Bridging vision with reality: a candid conversation on AI’s transformative power

In this exclusive interview, Tom Goodwin and Wouter Huygen dive into the complexities of integrating AI into today’s businesses, exploring the tension between transformative potential and organizational realities. They discuss the importance of balancing visionary goals with practical steps, the challenges of scaling innovation within legacy systems, and the human aspect of embracing AI. From the potential of generative AI to the importance of incremental change, Goodwin and Huygen delve into the complexities of driving meaningful progress in today’s fast-paced digital world. This conversation offers a look into the journey of bringing cutting-edge technology to life in a way that reshapes industries while staying grounded in real-world applications.

Wouter Huygen: Tom, you spend a lot of time thinking about data, AI, but also talking to industry leaders. What is the most intriguing question on your mind in this field?

Tom Goodwin: That's a big question. For me, the biggest challenge is reconciling, on the one hand, the incredible opportunities that we have and the extraordinary things that technology can do. And, on the other hand, the reality of people and organizations.

Tom Goodwin: Whenever I work with a big company, individually people are incredible. Individually people are expert, they're  well-intentioned, they work hard. You put all of these people together in a company and things get really messy. Most companies have to deal with cultures that are not great with change. They have to deal with legacy technology that they can't make a business case to change. So by far the hardest and the most wonderful thing I have to do is plot this line between possibility and what's realistic. You know, what can you rethink? How big a risk can you take? How do you make the business case for this change? And that's why I'm really interested to meet you and talk to you guys, because this is what you're doing.

I'm quite often the provocateur, I go in and I say, "Couldn't the world be amazing? What if we thought this way?" And then normally I get to leave before I have to do much about it. So I'm always interested to know in your position what it's like. How do you take the possibilities of technology and turn that into a real action plan?

Wouter Huygen: It's indeed striking the right balance between staying pragmatic and keeping eyes on short- or medium-term opportunities, versus going towards that vision or dot on the horizon, right? The big promise. Because ideally you move as a company to that big promise. We can all reinvent our business with AI. But it's not easy to paint that picture, but sometimes it's possible. But then it's only a picture. And you will only get there step by step. So how do you chop it up in steps that you can realistically take? Where do you start and how do you scope it?

Typically it boils down to defining value pools with use cases and doing that in a smart way. So making a smart assessment about what is the possible or the potential impact of these use cases, how feasible they are, and then doing a common sense prioritization. But that's only the very first step. And then the next step is how do you go the whole journey from conception to MVP, to scaling it, and to changing the business along with it, right? So that it doesn't just become this new fancy thing on top of a process or on top of a business, but that it is actually integrated into your processes - and the processes themselves evolve.

Tom Goodwin: There's always this really interesting tension, I think, between the fancy and frivolous, and new and exciting technology. And actually the degree to which really profound change comes from things that are very boring. So anything you can take a photograph of gets the client's imagination. A picture of a drone looks very exciting. A picture of a robot looks very exciting. In reality quite a lot of companies can be really transformed by people using collaborative software or with a better use of microservices layers or a better way to run meetings. Quite often the most profound technology doesn't look very interesting. So I always find it quite hard to balance the sexy with the profound and the stuff that can happen quickly and looks good in the company report and the thing that may take five or ten years. For me, technical debt is a huge problem because it's very hard to get people really excited about changing the foundations of a company when often you can't see those things.

Wouter Huygen: On that, sometimes the first step is changing a process using data and maybe not even very sophisticated AI, but make some data-driven common sense decisions. That could be a first step out of which you then become more sophisticated. Do you think you need to walk before you can run, or could you run a marathon right from the start?

Tom Goodwin: It's a very good question. I think it probably depends a lot on the business itself and their culture. There are times when if you aim too far, you end up losing people. But there are times where if you're not ambitious enough, by the time things get scaled back, it becomes very incremental.

It's always quite interesting for me because I trained to be an architect rather than something in technology. And for me, physical infrastructure and company infrastructure have a very similar parallel. There are times when you look at an airport and you think rather than adding a cabin or building a new terminal, we should probably just build an airport that's brand new in a completely different place. And I find that these incremental changes - because the capital is easier to justify, because they carry less risk, they can be done more quickly, and people can get on board with more quickly – I find that they tend to be the solution that people defer to. And normally that means that people work very hard just to maintain something that's not quite as good as it can be.

So it's interesting for me when you get a technology that's developing as rapidly as AI, because the growth curve of possibility is so rapid, it almost becomes easier to justify something which is bolder and more ambitious because you can do something with it now and the value that it adds can be so great. But it's difficult. People are not good with taking risks. People are not good with something that can't be quantified. The cost of doing nothing is often thought to be zero, when in fact, it's quite expensive. How do you motivate people to take a risk and to invest in these new ways of doing?

Wouter Huygen: Part of it is the business case. That's by far the most important thing. And it should be a business case that has a sufficient amount of realism to it. Because lofty business cases are easy. Back of the envelope calculations, we can all make them. And then it helps to have experience in actually developing these use cases all the way, up until the point they are embedded, implemented, scaled in an organization. Because then you actually learn what it takes to get there and to get the impact out of it. Just having the wheels running. Next time you are in the beginning of such a cycle it will help make a realistic estimate – not just of its theoretical potential, but how much we actually can get from it. And being able to explain that gives both the inspiration about the opportunity and sufficient tangibility around the reality of things. Those together, obviously, for an executive, is the recipe for saying, "Okay, let's do it."

Tom Goodwin: Does it limit your ability to be incredibly ambitious? What I mean by this is that it's very hard to quantify the value of doing something that's never been done before.

Wouter Huygen: It does. And it depends on the leaders. The ones that dare to take a leap and are a bit more willing to take risks, have a drive to innovate in their industry – those are the business leaders that stretch the boundaries.

Tom Goodwin: This event is obviously mainly focused on AI, but AI as a lever for accomplishing more. Are there particular elements or aspects to AI that you're most interested in personally or most inspired by?

Wouter Huygen: I'm curious by nature. I've always been curious about technology. What I like about AI is everything that doesn't work yet or is not yet very clear. Once it's clear, I tend to lose interest a little bit because then I know how it works. So this whole new wave of generative AI is actually, for me, a candy store because there are so many unknowns. There's so much stuff still to be found out.

At the same time, what I'm inspired by is not so much the technology per se, but what you can do with it. In that sense, we're shifting from what we now call traditional AI or predictive AI (which is basically machine learning, predicting quantitative values), to a whole new class, which is generative AI. I'm interested in both. This new class – GenAI – catches my attention because it has all these new promises, up until autonomous agents and autonomous organizations. But what I also wonder about is to what extent we are done with the first wave – that is, traditional or predictive AI. If I look at most organizations, they are still a long way from capturing the value from that.

Now everybody is all over the moon with GenAI. So there is, you could say, too much focus on the hype and the technology, but that's not what it is about. It is about what you do with it and how you change your organization with it. And you should not separate these AI classes. Actually, in most use cases, they blend, they work together. It's about systems thinking and how do you use different technologies to create new possibilities to innovate the way you serve your customers, or how you run your processes.

Tom Goodwin: A very interesting time, I think, because every time there's a new demo or a new launch from an AI company, it seems completely magical. But sometimes it doesn't seem that helpful. So I'm really interested to know what happens in the next year when people like you or I are comfortable enough with what this technology means to start bringing together user needs, bringing together companies' needs and then bringing together the technology and to make a much better way to do things. At the moment, it seems very pushed by Silicon Valley. It's like a metaphorical laboratory with people wearing white coats, juicing a formula. I'm fascinated to know what we're going to cook with it and what does it taste like.

Wouter Huygen: That's indeed the smoke and mirrors that I tend to talk about. And it's very hard: I read about it daily, maybe an hour per day. And even I have many open questions or can be confused. So let alone people that don't spend that much time on it. We've all seen the Klarna case. They automated 700 agents or so. And I wonder: is it actually a very sophisticated Q&A engine? A sophisticated chatbot? Or did they really integrate into the back end? Does the chatbot talk to back end systems? Does it solve problems? I don't know.

Tom Goodwin: This is the question. People are so quick to adopt these things that have become a UX layer. But actually, it's the system below that really matters. It's great to talk to a chatbot that sounds like a human, but does it actually have the power to refund me? It's great to have a chatbot that tells you if your flight's running on time, but does it let me change to a different flight? And it's fascinating because it's the stuff beneath the surface, these things can completely transform the world; they can give us better experiences than we've ever had before; and they can stop us having to phone up a call centre and wait for 30 minutes. Or they can just become another thing to get frustrated with before we have to pick up the phone and do it a different way. It's a very interesting time, I think.

Wouter Huygen: We were talking about chatbots and what is really the state of the art. And whether it's more than an interface, whether it's actually technology that is able to integrate with other systems to interact and carry out tasks. And part of that, obviously, is also the whole question around manageability and performance bounds on these agents. If you look at different companies applying this technology in a real customer facing setting, what is your view on that?

Tom Goodwin: We are very early on in this. I think companies have been very quick to embrace this because they think they can save a lot of money. So we can talk about Klarna and their apparent success so far. We could also talk about Air Canada having a hallucinating chatbot. We can talk about lawyers that are going into cases badly prepared. This brings home to me something which I'm really, really passionate about, which is that these decisions shouldn't be driven by the CFO. They should be driven by someone more like the CMO. Like technology is always a lever to get more out. And AI is an amazing lever to our humanity and our brains and our empathy. And I don't want people to put less in and then multiply it to get the same out. And I don't want people to put nothing in and get very little out. I want people to put the same energy or more into this and get even more out. So it can't be how can we have chatbots do our jobs. It should be how can technology make people who do customer service be even better at their jobs? How can it listen to conversations and suggest information to assistants? How can it automatically do the mundane work in the background? How can we keep humans in the loop? How can we do what we've always done with technology?

Technology has always made our jobs better and more valuable and more human by being a lever to us. Because of ATMs [cash dispensers] the people who were handing out money all day got to have conversations with clients and probably upsell them on a mortgage. How can we use this to make sure that we do better work rather than stuff cheaper and faster? What's your vision for AI? Rewire has been very, very quick to embrace this technology and to really understand what it means. What's your vision? How do you see everything going?

Wouter Huygen: Interesting question. I think the divide between the technology and the pace of change in organizations is going to increase, unfortunately. So there's a lot of talk about the pace of change when it comes to generative AI, the fact that it will come to market faster than new technologies did in the past. There is some truth to it because the infrastructure is there. Ten years ago or so, all these algorithms already existed. You could build an XGBoost model with a flick of the switch. However, the infrastructure to implement those algorithms at scale in an organization and embed them in a process was not there yet. So there wasn't kind of a vehicle for AI to diffuse in organizations. By and large, most large organizations are there now. So there's a fertile ground for GenAI, and yes, that will go faster. But as previously discussed, it's much more the fundamental change in terms of how organizations work, operate and how they create value. That's going to be the real change. And that's still going to be hard work and will require creativity in terms of rethinking how we can deliver value to our customers, how to change our systems and operating models and all the rest of it. That's going to take time.

At the same time, the technology will rapidly evolve and there will be all kinds of new applications coming out, but that's on top of incumbent businesses. And I think that the topic du jour, when it comes to GenAI, is whether this pace of change is going to continue. Because the industry says yes. And they all go by the scaling hypothesis, which until now still seems to be valid – more compute, more data, and performance will increase. But there are also AI researchers who point to the technology in generative AI, and give evidence that its flaws are a feature of the technology. And that won't change by scaling it even more, let alone that at some point we'll run out of data. In that case, we will hit a plateau somewhere in the coming years. And is that a problem? No, not at all, because there's so much value to be created with what we got in the last  few years. And how that has to be adopted by organizations and how organizations can innovate with that. So, to me, it doesn't really matter, whether we hit the plateau or not. Obviously to the industry, yes, because at some point the valuation of OpenAI will drop if that's the case. But to most organizations globally, it's irrelevant.

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About the authors

Tom Goodwin is the four time #1 in “Voice in Marketing” on LinkedIn with over 725,000 followers on the platform. He currently heads up “All We Have Is Now”, a digital business transformation consultancy, working with Clients as varied as Stellantis, Merck, Bayer, and EY to rethink how they use technology. Tom hosts “The Edge” a TV series focusing on technology and innovation, and “My Wildest Prediction”, a podcast produced and distributed by Euronews. He has published the book “Digital Darwinism” with Kogan Page, and has spoken in over 100 cities across 45 countries. With a 23 year career that spans creative, PR, digital and media agencies, Tom is an industry provocateur as a columnist for the Guardian, TechCrunch and Forbes and frequent contributor to GQ, The World Economic Forum, Ad Age, Wired, Ad Week, Inc, MediaPost and Digiday. To find out more about Tom, visit www.tomgoodwin.co.

Wouter Huygen is partner and CEO at Rewire.