The emerging world of machine customers - Interview with Katja Forbes

Speaker A: So welcome to the next edition of the PunkCX podcast. With me today I have Katja Forbes who is an author, advisor and keynote sort of speaker but I'll let her introduce herself in due course. But first of all, Katya, welcome to the podcast. How are you doing?

Speaker B: I am excellent today. Thank you so much for having me.

Speaker A: You are very welcome. Now for folks that are not familiar with you and your work, can you tell us a little bit about you and the work that you do?

Speaker B: Well, I've been working in digital design, CX and all of those associated fields for 30 years now and I say that out loud and just I could feel the agedness of my body. So I've been a practitioner. Mhm. On the tools amongst the weeds and also operating now more at a strategic level. I've worked in consultancy, I've built, run, sold my own business. I've also worked client side so in banking, working in journalism as well. And so I've had a really varied career. But the thread that goes through all of it is, it's always touched digital so far back to the days when we used to call it new media,

Speaker A: when the web was, was, was a thing that people like.

Speaker B: What's the web potential web when the web was born. But it's given me a really good long trajectory to have a look at things and um. Yeah, and get to where I am now with a really solid perspective on where we'.

Speaker A: Yeah, awesome. Now the reason I wanted to have you on the kind of podcast is because you've just recently published this new book and it's called Machine Customers. The evolution has become, has begun rather um, how AI that buys is changing everything and it's Machine Customers is this kind of thing. This is like an emerging kind of thing that's kind of blowing up on the back of kind of the, the whole agentic commerce sort of thing. And so I thought it was a be a really interesting kind of topic just to dive into. But before I dive into some things that, that I saw when I looked through the kind of book which is over there, I'm not going to go get it and kind of show it up but I've got it over there. Before I get into some of the things that stood out for me and to give um, to give people a flavor of the book, I wonder if you could tell me a little bit about the book and how it came about and some of the main headlines.

Speaker B: So the book is a field guide for CX practitioners, for business leaders, for people who care about their Customers to work out how they change their business models and customer experience when the customer is no longer human, when it's an AI that's buying, either as a delegated agent that I'm sending out into the world to go and shop for my shampoo, or whether it's something that's much bigger in scale, an autonomous factory that has predictive maintenance and the AI can order parts for it. So the agentic commerce part of it I think is a subset of the idea of a machine customer, that sort of agent going and buying. But machine customers are uh, they're cars, they're robots, they're factories, they're autonomous platforms, um, they're multi agent networks, smart homes, all the way up to smart cities. So exploring that in the book and trying to help people navigate from where customer experience has been over the last 30 years to where it needs to go in order to welcome in this whole new group of customers. That's what the book is about. And giving people practical things they can actually do rather than like hand wavy future predictions.

Speaker A: Perfect. I mean so just to dig into this whole kind of a machine customer sort of idea, I mean I'm glad that you expanded out beyond a sort of a consumer LED sort of like thing. But just if I zoom out a little bit, I mean just so I understand it, maybe their listeners understand it a little bit better as well. I mean, is a machine customer not just a customer that has captured, automated and enabled their preferences with regard to decision making and those particularly kind of purchasing and things? Is that not what a machine customer is or is it, am I missing something?

Speaker B: I think it's the autonomy that's missing in that and the way that AI is advancing at the moment in terms of introducing autonomous decision making into physical objects, into large scale procurement platforms. Um, thinking about it from a financial services perspective, let's go for something on a Treasury platform that wants to move money around on behalf of a company and autonomously makes decisions about, oh, I can see a whole lot of money that I've got in China and bills to pay in South Africa, I'm going to make that transaction. So there's the autonomy I think is the difference between the, what you described, which is, you know, I take my preferences, tell the agent about the preferences and automate it to execute on um, my preferences. Gartner M calls that um, almost a bound model of machine customer where it is bound by what the, the human has told it, it can and can't do. But where we're moving towards is a Fully autonomous machine customer that can determine what it buys, how it buys it, when it buys it, who it buys it from, how much it's willing to pay for it. And there is ultimate liability on either side of that of like the humans that run the business that's sell the things and the humans that either run the business or own the agent. But ultimately that autonomy that goes on in there is very different from us just going, here's my preferences, automate that and execute on them.

Speaker A: Okay. And because I know this is quite future kind of focus, it's like setting the ground. Sort of like saying here's the landscape, what it looks like right now, here's what's emerging, here's what look in the future, prepare yourself for the potential future. I mean if you think, I mean, I know it's hard. I mean thinking five years out, any, any point in time is kind of generally hard.

Speaker B: That's my favorite thing to do.

Speaker A: Okay.

Speaker B: It's literally my favorite thing to do.

Speaker A: Let's, let's stroke our chins a little bit and then think five years out. I mean what proportion of transactions do you foresee will be driven by this? I think you call it MCX in the book the machine Customers. I mean what are we kind of talking about now? Because we're pretty much almost like a, uh, it's a. It. Is it zero or is it a rounding error right now? But where. What are we likely to see given the nature of the world kind of right now?

Speaker B: Well, I think that if you look at the statistics on it. So worldpay has done a really great agent E commerce report just at the end of last year. And just looking at American consumers alone and just in the consumer market. Just in the consumer market, they're predicting 9% of purchases through AI agents within the next five years. But that's a $2.9 trillion E commerce market. Right. By 2030. So we're talking $261 billion of AI driven spend just in the US just in the consumer market. And the B2B number is much, much higher. It's just. That's less visible.

Speaker A: Yeah, I guess so. I think the thing to point out, I guess is that. And it's the. I got almost the elephant in the room every day all the time is that everybody talks about the B2C the uh, consumer sort of like space. But B2B is where what drives most economies.

Speaker B: Yeah. And it's where the money is for this as well.

Speaker A: Right. And so do you see those playing out more in the B2B space rather than the B2C space. And like over, over time.

Speaker B: Yeah, I do. Because algorithmic procurement is, it's already in market today. So, so Walmart has been piloting algorithmic procurement since 2022. Um, they run an autonomous AI that negotiates with their vendors for the best prices for the products that they want to buy from them and then put them in the Walmart stores and sell. And they're now operationalizing that and scaling that. So they have more than 2,000 vendors in the pilot that they were running. About three quarters of the vendors actually preferred to negotiate with the AI, I think because there was no kind of emotional backwards and forwards that you could say whatever you wanted to the AI about how crappy you thought the terms were that it was offering. Um, but AI procurement is absolutely, and autonomous procurement is absolutely going to be a massive, massive M market if you think about all the procurement that gets done in the world, the things that businesses buy, and all of the pain that goes into responding to requests for proposals or requests for quotes and things like that. And I've worked in big corporate, I know how deeply painful that our procurement processes were when I was working in there. So it's a natural fit for us to give that painful task to an AI in order for it to make the best, soundest decisions on behalf of the business. So that's going to be huge. I mean, automated reordering that gets more into the like we have a bound version of that that's already in market today, which is the uh, HP printers that can order their own toners and supplies. Yeah, but in 2021 that was like a half a billion dollar line item in HP's P&L.

Speaker A: Right.

Speaker B: So the machines that can automatically just order stuff that they need and the more smart machines that we're going to be having because there's more than 7 billion devices and things in the world that had the, the, the opportunity to act as a customer, like watches, smartphones, cars, you know, smart houses. And so as they start making determinations for themselves about things that they need to purchase, um, that is going to be a really, really big part of the, the pie as well.

Speaker A: Yeah, it also feels like the um, that, you know, over the years and I'm sure you've kind of tracked it as well, everybody's talking about there was a big blow up and it continues today. It's still a big field. It's like Iot was the next big thing and you're like going m, that's

Speaker B: where this got born. This was born out of Iot.

Speaker A: Well, yeah, exactly. And it's almost a bit like this sort of capability is going to make iot, almost like, kind of like come to realize its kind of potential in many ways.

Speaker B: Yeah. This was definitely born out of some gartner Research into IoT by Don Schibenreif, when his people leader asked him, what if the thing became a customer.

Speaker A: Right.

Speaker B: And then like, uh, this is back in 2016, 2017. Um, then I saw that research and was like, if the thing is the customer, what happens to customer experience?

Speaker A: Right.

Speaker B: Hence the thinking and writing and talking and then finally collating it into a book that people can actually purchase to help them navigate this.

Speaker A: Yeah. And also the other thing that kind of makes it, uh, uh, you know, it's almost like a historical reference is like, so there's the IOT thing and then there's the machine customers and how that all. And the new kind of like agentic capabilities that are starting to develop and how that's going to manifest itself Both in the B2C space, but also more importantly in the B2B space as we move towards this more kind of autonomous enterprise type of, uh, scenario.

Speaker B: Yes. I'm literally researching and writing about that.

Speaker A: Yeah. Which is fascinating. And it's almost a bit like, for people thinking about it, it's like, it's about having a self. It's like having a self driving car, but in the form of a business in many ways. But then the other thing I wanted to ask about is like, and this is a bit of a blast from the past or possibly slightly esoteric. I mean, this also feels a bit like Doc Searles VRM or vendor relationship Management. Sort of like, you know, uh, framework or philosophy brought to life. I mean, was. Does that feel sort of accurate?

Speaker B: Yeah, I mean, that's all about where, you know, the customer took power.

Speaker A: Right.

Speaker B: And it, it is that, but in a really sort of weird way.

Speaker A: Right, okay.

Speaker B: Where I guess when Doc Sal was thinking about this, he was thinking about the customers, and the customers were a bunch of humans who were trying to get things done and they were trying to also, you know, manage. Manage the relationships on their own terms.

Speaker A: Yes.

Speaker B: Right. I think we have the same opportunity here, but the person in the customer box is not a person now.

Speaker A: Right.

Speaker B: And it's going to be trying to manage those relationships on its own terms, and its terms are going to be completely different to anything that our rational, emotional human brains could come up with.

Speaker A: Right.

Speaker B: So I see a really interesting. I don't know, correlation to do there to go. All right, well, let's take that as the idea and then change the human for a robot that needs to buy parts for itself or get a battery exchange. What does that, the terms become in that conversation? How does it work? Um, and that would be a really fascinating exploration.

Speaker A: Yeah, no, I think it's fascinating because it's all Greenfield type stuff. I mean, there's also another thing that left field, something that kind of just struck me as you were talking kind of there, because in the B2B space, but also kind of sometimes in the B2C space, there's such thing as a buying group where people get together and then kind of like club together and put together their purchasing power and then go to the market and trying to buy sort of at scale and volume to get better discounts. I mean, I'm just thinking about like, how does that work in the machine customer sort of space.

Speaker B: Do you want to know my version of that?

Speaker A: Okay, go on.

Speaker B: That is, say we have a family.

Speaker A: Mhm.

Speaker B: Across multiple homes.

Speaker A: Mhm.

Speaker B: You know, cousins, aunts, uncles, et cetera, et cetera. Or even friend groups.

Speaker A: Yeah.

Speaker B: All of those homes are, uh, smart homes. All of those homes have an orchestrator agent running that home.

Speaker A: Right.

Speaker B: Those agents can connect with the rest of the home's family, agent network, friends, group agent network, and collaboratively come together to buy things to the advantage of that group of family or friends. And that's a version, I think, of the buying group where we have networks of agents who are actually able to work together in order to get better economic outcomes for themselves, for their human counterparts. Yeah, that's how I reckon that one could manifest.

Speaker A: Yeah, no, that's fascinating. I mean, I mean, so it feels like the, um, I mean, there's going to be all sorts of different types of agents kind of in this sort of like space to try and facilitate all of this. And it's like mind blowing and slightly discombobulating, kind of like thinking about the whole sort of like, thing. But who's going to provide those kind of agents? Because I think both at the consumer level and then also at the kind of the business level, because that feels like a layer there. I mean, I can see in the business, the business sort of space, the enterprise software space. Well, can we call it software anymore? Well, who knows? But at that enterprise level, I can see how you get. People are jockeying for position to be that sort of orchestration, kind of like layer. Um, so that kind of makes more sense. But at a Consumer level or even at a more local level where you've got like, as you kind of say, like you've got um, communities that might represent households or even just individual consumers who would be providing those sort of agents. Because that's only a really interesting kind of layer.

Speaker B: Yeah. And people are fighting this out at the moment. So we have options. You can build your very own agent. I have an agent, Tyler. Tyler runs on openclaw. And uh, the current model that it's working with is, ah, a Kimi 2.5 model. Uh, but I switch it around like when I want different things, I'll switch to different models.

Speaker A: Quick question, is surname's not Durden, is it?

Speaker B: It is not. But that's funny. No, no, it is not. Tyler. Um, yeah, Tyler is my erstwhile delegated agent for all sort of presentations and commentary that I do about delegated agents. Okay. But Tyler is real. Tyler has a credit card. Mhm. Through a credit claw option which is basically, it takes my credit card, obfuscates it and the credit card number is never actually passed to Tyler. So there is something that would allow Tyler to pay. So far the Internet is not super supportive of AI agents trying to use credit cards. So uh, we're still fighting that battle. But that's what the whole book is about is like, how do we make ourselves more welcoming in customer experience for a machine customer like Tyler so you can build your own. There will be people who have no inclination to build their own agent to do things for them. And for them those are the ones where we're going to see the larger players starting to offer capabilities through their platforms. Like Anthropic has already got quite a lot of agentic capability in its cord platform. OpenAI actually hired the guy, Pete Steinberger, who created OpenClore, um, and hired him into there to actually start looking at their consumer agent offering. So I can see that there will be probably a subscription or something like that that we'll be able to do with some of those big players to get an agent that works for you? For me, I like my own agent because I built it. I know what it does. Everything is in there because I commanded it to be. So I'm not entirely sure that I trust another party to actually create an agent for me. And we see, ah, an agent type at the moment as well. Um, that I've called an intermediary broker.

Speaker A: Mm.

Speaker B: Which sits on the platforms where people buy things like Amazon's got Rufus, Walmart has Sparky in Australia, Woolworths has Olive and they're all operating off different large language models to, you know, get them to help you buy stuff. In fact, you can actually delegate to Rufus. You can say help me decide and Rufus will just decide for you. But they don't work for you. They purport to work for you and they're showing up super helpful but they don't work for you. Rufus works for Amazon. Rufus prioritizes brands that have paid to be prioritized. You know this, I just check, just

Speaker A: check out the kind of the um, how much money Amazon makes for advertising every year and then it'll tell you a lot about their business.

Speaker B: Precisely. You are so correct, Adrian, so correct on that. They are 100% protecting their income that they get from advertising revenue, which is huge.

Speaker A: Yeah. And so I mean I've always thought about this because I think um, there's a guy called um, Jamie Smith who talks about, he's got a newsletter called kind of Customer Futures and he's been in that sort of like space. He's looking at customer uh, agents I think more on the consumer side and how it fuses with digital ID and digital wallets and all those different things.

Speaker B: Yes.

Speaker A: Oh, it's a fascinating sort of like uh, space. And I've known Jimmy for a wee while and he's, you know, it's what he's doing over there is what um, is, is brilliant. Um, the thing, it makes me think about how the mobile phone players may actually offer like a, I say in inverse of commas, more trusted platform, um, potentially. And you've got people like your Apples of this world. One of the biggest kind of players, Google and it's Android kind of like platform. But then people like Samsung and things are going to try and muscle into all that sort of like sort of space as well. Do you think that's prob. Fair shout as well?

Speaker B: I think everybody's got to have a crack at this.

Speaker A: Right.

Speaker B: And uh, I think the one who'll probably win will be the one that makes it friction free, effortless, simple.

Speaker A: Mhm.

Speaker B: And it also does what it says on the tin in a trustworthy ethical fashion.

Speaker A: Trust I think is the key word of the key. Can you. Do you trust them to have your best interest at heart as well?

Speaker B: Table stakes now. So I think about agentic commerce in particular at like three different altitudes. One being that foundational, hey, let's get machine readable everybody. Which is where everybody's kind of scrabbling about at the moment and taking their SEO ways of working and repackaging it. As aeo, and then giving you a checklist that looks a lot like SEO, but there's a lot of activity down there. But there's a middle layer as well, which is that operational trust, which is table stacks, which is know your agent Kya, who is the agent, who's the liability holder in that. Uh, can we trust it? Can the agent trust our business? Can we create a trusted transaction? All of those sorts of things sitting in that middle layer, which is again, table stakes. But I think that for businesses who are thinking about those, like, if everybody is equally discoverable and everybody is equally trustworthy, which is what it's going to have to be for this to function, then what separates anybody from anybody else. Sure. And my perspective on that is that it happens at the stratosphere, which is where we signal our values, who we are as organizations. Because in some experimentation looking at how AI decides to recommend you rather than just cite you, it cares about what you put out into the ecosystem as the values of the organization, matches it to whatever values that it is going and looking and searching for, and then checks third parties to make sure that you're actually living your values, not just espousing your values. So I think that this is a really great place for uh, organizations to start thinking. It's like, okay, so we say this about ourselves. We say things about sustainability, we say things about ethical sourcing, we say things about an ethical supply chain. Are we living those values? Can we prove it? Because an AI agent has got infinite patience to go looking for the proof.

Speaker A: Yeah, no, it's, it's interesting. I mean, and then I was thinking about it, um, I think before I was, we were talking, I was thinking about writing something around kind of this because I think it's a fascinating space, how it progresses, I don't know. I mean, and I have questions around how it kind of like how it kind of plays out and how it kind of like smashes against and changes. So it embedded kind of behaviors, as it were.

Speaker B: Yeah, yeah, yeah.

Speaker A: And so how does this impact people who like shopping? So let's. Because let's face it, it's the national sport in many kind of countries or like randomly, randomly browsing. And, and I was just thinking about it just then as I was asking or just as you were talking. And I was thinking about, well, actually that's true in the consumer space, but it's also really true in that shopping or preferred suppliers kind of like, like space, connections and relationships in the business to business sort of space. How does it affect that as well. Because I just think, hm, so what we, that everything's going to get done for us or how do we, how do we, how does it impact? What do you can foresee in that sort of space?

Speaker B: Well, I think it's really culturally different as well. So again, hacking to the uh, worldpay report, um, from. And this is a survey of more than 8,000 people all around the world.

Speaker A: Right, okay.

Speaker B: About how comfortable they are with agentic commerce. An agent making a buying decision. So we see at one end of the spectrum, we see China with 65% of the people who responded from that territory saying I already shop with an AI agent or I would totally be fine shopping with an AI agent or having it do that for me. And then on the other end of the spectrum at the very, very, very, very end is um, France where like a huge proportion would say I would never, I'd love to do a French accent here, but I can't. But I would never shop with an AI agent. They are the biggest resisters to agentic commerce as found in that particular survey. And so there's a cultural aspect to this of the comfort from that type of perspective. Looking at uh, the microcosm of America, about 35% of the Americans said that the reason why they wouldn't use agent E commerce is because they like shopping. That was one of the reasons I like it. That's why I wouldn't delegate it. But I think what we have to look at here is like if even 20% or 30% of transaction volume shifts to agents who've got zero tolerance for friction, zero tolerance for any interest or uh, zero interest in your brand story, then you've got to account for that.

Speaker A: Well, it's a massive market.

Speaker B: What it does mean as well is we're going to have to run parallel tracks for a bit as we figure this out. And I think particularly in the business to business example, the relationship aspect of being the preferred supplier. So there is going to be people coming with requests for proposals for I want to buy a thing from your business. Big procurement angle. They will have the humans who are wanting to have a human relationship conversation with the vendor saying, how are you going to support my three year digital transformation? How are you going to be my partner in this business? But they're also going to have an AI agent that takes the 900 questions that are in the response and interrogates them for accuracy, for compliance, for third party credentialing and proof to ensure that when you say you don't have any modern Slavery. You actually don't. So there's going to be this sort of two parts that have to run. I made up a job in the book about a human machine experience coordinator, somebody who makes sure that those conversations run parallelly and smoothly and that they come together at the right point for the decision to get made and that the AI decision marries with the human decision and everybody understands why they did what they did. So there's that aspect in the B2B space. But also we're going to have to continue to curate experiences for people. I just, I just wonder whether there will be a premium for having a human serve you.

Speaker A: Oh, 100%. 100%.

Speaker B: Yeah.

Speaker A: I mean it's just like uh, that's something that came out in my. So I do this annual end of year predictions kind of like kind of piece which is, which I've been doing for seven years now and but it's a curated sort of piece. So last year I got 777 different predictions from nearly 400 different people.

Speaker B: Wow.

Speaker A: And so I had to boil them all down into themes and I got 18 different sort of themes and I used about 60 roughly quotes and created a bit of a story. One of the kind of things that came out was that there's a trajectory and I think that it will be, don't be surprised if you'll see more um, AI free or kind of like human only, sort of like service, blah blah blah. And don't be surprised if people try to charge you more for it as well. And I think that's absolutely um, a way to go because I think there's value in that in the minds of the customer base. It's not just about efficiency. It's about kind of like value and the value exchange that goes on between a business than another business or a business and its customer. And so I think that's a really interesting sort of like space and um. But I think that it leads me on to something that you kind of alluded to is that the idea about what is you said before is like what does this mean for customer experience and customer experience kind of leaders and are we likely to see different types of roles and teams emerge in organizations to meet this new demand? I mean like are we going to have MCX M machine customer experience specialists and human customer experience kind of specialists? And is that the dual track you were mentioning? Or is it kind of are we going to have to kind of fuse them at some point or what's your.

Speaker B: I think it all fuses into customer experience at one point when we just recognize the fact that we have a new type of customer and it's just not a human, but it's all still customer experience.

Speaker A: Right.

Speaker B: So I think it all fuses together. But for the immediate future I think there needs to be the intentionality to go, I am going to get expert in how to deal with a non human customer. I am going to specialize in that. But I don't think a customer experience professional can just go, well I'm not going to do anything with humans anymore because it's just not practical or it's not how the world's going to work. There is always going to be human layers to even machine customer experience plus machine customer experience. So I think that there's toolkits that we use that just don't work for machine customers. Like an empathy map. Yeah, anything that we tried to do with an empathy map with a machine customer, at best you're going to get it to role play its training data for you and pretend to say do see and hear things.

Speaker A: Right.

Speaker B: Because that's what happens. Like Malt Book for example where they all went out into that social network for agents that it wasn't sentience, self awareness or anything like that, it was them role playing their training data.

Speaker A: Right. Okay.

Speaker B: And it's, it was fun and interesting but that's pretty much what would happen whenever you try to give them something that is human. As yet we haven't seen them use it in a way that is unique to agents. As far as, as far as my research has showed me, um, we see them role playing their training data. So empathy maps, you know, CSATs like how satisfied or dissatisfied, like that's not a thing that you would ask a machine customer. What you would ask a machine customer is about ease, customer ease. How frictionful or friction free was this? You know, but you can, don't have to even really ask them, you just get that from your telemetry. So we start looking in different places.

Speaker A: So I think the really interesting thing is that kind of, I think the framework that does work in this sort of sense I'm guessing is if you take a jobs to be done approach because it's really matter of fact you're like going what is this? Kind of like either customer, machine customer, agent, what is the job that they are trying to do and you have to then match that against are we helping or hindering in that kind of like the uh, achievement of that job to be done and then it becomes outcome based. Are you, does it kind of does it work or not?

Speaker B: Yeah, and that's a really, that's, that's a great pivoting of an existing tool. And this is what I want leaders to go away with. Like, we're not starting from scratch here. We have a lot of knowledge and a lot of tools and a lot of useful things we can do. And that is a great example of an existing tool that we can pivot to this new context to get value and to deliver good outcomes for these new customers who are asking us to help them. I don't know if you've ever tried to get one of the agent platforms to browse the Internet. It's deeply painful. I feel sad for them. I feel sorry for them because it's so bad.

Speaker A: Yeah, uh, no, I mean, um, I kind of like tinker away with some of this, like some of this stuff and keep a watchful eye of it. But um, no, the kind of, the browsing kind of part, I'm like going,

Speaker B: nah, take my word for it, man. It is a deeply painful experience. Have you ever done, and I'm sure people in the audience will, usability testing with somebody who doesn't really know how to use the Internet and is just so confused that you, you know that you're supposed to just wait and see how they use it, but all you want to do is help them? Yeah, it's like that. It's like one of those so painful usability testing sessions.

Speaker A: I remember, I remember a story that I learned from somebody I spoke to on the podcast years ago. And they're uh, they were, what were they? They were like a chief product officer at a. Appeared to be a lending platform called, I think it's Zopa actually. And they wanted to better understand their, their customers experience on their platform. And what they did is they brought a whole bunch of customers into their offices to do some real transactions live on a screen. And then they put a whole bunch of their engineers and their designers and at marketers and comms people in a room behind a, like a one, a two way, kind of like mirror and then watch them. Oh, it was also soundproofed as well. And then watched these customers trying to do uh, transactions. And the amount of hair pulling and screaming that took place when they were trying, watching people trying to navigate some around something and use something that they designed or tried to kind of explain

Speaker B: was

Speaker A: comical apparently because it's like, it's right there.

Speaker B: Why can't you see it? Are you an idiot? Yeah, I've sat in those rooms. Yeah. With all of the designers and product owners, et cetera, just going. But it's obvious. And I'm like, yes, because it's. You've been looking at it for like seven weeks. This person is seeing it for the first time. Yeah, yeah, in there.

Speaker A: And that's a big leveler. And, uh, particularly if you think about it from a. From a machine customer perspective, you like going. And you've made a mention to something about are you kind of machine readable? And I think you have to apply that to sort of, you know, just everything, you know, in this sort of like this, this new sort of space. But. So I think it's fascinating, but I think, uh, the old idea about machine readability and also kind of like thinking about it from a jobs to be done perspective, I think kind of like just brings it down to a level just to think about. Okay, yeah. Are we kind of meeting kind of those sort of things?

Speaker B: That's definitely worth an experiment. And, uh, if people on the court, if people on the show want to have that experiment and report back, I would be really interested.

Speaker A: Yeah, no. Fantastic. One final thing I wanted to ask. Digging into the book, I mean, I think you mentioned the person. You use a kind of almost a fictional character to try and explain this story. And I think the character is called Maya.

Speaker B: Uh, Maya.

Speaker A: Yeah. But later in the book, Maya reflects on what she calls a, uh, failed efficiency revolution. And I wonder if you can tell me about that and what happened and what lessons can be learned from that. Because that's almost like. What's almost happening in that. Something projected into the near future and almost kind of, if we can learn those lessons, then we can maybe set ourselves up for. To try and avoid them going forward.

Speaker B: Yes. Because that one is absolutely a cautionary tale. And for those who, um, haven't read the book yet, Maya, uh, comes into her wardrobe and notices that Tyler, her delegated agent, has bought 17 dresses in different colors because bulk pricing logic said that it was more efficient and that different colors would perform differently in different social situations for Maya. Now, um, Maya doesn't need 17 of the same dress in different colors. And so it's a cautionary tale about optimization without values encoding, which is what I was talking about earlier, where the values at that stratospheric level is what differentiates one person or one business from another. And so nobody programmed in sustainability there. Nobody programmed in sufficiency. Or does Maya actually need this? Um, and so the lesson coming out of that is about the values that you encode or that you fail to Encode into machine customers is actually going to shape commerce for decades. And it's one of these things that we can't fix it after the fact. This is our moment to get these values encoded into the creations that we're putting out into the world. Um, at just. Even the B2C small scale buy me shampoo to the really large scale smart city procurement that needs to buy, um, a bunch of widgets for its waste management system.

Speaker A: Yeah.

Speaker B: Or lighting and, you know, chooses something that's not a great energy efficient, sustainable thing versus something that was cheaper.

Speaker A: Yeah, no, exactly.

Speaker B: This is our moment. Yeah.

Speaker A: I mean, it's that you've got to think about this carefully and systematically and also systemically because otherwise you create kind of externalities that you go, oh, a bunch. My agent could have bought us. We wanted, we needed kind of a thousand it, um, of these widgets for, uh, replace all these things. But my agent seems to have got a great deal on a million of them and a million just showed up and with no returns policy and where the hell we have budgeted for that and where the hell are we going to put them?

Speaker B: Yeah, there's, um, a lot of guardrails and scaffolding that need to get put in place. Everybody's racing really fast to get the payment rails in place and all of the infrastructure working. You know, And China is racing ahead on this, um, just their Chinese New Year campaign to get everybody to buy bubble tea using Quinn, their AI model and then have it paid for and delivered via the Alibaba stack. 10 million orders in the first nine hours, 120 million orders across. Like the whole lifecycle of the campaign. It's. It's nuts. And that's because Alibaba owns the whole stack. Like MasterCard and Visa and Stripe aren't fighting in the middle of it like our, um, our other markets.

Speaker A: And is that all taking place in that kind of that WeChat sort of ecosystem, or is it in a slightly different one?

Speaker B: No, WeChat, that's a separate ecosystem. But Alibaba owns Quinn, Alipay and all of the delivery logistics that sit underneath it.

Speaker A: Right. Okay.

Speaker B: I think it's Tencent who owns WeChat.

Speaker A: Right. Okay.

Speaker B: Probably want to check that.

Speaker A: And they're like this.

Speaker B: Well, Tencent was pretty salty about this whole thing because they've got their own AI play that they're trying to do. But what Alibaba did was took a leaf out of Tencent's book because Tencent about, uh, I don't know, more than 10 years ago, came up with the idea of the digital Hong Bao red envelope for Chinese New Year. And that was the first way that they did this, which is like send digital money, digital Hong Bao to your relatives, to your children, et cetera. And that was an amazing campaign and did really well. So basically they took a leaf out of that book and went, okay, let's do a thing that gets people to onboard and delegate a buying decision to an AI agent. And they spent 3 billion yuan on that onboarding campaign. 431 million USD for those who don't think in yuan. Um, mhm.

Speaker A: Perfect. Oh, one final thing I wanted to ask about, and this is more about this whole, uh, concept of machine customers just broadly. And it. Because I was thinking, I was reading about uh, Nomsa and Kosi in the book.

Speaker B: Another fictional character for the book. Yes.

Speaker A: Yeah. And. But I think it's a fictional character, but it sort of leads to a kind of a point around whether kind of a machine customers or machine customers reason will be for everyone and every business and everywhere. And I wanted to just get your perspective on that because all. Because otherwise it can feel like, oh, it's like tech jazz hands.

Speaker B: Yeah, exactly.

Speaker A: Everything for every, everything for everyone, everywhere sort of thing. And it's like m. The reality is possibly a lot more nuanced than that.

Speaker B: Yeah. Nomsa's story is the one that really needs to keep us honest. And so Nomsa owns a Spaza store in South Africa which is part of the informal economy. So people who run shops out of their houses, roadside stalls, things like that. Um, and Nomsa participated in a fintech startup that is an actual legit in South Africa fintech startup called yoco, who provide point of sale devices for people who are in the informal economy so they can be brought in and participate. It's about financial inclusion. And about 80% of Yoko's customers, that's the first time they've ever taken a credit card. They've never taken a credit card before. And so that financial inclusion is fantastic. But in the story, Nomsa's nephew comes in and says, hey, look at my AI agent and what I can do. And I can buy bread. Look at me buying bread. And so the agent, because it's looking for machine readable places where it can buy bread and actually do the transaction, goes to one of the biggest stores. And so Nomsa has been lifted up by the inclusion with. You can take a credit card now using a point of sale device. But because she has no digital presence and there's nothing there for her to help her participate in this new agentic commerce, then she's excluded all over again. So all of the ground that was gained is lost. And this is something I think is really important for us to consider because if we think about the voices who are shaping this, it's predominantly Western, predominantly privileged people who are shaping what this looks like, at least out loud. Um, China is racing, as I said, but they're doing it very quietly. And so we need to look at who's not in the room, whose voices are not being heard. Because the informal economy just in South Africa alone is about a $6 billion economy of people who just aren't really part of how the ecosystem and money flow works from an official capacity. So figuring out how do we actually create inclusion for those parts of our market and that they don't get left behind and suddenly, uh, excluded all over again is a real conversation worth having. And um, I think you know, at that time, as me and Dean Broadleaf from Yoko are the only two people having that conversation in the whole wide world. Which made me kind of sad. But I'm hoping that putting it in the book and asking people to think about it and inviting perspectives that are not privileged, white Western perspectives to be in this conversation, that we can actually try to create an MCX for everyone, an agentic commerce for everyone that lifts everybody up rather than disenfranchises and excludes people all over again.

Speaker A: Yeah. And I'm sure that things will evolve and also the uh, innovation may happen that are possibly closer to those markets that feel a bit more aligned with those kind of, the needs of those kind of markets. Because it's not about taking ideas and going and then transplanting them. Sometimes it's about kind of figuring out kind of what's the right solution for that particular context and how that particular, uh, context is going to evolve as well. So. But I think it's a fair, it's a completely fair shout. And I love the story. And I just thought actually, you know what?

Speaker B: Yeah.

Speaker A: It might not be for, you know, for everyone and every business everywhere because of kind of what they do and how they operate.

Speaker B: Yeah. They're facing genuine exclusion risk. And uh, you know, this is not a neutral technology story by any stretch of the imagination.

Speaker A: Mhm.

Speaker B: It serves those who are, uh, designing it.

Speaker A: Yeah. No, indeed. And like, like many kind of these kind of like mass, kind of like these uh, technological solutions that have a mass kind of like impact. It's. Yes, there does. They are designed to serve themselves, um, rather than actually conserve them. Which is ironic given it's supposed to be about improving customer experience. But anyway, let's not get into that rabbit hole that's it was for the things that, um, that I wanted to ask, I wanted to, before I move on to some quick fire questions just to wrap up, um, anything else that you'd like to add or highlight before we get into the quick fire?

Speaker B: Yeah, I think that for, uh, for business leaders who are listening, for CX leaders, for people who are trying to figure out how to navigate this, I want to drop the term commercial sovereignty. Because this is the right for your business to set the terms under which machine customers can engage with you. You should be able to maintain your commercial sovereignty. Um, and I think this is a strategic frontier for most businesses that they're not really thinking about. Um, because what's happening is so many of the players in this space, the infrastructure players, the payment Rails providers, the AI players, they're all kind of doing their thing to kind of grab their bit of the revenue and, you know, grab their bit of territory and stuff like that. And uh, just in the last week I've seen an example of those Payment Rails providers not asking merchants whether the way they think that agents can interact with them is going to be okay around Buy Now Pay Later. Right. So that's not just like doing a credit card transaction. Buy Now Pay later is a conscious decision that human beings make to manage their cash flow with associated risks and often under duress because they don't have the money to buy the thing that they actually want to buy. And about 41% of buy now Pay later is defaulted on. And Klarna and Stripe have added Buy now pay later to agentic commerce to allow agents to use that as a payment mechanism, which is a completely different thing from speed spending money that a person might have or have authorized on their credit card.

Speaker A: And the default risk is going to be carried by the merchant. I'm, um, guessing correct?

Speaker B: You are correct, yes. Yeah, it is absolutely carried by the merchant. But Klarna and Stripe did not ask their merchants, hey, do you think that's okay? Are you all right with that? So I think as part of planning your machine customer experience, part of what you have to investigate and decide and articulate is what your commercial sovereignty terms are. How are they allowed to come and interact with you?

Speaker A: Wow, great point. Thank you for sharing that with me. Anyway, and it's kind of a lot to think about, but a few quick fire questions before we um, before we Finish up. So first one is I always ask people to boil things down, give me their best advice. And the way I've been doing that is to ask them to complete the sentence. And the sentence is this. Katya, what would be your best advice? Well, actually that's my kind of me reading my question is like the question, the sentence is, if you want to improve your customer experience, Katya says, do

Speaker B: this design for the customer that you have and be very clear on who or what they are.

Speaker A: Perfect. Now second one, Punk one obviously, um, what company or brand do you think takes a more punk approach to customer experience and why?

Speaker B: Okay, Patagonia is my pick for this one. Because Patagonia is so deeply invested in circular economy and sustainability that they will go as far as to say, don't buy our product if you don't need it.

Speaker A: Mhm.

Speaker B: Don't. Just don't. And if you do buy our product and you rip it, it needs repair, give it back to us, we'll repair it for you and give it back to you. Don't buy a new one. Don't buy a new one. And they're really upfront with how they make the statement that every product that they create has got a sustainability impact. And they're really bold about that statement. And they are open with their data. They've created, uh, the Footprint Chronicles, which is basically data about all of their products. The recycled content, the repair versus reuse, like all of the things that signal out how they live. Their commitments to, you know, creating sustainable futures for us and the planet is embodied in, in all of these activities that they do and in their customer experience. But I think a company that says don't buy our stuff if you break it, don't buy a new one yet still is really successful, is that's punk for me.

Speaker A: No, no, I love it. And also, I don't know if you know about this. You know, they've given themselves to the planet as well.

Speaker B: Yes, yes. This is another thing. Absolutely. They, uh, are they just. Yeah, I think they're just such a great punk CX brand.

Speaker A: Awesome. Love it. Final question before we wrap up. Tell me a good news story, Akatia, because the world is a weird place right now. So let's, let's end on a good news story.

Speaker B: All right, There is a good news story. There's a good news story from literally this morning at the time of recording, which actually comes in from American Express. Okay, so they launched, uh, just yesterday, US time where we're at the 14th of April. They launched something called the ACE, their agentic commerce Experiences Developer Kit.

Speaker A: Okay.

Speaker B: Um, and alongside it, what they also did was they made a consumer commitment that AMEX will cover your losses if a registered AI agent makes a transaction that goes wrong on your behalf. Okay. Right. So what that means is like one of the biggest trust barriers to agentic commerce. It's always like, what happens when the agent buys something I didn't actually want. Well, AMEX answers that question and has explicitly backstopped machine customer transactions with fraud protection. And out of the payment Rails providers, they're the first ones to do it. Now, that said, obviously, it's registered AI agents that it works for. What about unregistered AI agents? Uh, there's a lot of what abouts there for sure. And there is also, you know, hey, we've also got chargebacks and things in place that already does that. So they're not doing anything new. But the mechanism might be similar. But this is the first organization that has publicly made a commitment to consumers that we will take care of it if your agent does the wrong thing and cover the losses. So it's not about technology at all. That one's about trust design. And, um, yeah, I think that that's a really good news story and it's a nice human promise to make in this space where AI is all the noise.

Speaker A: Well, I love that. That's great. Um, so that's it. I just want to say, Katya, one, First of all, congratulations on the book. I know kind of like how much effort it takes to kind of get into one of these things and get through to the end of it.

Speaker B: Thank you.

Speaker A: And just want to say thank you so much for spending some time with me today and sharing your insights and your. Your expertise and your perspective. I think that's been super cool.

Speaker B: My genuine pleasure. It's been fabulous to be on the show and, uh, you know, have a bit of a punk afternoon. I like it.

Speaker A: Perfect. Thank you. Wow, what a great interview. I hope you enjoyed it. I know I did. Find out more about me and the work that I do@adrianswinsco.com do leave a review on your favorite podcast platform. Um, and if you have any comments, feedback, or questions about the podcast, then feel free to send me a message to podcastdrianswinsco.com and, um, do check, tune in again. Thanks very much.