now, and they need to be a, a part of it. Uh, that's been what we invested in. We've seen work re- really well. But obviously, like I said, there is no blueprint, so we're kinda testing and seeing and, and adapting from there.
Speaker 1: Yeah, it's really interesting, isn't it? 'Cause it goes back to my earlier point around just saying how, you know, you are genuinely inventing the future, right?
Speaker 2: Yeah.
Speaker 1: And again, mapping that back to kind of our industry, you know, along that way we've kind of come up with frameworks and standards and regulations. Kind of where are we kind of on that kind of maturity curve in terms of starting to kind of operate kind of in a consistent, coherent way in this regard?
Speaker 2: I think we're, we're still very early stage because of things like training and adoption being one, and there's, there's also a lot of hype around AI. And unfortunately, when not done right, AI does fall on his face. You know, what we see from brands that, that truly fail at delivering value are the brands who take AI and overlay it on top of their existing workflows. And the problem with that is you get 20, 30% efficiency savings, but if you count the cost of building it and then the usage cost, that's negligible. So a CFO takes a look at, at that and says, "What am I paying for?" But where we've seen success is when you take AI and reimagine your workflows. That's when that 20, 30% turns into 20, 30x.
Speaker 1: I'm a CMO or a CEO, and I'm listening to this kinda conversation right now, and I wanna move fast, right?
Speaker 2: Yeah.
Speaker 1: You know, what are the first few things that sort of I, I need to be thinking about and doing inside my company to make that happen?
Speaker 2: You need to start thinking about training, enablement, and governance. Those are the things that will almost become a, a rocket ship for AI adoption. Obviously, you have to have the tools. Obviously, you have to have the solution. Obviously, you have to really figure out what you're building and how it makes sense for your business. But at the end of the day, the separation between good companies that do this well and, and not is training, governance, and enablement.
Speaker 1: And what are you seeing in the companies that kinda get past that kind of initial phase, you know, and, and, and really start to kind of move the needle on a kinda day-to-day basis?
Speaker 2: Companies that invest in the right partners. So being able to sign up with the right partner that has done this before, leveraging the right tools, they can sit down and really work with you to help you achieve those goals. Because at the end of the day, this is yet another technology innovation and, and, and change that we have to adhere to. And yes, AI makes things simpler, but it also makes things unpredictable. So for us, working closely with our clients, that's exactly what we focus on.
Speaker 1: You've been quoted talking about sort of reliable agentic AI in the enterprise.
Speaker 2: Yeah.
Speaker 1: Where are people kind of getting it wrong right now?
Speaker 2: People are getting it wrong when they try to use AI as a accelerator for their existing work because they think of AI as ChatGPT.
Speaker 1: Hmm.
Speaker 2: You know, that's where a lot of the existing investment is happening, is you sign up for a chatbot, you adapt it internally, you, you build your bespoke solution, and now you're stuck with that. And you expect your employees to figure out where they fit in that world. So you have to focus on taking your business goals and reinventing your processes using AI, and I think that's, that's the goal.
Speaker 1: And is that sort of like what's happened to you? You know, you've been, you've been on this journey. Like, you know, once you've kind of, you know, uh, gone beyond the kind of the excitement and the kind of early kinda hype curve, I suppose, in a way, and got really deep into kind of the real kind of deployment and transformation, has, has your view changed?
Speaker 2: Uh, I think my view has gotten clearer.
Speaker 1: Yeah.
Speaker 2: Because before it was more, more, most of the time just a thesis, and now it's been validated. That's what we see the success versus the not so successful. Uh, historically, what I've done in the past with, you know, at Omnicom Group, when I, uh, built and, and deployed Omnai, which is our agentic platform, you know, right off the bat there was a lot of excitement around the platform. And just seeing that with, at the hands of 100,000-person organization was obviously very promising. But what I wanted to do was to find a way to take that level of solution and deploy it to every client, and this is what I'm doing at Apply.
Speaker 1: And what kind of work, you know, is best suited to agents today, and where do you still sort of want to have a kind of a, a human in the middle?
Speaker 2: So you always have to have a human in the middle because at the end of the day, AI is unpredictable. It is indeterministic. It is still uses a lot of probability and produces things that sometimes are wrong, unsafe, and very risky for your brand. So you always have to have a human in the middle to be able to authenticate and authorize these things from being published. But if you take- if you put that aside, I think that what AI does really well right now- nowadays is it helps you produce from concepting to ideation to streamlining the scale of content really, really well. So in the agency world, that is a huge advantage in terms of how to really activate and accelerate your work.
Speaker 1: What's your kind of response when someone, you know, is, is very, very keen to, you know, kind of deploy kind of reliable kind of AI, but the stakes are really high, you know, and the business just cannot afford kind of another, you know, innovation or science project?
Speaker 2: Well, it's, it's ... That's no longer the problem because, you know... I remember last year here at Cannes, the year before, AI was so, like-
Speaker 1: Yeah
Speaker 2: ... is it here? Is it, uh... Are we-
Speaker 1: Yeah
Speaker 2: ... are we doing this? I think that ship has sailed.
Speaker 1: Yeah.
Speaker 2: I think now it's absolutely clear. Uh, brands who are doing this and doing this right are seeing tremendous impact, both from raising the floor with efficiencies to raising the ceiling with potential outcomes that they've never been experienced before. So I think, you know, that argument, you know, you, you won't, you will not see that argument in many circles around here this time of year.
Speaker 1: Going back to the kind of the business model and the shift that you're sort of seeing and-
Speaker 2: Yep
Speaker 1: ... making, like, AI's making teams more productive, you know, massive increases in operational efficiency. Tell me more specifically about kind of what happens to the kind of the fundamental economics of a services business.
Speaker 2: So I think the, the fundamental economics in a services business rely-
Speaker 1: Yes
Speaker 2: ... on the people itself, right? We are in the people business. We don't have products. Our products are our people. So that's our separation, that is our moat, is when we have the talented people that we do, it gives us that advantage and then, and that, uh, really differentiation. With AI, 'cause AI amplifies everything. If you have a good team, AI will amplify them. They'll become a great team. If you have a bad team, AI will amplify them. They'll become a really bad team. So for us, it's how do we make sure that our talented people, mastery of their craft, have these tools under their belt to start doing these amazing things that we see AI do. And I think that's the shift in the service industry. Historically, it's been about bodies and, uh, and s- and, and seats. Now it's about the top talent that are masters of their craft because that's when you get the multiplier with AI.
Speaker 1: And, you know, when you're educating, working with the C-suite, obviously there's a whole raft of kind of different touchpoints in the shift that you're helping your, your clients to make. When you're having the conversation with the, the FD or the CFO, kind of how are you helping them get educated, I suppose, in the shift that their business is gonna make around the P&L, CapEx, OpEx, and the balance sheet?
Speaker 2: Honestly, what I've seen work really, really well is sharing our experience at Apply because we went through the same thing that we ask c- our, our clients to do, to go through. So sharing our experiences in terms of what we face and how we went through this transformation, what were the challenges, the training programs, the enablement, the tools and, and, you know, the governance around all this. Sharing that and, and sometimes giving them a lot of the, the recipes of what we've seen work is the best tool that we can give our clients.
Speaker 1: And are you a, a sort of glass half full kind of, you know, uh, proponent of margins, you know, increasing, being maintained? What's your, what's your view on that?
Speaker 2: So my, my thought on that is when I meet with clients to talk about AI in our project work, is we need to make sure that both sides are incentivized to innovate. And when both sides are incentivized to innovate, we go with the outcome-based engagement, then everybody benefits, right?
Speaker 1: Yeah.
Speaker 2: They get the, the, the benefits of that AI discount, we get the benefits of our margins, our people get the benefits of the tools, and then the c- the outcomes are, are the leading result there. So everybody s- uh, enjoys that. When it's one-sided, it- it's not a healthy relationship.
Speaker 1: Not always. 100%. Lift all the boats, right?
Speaker 2: Yeah, exactly.
Speaker 1: Yeah, yeah, I love it. And you know, like, I, I totally with you on the whole we're here, we're in, we're in the now phase, right? This is not a, a, a wow or how phase as Martin Sorrell said, you know, a while back. Um, I think, you know, for me, I really wanna find out from you, like what does the, the sort of the agency, you know, of next year, maybe the year after look like in terms of fewer people, different people, or just a different mix of work?
Speaker 2: I think it can be the same amount of people if those people are willing to enable and empower themselves on AI. So it falls on the agencies to provide the tools, to provide the resources, to provide the commitment, and it falls on the people to upskill themselves. So I think if both of those things are achieved, there is plenty of work to make sure that everyone does the work. But I will, I do think that some people who are gonna be detractors will find themselves out of a job, and those who are enabled will find themselves in very high demand. So I think that's gonna be the separation in the workforce.
Speaker 1: So-
Speaker 2: I always tell people AI will not replace your job, someone with AI will replace your job, and I think that's the differentiator.
Speaker 1: Hence your point around education and-
Speaker 2: Yeah
Speaker 1: ... well, get it. Um, let's talk about building for enterprise scale for a moment. So going back to your time at Omnicom, you founded Omni AI, and you helped build something that was being used, you know, across hundreds of thousands of people, um, 20,000 AI agents or so. Um, what does kind of building AI at scale teach you that smaller teams often miss?
Speaker 2: I think building AI at scale is easy. Getting people to use it is hard. So this was my bigger challenge at Omnicom, is how to make sure that people use the tool because, I mean, like I said, the jury's out. The tool is amazing. AI can do the job, AI can help you become a better professional. But getting people to figure out how to use it, getting them trained, getting them enabled, that's the bigger challenge. And for us, you know, this is how we focus on deploying our people with our solutions to clients to make sure that that is achieved.
Speaker 1: Yeah, and you've talked a- about a few of those techniques, but tell me more about kinda how you really drive kind of adoption, not just once, but sort of embed it in the daily workflow, lifestyle, work style.
Speaker 2: So we, we have made a decision a while back that we are going to focus on Google and the Google Cloud platform because in, in our mind, it is the only cloud platform that spans from models, agents, cloud, and data. Uh, so for us it's building purposeful point solutions that we can deploy to clients that they can use because they find it as not so, you know, uh, new, brand-new plate, but it is built for their business and their business model. Uh, that's what we found works really well because clients tend to gravitate towards what works. When you show them the solution and they see how it works, it's becomes an easier way to kind of picture yourself using it versus seeing what the possibilities are.
Speaker 1: And when you're deploying, how do you decide what should be shared across the whole business and what should stay closer maybe to individual teams and clients?
Speaker 2: That is actually a, uh, a very rigorous process. So we're focused on getting our ISO 42001, uh, which is the first, uh, AI, um, um, focused, uh, certification on AI governance and AI safety and security. Uh, and it is a very rigorous process to make sure that you have the right pro- Uh, security, safety, governance piece is in place to make sure things like access to data, things like access to models are not cross-pollinated. And it becomes really, really difficult to maintain when you have your data connected to your AI models that that partition has to happen for every single account. So it is something that's not easy, but it has to be done.
Speaker 1: That's really interesting. We've gone through various ISO sort of like, you know, paradigms over the years. What's particularly different about this new standard?
Speaker 2: It is focused on AI usage. It is focused on the nuances of AI models. It is focused on AI connectivity to tools and how that partition of data access is all of a sudden multiplied and has to be managed correctly. Uh, so this is almost like a, a, not only just to get the badge. For us, it's the process of doing it is teaching us on how to be better at governance.
Speaker 1: And in that world that you're building with AI tools, you know, kind of ubiquitous and agents in circulation, is it too simplistic to ask you kind of what the hardest part is? You know, is it the tech, the people, or the rules?
Speaker 2: I think the hardest part for me are the people. There is obviously a understandable frenzy and, and fear for people around their job security. And you know, I, I think that's... When you start from there, that's a... You're starting from a place of fear and panic, and you can't really help adopt and help enable people who are in that place. So you ha- you have to help them overcome that first. And then once, once they overcome it, then you have to really help them adopt. I mean, for me, I've seen people break into three categories. You have the evangelists who are m- ahead of you. They just want access to the tools, and they want, they wanna run with it. You know, there's nothing you need to do for those people. They're on top of it. And on the other side of the spectrum, there is the detractors. And there's almost nothing you can do to change a detractor's mind. It is the people in the middle, the overwhelming majority, who are open, but they're scared, they're busy, and they're tired, and they're unsure. Those are the people you have to invest time and effort to help them come across and learn the tools and the technology to be upskill as better professionals.
Speaker 1: When you are deploying, what happened to your thinking about the old adage of speed versus control once you were building at that sort of level? 'Cause this stuff is probably pretty hard to kind of rein in or roll back.
Speaker 2: Yeah, and, uh, well, we're starting to see, and not just at Apply but really all in the, in the industry, you're sort of seeing this graveyard of applications are being built. Because historically it is so easy, uh, uh... It's not, it's not so easy to build an application. You have to sit down with a team of developers, some product managers, creative strategists, and really figure out what you're trying to build because it takes time and effort a- and money. Now, anyone on a whim can pull up cloud code and build up an application. So we're seeing this graveyard of applications that's being built r- really with no purpose. So the first thing that we wanted to do is to avoid something like this, and also to avoid redundancies. Everyone's gonna create a cloud, cloud skill to connect to their Gmail. Everyone is gonna create a cloud skill to connect to Slack. So we're building internally a marketplace for our people. All the skills, all the tools, all the applications are published in a place where people can reuse, improve upon, and even discover, like, what else they can do with this technology.
Speaker 1: Let's talk a little bit about talent and go a little bit deeper on the someone replacing your job kind of analogy, which I totally buy into. But I think, you know, we, we're definitely... You're, we're, you're way further down that line now. When you're hiring across engineering, delivery, leadership, you know, kind of for you, kind of what kind of people become more valuable in an AI first business? You know, what are the skills, competencies, mindsets that you're looking for?
Speaker 2: I think that's really simple. It's EQ. I think EQ will become the separator because with AI, a lot of the, the nuance of coding and rolling out development-
Speaker 3: Yeah. Okay. Thank you
Speaker 2: ... becomes so easy to do for really everyone. It's the EQ and the, the, the mastery of, of one's craft that's really gonna matter. So people who have that, um, really forethought of being able to make a, a, a decision and understand the nuance of those decisions, that's, that's the skill that we really look for. That's the skill that's gonna separate the people who are gonna be successful versus not with AI.
Speaker 1: And how do you, how do you recruit for, for high EQ?
Speaker 2: So internally we have our own processes. So we, we, um... The, the interview process, we, we actually label them with there's a skill interview, uh, there's an EQ interview, there is a, um, demonstrate your abilities interview. So we break those down into different classifications, and then for each interview we tackle that interview specifically with that purpose in mind. So an EQ conversation is truly trying to find out things like empathy and recognition of situation and how to identify with the person across from you. Like, these are the things that will make you a better professional, but these are the things that are also hard to attain. They're not impossible to train, but they're hard to, to come by. So for us it's how do we make sure that we bring people that have that, or at least are susceptible to learn that?
Speaker 1: And does the same apply to practitioners, managers, and leaders?
Speaker 2: I think across the board. I think what AI's gonna do, it's gonna elevate everyone's role to focus on those high EQ, high thoughtful tasks because AI's gonna start eating a lot of that automatable, simple, mundane, rudimentary tasks at the bottom. So everyone has to elevate their game and become more f- more and more focused on those higher order thinking when it comes to, uh, their work.
Speaker 1: And in terms of upskilling, and you talked a lot about sort of the academy and training and retraining, how do you kind of go about sort of, you know, deploying that sort of in, in enterprises, you know, with people whose jobs are being re- reshaped almost in real time?
Speaker 2: The actual academy itself? Uh, we've had conversation with clients. We- I told you that earlier, we, we like to share our experiences with our clients-
Speaker 1: Yeah. Yeah
Speaker 2: ... because we've been down this road, we know what their, uh, anxieties are and what they worry about. Uh, and in some cases, we've actually shared our training framework and shared the way we've done this, uh, because for us, this is the best way to teach an organization how to do things, is to show them how another organization has done that.
Speaker 1: Seems to be a bit of a thread running through your career. You know, freelance developer, co-founding Distiller, building and selling TA. What have you kind of kept from that kind of founder mindset, you know, even inside much bigger businesses?
Speaker 2: I think what really triggered my founder mindset is, uh, when I got my doctorate. Um, I got my doctorate in video game psychology and educational theory, and what really propelled me, uh, after that was it taught me how to critically think about every conversation. It taught me the EQ that I was talking about. That is what a lot of this kind of mentality came from, and I think that has helped me a lot in my career because even, like you said, in larger organizations like Omnicom, I wanted to build something versus just be part of the larger machine. Uh, and I think it's, it's much more, um, it's much more satisfying when you have the pride, the, the founder pride in something you do. Uh, and then obviously you have to, you have to be really good at what you do, but you also have to get lucky. I mean, I got lucky with Apply with a really good team, and, you know, walking in and seeing the, the, the breadth of the team and the clients we work with and the work that we've done, uh, it's really, you know, have propelled us into the situation we're in right now.
Speaker 1: You're very driven, you know, and, you know, it feels like, yeah, there's, alongside the founder side of things, there's kind of a very much a kind of instilled belief, you know, in, in a world that you're yet to see come to reality. What, what have you kind of held on for a long time that perhaps the market is now just about starting to kinda catch up with?
Speaker 2: You know, for a long time, I've always thought to myself, what would I do in 2000 if I'd known now what I knew back then-
Speaker 1: Yeah. Great question
Speaker 2: ... because of the whole digital experience and then evolution. Uh, and now I don't have to wonder anymore because I'm living it, and I'm living something even bigger. So, you know, the ability to actually create a segment and lead it from a very small organization like Apply with only 700 people, uh, to me, that is truly satisfying. And it is, like I said, terrifying at the same time because you have to really kind of pivot really, really quickly because there's no blueprint. We're building the blueprint as we go.
Speaker 1: Things that's, you know, back to the point about kind of where we are, you know, we're very much in the now phase, right? You know, I totally subscribe to that. So let's kind of look three years out, you know, here we are, Cannes 29, you know. What will clients be expecting from, from a services partner that they do not expect today?
Speaker 2: The one thing I will not dare do is predict three years from now. Um, if we've seen one thing with AI, is that the, the, the rate of change-
Speaker 1: Mm
Speaker 2: ... is unlike anything we've done, and that's only going to accelerate. So, you know, I might be able to predict what six months from now is gonna look like. Three years out is, is very bold, very, very bold.
Speaker 1: Give me, give me six.
Speaker 2: So I think six months from now we're s- gonna start seeing organizations that really buy into the idea of the 20/30 X versus the 20/30%, which is taking AI solutions and truly, truly reimagining the way they do work. Because at the end of the day, when you do, getting 20/30 X, usage and cost and investment are negligible. But when you do the 20/30%, then all that matters because everything will become, you know, significantly the same.
Speaker 1: And if you were to be able to give kind of one piece of advice to the kinda Ali, you know, that started way back then, what would it be?
Speaker 2: Um, I think the one piece of advice that I would give Ali in 20 years ago is, uh, don't settle. Always challenge yourself. You know, I've, I haven't really settled in, in one company for more... I mean, TA obviously I've, and I sold it, but other than that, it's always been two or three years because I've always gotten to a point where I get bored and, and, you know, my wife always tells me is that, she said that that twinkle in your eye goes away after two to three years. So I need to find a ch- new challenge and, uh, I think that has benefited me in my career. Uh, and hopefully now with Apply we should be able to see that, uh, really come through with the work that we're doing.
Speaker 1: Ali, it's been really, really great chatting to you today. Thank you for joining us.
Speaker 2: Pleasure. Pleasure. Thanks for having me.