“AI is fantastic, but we also really need to think about how we, as humans, thrive in a world that is AI-powered.”

Anneke Quinn-de Jong is the perfect guest for this episode of The Persuasion Game podcast, where we take a human-centric look at the world of Artificial Intelligence.

It’s easy to be blown away by the technological advances that have been made, but where do people fit into the conversation?

Anneke believes the way we work, live and learn is fundamentally changing, the question is: are we ready for the scale of change that’s seemingly heading our way?

The conversation covers the impact AI is having on insights teams, how to get the most out of the technology, and what needs to be done to make sure the next generation of workers is ready for an AI-powered world.

Anneke was  the former head of Brand and CX Insight at Philips, and has since set up her own consultancy, AI & U, which helps companies and businesses adapt and thrive alongside AI.

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Want to know more about us? Visit our website here: thisistheforge.com

This is an 18Sixty production for The Forge.

CHAPTERS:

(00:00) Introduction

(01:51) Understanding AI and Generative AI

(04:26) The Human Element in AI

(09:32) AI as a Sparring Partner

(14:34) Preparing Tomorrow’s Workforce for AI

(18:32) Human-AI Collaboration Success Stories

The Persuasion Game is available on all your favourite podcast apps: https://link.chtbl.com/PersuasionGame

Episode transcript:

Anneke: AI is fantastic, but we also really need to think about how we as humans, with our human intelligence, thrive in a world that is AI powered.

Adam: Welcome to The Persuasion Game, The Forge’s podcast about growing brands and persuading consumers in the modern age.

Good afternoon, Laura.

Laura: Hey, how are you?

Adam: I’m really well, thank you. I am very, very excited about today’s interview.

Laura: Yes, we are very lucky to be having a human-centric take on the world of AI today, right?

Adam: I know we’ve been a bit in a bit of an AI series almost. Officially, unofficially.

Laura: I won’t say we’ve cracked it, but we are starting to understand it.

Adam: So look, today we’re really excited to be welcoming Anneke Quinn-de Jong.

She worked at Phillips for 14 years in the insight team. The final role that she had there before setting up her own consultancy was the head of brand and CX insight. Now she set up her own consultancy, so she’s the founder of AI & U. It’s a human centered AI consultancy. It specialises in helping corporate startups, scale ups, understand the world of AI, and helping the individual teams and organisations thrive in an AI powered world.

Laura: I think we have so many questions to ask both personally and for our audience.

Adam: Yeah. Like what is AI?

Laura: Speaking of, we have actually asked Anneke to help us with a quick primer so that, well, I was not staring blankly the whole way through as she got into increasingly obscure three letter acronyms.

So, we’ll hand over to Anneke to give us a little bit of an overview about what we are talking about and then we’ll get into the interview.

Adam: And just before we hear from our guest, a quick ask from us. We’d love it if you can tell one person about this podcast in the office or on a call today, or leave us a five star review in a rating wherever you listen to your podcasts. Okay. Here’s the interview.

Anneke: Alright, I’m gonna tell it to you in quite simple language. Yeah. Because otherwise it’s gonna be too lengthy. But think of AI or artificial intelligence as the technological fields that’s focused on creating machines with human-like capabilities. So, problem solving, reasoning, et cetera. Gen AI is then the sub fields that actually can create new content. So if AI is the bigger field, which is all about recognising patterns and algorithms and making predictions. Generative AI can actually generate new stuff like images, video, and obviously text.

And then within that it’s probably quite important to also note that large language models, LLMs is also a term that is often used and LLMs are actually the part that are focused on language. So if you think about Chat GPT. GPT 4.0, which a lot of people are using, is actually a large language model. And around a large language model, you can build applications. And these applications is what we often call wrappers because they’re not actually the models themselves. They’re not being trained, but they are application software that are built so that they can be used for specific business applications, for example.

So think about Microsoft Copilot, that’s a wrapper. So if we go back to the large language models, there’s only kind of a handful. And the most well known are of course Chat GPT. But we also have Grok, Claude, et cetera, DeepSeek from China. Those are the large language models.

Laura: Perfectly put. That’s brilliant.

Adam: Excellent.

Anneke: Yeah.

Laura: Yeah, that’s great.

Adam: Anneke, could you just share a tiny bit about your background and what led you to start AI & U?

Anneke: Yes, of course. So most of my career I’ve worked in innovation and insight. So that means that I’ve always looked at new tools and new ways of understanding consumers. I’ve always been very interested in that area.

So when at the end of 2022, generative AI kind of became mainstream with the introduction of Chat GPT 3.5, myself and my team are very keen to start experimenting with it, which we did. And as we did, quickly realised looking at the output that this was something big, this was something that was gonna change all of our lives.

This was gonna change the way that we work. And the more I looked into it, the more that feeling rose like, hey, this is bigger than we actually think it is. At the same time, I realised that most people were really talking about the technology. So they were talking about IT infrastructure, the algorithms, the pure tools that they could generate, but no one was actually talking about this change as having an effect on humans.

Even back then, but even more now, we know that all of our jobs are changing and have already changed and will change even more. And a lot of the skill sets that we currently have will become redundant and there will be new skill sets that we need to develop.

So, that feeling became bigger thinking, there is something here, which I don’t think we’re paying enough attention to – there is this human element. And that’s when the idea came to start a consultancy, which I then called AI & U, which is really about helping individuals and organisations understand what these changes are, but then also really try to help them take ownership over these changes. So we don’t just let it happen to us, but we can steer it and we can steer it in the right direction. And we can work towards a, what we call, co-intelligent future where artificial intelligence and human intelligence thrive together rather than just thinking of AI as a technological tool.

Laura: I love the way you’ve explained that, where you talk about it being not a tool, but a capability, because I think that absolutely captures how people were thinking about it initially.

Anneke: Yeah.

Laura: Like, oh, it’s another instrument that we need to master, as opposed to if this capability comes in, our capability’s gonna have to flex and adjust accordingly.

So even in that explanation, I think you’ve made it feel more accessible.

Adam: Absolutely. And you must have a huge amount of, like now insight into different organisations and kind of the stage that they’re at in adoption and who’s, I guess, embracing it and, and who’s not. What would your take be on where as an industry, if we were to call, I don’t know the insights function, the marketing function, how are people embracing it and like where are we on the adoption curve?

Anneke: Yeah, before I give you a straight answer to that, let’s think about this term adoption a little bit, because I think there’s a lot more to it than just adopting something, like learning how to work with it and, what we often see in organisations that when it comes to AI adoption, there’s three waves.

Where the first wave is very much about efficiencies and cost cutting. So a lot of organisations their starting point is, oh yeah, we need to use AI. We need to make sure that we learn how to use AI so we can become more efficient.

The second wave of AI is when organisations start thinking about AI as something that can augment what they’re doing. So it’s improving quality. Yeah, getting more quality outputs.

The third stage is actually really about disruption, because if you think about it, AI as a tool, it’s amazing and it can really trigger our own thinking and our own brains as well. But not many companies are there yet. They’re just thinking about it as an efficiency tool.

So this whole idea of adoption, I think has different layers. What I see in general with a lot of organisations that are still in the first wave, and I say still because I think it is a bit of an evolution, and a lot of them are coming to me and they say, oh, we need to do something with AI. And when I asked them why, I don’t often get a straight answer.

Laura: Because it’s there!

Anneke: And I think that’s one of the first questions I always ask, why do you want to start with AI? Because if you don’t link it to your business purpose, to your business needs, then again, it’s just a tool for the sake of the tool. So that’s a little bit about adoption and if I go back to your question, what do we see in the research industry? I think it’s not very different from what I see in other organisations in other domains as well.

But one of the things I do see, I think, in research is that of course a lot of the work lends itself for having AI as a partner or as a tool because it is about having a sparing partner if used in the right way, right? And trying to get to new insights. It needs to be done in a way that AI is also triggered to start thinking about these new insights. And I think at the moment what we see in the research unit industry is that people are trying to find that balance between trying to become more efficient when it comes to data analysis or transcript analysis, et cetera. And at the same time trying to think about how can we get to better insights?

So a lot of the companies are still searching for that. And we also see a lot of experiments with synthetic data, synthetic personas. And no one has found, of course, the holy grail because there is no holy grail because every organisation needs to figure out how they’re gonna work with AI themselves. Yeah.

Laura: You used a really interesting word when you said sparring partner.

Anneke: Yeah.

Laura: And I think that’s a really interesting dynamic, which kind of runs counter to how AI is often set up, which is to be harmonious and to be agreeable. And I’ve read a few strategists online talking about how they sort of prime AI to be more of a challenger, to be more combative.

Do you have any tips on how people should actually try and set up that augmentation to thinking, rather than like, “Yes, what a great idea! You are really acing life today”, that kind of vibe that you can get back sometimes.

Anneke: Yeah, of course. And there’s of course a more than a dozen different frameworks out there that you can use to create better prompts, right? So make sure that you give enough context, but also give your AI a persona. So say like, “Hey, you are the most critical CEO I’ve ever met, and I’m gonna present an idea to you, and I want you to really, really, really shoot it down and give me all the negatives”, for example, because you’re right, AI in its essence is trying to please you.

So it will always say, that’s a very fantastic point. Ooh, great insight. So you feel really, really good. You’re like, yes, I’m on a good track. But if you really want to break it and you really want to get further than just, you know, what everyone else is getting, you will need to prompt it. And you can ask things like I want you to find, an insight in all of this that your average person will never find, for example, and then you already send it out on a different path because it will try to look for different things.

Laura: And you actually mentioned that this was a strength of AI compared to humans in the past as well. I remember when we were first talking, you said, you know, humans tend to follow along a very linear path in terms of how we think and the data that we get, that sort of supports our way of thinking.

But AI is different in a way that can be a strength. Can we speak a bit about that?

Anneke: Yeah. And, maybe let me do a little experiment with you guys. I’m gonna ask you two questions, right? And I want you to give me the wrong answer. Yeah? So the wrong answer. So here’s your first question. What country are we in right now?

Laura: France,

Adam: Belgium.

Anneke: My second question, and again, give me the wrong answer, is how many fingers do I have?

Laura: Seven.

Adam: Nine.

Anneke: Thank you very much. So that’s the first part of the experiment. I’m now gonna tell you what my answers are. So what country are we in? Wedding dress.

Adam: Uh…

Anneke: How many fingers do I have? Banana.

Laura: This sounds like my son’s jokes!

Anneke: And my jokes as well.

Adam: I like his sense of humor.

Anneke: But you see what I’m getting at, right?

Adam: Yeah.

Anneke: So even with a clear permission to give the wrong answer, your brain is wired to default to these categories that are expected. So you still answer, oh yeah, a country or number of fingers. But actually our brains are also capable of breaking free from that.

Laura: Mm.

Anneke: But we need to allow our brains to do that. And the same with AI. AI is built on patterns, and these patterns come from us. So if we ask it what is expected to be asked. It will give you answers that are expected. So if you want to break those patterns, you just need to also break your own patterns and think outside of these expected categories, and that’s when you will get the most out of AI.

Laura: And that’s kind of what you are championing, isn’t it? Like the importance of the human element and the right kind of human element that you bring to the process.

Anneke: Yeah.

Laura: What do you see as the best opportunities that it offers in terms of marketing and insights, capabilities? What do you think could be really exciting?

Anneke: Well, actually, I think part of what we were just talking about breaking patterns and being able to go further. I think that is the heart of insight, isn’t it? It’s the heart of research in general. Researchers are very curious people. They like to explore different things.

So I think researchers, maybe I’m a bit biased because it’s my own background, but I think they’re one of the typologies of people who are probably best posed to work with AI because we know how to ask questions. And there’s plenty of people out there and, and especially maybe even the younger generations who are still learning how to ask questions, who actually do not know how to ask deeper questions and who will stop with the first answer. So I think researchers as a profession have that huge advantage that we tend to know how to ask questions, and we tend to know how to ask deeper questions to really get to something which feels like a gem, feels like an insight. So I think we can capitalise on that in the way that we interact with AI as well.

Laura: It’s gonna be so fascinating, isn’t it, for the next generation of children because we already hear so much discourse about how people learn differently and look at the world differently.

Anneke: Yeah.

And actually we’re gonna realise that there are gonna be some types of intelligence that are really uniquely capable of asking these kind of more adjacent questions, these more provocative ones that perhaps in the current school system or the way we currently look at skills won’t be there. It’s quite exciting from that perspective, isn’t it?

Anneke: It is exciting, but also frightening a little bit because if you would ask me what would I lay awake for at night, then it is actually education and the younger generation because I’ve got a huge respect for education and educational professionals. But quite frankly, for most what I’m seeing is they’re really lagging behind when it comes to this, and they’re so stuck in their old ways of educating and learning.

And what we already see at the moment is this huge risk of what we call cognitive offloading. Or another term that we use is lazy reliance on AI, because the easiest thing is, of course, when you have a question to offload it to AI and you get your answer. But that means that you’re not thinking yourself anymore, right? And it makes me think, I recently visited Athens and of course Athens and great philosophers came from Athens as well, and Socrates walked around there and spoke to people about critical thinking. He was actually afraid that if we would write down stuff people would stop to think because then it would already be available in a book. So why would you think yourself? So even back then, they were worried about it. So, okay it’s reassuring that we can still think of how many thousands of years later, but I think in this case it becomes so easy.

Laura: Yeah.

Anneke: to not think for yourself.

Laura: We only have to see people driving Waze into a river or whatever and then being like, oh right. You know, the Waze told me to do it. And like it’s quite easy to switch off.

Anneke: Absolutely. Yeah. So I think, yeah, that’s something that we need to be really aware of and to be honest, doesn’t get nearly enough attention in my opinion. AI is fantastic, but we also really need to think about how we as humans, with our human intelligence, thrive in a world that is AI powered.

Adam: You know, you’ve got two pictures of the future, the utopian and the dystopian, and I think on the utopian side, it’s, we will offload a lot of this kind of process based stuff that we don’t want to do and have lots of freedom and time to be creative and kind of be, and it it’ll push us to be more human or the most human versions that we can be, you know, the creative and the fun and the emotional.

But yeah, there is a very, very kind of strong nagging thought in the back of my head that it’s gonna be a mix of those two. Right? So some of those things but, where are the jobs of tomorrow going to be? And you know, how do you get the next generation into the world?

Anneke: And I think it’s also, how do we manage their mental state? And there’s been research that was published, which was amongst Gen Z in the United States. 52% of Gen Z, the older Gen Z, thinks that their study is redundant, a waste of money because they’ve seen the world change so much in the last three years or so, that they think that what they’ve learned at school is no longer valid. We need to help this generation. We need to help them figure out, what are those skills that are gonna set you up?

And even if we don’t know it, because I don’t think we know it exactly, we have to help them with the knowledge that we currently have. I’m a positive person. I am always like glass half full. So I think we’ll find a way, but we cannot just let it happen. We do need to play an active part in that.

Laura: So let’s think about some examples of where human intelligence and AI have actually worked really well together.

Anneke: Yeah, and the first one that springs to mind actually is, a paper that came out a few months ago, and Ethan Mollick was the author of it. Ethan Mollick is also the author of a book called Co-Intelligence, which is a little bit my bible, because Ethan Mollick really explores that whole field of human intelligence working together with artificial intelligence and really partnering rather than just seeing it as a tool.

But in this research, MIT, Ethan Mollick and Procter & Gamble worked together on an experiment, and in the experiment they had four distinct groups. And each of these groups worked on specific business challenges, real business challenges, and the first group consisted of individuals. The second group was an individual paired with AI. The third group were human only teams, and then the fourth group were hybrid teams, so those were teams working together with AI.

Now, some of the outcomes of this whole paper might be as we expect. So the group that performed best in terms of speed and quality output was the fourth group, so the hybrid groups where humans worked together with AI. So they had a faster output, the quality was better. And this was of course all measured on objective benchmarks. But one of the really interesting outcomes was as well that the experience of working was most positive for that fourth group as well. So the emotional experience of the people in that group was superior to all of the other ones. So that means that actually people also enjoyed working together more.

And where the paper doesn’t really drill into this, I think a hypothesis could be that the knowledge is a little bit more accessible for everyone.

Laura: Yes.

Adam: Yeah.

Anneke: So you still have experts of course, but the people who are maybe considered a non-expert on that specific topic can still enter a dialogue because they have access to at least a really solid grounding of knowledge and facts that can allow them to enter a dialogue and ask questions.

Adam: Yeah.

Anneke: So it was really interesting to see that outcome in a quite large pilot because I think there were 776 people involved in this. So it was quite a big experiment that they did.

Laura: You’ve mentioned a couple of ways that actually AI can be a democratiser, right? So it can give people ideas faster and give them more of a seat at the table, and it can also, as you say, create a sort of a quality of ideas so that people can focus on what really matters and what we’re going to do about it.

But we are still seeing a few barriers there, aren’t we? We, that we’re not quite at that golden age of everyone just being totally enabled by AI. What do you think that’s about?

Anneke: Yeah, I think a lot of organisations are maybe struggling at the moment to implement AI because initially I think when Gen AI burst onto the scene, as we refer to, in the beginning it was seen as a technological challenge.

So IT teams were put on it like, hey, we need to invest in tools, let’s do it. But what they didn’t realise necessarily in that time was that the success of your technology or your tool really hinges on people working with it and people using it. Otherwise, it’s just gonna be a technology and it’s not being used.

So I think now almost, well, two and a half years along the line, we see that a lot of companies have actually walked away from some of the AI initiatives. I think there’s actually staggering figures where 80% of the AI projects have not maximised on what they could potentially achieve. And I think 42%, or along that way, actually have been abandoned because people haven’t seen the results that they wanted to see.

And I think the blind spots are really into two categories. So one is people seeing the change only as a technological change as already talked about, and not taking that human element into account. So think about change management, think about culture shift, mind shifts, talk about skills developments, career paths, et cetera.

That whole spectrum of things that have to do with human elements. And the other one is as well, that people tend to focus on the kind of the magic and the shiny tool syndrome as we call it, where they think like, okay there is one tool that will solve everything – which quite frankly, I haven’t found it yet.

Adam: Yeah.

Anneke: And I don’t think we’re gonna find it. So I think it’s, yeah, these two blind spots that are holding people back, or organisations back at the moment.

Laura: That’s quite funny. It’s very human, isn’t it? That we just want simple narratives and not complexity and we want the technology to do the same thing.

Anneke: Yeah exactly.

Laura: Just be a simple one size fits all.

Anneke: Yeah.

Adam: I think that’s what’s so challenging about it, right? It’s so much easier in human nature to go, right that is the tool.

Anneke: Yeah.

Adam: And I still do the same thing that I did before. Like actually what we’re talking about here is transformation.

Anneke: Absolutely. Yeah.

Adam: And everything needs to change the systems, the tools, the processes, the ways of going about it, the behaviors. And that’s what I think is so difficult, I think. And I think when we first spoke about this, you mentioned there were a few typologies of different people.

Anneke: Oh yeah.

Adam: There were the scramblers and the…do you wanna share a bit about who they are?

Anneke: Yeah. This is actually, I stole this from a podcast with Brené Brown who was talking about, she actually did a great series of podcasts already a few years old now, but it was about AI and she spoke about ostriches and scramblers. So people basically react to AI in two ways. And there’s a spectrum.

So you’ve got those people who stick their heads in the sand and they’re like, this is not happening, this is not happening. And you know, it will go away. And if they need something from me, I will hear it. But for the time being, I’m just closing my ears.

And then there’s the other end of the spectrum, which we call the scramblers. And the scramblers are like, oh, this is so amazing. And I’m just going to run ahead and I’m going to try everything. And they really don’t think about the consequences. So they just like want to be on the front. They’re running, they’re pushing everyone to the side. And of course a lot of people are somewhere on that spectrum.

But think about it. If you have an organisation, there will be a mix of people there. Yet, the way that AI is often implemented is like one dimensional, is one way. But how are you gonna get your scramblers? How are you gonna get your ostriches all on board? And this is on a team level, this is an organisational level. So I think that’s a very interesting way of looking at people and it can even differ per situation, right? So if it comes to maybe more confidential data, I might be more of an ostrich or if it gets really scary, like, ooh, what people are gonna actually play with AI, I might be an ostrich, but in other situations I might be a scrambler.

But it’s a helpful way of thinking about how people react to it and it’s not one dimensional. At all. But yet we approach it often as a one dimensional thing.

Adam: I mean, I can absolutely see ostrich tendencies of mine with some of this stuff, but also scrambler stuff too.

Anneke: Yeah.

Laura: Often at the same time, right?

That was wonderful. I really enjoyed that. Thank you so much, Anneke.

Anneke: You’re welcome.

Adam: Thank you so much for coming on Anneke. That was a brilliant conversation.

Anneke: Thank you.

Adam: So Laura, I thought that was a brilliant interview. I’ve been looking forward to that one. She really helped us bring the human point of view into the AI discussion. I think so much of it up until now has been about tools and tech and process, but actually what we’ve really understood here is like some of the human barriers that are kind of really preventing AI take up in the way that we probably need to happen.

Laura: Yeah. And also I think putting it back on us in terms of responsibility, so it can be very easy for all of us, including me, to feel like all of this stuff is happening to us. And you know that it’s very hard to sort of work out how to respond and I think have very human insights about scrambling or ostriching is something we could all relate to.

But really asking ourselves, how are we going to use this capability? How are we going to bring teams along with us? How are we going to be more human in a way that actually makes sure that we are bringing that human intelligence and AI equation together is a really good one.

It’s not got easy answers and I think that’s why her provocations are so interesting. But I think it does remind us that this is a great power. How are we gonna wield it? And doing so responsibly is the key for it becoming successful.

Adam: And we haven’t lost our agency in this, you know, this stuff hasn’t been built yet. We’re still learning how we’re gonna implement this complete transformation that’s going to happen. So it’s all up for grabs.

So I think personally I’m gonna take out of this that I need to now look at almost everything I’m doing and working out which bits of it can be turbocharged with AI, which bits of it I actually absolutely need the human touch. Where can we scale things up? Where can we find efficiencies? I’m gonna come away from this interview looking at everything that we’re doing at The Forge in marketing from this perspective, but I think everybody listening to this podcast is probably thinking the same thing in terms of their role.

Like whether you’re a scrambler or you’re an ostrich in this, I think it’s now time to take action and think about what it is you’re going to be doing next.

Laura: And I think another thing that she said that was super interesting was that, almost a framework of, where does it democratise?

Where does it bring people into the conversation? Where does it make more parity between the conversations you have and where does it start to exclude humans, either excluding your own brain because you’ve kind of switched off, or perhaps not letting people who are earlier in their career journeys on the path, or people that aren’t feeling as proficient in terms of understanding the capability. How are you making sure that you bring people along with you?

Adam: I think that’s a really, really good point.

Laura: Well, what a meaty topic, but delivered in such an engaging way. We’re very lucky to have Anneke on board.

Adam: Yeah, excellent episode. Looking forward to the next one.

Laura: See you next time!