Rethinking Customer Experience in the Age of AI

Today we’re talking about how AI is changing the nature of customer experience. Not just what customers see and experience, but also how organizations are actually designing and delivering those experiences behind the scenes. Jess Leefe is managing director at Tobias, a specialist strategic design and innovation agency with deep experience in AI customer experience transformation. So no one better to have this conversation with today about what’s changing. So Jess, for those who may not know Tobias, could you tell us a little bit more about the work that Tobias does?

Yeah, absolutely. So, as you said, we’re a strategic design and innovation agency. Many, many years of experience in the field. And really, you know, I think the way that we talk about what we do is solving really complex problems actually. And starting by really understanding and interrogating those and working alongside the people who are trying to solve those problems, but alongside the people that are impacted by those problems as well. We live in an incredibly complex world with a very fast rate of change going on. It’s getting bigger and faster. The problems are more complex. And we use I guess our tried and tested tools, tricks, methodologies, namely human centred design, thinking more now about human systems design. To really understand all of the different components of a problem to try and solve and generate and create a better world for all of us to live in.

Fantastic. Yeah. Well, customer experience in this topic that we’re talking about today and depending on what sort of organization you are, maybe you call it citizen experience or member experience or you might have a different label, but the experience that you deliver to your customers has been a focus for organizations for a long time in terms of how they deliver better value to their customers, but also commercially as a business through better customer experience. But things have been changing recently. How do you think about what’s changing in customer experience today?

Yeah, absolutely. I mean, obviously you can’t avoid AI. It’s everywhere. And the reality is that that’s a big driver in terms of, you know, the way that customer experience is talked about, thought about. Having said that, you know, it’s not new. It’s actually been around for a while and the sort of the backbone and the foundations of how many organizations are thinking about, you know, personalization making things easier, the way that we operationalize and organize. I think the difference now is just how sophisticated some of that technology is and the pace at which it’s changing and moving and what that means for as you say, could be customer if you’re talking about organizations, could be citizens if you’re working with government. But really you know their experiences and their expectations continue to shift and change. So I think one of the biggest changes is probably you know just the way that things are almost conflating and coming together. You know previously I think we thought more in a in a layered kind of way about you know customer experience is layered on the top. Digital is a channel or it’s a touch point. We have other channels and touch points. And a customer has a problem that needs solving. And I think now we’re starting to see that those worlds or those layers are conflating and becoming one. And perhaps the expectation that customers have and the thinking that organizations are starting to work with is more you know how do we support and help people in decision-making, the next best steps and actions, but really working in a more cohesive way through that experience layer, the channel layer, the process, the policies, the operations, all kind of conflating into one space and operating as more of a dynamic system.

Absolutely. Yeah. So let’s talk a bit about where you see organizations struggling with some of the things that we’re talking about. What’s hard, or where do you see organizations getting stuck?

So there’s probably a few places. I think it would be fair to say that when new technology comes along, we all get very very excited and we want to see what it can do. We also think that it’s going to solve every problem. That sometimes can lead to us just wanting to get on and use it, try it, put it out there, you know, beat our competitors. Everybody else is using AI. We need to as well. What comes with that though can be forgetting to ask the question why. What is the actual problem that we’re trying to solve for those customers, for those end users or for ourselves? And it becomes more about the shiny thing and getting out there first to market rather than really questioning what do we want to achieve with this technology? How does that sit within the system? What is the problem that needs to be solved? And it’s funny because that’s actually been a common thread in our conversations for as long as I can remember and as long as I’ve worked in this field. It’s not so different to the internet coming along, you know, and us all of a sudden being so excited about what we could achieve and the technology leads us and now the tech does this and now the tech does that. But to what purpose? How is it actually going to be useful in a human being’s life to help them solve the problems that they’re wanting to. So I think in terms of mistakes that are being made, we’re probably seeing a lot of let’s just get out there and do it and not remembering to think about what is the problem that we’re actually trying to solve and for who and why. I think the other thing is wanting to have all of the solution ready. Let’s build the whole thing and put it out there into the world and it’s done. And again, you know, one of the ways that we work and we have done for a very long time is through rapid experimentation, testing things out and putting them out there to learn, to iterate quickly and then go again to be really confident that we are solving the right problem. So we use the British Design Council’s double diamond process and that really talks about starting with making sure that you are designing the right thing, you’re solving the right problem, and then you’re designing it right so you’ve understood that it works for that context and for that problem. And that hasn’t changed for as long as I’ve been working in this space.

Sounds like a lot’s changed when we talk about AI but also nothing’s changed when it comes to the principles of good strategic design. If you could help just spell out for us, when you see this working well, what does good look like in terms of how organizations work, but also in terms of what great customer experiences look like that are empowered by AI?

Great question. It’s funny because the thing that actually comes into my head first before anything else is remember the human. Remember the humans at all of this. Whether it’s internal operational teams, whether it’s a customer wanting to solve a query or plan for a life moment or an event that’s going on for them, whether it’s your operational team trying to help solve problems out there in the world, let’s remember the human first. So I think that’s something that people do really well. The technology is fabulous but a question that we get asked a lot and did so recently is how do we design this AI enabled experience with an AI agent but keep the empathy, keep the human, so that it really supports in a way that humans need that human to human interaction. So we ask and explore a lot of that and I think people who are thinking that way are going to be more successful.

I think leaders and authorizing leadership environments is another place for success. As a leader, the people that I can see doing work really really well are people where the leadership in the organization are really leaning into giving the problem to and letting their teams explore that problem. So it connects up to that view of we need to really understand and interrogate the problem. What can our leaders do? Our leaders can create the authorizing environment for their teams to thrive in that space. Give them the room and scope to understand the why, solve for the problem, focus on outcomes. And that makes a really, really big difference. And, you know, as I was sort of talking about before, test, learn, iterate, you know, fail and learn from that. They’re the same principles. But they’re the people that are doing it well. You know, it’s really that innovation mindset and that growth mindset that thrives.

And thinking about things that have changed. I think you touched on earlier in the conversation how traditionally we think of customer experience in layers, you know, policy, operations and experience. But those are now collapsing. What does that mean in practice?

Yeah. So I guess one way to think about this: if you imagine a typical sort of service experience, you know, typically we might have a large call or contact centre. You might have a customer that’s wanting to manage an inquiry or they’ve got a change. They’re wanting some advice and some guidance. Historically maybe what would happen is a customer would call up an organization. They’d ask the first question, they got a problem to solve. They’d be put through one layer then another layer. Our contact centre teams are set up to handle a certain query and then maybe pass on to the next. And we’ve got time limits around what good looks like in terms of those handoff moments. Maybe in really good practice, we’re able to hand someone off between different teams or channels as the customer sees fit. But what’s really shifting now is that that could — and in an ideal world would — all be done in that one moment because if your contact centre person is enabled with the right tools and the right AI, that AI is helping them with the decisioning and helping them with the process. It’s bringing that right information from the policies that exist. It’s thinking about those relevant next steps and those moments that could be really important. It’s pulling the right guidance. It’s thinking about the rules and regulation. And that person that’s sitting having the human-to-human conversation isn’t having to sit, you know, reading through all of the material before they can answer that question. They’re actually able to go, “Ah, it’s right here at my fingertips.” And therefore, I can have a much more educated, informed, supportive, enabling conversation with you to help you make that decision and give you guidance. What’s challenging is where there’s regulation involved. But, you know, again, regulation is brought to the fore and our people are much more equipped to service in a more informed and educated way much faster.

Yeah, I can see what you mean now in terms of the shift towards systems thinking. To deliver on that sort of promise — like the example that you just gave — what needs to come together in an organization to make something like that happen? Having things like a single view of a customer and having all the data in one place and surfacing that data or that insight at the right time — these are difficult things. What in really simple terms needs to come together in an organization to deliver on this sort of promise?

People behind a unified vision. So a clear view of where they want to go, what the problem is that they want to solve. We’ll often talk about having that north star. We’ll also talk about bringing people on the journey. So realistically a lot of the work that we will do will look at, very early on when we work with teams, working with the right people in the organization bringing them together early and saying, you know, what is the vision that we’re trying to achieve, what is the problem that we’re wanting to solve, and who in your organization needs to come together, cooperate, work together to achieve that. And so these layers that typically we might have worked with — a tech team over here, a people culture team over there — they’re now coming together and we’re saying, let’s bring them all together. And we have a bit of a philosophy that you can pretty much change the world with a team of six. And it’s probably not far off to be honest. If you’ve got the right people, the authorizing environment, strong leadership, and a clear vision, and you’re set on focusing on the outcome, it’s a good start.

Yeah. Makes a lot of sense. We talked about a couple of times through this conversation how those principles of good strategic design, they’ve stayed constant. That’s not changing. But is anything changing in your ways of working and how you work with clients these days?

Yeah. Yeah, absolutely. I mean, what is changing? It’s a great question. I mean, I think there is, the complexity for us is changing. I think the other thing is that is a slight shift — more and more people are asking us to work alongside them. So once upon a time agencies like ours would be asked to come in and solve the problem. It’s an outcome based delivery and we want this by this time. Now increasingly what we see — and it works, so I’m glad that this is what is being asked for — is: support us, augment our teams, help uplift our capability. We talk about applied capability. So doing the work while upskilling people in the organization with the mindset, the tools and the doing. So I think one of the big things that’s shifting in terms of the way that people are working with us is wanting to partner more, wanting to learn from and with and do alongside to get to those outcomes and then ultimately have more of that kind of strength and capability left for themselves for the longer term. And that’s great. I think that’s a win.

I mean I actually think it’s critical to be skilled and be practicing and exploring. There is absolutely in my mind no way on the planet that we should be supporting folks to embed AI in their organizations, run transformations, think about the role of AI if we’re not doing it for ourselves. We’ve got to be able to understand it. We’ve got to be able to know and use it and we’ve got to be able to be really clear about what the guard rails are. So we’ve been investing a lot in the way that we work with AI both for ourselves and our own understanding and learning, but so that we can stay at the forefront for our clients and partners too. You know, that might look like exploring how different types of AI assistants and agentic AI can support our workflows. We follow a pretty solid human-centred AI methodology and that’s included for us looking at our end-to-end process right the way from how we mobilize to how we deliver work and exploring what is the role that AI could play, what role should it play, and how will we make that come about. So if we’re doing research, you know, do I want to have AI assist me in my analysis and my synthesis? What’s going to be useful around that, what’s not, where is it going to speed things up, where are the complexities or the challenges, what does it look like in coding and delivery when we’re in that kind of tech development world. Really, really different. So we’ve absolutely been exploring how we use it in our own practice and therefore what does that mean for the way that we work with clients, but also how do we stay across what’s possible and what’s out there so that we can give them the best guidance and advice and partnership, too.

And in a team like yours, there’s room for that sort of ongoing exploration and testing and learning. I can imagine that’s also why your clients are increasingly asking you to work alongside them as joint teams, so that they also get that capability transfer from learning where you’ve experimented and proven some of those use cases. This has been a really good conversation, Jess. We’ve covered a lot. To bring this to a close, if you were a senior leader watching or listening to this right now, what should you prioritize? If you’re thinking about where your customer experience needs to change or how to make the most of the opportunities that are there now with AI, what should they be thinking about?

So good. And I don’t I really don’t think my answer has changed. It’s understand where your customers’ problems are, what needs to be solved, organize your teams in a way that they can self-organize around outcomes and create the environment for them to experiment, to test, make it okay to fail for learning and bring an innovation mindset. Let’s see what we can do.

Yeah, those things have stayed pretty constant. So the flip to that — when we’re thinking about AI and customer experience, what’s one thing you see happening that you wish organizations would stop doing?

I wish they would stop leading with tech and I wish they would start leading with the human and the human’s problem and figure out how tech can solve that problem.

Yeah. Which is also — for all the journeys that we’ve been on with digital transformation — also remains a truism. Inside organizations, the responses that you see to AI and what it means for customer experience. Are you seeing behaviours that are similar or different to how we’ve responded to other new technologies in the past?

Pretty similar. I think you know and I think that comes from different points of view. So for some it’s real excitement — some people are excited, they’re early adopters, that means they want to jump right on it. They want to use it, want to experiment with it, they want to learn. Great. Fabulous. Others are terrified, don’t want to touch it, don’t want to move at all. And then there are those in between and some who are just a little bit confused about what the heck does this mean? And I don’t think that that changes because this is human behaviour. This is how we respond to change and technology is a constant change actually in our lives. So realistically, the way that people are behaving and responding, I think, is pretty similar to any other wave of technology shift that we’ve seen in the past — with maybe a couple of caveats. So I think perhaps that fear factor is a little bit greater in the world right now. And perhaps also the confusion. There’s a bit of a moment of like what do we do here with this? So a little bit of stalling, a little bit of pausing, a little bit of reflection on is this going to change actually our value proposition? So I think we’ve seen a few folks just take a moment and go, what does this mean in terms of our people and our value proposition and what do we have to do to respond? But those who are the early adopters and the excited ones are leading the charge and ready to roll.

What’s perhaps different about AI is it’s more of an existential threat to the people inside organizations who are making these decisions because we’re increasingly having a conversation around what does the future of work look like and will AI replace all of our jobs. So with that backdrop, it’s understandable that there’s sometimes a resistance or a reluctance or that it’s hard to progress on making the most of this technology inside your organization.

Absolutely. Yeah, I think that’s really really true. And I again, you know, human nature. That’s the way that I guess we’re wired. But yeah there is a significant concern for us. I think that’s part of the — dare I say — opportunity. It’s to work with and really understand, well what does this mean, what else could be possible? Maybe I take the optimistic view on it. But there’s so much that can be enabled and unlocked and freed up — that’s potentially about enabling really human experiences where they actually matter. I’ve spent a lot of time in healthcare for example where perhaps it matters more than anywhere else. All of a sudden we are creating some space for people to have more supportive, engaging conversations with people in the times in their life when it really matters, because we’re not worried about the admin tasks and the chaos. So there are potentially big opportunities — the ability for consumers, citizens almost — to level up and to challenge some power imbalances in the world. So thinking about inclusivity, you know, there are huge opportunities as well that are created in the real human to human space when some of the day-to-day logistical, difficult nuts and bolts of operationalizing things are cleared out of the way by the technology.

That’s really good. What’s different when we shift from thinking about experience as layers to a more systematic approach?

Yeah. I mean many things. If I take the citizen or the customer view to that I think it’s almost enablement — it is a shift in my ability to feel supported. So what is the outcome that I’m hoping for when I’m planning for my retirement or I’m growing my family, selling a house, facing a health crisis or a significant health concern? At its core, what we care about is protecting the ones that we love. Continuity for our families. Managing and minimizing the stress through those times. Sometimes they’re exciting moments — sometimes someone’s getting married and I want to be able to support them. Whatever these kind of life moments are for people, that organizations play some role in enabling, supporting or hindering — I think what’s changing is the ability to have much more human, supportive, enabling interactions that meet people where they’re at. And if that means minimizing the role that we play in their lives, actually they might be really grateful for that and think about an organization in a better way because something was easier to do, wasn’t something they had to worry about. You know, if it’s actually playing a more significant role in supporting a conversation or stepping through a decision that needs to be made, those things become more possible because the information, the data, the way that the data flows behind systems, the interoperability of that data that helps solve a problem — all is at their fingertips and is all digestible. And therefore it makes it a much more seamless, easy to navigate experience in that life moment.

Jess, this has been a really interesting discussion. Particularly like that shift in thinking from experiences as layers to thinking of them as more dynamic systems in an age of AI. But I also really like what you talk about in terms of how those principles of strategic design still hold absolutely true for the age that we’re in today. So, really enlightening. Thank you so much. Thanks for making time.