“From AI anxiety to AI agency: how design leaders can navigate the shift" ft. Melanie Yencken

[00:00:01] Melanie:  Now, literally anyone can build anything, and so the only differentiator in product is gonna be the level of quality, how much it actually solves the problem, the craft, the experience. But you're already seeing so many products that are just AI slop, like, they all look the same. 

[00:00:20] Naomi:  Welcome to The Future is Human. I'm Naomi Tricke. And in this podcast, I talk with leaders from tech and hospitality businesses about how both they and their people are navigating the edge between humans and technology in an increasingly automated environment. We break out of the mould of a standard business conversation to hear more informal perspectives and reflections to understand what people are really thinking about the future of work.  

[00:00:49] Naomi: Welcome to The Future is Human. Today, I am joined by Melanie Yencken. I think this is a correct title: Senior Director of Product Design for Growth and Premium at LinkedIn. So, Mel is shaping how AI native experiences are designed, built, and measured at LinkedIn. And Mel and I have known each other for quite a long time now in our previous life, but you've had an incredible career, really, sort of spanning Google, eBay, and now LinkedIn. And you've led design teams across the globe, and you've really championed user-centric products, which are now used by millions of people. And I think what's super interesting as well, and we're gonna talk a bit about this, is you're increasingly starting to make AI a core part of the product development process, but at the same time, fostering cultures where designers, engineers, product builders can experiment, collaborate, stay connected to their audiences, and design remains at the heart of this. And then I would be very remiss if I didn't also mention something from the, so you're the founder of Tech Ladies, aren't you, which is a member network empowering women in tech.  

[00:02:00] Melanie:  Yes.  

[00:02:01] Naomi:  And I think that in a world where technology is changing, how we build and who gets a voice, I think you're really helping shape the next generation of designers, leaders, and creators. And I know that shaping the next generation is a really important area for you. It's an important way in which you think about your work. So, I'm really thrilled to have you on the show, so thank you. 

[00:02:24] Melanie:  Thank you. I need to hire you for my PR. That was a lovely introduction. 

[00:02:28] Naomi:  Anytime. You make it easy. So, I'm just gonna kick off with some questions, and let's get started. So, you've built this really incredible career, and it's at the intersection of design and technology to the point at which you're now undoubtedly a leader in your field, as I said, developing and impacting others on a similar path. What was the moment that first inspired you to pursue a career in design, but not just design in its purest sense, but one that sort of blends technology with human impact? What was that moment? 

[00:02:59] Melanie:  A kind of random and kind of a funny story, but I was actually specialising in science, doing physics, chemistry, biology, and advanced mathematics. And I was gonna do veterinary science, and then, actually, did a placement at a vet. So, I was like, “Hey, I wanna experience this.” What will this actually be like? I wanna be with a vet and do that, and then, like, really have deep empathy and understanding, and I really hated it. I literally passed out in the operating room because I just love animals too much. And I'm so glad that I had the instinct to go and just, like, live the experience, and that's kind of a theme of what I do in my job, is, like, how do we put ourselves in the shoes of the user themselves? And then I had a passion for art, so I was like, you know, I'm just gonna specialise in design and ended up studying visual communication. And then I graduated as, like, a graphic branding designer. And as I was just doing those projects, I would visualise the brand on the digital assets that the company would have, like the website or whatever, you know, the app. And I loved that part of the experience, and part of it for me was that the brand itself was very subjective, but the digital experience was something that we could quantify. And, like, I could test and research and understand and, like, really build a solid rationale about why this thing solved a problem for a person and was the best way of solving that problem and quantify it. I've been someone who potentially had that analytical background, was very motivated to move towards that, but always at the heart of it, like, understanding what is the real person behind this, what is it they're trying to achieve, how can the thing actually just be an interface that helps them achieve that thing. And, yeah, that's how I ended up eventually in digital design, where I am today. 

[00:04:52] Naomi:  Amazing. And I think what's really interesting and what's formed as a component of our conversations in the past and now really is that you are a very strong advocate for design to have a seat at the top table. So, it's not just design because branding matters or anything like that. It's actually at the top table, and I'm really interested in how you think about that. What does it look like for design to influence not just interfaces, but business roadmaps, ethical decision-making, and so on? And in what ways is this new sort of AI-led world affecting the need for design, like a need for design to be sat at that table? 

[00:05:32] Melanie:  So, I think the way that this is done effectively is where design is set up to be a true partner with product management and engineering, and the roles between those three functions actually get quite blurred. And that's what we're seeing in AI, and we'll talk a lot more about that later. And design, instead of just being responsible for the mocks, the design of the UI is acting as a facilitator for product and engineering to, like, more deeply understand the problem, to partner with research, and, like, really get close to what is it that customers are actually doing, what do they care about, what are the problems that they're experiencing, and being the voice for that in those discussions. And then creating this sense of design thinking, which is essentially that customer obsession, and then using creative problem solving to explore what the potential options are, using the other functions as an extension of that, like, actually bringing product engineering along with the process. We used to do that pre-AI. We had design sprints. We would, like, bring everyone together. We would go through this whole process. It's like AI is definitely changing how we work even more, but getting a seat at the table is earned, earned by showing that you actually understand the business and you're able to translate customer obsession into business impact. And this is where I've definitely seen some designers fall short, is that they don't speak the language of the business and turn what their idea is into, like, hey, this is how solving this problem can actually help us achieve whatever goal it is that we've set out overall. And that's where more partnership with product management can be extremely effective because they're much more likely to already speaking the language of the business, understanding, like, how we were able to improve a certain metric or whatever it might be. And so I've seen design get a seat at the table by proving that they should be there and then having other people say that they have to, inviting you there and actually saying that it's essential to have that, to actually create a great product that will help us deliver impact. 

[00:07:35] Naomi:  Has it changed how you think about creativity, having to demonstrate that business value and reshaping that narrative? 

[00:07:44] Melanie:  It's like, what is creativity? You know, you can make a creative, beautiful thing that doesn't solve anything, and then what's the point? So, like, I'm obsessed with creativity that actually helps a human achieve something, and sometimes the most creative solution is something that's almost invisible because it's just receding away and helping the user actually achieve the thing that they wanna do. And, actually, those designs are the hardest to make is to strip it back to what is truly just the most essential thing to get out of the way and help the person just do the thing they're trying to do, which is why I think, you know, we've seen a lot of move towards chat-based interfaces, which we're just stripping down to having a conversation. Like, anyone can have a conversation, and now that's all you need to do to actually build a product. 

[00:08:34] Naomi:  Super interesting. Sort of, listening to Jenny Wren, I think, who's a designer at Claude, and she was saying that chat is never going away because it is such a sort of fundamental component of how we users interact with services, right?  

[00:08:50] Melanie: Yes.  

[00:08:51] Naomi:  That must be the same with LinkedIn. I imagine it's the interaction that drives it, right? It’s such a key feature. 

[00:08:57] Melanie:  Exactly. I think, you know, with Jenny, she obviously works at Anthropic and, like, works on Claude and builds everything with Claude. And so, you know, they're on the bleeding edge. And we're trying to build a product that creates economic opportunity for every member of the global workforce. So, we're trying to apply AI in a way that is actually helpful to someone's experience and not just AI for AI's sake. I think we saw a lot of, like, AI just show up in everyone's product because they felt like they had to have it. We're trying to be more intentional about where it will actually solve the problem and doing it effectively. 

[00:09:35] Naomi:  Yeah. I love that. And I love your reframing of creativity as well as an incredibly pragmatic thing. In terms of that sort of creator piece, I'm interested in this idea of being a creator and the value of being a creator rather than just a consumer in an increasingly automated world. And I'd love you to take us on that journey that you've gone over the last, probably, a relatively short while at LinkedIn to turn you, not only yourself, but others into creators of AI, and why this was a really necessary step. 

[00:10:08] Melanie:  I think it's been really interesting where people, even including myself, maybe had our, like, heads buried in the sand a little bit, kind of obsessed with the experience that we've had in our career and the expertise we've built in, like, prior ways of working. And I think we've all had a, like, shakeup moment that has motivated us to make change. Mine was hearing Kevin Hu speak to the LinkedIn leadership team. He was the head of product engineering at Wind Surf AI, which was the early, very popular AI coding tool, and he said how everyone builds and ships. He actually built and shipped something himself while he was eating breakfast that morning. And it just really showed us that, okay, we have to change. We have to evolve. Because across the industry, whether or not you're changing your job, your job is changing. And if you're hoping it's gonna stay the same, there's no good news for you. It's happening. And so for me, I spoke to my team about this after I had that moment. We had a whole day about, okay, what does it mean for us to start actually being in control of what AI instead of, like, having it happen to us. We spoke about the industrial revolution, where there were, like, these three categories of the roles that people played in the spectrum of, on one end, there were the designers of the machines, who actually innovated the engineers who built the machinery. And then in the middle, there were the designers who took that machinery and created new work processes for it. And at the other end, there were the operators who were then controlled by the machines. Their jobs were dictated by the machine's operations. The same thing is true for AI. On one end, you have people who will actually define AI, who are designing the AI models, the systems, the tools. In the middle, you have people who are taking those and then designing new workflows and new practices. And then on the other end, AI is just gonna happen to you, and workers' jobs are gonna be impacted or directed by all of this change. I said to my team, like, hey, we need to be in the first two buckets. We need to be defining AI, and we need to be defining how to use it so that we're proactively in control of it, and it's not just gonna happen to us. Why would we give away our agency in this moment instead of participating and being on the forefront of innovating with it? And so we've really taken that approach to be more in control as much as possible and actually carve out a space for the team to spend quite a lot of time. At least 10% of their time is expected to be testing and trialling new AI practices that's not necessarily attached to the work that they're doing. And then all the work that they do, this will obviously then be using the new tooling and thinking about how to bring it in. But it's not always that they're actually, you said, like, creators of AI. We do have some people in the team who are working on how do we have AI in the product. But I'd say the majority shared experiences, like how we're changing how we work with AI. 

[00:13:03] Naomi:  Got it. So, what are some of the programs of work at LinkedIn in this area that you're most excited about? And I know you're a real stickler for measuring the value of the work, so I'd love you to also touch on that. Like, how are you measuring the success of the work? 

[00:13:18] Melanie:  Yes. Yes. Okay. So many, and I'm so excited about this. At my heart, I, like, love experimentation of things. You know, it's why I've ended up in a growth role. And so I've been involved in starting lots of different pilots where we're setting up different ways of working. So, one of those is the associate product builder program, where we amalgamated our associate product manager program, and our design intern program and some of the engineering interns together into one role. And we have our first cohort who've started now, and we have seven of them in place. And they're basically helping us test this hypothesis that a single person can take an insight, a problem, something that we've learned about the customer, all the way through to shipping that thing. So, covering all the traditional responsibilities, not necessarily old ways of working, but traditional realms of product management, design and engineering. So, we were a few months into that experiment, and the hypothesis we're trying to test is that we can work faster but also ship high quality product. And we're measuring it via many metrics. Like, we're surveying the managers of these roles, the people themselves, the mentors who work with them in each of the functions. And definitely some learning so far is, like, it's hypothesis was that we hire entry-level talent who are AI native. That's literally the only way they've worked in, like, building is with AI, is that they're much more likely to be able to innovate and work with different processes and change, versus the old traditional methods. That's definitely true. Like, they're just not attached in the way more experienced people are to the old ways of working. They have more fluid, more flexible, more willing to, like, just pick up a brand new tool and then use that thing. That's great. I think we're definitely seeing that whilst they can move faster, they can move faster on the parts like engineering, where engineering's definitely been completely innovated with AI. Whereas a lot of what product and design do is still latent disruption from AI, where applying taste and judgment and figuring out, like, what we should actually build. Is this the right problem to solve? We can't delegate those things to AI yet. So, we are still early in that program, but it's really interesting so far. And then we also have a bunch of programs where we then take aim for our existing workforce. How do we set them up to then be successful in this new AI era? So, we have a track of building agentic capabilities that have trained on our individual functions, like an analyst agent, a growth agent, a trust agent, a product jammer agent, and those all have the context that is important for them to be right at LinkedIn and access to our unique information. So, it's a lot better than just working with traditional ChatGPT or Claude because it has the context that it's working from. Like, what's on our existing road maps? What mitigations have we already done for trust? What experiments have worked before for growth? What’s all of our data for the analyst agent? And so for those, we've been measuring, like, what's the increase in speed, what's the increase in the quality of the output in terms of the project, does it improve, and adoption rates, those kinds of things. And then another pilot, which I've been really enjoying, is how do we take traditional people like the designer and extend their role beyond just design? So, we've had a programmer. We've had designers acting as a product manager, so they actually PM feature completely so that they can understand what is the PM stack. How does the PM actually have to think? What do they have to do to actually make sure something ships? What are they measuring? How are they writing their PRD? So that the designer can have a lot deeper empathy and influence at a higher level. And then on the other end, we have designers in the code shipping work themselves and actually making small changes and then having a much closer collaboration with engineering to just work together in the build rather than having the intermediary step of, like, having to own create marks. Instead, it's like, hey, let's just look at the actual source of truth, which is the code, and then let's experiment and make changes there directly. So, that's been exciting. But, yeah, I still think we're still in the early stages for, I think, probably the next year or two, we're gonna see more for design specifically in terms of how AI is gonna start to replace more of the design process. 

[00:17:44] Naomi:  That's so interesting. And because I think I am interested in where, well, obviously, the purpose of this podcast really is to explore that edge between the human and technology, which only that edge just seems to be blurring minute by minute. And design is one of those functions where I think it's still very grey. Much like people work, it's still very grey. And so I guess that does beg the question why it was important to you that this work was maybe you don't design it, define it as design-led, but I hear it as a design-led initiative. And we've got product builders, for example, at Mews, and they're off they're often engineers predominantly. Why do you think, if we think about the broader context of human skills that can't be replicated by machines, why is the design skill set a really critical one for you? Do you think, still? 

[00:18:38] Melanie:  Yes. Yeah. If we just focus, then, like, what are the critical skill sets? So, if we look at the whole product building process, the things that I think AI is gonna be the hardest to replicate, maybe eventually, superintelligence it will. But right now, it's still very much human-required is, like, the ability to create vision, the ability to have deep empathy and understand a problem, creativity, and then, I think most importantly, judgment and taste. I think AI has come a a lot further in the last year in terms of taste, but still, it's like the things that it comes up with is is we go from zero to a million, and then what you're doing with AI is, like, how do you chisel it down to something that is actually the right thing to execute on? But I think, really, it's about, like, the soft skills, like communication. So, if you think about what you actually do at work to make things happen, the actual building or designing is, like, one part of it, but what actually makes things happen is the ability to navigate internally, to convince people, to get alignment, to resolve conflicts. It's like these important soft skills around influence, emotional intelligence, conflict resolution, and negotiation skills. Even if we have AI that we're able to delegate a lot of our work to, we're still gonna work in a team. We're still gonna work with other humans. And so the ability to navigate with each other, I think, is still essentially this human experience, and that's still gonna be the most fundamental thing that we need to be effective, even if we have 10 different AI agents that we're working with at every moment. 

[00:20:10] Naomi:  I'm interested in, I agree, and I think I'm also seeing a shift, an accelerating shift, actually, in terms of how we think about management, because emotional intelligence is increasingly important in terms of the new form of management that is needed. And in terms of sort of staying influential and empathetic and impactful, these are all management skills as well, aren't they? Especially when you're managing multidisciplinary teams and agents in some cases. 

[00:20:37] Melanie:  Exactly. 

[00:20:38] Naomi:  Yeah. It's interesting. So, as design, and product, and engineering end up working more closely together, what behaviours or cultural shifts have you found that are really essential for teams to build these AI products responsibly? How's that shown up for you? 

[00:20:57] Melanie:  Yeah. So, there's a few ways. I think right now, a lot of the gains from AI are around engineering, like how and, actually, the rate of building is much faster. And so even if we're able to build a lot more faster, we still have to make sure we're spending enough time and applying our efforts that we're choosing the right problems to solve and that we're applying our judgment to figure out, is that actually the best way to solve it? And this is where I think design is playing this great role, where shifting to actually becoming, like, a consultant where we work with PMs and engineering who are just, like, spinning up prototypes. And we're like, okay, but is that actually, do we even care about that thing? Like, great. That's a cool thing you've made, but it's not actually solving the problem. Let's think about it in this different way. And so, like, working with the rest of the organisation to understand that, but and then in terms of AI itself and how we're implementing it responsibly, it's like the unlock really is evals. Like, how are we evaluating and testing the LLMs properly? How are we actually structuring our evaluation so that we're considering who the different profiles are that we have in the user base, what are their expectations, what are the quality benchmarks that we wanna put in place to make sure that it's actually acceptable, and it's doing what we want it to do because it's such a black box, the LLMs. They're just like so, actually stress testing and evaluating it before you're shipping it properly is, like, so important to make sure that you have something that is actually gonna work in the way you expect. 

[00:22:38] Naomi:  It's kind of shifting how we think about all of our jobs, obviously. But from design, it's quite a deep shift, isn't it, really? And you mentioned earlier that you've been bringing on AI natives. And I imagine one of the advantages of bringing on an AI native is that you don't need to teach them that AI isn't just an add-on. It's a core component of design work. How do you go about shifting teams who aren't AI natives in that thought process? So, moving from thinking of AI as an add-on to treating as a core component of design in terms of the material, the tools, the working practices, and so on. How do you affect that shift? 

[00:23:20] Melanie:  Exactly. So, I talked about a year ago, I think we had that AI on-site day where it started by, like, sharing my own personal experience of realising, like, I can't have my head in the sand anymore. Like, I need to be using all these tools. I need to almost be eye seeing myself as a leader to really be in deep empathy and understanding, like, what is the design process gonna change? How is it gonna look? And it's the most hands-on I've been, I'd say, in the last 10 years because I've heard a few other design, any design VP said recently, he feels like an intern. We're having to go back to the beginning, and if we don't get into detail, then how are we actually able to act as a leader and a manager for the team? And so that day, we did a lot of exercises where we had new tools that we had access to. So, we had done some pre-work, to then, like, okay, let's try to replicate a design process that we've done recently, but let's do it instead using the AI tools. And everyone was sharing what they're working on, and we started that day AIP groups, where we put together people, and they were meeting each week, basically saying, oh, well, this is what I tried, this didn't work in this safe, small space of, like, three or four people from a design group, and then we said the expectation that they should be spending at least 10% of their time just constantly learning and trying AI tools. I think because I've been so passionate about it, my team has then been on the forefront of being in all these pilots. So, basically saying to them, like, that they have the permission to then participate in these things and spend time, not just doing the work, but then helping us figure out the new way of working. So, quite a lot of my designers were in the code program where they're shipping front-end experiences, or they're in the PM program. And so, basically, making it safe for them to be in this trial and error. I think one of the, I've spoken to some colleagues at other big tech companies, and they've felt this increasing pressure to just be more productive constantly and that there hasn't been enough space for them to really learn, and test, and trial with these things in a safe way. So, I've been very intentional about creating that space where we share our learnings of, like, not just what's working, but what didn't work and, like, where the gaps still are so that we can keep feeding that into our team who's working on this whole transformation internally for the product org. 

[00:25:37] Naomi:  Interesting. Yeah. Because there's a question I wanna ask you in a second about that. So, let's move to leadership a bit more specifically now, off the back of that. How has what is expected of leadership changed as a result? I mean, you've started to talk about that, but could you sort of say a bit more about what it means for you as a design leader? 

[00:25:59] Melanie:  I think just like now, we can use these tools to almost like be an IC again. And in a way, we need to do so that we can help our teams to move through this transition and change, and we can't just lean on everything that we've learned in our careers because everything is changing. So, then we have to have that hands-on nature to be a part of leading the innovation of this way of working. And so, yeah, I've seen leaders, or in the code, we're shipping bug fixes were actually in the detail of the work. I don't think the extensible model is that I would be icing a large percentage of my time, but I think the change is really that leadership's getting much closer to the work, and the role of the middle manager, I think, is dying. And instead, we're in the move towards these, like, flatter organisations where leadership are much closer to the work itself. So, I'm hiring a new manager right now in my team, and they're gonna be a hybrid player-coach. So, I just wrote the job description yesterday. It literally says, I need you to be both a principal designer and a manager. You'll have a small team, so you need to still be successful at setting up that team and managing and leading them through this, but at the same time, creating strategy and vision yourself and being really hands-on. And with these tools, like, that can be relatively easy. It can be about having a point of view and spinning up a prototype, you know, even if it's a low fidelity that helps people understand what that is versus our old, you know, just months creating these visions and these, like, really long-term exercises. So, now we can all be in the details, actually, creating work ourselves. Not that it'll be the final artefact, but it's the thing to, like, tell the story. 

[00:27:48] Naomi:  That's interesting. Yeah. I don't know if you've seen Jack Dorsey has just published a thought piece on player-coach as one of the new roles. 

[00:27:57] Melanie:  I haven't. I need to read that. 

[00:27:59] Naomi:  It's on Twitter, obviously. And so take a read. And I think we're thinking the same thing. Like, it really is changing. It's decentralising power, but that forces people who lead teams to engage with where decisions are made and where actions actually happen, where work actually happens. It's very empowering for those at the edges, and it's a real challenge to the ego of people who have traditionally held power, right? So, this kind of collaboration could really amplify creativity. It can really share in the decision-making. It can give autonomy and authority to people who historically may not have had it. But it can also really lead to isolation, I think. I know this is another topic that's very close to your heart. What practices have helped your team stay connected and human whilst building in this new world? 

[00:28:58] Melanie:  I'm definitely seeing that. I think, and we see it in general in LinkedIn, like, the amount of anxiety has never been higher, and it's a very reliant AI anxiety, and then the layoff anxiety that's coming with that because of the climate that's been created. And so then there's pressure that people are putting on themselves because when you have to reinvent yourself, when you have to be, like, constantly learning, especially if you maybe already had the experience that you were relying on, it's a high-stress environment. And so in this environment, it's never been more important to prioritise well-being and set boundaries. And I did a talk last week at our design week to the whole LinkedIn design organisation, telling them about my journey with burning out at Google. I speak about it very publicly and how that experience, whilst it seriously negatively impacted my physical and mental health, reaching that rock bottom helped me realise that I have to have boundaries. I have to create space to, like, completely step away from technology and have a life outside of work where I can be creative and fill up my bucket with activities that are about connecting with other people or connecting with nature or literally, sometimes I say to someone at work, like, I feel like you need to go and touch some grass right now because you're so wrapped up in this problem that actually, in a month from now, no one's gonna care about. And so we need to go and get some context and step outside of this so that we can come back tomorrow and have more resilience and be able to manage the stress. And so I champion that for my team. I try to talk about it very vulnerably and visibly, so I make space for them. I take a lot of time off, and I do that very visibly. And so then that gives other people permission to do that. LinkedIn is great at that, and then we have lots of, we, like, shut the whole company down for multiple weeks throughout the year, and then we have unlimited leave, and people actually do take a lot of that unlimited leave. So, yeah, finding that balance so that you can come to work and then be your happiest, healthiest self, who's then the best possible version of you to creatively problem solve, to connect dots, like, all of those things you need to be happy and healthy and well rested and all of those things to do that. But then also, what I'm seeing with AI is that the way it's implemented right now is this really individual exercise. It's quite hard to collaborate in those spaces, whereas our other tools that we've used for design, especially with Figma, it’s so easy to have multiple people there. It's this tool that really helps you come together and explore options together, hopeful that new versions of AI, like Cowork for Claude, is, like, how do we start to actually have more people coming together around this? Because when you start to collapse roles into a single thing, it's like, well, that can be really isolating when you have one person. So, I still believe that we'll need teams and we'll have generalists and specialists to work together. And ultimately, the way that you, like, amplify creativity and problem solving within the team is that they have to have psychological safety. They have to have a baseline of trust. They have to understand who they are as people and then have good relationships in place so that they're able to work effectively together. So, it's like, even though everything's changing about how we work, the fundamentals of building a psychologically safe team that prioritises their own well-being, those things that haven't changed. They're existing paradigms that I've been preaching for the last 15 years of my career. And in fact, they're even more important now as, you know, we're getting even more stressed with all these changes. 

[00:32:25] Naomi:  Yeah. 100%. And I think the pace at which AI is both accelerating but also forces us to work is another part of that. How do you protect your teams? Or how do you help your teams balance that sort of pace piece with the time for deep thinking for a designer? 

[00:32:44] Melanie:  Yes. Exactly. I think AI actually can be a great brainstorming partner. And I think what we're seeing with engineering is what we're gonna have for design, which is why engineering has moved instead of being the executor who's coding everything. They're like the creative director, the producer. They're then managing the agents who are doing what was, you know, the tedious execution task underneath. And instead, they're up above thinking about, okay, well, what overall architecture do I wanna create? What's the overall strategy I wanna employ, and how am I gonna use agents to then go and work against that? And I think that's what we'll see with design is that we move to this role of being, like, the creative director or the art director, and we're able to delegate some of the more tedious activities. And that transition is, I think, is gonna be good for people because it's actually nice that they're able to spend more time doing less of the tedium activities and more of, like, the creatively fulfilling activities, but those also take a lot of energy to do those things. So, we just spoke about, we need to then balance and make sure that they're prioritising their own well-being. And what we're seeing with the associate product builder program, as I mentioned earlier, is that engineering is able to move much faster. But we saw a few of them just jump straight to building, and we're saying, no. Like, actually, you still need to spend quite a lot of time understanding the problem, making sure it's the right problem to solve, and then thinking about the craft and the actual experience of that solution. And so we've always had to, like, slow them down a little bit so that they can spend more time on that because, like, anyone can build anything, but is it actually gonna be something that solves a problem and is useful in the world is the bigger question. And that's why I think actually design has this amazing role in this innovative area, because now literally anyone can build anything. And so the only differentiator in product is gonna be, like, the level of quality, how much it actually solves the problem, the craft, the experience. You're already seeing so many products that are just AI slop. Like, they all look the same. They all have the same gradients. They all have the same UI. So, it's like what is actually standing above and beyond that, and what's gonna resonate with people will be the result of spending time to do this deep thinking and not just jumping straight into building. 

[00:35:03] Naomi:  Nice. I love that. And I suppose the same applies to experimentation, doesn't it? That it's a core component of how design works. And in order to differentiate and continue to differentiate, that experimentation piece is absolutely critical and needs to be time for it needs to be preserved. 

[00:35:23] Melanie:  Exactly. Yeah. I think the big change with AI is that we are able to get to a pretty good fidelity of an idea very quickly. Whereas before, like, that used to take us quite a lot of time. And so we actually have the ability now to just start testing and learning and working iteratively much faster than we did before. And this is where, being in a growth org, that is a way that we like to think in general, and, you know, not just spend 6 months figuring out a big vision and having this big ship moment. It's like, instead, how can we, like, think about what's the fastest way for us to test this hypothesis, ship a small thing, learn, iterate, and do the next thing? And so this is where design's role is changing, where it's like, how do we actually partner with engineering or whoever it is that has the idea to help them figure out what's the minimum amount of experience or quality that we can add to a thing to then ship it so that we can then learn and then we can iterate on that thing and, you know, we can improve it over time. And that's why I'm excited about because that's the way you build a better product. Because when you are building in a vacuum, and you don't have that iterative loop, yes, you have UXR, and you can go and do research and get that point of view, but it's not until you get the actual behaviour as well that you can, like, really deeply understand if this thing is helpful for people or not. 

[00:36:46] Naomi:  No. 100%. I understand that. I'm gonna sort of change tack slightly with my next question, but I think it is still a leadership question, really. You founded London Tech Ladies. It was about helping women in tech through development opportunities and time spent together and learning from each other, and you've supported thousands of women in tech in doing so. Do you think there are unique challenges and opportunities for women leading in the AI-first era? And how would you characterise and solve those challenges as someone who is a leader? 

[00:37:23] Melanie:  Yes. I think the AI era is both amplifying inequities and also creating a window to reshape them. So, I think in the first point, when we have representation gaps, like women are still underrepresented in technical AI roles, which means the systems that are being built can then reflect a narrower set of perspectives, or, you know, we even see bias at scale whose AI systems amplify bias faster than previous technologies. So, those things are true. But I think what's different and exciting to me is that AI is meaningfully lowering the barrier to entry, so you don't need a deeply technical specialist background to build an experiment anymore. And so that opens up a huge opportunity to redefine who gets to participate. I'm being very optimistic about this. It's not that I'm necessarily seeing that happen yet. I am seeing it to an extent, but where we have to lead through it is that we have to think about, like, how are we actually creating the access? So, how are we closing the skill and tooling gap? How are we actually making AI tangible and usable, and not intimidating? How are we creating safe spaces to learn and understand it? The peer groups that I was talking about earlier, that hands-on learning in safe spaces. And then, as a leader, how are we still thinking about representation? So, to the points I talked about earlier around the representation gap so that we then have a narrow set of perspectives or the bias that we might be seeding into the AI, how do we make sure the teams are thinking about those things, acknowledging that those are true, and therefore trying to design either, like, to have more perspectives to actually build it or at least be conscious of those things so that we're testing and mitigating for those biases upfront. And then I think, you know, one thing is with women or any underrepresented group as a leader, there's always this need for, like, amplification as well. So, how are we amplifying the work of those people? Because their work's more likely to go unseen versus, you know, male counterparts. So, yeah, I think between those of working on access representation and amplifications, it's like where I'm focused right now. 

[00:39:37] Naomi:  And I think it's what you're talking about is a level of intentionality about it. It's not always easy, but it requires people to actually lean into the problem, both in terms of articulating it as a problem, as well as designing solutions, very pragmatic solutions for it. But it does require deliberate intention. Some quick-fire questions for you now before we wrap up. What's one outdated belief about design and tech you love to retire forever? 

[00:40:09] Melanie:  There's a lot of resistance right now in the past, the design community holding on to the old ways of working and fear that we're gonna lose craft and quality with all this AI change. But I just encourage those people to, like, see this as an opportunity. And speaking to the roles I talked about earlier, like, be a part of actually figuring out what we can do with it. Because I think AI is gonna make us move so much faster than we talked about earlier, have that test and loan cycle rather than spending endless months in a discovery process. So, yeah, my message to people is get your head out of the sand like I used to have and instead be an active driver of this, rather than having it happen to you. 

[00:40:52] Naomi:  Yes. 100% agree with that. What's a creative ritual you encourage teams to protect even when the pace of building speeds up? 

[00:41:01] Melanie:  This one is so interesting because what AI tends to do is it pushes you into one path, and then you iterate on that path, but very quickly, you end up on one thing. What we have to intentionally do is still even used to using AI. It's like, how do we go broad? How do we explore 10 different ways to solve a problem? How do we really explore a creative breadth? And that's where, like, there's still a need to use tools like Figma, where you have a canvas, and you can, like, lay things out and look at multiple options. I'm excited to see how AI is gonna blend more into that world over time. But, yeah, it's really, like, how do we protect the divergent activity that is really important for us to this, that we don't just get stuck on one solution and iterating within a very local maximus of that particular thing. 

[00:41:48] Naomi:  That's so interesting. And that can apply to any, it's not just design in its purest sense, but any of us that are involved in leading and creating ways of working. 

[00:41:58] Melanie:  Yeah. Like, a really practical way to do it. Sometimes I'll have Gemini, ChatGPT, and Claude; I give them all the same prompts, and I also give it to each of them 3 different times. And every time you get a different output. So, I end up with 9 things. And then I'm like, okay, very quickly, I've gone, like, super broad now, and then I'm using my judgment and taste, then feel like, okay, well, this seems the most interesting. So, now I wanna go into that, and I can go deep then, in that path. 

[00:42:26] Naomi:  That's so interesting. I love that idea. Slightly left field one. How do you come up with new ideas? 

[00:42:31] Melanie:  First is having a foundation of deep understanding. So, we're in the middle of an exercise right now to solve a particular audience that we haven't traditionally solved for. So, we spend a number of weeks upfront just looking at the data, looking at our research, and talking to those actual people. And then, purposely, people kept trying to talk about ideas. I was like, no, not doing that yet. It's like, first, we're getting a full, steep empathy and understanding, and we need it for two weeks. So, we're gonna get to ideas soon, but we're just gonna spend these two weeks right now in deep understanding. And then that just improves the quality of the ideas after that. And so when you, then I use all that understanding as a stimulus. Basically, break it out. I did work with AI. I'd be like, extract these out 20 or 30 individual findings, and then each of those are brainstorming point of, like, okay, well, I understand the nuance of this individual insight, so therefore, his ideas that branch off that, versus brainstorming in a vacuum, where it's not based on the reality of some insight of an actual user. 

[00:43:33] Naomi:  Brilliant. That's such a great idea. And I really like that it's a common thread throughout a lot of your answers, actually, about ring fencing time. And in a world that is moving so fast, it's such an obvious maybe, but such a critical step is about ring-fencing time for thinking and thought. 

[00:43:49] Melanie:  Yes. And then making sure that you have the right context. So, the quality of your output that you get from working with AI is based on what you give it. Like, the quality of your prompt and, like, how good you are at prompt engineering, but also what context you give it, what information, what artefacts, and it's the same with an individual brain, when you brainstorm yourself, what stimulus you have to react to. And, actually, that activity is then useful for you to do during brainstorming, but then also to feed to AI. And we actually ended up making a GitHub repository of all of our insights and understanding that Claude has access to. And so when we're working with Claude to brainstorm, it's using a repository of all the data analysis and all the UXR and the customer interviews that we did as its basis. 

[00:44:33] Naomi:  So, it's learning, essentially. What's one thing every leader should do to give design a real seat at the table? 

[00:44:40] Melanie:  I think I said it earlier, but it's, like, really show value so that people want design there. Like, demonstrate how design thinking delivers significant business impact. There are ways that you can do that. I guess, as a design leader, we do things keeping a track record of, like, what was the impact of this design change? Like, where we made this, it changed the experience, what was the result of that, and we have this, like, running track record of that over time. And then when we have success stories where design-led thinking was a key agent in creating a particular feature or functionality, then, like, making sure people understand, hey, we work differently with that. We used this process, and therefore, that's why we had this outcome. So, we just have to continually demonstrate, like, what was the value that was driven by that. 

[00:45:265] Naomi:  And, again, that's another thing that I think applies to lots of different fields, with people work. I was talking with someone earlier, an investor firm, they were asking me how we handle DEI, ESG and climate, which is in question. It's like you demonstrate business value. And, actually, the maths of it, it's a relatively easy thing to do, honestly, because it expands the pool. And the same thing, if you have a designer at the table, you expand the thinking and the available insights. So, it sort of makes a case for itself. We're nearly at time. It's been really interesting. I've got a couple of closing questions, if I may. I wanna give you an opportunity to give one message to the next generation of women, particularly, actually building careers in AI and product design. But if you wanna expand it out to anyone building careers in AI and product design, what message would you give them? 

[00:46:19] Melanie:  Start building. Start creating products and putting it out into the world. The barrier for entry is so low now. I'm seeing entry-level portfolios of people who've actually shipped product. Like, they just pushed it live and then got organic usage for the associate product builder program, which is an entry-level program. We didn't even require a resume. We just asked you to share the needs built and shipped, and what was the impact of that thing, and how did you use AI to do it. So, I think we're gonna have more and more focus on what have you actually been able to build using these tools, and so I think we need this move. Hopefully, education is making this move. It's like moving from being theoretical to actually just doing it, just trying and building and shipping and learning. 

[00:47:06] Naomi:  I read recently a really nice phrase which has stuck with me, which is that the price of initiative has collapsed. And I think that's so true. The barrier to getting involved in building has just disappeared, really, in so many ways. Finally, looking ahead, what gives you most hope about the future of human creativity in this AI-driven world? 

[00:47:29] Melanie:  I think maybe I'll build an answer I said earlier, which is, like, I see AI as an accelerant or like an extension of you. And there are quite a lot of tasks that we'd spend our time on, which truly are tedious. And if we can delegate those so that we can then, to AI, so that we can then be focusing on the more impactful parts of our role, which is soft skills internally, how we're collaborating, but then also being a creative director, being the producer of AI, figuring out where you should direct it and what you should be focusing on. I actually think that's spending more time on the most interesting part of our job, and it's exciting for me and hopeful because then we have to focus more of our job on creativity, like, which is the thing we should solve, how's the best way to solve it, instead of just the implementation of how do we actually execute against that. Because that was the vast majority of what we used to do, is, like we had a great idea, and then we spent so much time actually trying to make the thing actually come to life with small creative moments along the process instead of the bulk of it being purely on creative exploration. 

[00:48:37] Naomi:  I love that. Just makes work better for everybody, doesn't it, really, in so many ways. Thank you, Melanie. I really appreciate your time and your insights, and I'm sure other people will. And it's been a real pleasure to have you as a guest. 

[00:48:52] Melanie:  Thanks for inviting me and for creating the space for the discussion. 

[00:48:58] Naomi:  The Future is Human is brought to you by Mews, the cloud-based hospitality platform. If you want to learn more about what we do, visit mews.com. And if you'd like to listen to more conversations like this one, find us on Apple Podcasts, Spotify, YouTube, or wherever you listen. Subscribe so you don't miss future episodes. Thanks for listening.