The Technology Works. The Deployment Is What Fails
Episode Summary
Tallulah Le Merle spent most of the last decade on digital and data transformation with large corporates before moving to the investment side at Fifth Era, and describes the same failure repeating with AI. A leader hears innovate or die, goes to a conference, gets excited about a tool, and licenses it for a year or so. What follows is a scatter gun deployment that never started from strategy, and an executive who has spent heavily and cannot see the dial move. Her alternative is to flip the equation: start from the needs of customers and employees, find where the organization is leaking value, get specific about use cases one value pool at a time, and only then look at tools. She is relaxed about being late, arguing that fast follower is a legitimate position when innovation is moving at this pace. The second half turns to the workforce, the emotional use cases, and what any savings are for.
Key takeaways
The failure is deployment, not technology. She cites an MIT report finding that 90 percent of organizations are not getting the return they expected, and reads it as a problem with how the technology was deployed rather than with the technology or its promise
Flip technology first into needs back. Start from the needs and pain points of customers and employees, find where the organization is leaking value or running slow manual processes, and work from those value pools to use cases and only then to tool
It is fine to be a fast follower. With innovation moving at this pace there is no requirement to be a first mover, and looking at what competitors or similar organizations in other industries already use is a legitimate strategy
Diligence the tools properly, because a lot of them are wrappers. There are at least ten teams building for any given enterprise use case, so time spent comparing solutions against a specific need is time well spent rather than a delay
Change management is the part nobody wants to do. People focus on the platform and the software rather than the capabilities uplift, on helping employees understand what it means for their day-to-day role and how they are incentivized to grow within it
Nominate champions and give them the job. Find the rising stars who are excited to get hands-on, make them responsible for being a hybrid human and AI employee, for making the solutions fit the company, and for teaching everyone else
This cannot be delegated to IT. Her argument is a chief AI officer at the ExCo level reporting to the CEO, because it has to be cross-functional and thought through from the top rather than solved bottom up inside silos
Cost is not the only prize. She puts most enterprise adoption right now at efficiency and productivity, and argues organizations should also be looking at top-line growth, new business and revenue models, and getting to market faster out of slow multi-month R&DEveryone underestimates inertia. She cites a Goldman Sachs report putting the peak unemployment effect at 0.5 points, against five in the last recession and around eleven in COVID, and reads the shift as gradual and mostly about augmentation
The emotional use cases need people who are not necessarily technologists. Her argument is that the best placed people to build for those needs are the ones who understand them: creatives, caregivers, right-brainers, social scientists, psychologists and neurodivergent people
About Tallulah Le Merle
Tallulah Le Merle is a partner at Fifth Era, working on the investment side of AI, and describes most of the preceding decade as time spent with large corporates and enterprises on digital and data transformation and on AI strategy back when it was machine learning and big data. She is also a fractional executive and a consultant, and her platform sits at the intersection of AI and humanity, where she argues what she calls the case for hope: lucid about the risks of safety, security, data privacy, cognitive offloading and bias, and unwilling to concede the trajectory to them. On the firm, Fifth Era takes its name from the fifth era of human evolution, after hunter-gatherers, agrarians, industrialists and the information era. She spent twelve years in the UK and France and is now back in the Silicon Valley Bay Area, where she grew up, and describes her own edge as bridging the world of large corporates with the world of fast-paced innovation.
In this episode
| 00:00 | Welcome and guest introductions |
| 01:45 | Ken: GDPR, privacy, and the road to AI governance |
| 03:32 | Bob: when the models changed and the controls did not fire |
| 04:50 | What governance as code actually looks like |
| 05:54 | Where the policy code lives |
| 07:06 | Why gates also have to run at runtime |
| 08:21 | Will the CI/CD vendors build this? |
| 09:20 | Why the tooling is open source |
| 11:07 | Agent swarms, or viruses with credit cards |
| 11:52 | Ford: the puddle that flipped the car |
| 14:10 | GM: governance treated as a safety system |
| 15:14 | Over-the-air updates and automated targeting |
| 16:07 | Governance After Hours in San Francisco |
| 16:42 | The biggest misconception: governance as a brake |
| 17:34 | Unsafe at any speed |
| 18:00 | How much testing is enough |
| 18:24 | Red teaming and adversarial testing |
| 19:14 | The security analogy: shift left |
| 19:53 | Getting past the maybe gate |
| 20:33 | How many models do you test against |
| 21:05 | Inside Beacon |
| 22:19 | Umbrella, Lantern, and the audit layer |
| 23:14 | The Lean AI Handbook and the learning loop |
| 24:47 | Blast radius control and rollbacks |
| 25:27 | Data as the new oil, refined |
| 26:54 | Bob on where to start |
| 28:23 | Ken on his free e-book |
| 29:26 | Executive clarity and prototype theater |
| 30:33 | The one thing to remember |
| 31:26 | Wrap-up |
In Tallulah’s Word
“It’s not a problem with the technology and the promise of the technology, it’s a problem with the deployment of it”
— Tallulah Le Merle (08:14)
“Instead of being technology first, it’s about being people back or needs back”
— Tallulah Le Merle (05:35)
“It’s okay to be a fast follower. You don’t need to be a first mover in adopting tech”
— Tallulah Le Merle (07:00)
“But the truth is, everyone underestimates inertia. This shift will be much more gradual than what we’re hearing.”
— Tallulah Le Merle (15:35)
“This has to be cross-functional. It has to be thought of right from the top and then trickle down across functions”
— Tallulah Le Merle (13:18)
“The best placed people to build for those use cases are not necessarily technologists, deeply technical experts, et cetera, but it’s the people who understand the needs”
— Tallulah Le Merle (21:17)
Resources
Tallulah Le Merle and Fifth Era
Tallulah Le Merle on LinkedIn: linkedin.com/in/tallulahlemerle. The one-stop shop she names on air, which she says carries links to her website and podcast
Fifth Era: The firm where she is a partner on the AI investment side. Named for the fifth era of human evolution, after hunter-gatherers, agrarians, industrialists and the information era
Her daily AI news show: Described on air as the first fully AI-generated news show about AI, 15 minutes or less, delivered by her AI twin. Wednesdays are Workforce Wednesday, covering enterprise AI and what some of the key organizations are doing
Ideas and frameworks discussed
Needs back, not technology first: Her core reframe. Start from the needs and pain points of customers and employees, identify the value pools where the organization is leaking value, get specific about use cases inside one of them, and only then evaluate tools
The fast follower position: Permission to not move first. With at least ten teams building for any enterprise use case and innovation at this pace, watching what comparable organizations adopt and trying it once it is proven is a strategy rather than a failure of nerve
Champions rather than mandates: Nominating rising stars in each function, making them responsible for being a hybrid human and AI employee, for making the solution fit the company, and for teaching the rest of the organization
A chief AI officer at ExCo level: Her structural answer. Reporting to the CEO, cross-functional, working top down across functions and operating companies, rather than pushed into IT or left to individual silos
Cost out versus top line: Most adoption right now aims at efficiency and productivity. She argues organizations should also be looking at new business and revenue models, serving customers in new ways, and cutting multi-month R&D cycles
Where the savings go: The question of whether efficiency savings are reinvested in people, markets and products or passed to shareholders. She says it is too early to generalize, that the answer will differ by organization, and that reinvestment is how augmented roles get reabsorbed
The emotional use cases: Her argument that the needs surfacing now are best understood by creatives, caregivers, right-brainers, social scientists, psychologists and neurodivergent people, and that whole product categories and industries may arise there
The case for hope: Her platform at the intersection of AI and humanity. Lucid about safety, security, data privacy, cognitive offloading and bias, and built on the argument that the people who care about those risks have to stay in the conversation rather than leave it
Named on air, as she cited them in October 2025
An MIT report: Cited for the finding that 90 percent of organizations are not getting the return they expected from their AI investments
The McKinsey State of AI Report: Cited for 80 percent of organizations adopting AI at scale in three or more functions, which she reads as the end of AI as an experiment
A Goldman Sachs report: Cited for a peak unemployment effect of 0.5 points, which she sets against five points in the last recession and around eleven in COVID
A World Economic Forum report: Cited for roughly 70 million jobs that could be replaced by 2030 against around 160 million new jobs created
The GPT-5 reception: Her example of the emotional use cases surfacing. The complaint was that it did not have the same tone, texture and feel of interacting that 4 had, which she reads as evidence people were using the technology to feel seen and heard
AI Realized Summit: She moderated a panel at the summit on 5 November 2025, a few weeks after this conversation was recorded
Related AI Realized episodes and events
Agentic AI and Revenue Work: What Actually Pays Off: Christopher Penn on proving lift to a finance team, which is the measurement problem she raises and does not solve.
AI for Go-To-Market: The New Revenue Team Playbook: Jonathan Kvarfordt on revenue operations and AI, which is the top-line growth she argues organizations should also be looking at.
Shadow AI Is a Permission Problem, Not a Tool Problem: Bob Mitton on adoption designed rather than left to happen, which is the same argument as champions and capabilities uplift.
Frequently Asked Questions
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Most enterprise AI investments fall short of the return that was expected because the deployment fails, not the technology. Tallulah Le Merle of Fifth Era cites an MIT report finding that 90 percent of organizations are not getting the return they expected, and reads it as a problem with how the technology was put into the organization rather than with the technology or its promise. The pattern behind it is a leader who hears innovate or die, gets excited at a conference, procures a year-long license and deploys without a strategy underneath it. She adds a second reason that is measurement rather than failure: return is genuinely hard to quantify when roles are being augmented rather than replaced.
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An AI strategy should start with business needs rather than with technology. Tallulah Le Merle of Fifth Era calls the alternative a scatter gun approach: technology first, driven by what looked exciting at a conference. Her sequence runs the other way. Start from the needs and pain points of customers and employees, find the value pools where the organization is leaking value or running slow manual processes, get clear on specific use cases inside one of those pools, and only then go and evaluate which tools meet that particular need.
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[00:41] Christina Ellwood: Welcome to AI Realized, the podcast for enterprise executives leading AI deployments. From tackling security, data, and operational challenges to navigating organizational transformation, AI deployment offers a unique opportunity to redesign organizations from the inside out. I'm Christina Ellwood, your host for today's episode, and we're talking today with Tallulah Le Merle, partner of AI investments at Fifth Era Partners. In addition to being an investor, she's also a fractional executive, a conscious consultant, and helps organizations to navigate change at the intersection of business, technology, and human well-being. For enterprise leaders deploying AI, her perspective offers a fresh lens, how to lead responsibly, engage people in transformation, and ensure adoption is not just fast but lasting. Tallulah, welcome to the show.
[01:40] Tallulah Le Merle: Thank you so much. It's great to, great to be on this
[01:44] Christina Ellwood: I'm delighted to have you, and as you are in the, the, you focus on bridging business technology and human wellbeing. How do you describe your work to executives who are navigating today's wave of AI disruption?
[01:57] Tallulah Le Merle: Yeah. I, my role now as a partner at Fifth Era, I'm more on the invest side of the equation when it comes to AI, which I can speak about. But my journey to get there, and indeed most of the last decade, was spent working with large corporates and enterprises on these sorts of topics, digital and data transformation, AI strategy back when it was more machine learning and big data, and nothing like what enterprise leaders are looking at today in terms of, you know, vertical and applied AI is all over the place. There's solutions for every function within their companies, for every industry. So it's just, it's, there's a lot of noise. It's a kind of an ocean to wade through, and it's been interesting seeing that from my perspective because that's where I started my career, was working with them on these sorts of things and how to embed and deploy digital data AI throughout the organization in the right ways, and how to go about it strategically. And I can see that it's not easy for these people now to know where to start, how to navigate, how to bring people along the change, whether it's their internal customers, i.e. employees, or external customers. Yeah, that's, I'm not, I don't necessarily spend... I, I still am an advisor and sort of strategist on these topics, but my primary hat these days in the invest side of the equation as well.
[03:24] Christina Ellwood: I'm sure you're investing in companies that are actually selling to these very enterprise executives who are struggling with these challenges. And so when your customer is struggling and you're helping them to address those issues, it's important to understand them from the inside of their organization and as well as from the outside. Yeah. So when you're working with startups, it's obviously very different than working with enterprises, but there is a place in your experience where there are common elements. So when you've worked with FTSE 100 companies and now with scale-ups and visionary companies, what are the lessons from your experiences that apply directly to the enterprise? And then we'll talk about how they apply to your investing.
[04:07] Tallulah Le Merle: Yeah. It's interesting 'cause the approach to these things, or kind of the best practice approach, I think has always been the same, and the problems have always been the same. So the problem is that it's very easy to get excited about technology when you're a large corporate and you're hearing all these things like, it's innovate or die, innovate or be left behind, adopt AI or you'll become obsolete in a few years' time. So that's really sympathize with the types of things that they're hearing, and that's always been the case. So they hear these things and as a result they go out and they'll see, maybe they go to a conference or they meet with a startup or scaleup and they get really excited about a new technology and they just wanna adopt it quickly. And they may do so. They may g- procure a license to trial this for a year or so, whatever it may be. It's a little, but it's a little bit of a scatter gun approach, right? It's technology first. It's getting excited about what they see in terms of the solutions and just wanting to go ahead and embed that. And that leads to issues because it's not starting with strategy, it's not leading with a more structured approach for how this is gonna be deployed within the organization. And therefore, when it comes to adoption or usage or actually seeing a return from those technologies they've adopted, it's not always there. And that's where big corporates and enterprise leaders, they get disillusioned. They're like, "We tried this thing, we spent all this money," in some cases millions of dollars of budget on technology, "and it's not moving the dial in the way we thought it would. It's not, we're not where we thought we'd be." And so My work has always been working to flip that equation, and instead of being technology first, it's about being people back or needs back, right? So you start with the needs of, again, either external or internal customers. So the customers you serve or your employees, and it's what are their needs? What are their pain points? What are the biggest areas where we're leaking value today or we're just, we have really manual, laborious processes that are just taking so much time and energy and capital as an organization, and being very strategic about it and understanding where those value pools are within the organization. And then for any given one of those, so say it's like a back office function, say it's in finance where we have all these really, yeah, again, slow, long processes and period-end reporting and all these sorts of things. For that specific value pool area, it's then getting clear on specific use cases and then going out and really diligencing what are all of the best tools and solutions that would meet this need that I have as an organization. And having that be, not being afraid to spend time in that process and looking at the best solutions, 'cause especially when it comes to AI, there's a lot of wrapper solutions that have been built, right? There's a lot of... There's at least 10 teams building for any enterprise use case, and many of them, there's a market now, so they're not to be disparaging against the teams, but that's what we're seeing. So spending time in that diligencing process is really important, and I would also say it's okay to be a fast follower. You don't need to be a first mover in adopting tech, especially when we're seeing the scale and pace of innovation that y- we've never seen before. It's okay to look at who are competitors using or other similar organizations in other industries and, and why don't we try that ourselves once it's been a little bit more tested and proven. And then the last thing I'll say is then once you have chosen what these solutions are going to be- It's a tale as old as time that when it comes to technology, people wanna focus on, again, the tech, the solution, the platform, the software. They don't love to think about the, and I hate to say like so- the softer sides of the equation, but it's so the change management, the capabilities uplift in the organization, helping employees in functions understand what does this mean for me? How is it gonna change my day-to-day role? How does it change the way I'm incentivized to grow within my role? Nominating champions within different functions. You can find your r- emerging, you know, rising stars or emerging talent who maybe are excited to get hands-on with this technology. Make them a champion. Make them responsible for being a supercharged hybrid human AI employee, and making these solutions fit for purpose for your company, and then going out and teaching others within the organization about it. And bringing people along the journey, it's so critical 'cause this MIT report that came out talking about 90% of organizations aren't getting the return on investment they would've expected from their AI investments I think it's, it's not a problem with the technology and the promise of the technology, it's a problem with the deployment of it. And that largely comes down to getting the usage and adoption that you desire from these things, and other complexities too, right? Like ROI is hard to measure when you're talking mostly about augmentation of roles rather than fully replacing them at this stage because of where we are with the, and the solutions themselves. So anyway, I've spoken for a while about that. I'll pause for a second, but those are some of the things that were applicable before AI had its coming of age moment in the past few years and are still very much applicable today.
[09:16] Christina Ellwood: Yeah. I think we're certainly hearing this from our community of executives that right now it's not really about the technology. The technology is more than they could absorb as it stands today. It's actually about the people. It's about the transformation of the organization, the upskilling, et cetera, as you pointed out. So if our biggest challenge is in the organization and not in the technology, how does that play out in the opportunities for the startups that are trying to sell technology to these companies? Is this a moment of buy versus build as opposed to maybe if a year ago it was more about build, or is it the type of product that they're gonna look to buy in order to make adoption the priority rather than deployment?
[10:06] Tallulah Le Merle: Yeah. The teams building, there- there's not much incentive for them to really worry about how it gets deployed within the organization. They may start to consider are there services on their side that they offer to help employees get up to speed with the tool itself or helping with the capabilities uplift within the organization. I think there's an interesting point for enterprise leaders to think strategically about who they want to be in the era of AI. What is the mission and vision of their organization? Who do they become, and what do they wanna stand for? And that requires the leadership team to also have a relatively high degree of literacy on AI themselves, right? And to be able to almost envision futures so they can work backwards from that. I think, I know that's not necessarily a direct address to your question, but I also think there will be-- there was a flurry of activity in the past couple of years with enterprises adopting. The McKinsey State of AI Report last year said 80% of organizations are adopting AI at scale now in three or more functions. It's no longer just an experimental thing that they're doing. As a result of some of the rhetoric and promise versus reality when it comes to having to deploy it in the organization, there may be a little bit of a cool down, which I think is necessary as organizations start to be a little bit more strategic about how this is actually going to look for them. And I don't think that's a bad thing. It also means that on the side of the startup scale-ups, et cetera, who are building these solutions, a lot of the, quote unquote, "tourist interest" and tourist capital exits the scene, which means that only the top technical b- builders and teams who have really designed for longevity and scale and have very strategically thought about the type of solutions that they're offering, those are the ones that remain, which is a good thing. So I envision a lot of the noise com- coming down to an extent.
[12:07] Christina Ellwood: What do you see as the biggest blind spots leaders encounter when they're driving tech transformation?
[12:15] Tallulah Le Merle: Well, it was some of the things we mentioned. Getting excited about technology first instead of needs first, not approaching it strategically from the very top of what's our mission and vision and what we wanna stand for when it comes to digitizing or our core business, and therefore how does that flow into value pools and then solutions, the change in capability side of the equation. Often as well, when it comes to digital or data, AI, anything technology-related, sometimes the push is given to IT or to different silos of the organization to figure this out themselves. And especially now more than ever, I think it's critical to have a chief AI officer or, or chief, we used to say chief digital and data officer, at the ExCo level reporting directly to the CEO. This has to be cross-functional. It has to be thought of right from the top and then trickle down across functions, across countries or opcos, however the business is operating. It can't happen bottom up. It can't happen in silos of... A lot of the solutions maybe are function specific, but even... And then the other thing I would say is, and it's fled me again. It's such a good point that I keep wanting to make, and then it goes. I don't-
[13:42] Christina Ellwood: When it comes back, we'll find a way to weave it in. Yeah. What practices, other practices and structures besides the, maybe the appointment of a chief AI officer, help teams adapt faster and more su- sustainably when disruptive technologies like AI enter the organization, in your experience?
[14:03] Tallulah Le Merle: Having a really incredible strategy from the top that everyone's aligned about and can get behind that is visionary, not in terms... So here's another interesting point. When it comes to AI, everyone's talking about efficiency and productivity, i.e., reducing cost. And that's often where most of the enterprise adoption right now is probably for those sorts of angles. But there's a huge opportunity for organizations now to also think about top-line growth and how they can entirely new business model, business and revenue models and opportunities for them to serve their customers in entirely new ways, and/or get to market faster. If they have, like, very slow multi-month R&D processes that slows them from getting to market with new products and offerings, AI provides massive opportunity to speed that up. Like, that's a ge-generalization, but across every industry, that's broadly true. Agentic AI is something that will almost be a paradigm shift away from software as a service and allow them, in theory, to engage with endless, N number of customers in real time, in their language, 24/7, et cetera. So there's- Real opportunity here, but it requires stra- requires strategy. And again, I keep making that point of, like, top-down, cross-functional, whole leadership teams to get behind it, and be very cohesive in terms of what this means, and bring people in their organization along the journey. There's a lot of fear about it replace, AI replacing jobs, and understandably, 'cause there's a lot of already solutions that will augment what people do. But the truth is, everyone underestimates inertia. This shift will be much more gradual than what we're hearing. The Goldman Sachs report that looked into this suggests that the peak unemployment effect will only be 0.5 points. So to put that in context, the last recession was five points, and COVID was around 11, or 11 and a half, 'cause it, that was such an abrupt furloughing or almost overnight. So this will be very gradual, and it's more about augmentation of roles. It's more about hybrid AI roles and career paths in future. And then people focus on the 70 million or so jobs that could be replaced by AI by 2030. In that same WEF report, they're anticipating around 160 million new jobs to be created as a result of this technology. Similar to the internet, it's like entire industries arose because of the internet that never existed before, and indeed, we use it in almost everything we do today when it comes to corporate work. I think communicating these sorts of messages to employees, my platform, as you mentioned, is massively at the intersection of AI and humanity. I talk a lot about the case for hope in the age of AI, and I address a lot of these perceptions. And while we're lucid about the risks and that there's a job transition to navigate and that there's risks, we haven't even talked about risks around safety and security and data privacy, cognitive offloading, bias and inference in the models. There's a whole swathe of things here, let alone the macro risks when it comes to impact on humanity. But there's also, A, really smart teams addressing those. C, there's a precedent for this historically with the internet and cloud and mobile and other kind of disruptive technology, evolution, revolution, whatever you wanna call it, that we've seen in past, and it will be more gradual. Again, I ca- when you've worked with corporates for a long time, you understand there's inertia when it comes to these things.
[17:24] Christina Ellwood: And Tallulah, well, you mentioned, uh, uh, something else that I think is really important to, to pull, the thread to pull on here in this discussion. You mentioned that there's this top-line opportunity to grow. So the efficiency savings that we're realizing can be deployed in a number of ways. Right. You can use that to invest in new markets and new products and new opportunities. What are you seeing across industries for that reinvestment? Are they reinvesting in their people, new markets, and new products, or are they putting that savings to the bottom line and into the pockets of the shareholders?
[17:58] Tallulah Le Merle: Yeah, I think it's a little bit too early to be able to make sweeping statements about. That's only really in the past couple years that we're seeing enterprise AI adoption really boom. And again, they haven't necessarily been seeing those returns yet to be able to make strategic decisions about what to do with them. But I think the right answer will differ by organization, and that's really a choice they can make. There will be more and more... Again, the focus has been on taking cost out of the cutting cost and these sorts of things, but there is the opportunity to really drive top-line growth. And when there-- A- and that's where you can also reabsorb individuals whose roles do get augmented or whatever this may be. So I think having a strategy that involves both also makes a much more compelling case that you can communicate to employees and say, "Yes, yes, we are cutting... We will be aug- augmenting or replacing some of this manual data entry or very manual, slow, cumbersome processes." But by the way, you know, who as a kid raised their hand and said, "That's what I wanna be when I grow up"? A lot of that work, to me, doesn't really dignify the human experience, that we sit there on screens all day doing these kind of low imagination, low creativity type roles. So if we can offload some of that to a tool that can do it much more efficiently and, and eventually more intelligently than we can, what does that free up? Time, energy, resource. People's... unlocks their potential to do other things. And if an organization can communicate that, we wanna move into X space, X in- we wanna offer these new offerings as a, that are supported, facilitated by the power of AI, that's really compelling for people, and brings people along a journey and helps them understand what may happen to them Now, but also where they're going
[19:42] Christina Ellwood: So AI allows us to move faster, so we get velocity, we get some empowerment for more creative endeavors. What are you personally most excited about in the intersection of this technology and human development?
[19:55] Tallulah Le Merle: I am s- I'm so excited about many things, and I know we sp- I would say it's that, it's what I mentioned, that if you look on a macro level, and again, I'm very lucid about the risks and the pain of the job transition and navigating that, but there's a opportunity to, in the information age, we work in this 9:00 to 5:00 kind of Fordian model, but on screens all day, where we're disconnected from ourselves, from our bodies, from community, from nature. We spend nine times more time on screens than out in nature on average. And to me, that's the dystopia. So what does it mean when you disrupt this model? That's a, it's a open question. It's one I discuss and find myself engaged with often. I love the fact that when ChatGPT-5 came out recently, there was this big, not uproar, but there was quite a backlash because it didn't have the same tone and texture and way, feel of interacting with the user that 4 had. And so while everyone's been using AI for efficiency and productivity solutions like we've discussed, there was suddenly this little bit of a eye-opening moment that wow, there are actually... People are using this technology also to feel seen and heard and validated in a way that they haven't before. And so it's almost the, what's coming to light now are the emotional use cases of AI, and not just as a sidekick or a coach or a sycophantic AI, which is something we hear about. But deep emotional needs that can be met, and for which we can start to build for. And what I find promising about that is then the best placed people to build for those use cases are not necessarily technologists, deeply technical experts, et cetera, but it's the people who understand the needs. And those are creatives, caregivers, right-brainers, social scientists, psychologists, neurodivergent people who deeply understand the needs. And so this also, that's one of my, the things I'm most excited about is almost entirely new product solutions and possibly even industries arising at this intersection. But also the fact that, but it ties to my greatest fear, which is that those people I mentioned are right now leaning out of this dialogue because they fear that this technology won't ultimately be good for humanity because of all the real risks we see right now. And that's why this platform, that case for hope and that I find myself almost incidentally fell into it and am talking about, but I think it's one of the most important things, is just yes, there's a lot of real risks right now. The trajectory could go many ways, but unless we have the right people who care about these things leaning in- And for me, that's the real key to ensuring we m- we keep moving in the right direction.
[22:37] Christina Ellwood: So as we wrap up today, where... W- what is the guidance that you have for executives who are early in their adoption journey?
[22:47] Tallulah Le Merle: My guidance is come together as a leadership team, get on the same page about AI in terms of literacy and understanding, and for whatever your industry specifically. Educate yourselves first, and then put a stake in the ground strategically. Who do you wanna be in that sort of future? What do you think you stand for? Think about the value pools across the organization from efficiency, productivity through to the top line growth we mentioned. Craft, look strategically at what solutions there exist today across the board. Don't be afraid to be a fast follower in some of those areas. Trial things. Don't commit to massive one-year licenses. Do a month or s- be nimble. It's like this agile ways of working thing everyone always used to talk about. But trial things out, and nominate and empower people within your organization to become champions of the change. Think very carefully about capabilities uplift, and it's not holding their hand, but it's help them understand what this is gonna mean for your employees.
[23:42] Christina Ellwood: Also, and also you mentioned bring these other thinkers to the table, the people who do have an under- deep understanding of the human side of it. Make sure they have a seat at the table as well.
[23:52] Tallulah Le Merle: I think so, yep.
[23:53] Christina Ellwood: Yeah. What resources can you recommend to listeners who wanna learn more about you and your work?
[24:00] Tallulah Le Merle: I'm happy to connect. I'm on LinkedIn. That's probably the best one-stop shop. It has links to my website and podcast. We, I have, we have the first fully AI-generated news show on AI. It's daily, and it's 15 minutes or less, but it's my AI twin delivers it, so it sounds like me, but it's actually AI me. And she's very savvy and smart, but I think for leaders, that's a good... We do, on Wednesdays, it's Workforce Wednesday, so that talks about enterprise AI and what some of the, some of the key org- key organizations are doing and how they're thinking about this. And every day has a different theme, so that's an interesting one. My day hat, which we haven't spoken about at all, is as an investor. We work with the best VCs in this space to get access, and we do select direct and co-investments, but at later stage. We talk to institutional investors quite often who are interested in our strategy and getting access to early-stage AI because it allows them to have insight into what's happening early stage in this ecosystem. And we play that role as well ourselves as a intermediary between what's hot in the ecosystem and what corporates and enterprises care about and need to know about. I didn't talk about that hat at all, but that's something I'm more than happy to talk to enterprise leaders about, too.
[25:13] Christina Ellwood: Sure. And as a, an, a leader yourself, in this AI revolution, what's your defining edge in guiding AI leaders?
[25:26] Tallulah Le Merle: I like that question. Me personally, but also our firm, we ... Many of us were former consultants or worked with advise, advised large corporates at some point in our career. So we really bridge the world of big enterprises and corporates and the world of fast-paced f- innovation, cutting-edge tech. That's always been our sweet spot. We understand the needs of both. We understand the, um, vantage points and perspectives of both and what matters to them. So we bridge that. I spent the last 12 years in UK and France, but I'm back now in Silicon Valley Bay Area, where I also grew up. So again, I understand even geographically we have that diversity of understanding. And then it's this, you know, our name Fifth Era is talking about the fifth era of human evolution. We were hunter-gatherers, agrarians, industrialists, now in this information era, and moving into whatever comes next, this fifth era of human evolution. We care deeply about that intersection of technology and humanity, of innovation and what it means for the way we live our lives, work, interact, et cetera. And, and I, so I would say that's the third, is just being squarely a bridge between, yeah, corporate and startup, geographies, investors and builders, and technology and humanity.
[26:43] Christina Ellwood: I love that. So thank you, Tallulah, so much for joining us today. We really appreciate you being on the show, and we hope to, uh, see you soon. I know we're gonna ... You're gonna be at AI Realized Summit on November the 5th.
[26:56] Tallulah Le Merle: Yep. I will be moderating a panel on many of the things we discussed today, so thanks so much.
[27:02] Christina Ellwood: Excellent. Thank you. We really appreciate your time today.