Your AI Strategy Isn’t the Problem - Your Readiness Is ft. Elaine Barsoom

Speaker A: One of the biggest mistakes that I see is underestimating fear. People won't say, I'm afraid that this is going to make me less relevant, or they say I'm too busy or the tools are not ready or we need more guidance. And sometimes that's real. But that's the language people are using when they don't feel safe to use that tool. So it's really getting those, uh, signals very early on to kind of work through those changes within an organization.

Speaker B: Welcome to between the Briefs, a podcast by Steno. We're here to bring you practical tips, expert insights and real conversations about the pre trial process, court reporting and the legal technology shaping the future of litigation. I'm your host, Adrian SEO.

Speaker C: And I'm your host, Joe Stephens. Whether you're an attorney, paralegal, or just curious about how technology is changing the legal industry, we've got something for you. Each episode will break down complex topics, share behind the scenes intel and talk to the people leading innovation in and out of the courtroom. So grab a coffee and let's get into what's happening between the briefs. Welcome to between the Briefs, your go to podcast for legal innovation. I'm Joe Stevens.

Speaker B: And I'm Adrian seop. Today we're excited to welcome Elaine Barsom, AI and innovation leader, venture partner at Silicon Foundry and a strategist who has spent more than two decades helping global organizations turn emerging technology into real business outcomes.

Speaker C: Elaine has led AI Centers of excellence, enterprise partnerships and transformation initiatives at world class organizations including Nike and American Express where she helped scale innovation across customer experience, operations and growth.

Speaker B: Her focus today is helping leaders build human centered AI strategies rooted in trust, governance and measurable value creation. Elaine, welcome to the show. It's great to have you here with us. How are you doing?

Speaker A: I'm great. Hi Adrian. Hi Jo. Great to be here. Thank you.

Speaker B: Of course, we are very excited to have you on. So how about you just give our audience a bit of background about yourself, tell us more about who you are.

Speaker A: Sure. I have spent large majority of my career at the intersection of innovation, emerging technology and partnerships and growth, frankly. And really, how do you turn emerging technologies into measurable business impact? So glad to be here today. I've spent a large part of my career at large organizations like American Express, at Nike, but also built startups from zero to $12 million and have been at companies like Airbnb and I like to say long before AI. My real passion was in international politics and economics since I was quite young.

Speaker C: Give us pick one, Pick something and Give us an application of your skill set. A nice example of a project you've worked on, a company you've worked with, something that really demonstrates your expertise.

Speaker A: Oh yeah, so many to choose from. But one that I really love to talk about was a joint venture that we did while I was at American Express with a company based out in France, I guess during back then, the E commerce Internet boom. And we partnered with Font Privais, which was a $2 billion pioneer of the Flash sale. We brought them to the US and we name them Montpelier with American Express and we grew that company from zero to close to $200 million from scratch as, uh, a joint venture. So that was quite exciting. One of my big high points in my career. So building new growth and distribution companies within companies.

Speaker B: It's fascinating. And one thing we learned in our research and learning more about your background is you often talked about being the architect of that execution layer. So what does that mean in practical terms when you're working in your day to day?

Speaker A: Sure, it's being the architect of, um, how do you rewire workflows and workflow designs with human capital? So execution layer is not about just deployment of a tool. It's actually what are the underlying friction points that you need to really look at and where can you apply technology to unlock value for humans, really at the core of it all. So when I say the architect, it's actually bringing the systems, the processes and the humans altogether of that execution layer. So it's between the strategy and the result.

Speaker C: Where do people make mistakes there? If transformation work dies somewhere in the middle, where does that happen in like mid level management? I mean, imagine a number of places. But give me a thought.

Speaker A: I'll give you two thoughts, really. Kind of like on the deployment, people think, okay, right now AI is a mandate. The board is talking about it, the CEO and they're like, okay, we're going to buy these AI tools, we're going to deploy them, it's going to make us go faster. And so basically activity is showing that progress and activity doesn't actually correlate with progress. We have to understand where is the underlying work. So go slow to go fast. What are the problems that we're trying to solve? How do we craft decision rights? Who owns that in this day and age? Human values trap. But who's owning the actual outcome of this? Who's owning the AI agent? If you're building AI agent, so this tool can save time, but if no one changes actually the workflow, then the value really disappears. So you have to create better judgment, faster decisions and more capacity for people to do higher value work. And that's the real transformation.

Speaker B: And you're seeing it from a macro perspective and then also right into the micro, which is that workflow. Well, how does this actually change the way people are working and handing off work to one another? How has all of these experiences in your career led to you becoming a better advisor?

Speaker A: Oh, absolutely, because I sat with the legal teams when they have to evaluate risk and judgment. I sat with the engineers when they're trying to make the tool work. I sat with the business leaders when they're trying to figure out what is the strategy. So I've really crossed all gamuts working with the various stakeholders in actually designing how can we implement this in a thoughtful and adaptive way that actually drives value creation for the organization. It's not just an engineering function, it's not just a business function. And it's not just like senior leadership. You really have to bring people together. And biggest the hardest part is the cross functional alignment and the decision rights and really underscoring all of that. So that's what really helps me make a better advisor. I've sat on every seat.

Speaker C: Yeah, I would have to imagine that that experience matters tremendously. And also from a credibility perspective, if people know that you've been there before, they're probably more willing to listen to your thoughts. When it comes to suggesting a different way forward, there's so many soft skills involved with that. You're really having to listen and pay attention and then deliver results. What's an example of a way that you were able to bridge that gap between teams?

Speaker A: Oh sure, absolutely. I can give you a clear example where I was working with a company CFO had a mandate drive AI. We just want to implement this. The engineers had a thought of how we can drive decision intelligence and undervalued make our predictive analytics better because we have all this data that we can uncover. So we looked at a tool, we evaluated the partnership. We knew that this could be super productive and really unlock millions and millions of dollars. The engineering team wanted to build it because they wanted to own the IP and we wanted to go through a partner. And I was able to really bridge that grab of okay, what are the things that we want to own and how can we have a hybrid mod of bringing our engineering team with a partnership together? And so it's really what are the decisions? Right. The engineers want to help and control the ip. The legal team had a risk of not allowing Our data to be shared with the data. The senior leadership wanted to go fast. So bringing all. Everybody has got different incentives and different decision rights. So it's a matter of bringing all of them all together and then coming together with the prioritization framework and what really works for the organization. So that's just one of many examples of where you have to get everybody's incentives aligned towards one outcome.

Speaker B: That's great and it makes a lot of sense. It makes me wonder at uh, what point does an organization know that they need your expertise? What is that key identifier that they're like, maybe we need help with this?

Speaker A: It's interesting. I'm working with an organization right now that thought they just wanted to hire some forward deployed engineers. And it's really uncovering the questions of what are the problem that you're trying to solve? What are you trying to rewire for? Do you have the people that are involved in this decision? Where's the friction points? Do you have the data ready for this? Because an engineer can only do so much if he has the answers to all those questions. But if you're not even at that point where you know the problem you're trying to solve, then bringing in engineers is you're kind of putting a tech stack on top of potentially a tech stack. So just being really cognizant of that and asking the right questions and doing the discovery first can make everything all go faster and stuff. So I think that's super important when people ask me, well, where do you fit in and stuff. So.

Speaker C: Elaine, I'm a lawyer myself and the idea of asking the right question matters tremendously to me. So I really appreciate that and I actually agree with you that setting things up from a table setting perspective with the right question is obviously really critical. And I'm also happy that you weighted in on or just mentioned the legal engineer. We haven't really had many discussions about that role on our podcast, but it's obviously an emerging one. And what's the theory? Give me the theory of the legal engineer. Give me where they add met max value or the right time to start hiring for that position.

Speaker A: The legal engineer is so critical and I've always said, uh, kind of my biggest champion. And you have to make them your biggest because they see the organization from a very different lens. They've probably have seen everything within the organization, the risks, the potentially lawsuits, the strategies that uh, have evolved. And you have to stop thinking of them as just protecting the organization but actually accelerating the work that you're doing. And so I've always looked at the legal engineer as an accelerant to the work I'm doing is like, okay, how can we think about this in a different way so that we can move faster, so that we can ensure that we're not taking risks, but we're also crafting the right solution for the right problem. And I think lawyers are incredible at that. They're incredible at asking the right questions and helping you really break down the problem so that you can implement and craft the right strategy. So I'm a big, uh, proponent of really involving your legal team early.

Speaker B: One thing you mentioned in the past is that a lot of companies are just wanting to implement AI for the sake of implementing AI. I think there's this fear of if we just don't do something with it, we're going to get left behind. But why is that such a problem, would you say?

Speaker A: Because AI has created pressure before, it has created clarity. The competitors are announcing things, board is asking for a new strategy, employees are experimenting, and the market is just moving so quickly. So the instinct is to just respond with activity, like pilots, tools, roadmap, announce an AI program. But the activity can create the illusion of value creation. And I think about the early industrial shifts, right? So the instinct is often to put a new machine into the old process. You can gain some efficiencies, but the value is still stuck in the system because it hasn't been redesigned around the new capability. So I think about the same things for AI. The better starting point is a little bit more basic. Where is value stuck? Where is knowledge trapped? Where is judgment overloaded? Where is the workflow creating drag for clients, for lawyers, for customers? And so if you ask whether AI belongs in that problem, that's where you get the clarity.

Speaker C: So if we can stay there for a second, because I agree with you, I mean, uh, it seems like this AI mandate problem is real. You're demanding strategy before defining the problem. But is there some truth to the idea that top down pressure is one of the only ways you can break some sort of organizational inertia?

Speaker A: That's an interesting question. I think it's a combination of both, right? You have to have obviously leadership buy in. You know, there's an investment of millions of dollars, but you also have to ensure that you're bringing the employees and the people that are working with it along as well. So we were crafting even an AI strategy at, uh, Nike. We were very thoughtful about going from top down to each of the different areas to the senior leadership to the people that were actually working on the problem to even understand what, what is halting some of our production or our processes in place to get to the real root cause. Because the leadership can create the urgency, but then it's really getting the buy in from the people, the marketing team, the customer service team, the engineering team that are actually doing the work to fully understand that, to actually drive a holistic strategy. So very important to do is bringing it all together. So I think we've seen that a lot in all of these shifts. When the Internet boom, our, uh, retailers, they don't want to go online, whereas no, the leaders are afraid of their brand identity. It's tough. So I think we've seen this pattern before.

Speaker B: I uh, like how uh, you mentioned the top down and like literally evaluating every layer until you get to the ones who are actually using it. Boots on the ground type of idea. That brings me to a question of how do you know what workflow to prioritize? Right, because you mentioned it is expensive. It can be very challenging to bring on this new tool or you're just building an entire AI infrastructure. How do you know what to prioritize over others?

Speaker A: It's certainly, I just go back to the discovery and I think it's a number of different facets. Or is the team ready? What's the readiness of the team? What is the problem and can it be applied? With AI, do we have the underlying data or the readiness of. So you have data readiness, you have a human readiness for, and then you have just prioritization across the ease of inflammation, the cost. So it's multifaceted. I don't like to say it, but really one of the big underlying values that you have to answer for is just the human readiness. Because if the team is not able to absorb the redesign, then you just have a tool bolted onto a problem.

Speaker B: Makes sense. That actually makes a lot of sense. How ready are you actually ready? How ready are you to take this on and actually move with it, uh, hit the ground running instead of just spending all this money and they don't know what to do because they haven't been trained or don't understand it, whatever it may be.

Speaker A: One of the biggest mistakes that I see is underestimating fear. People won't say I'm afraid that this is going to make me less relevant, or they say I'm too busy or the tools are not ready or we need more guidance. And sometimes that's real, but that's the language people are using when they don't feel safe to use that tool. So it's really getting those signals very early on to work through those changes within an organization.

Speaker B: So I want to give a follow up here just to dig into that a little bit more. You mentioned fear and um, wanting to use a tool but you're afraid. Do you think there's actual fear in organizations where people don't want to use AI because they're afraid that it will replace them? So they are like, if I don't use it, can't replace me 100%.

Speaker A: I see it all the time. There's resistance and hesitation. Spreads faster than any tool. And if there's one team that's. And fear can come in lots of different shades. Like I said, we're too busy, we have too much work in our hands. And so that's one of the underlying biggest blockers is just how do you reskill? And it's not even, I don't even call it about upslaughter. How do you reskill an organization to work with tools in a very different way than they have in the past? The best value add you have is the context of the people, the judgment. You can have people that have been working in customer service for years and they have that context. They know how to answer that. No tool is going to replace just the context of that emotional relationship that you have with the customer on the line. And so that is the strongest way that you can actually use and unlock human capital.

Speaker C: What are some other signals there? I love these ones because they're hesitation, fear. They're almost impossible to quantify other than people could be using it more, but they're not using it as much. So therefore there's some combination of hesitation or fear there. Are there tangible signals that you're looking for? And I guess this is a real question about adoption in general. Are there things that. What are companies doing correctly and incorrectly when it comes to adoption?

Speaker A: I read a story, I want to share this because this I think so important about Ikea and their customer service and how they were going to use. I think it was 8,500 employees. And they ended up retraining those employees to be like interior design consultants. So we know that there's going to be areas where AI may be not replacing people, but you won't need as many workers. But that's where the reskilling is coming in. The World Economic Forum came out last year and said that they surveyed like a thousand employees and they said that 59 of the hundred or 100 people will need significant retraining by 2030 or reskilling and so 11 are unlikely to receive it. That's 120 million workers that could be left behind. That's a staggering number. And so retraining those workers and I believe that I'm not a doomsday person, that AI will unlock tremendous value and opportunities and new companies. And it's just that reskilling that we really need to invest in and that unlocking of the human capital and EBITDA growth. So absolutely, I would love to hear

Speaker B: your perspective on what that could look like. Who in your organization would you have to work with to implement like a reskilling process? And if you are implementing it, what could that look like?

Speaker A: Well, I think it's going to look different than what we think about it, than what it looks today. But it is certainly on the future of work involving your human resources and your HR capital. But it's also a leadership and it's very much of a team based. So do you think it's going to take a consortium of leaders of what is this team going to look like or what is this function going to look like tomorrow? And that's where the future of work, because it will look very different. It's not about just we can make an announcement and put out more communication and put out more training resources and try to launch a champion program. But if we're not actually getting into how are we going to work together and what is that workflow? I call that the quiet rewiring. Then you're not going to have a re skilled workforce per se.

Speaker C: So Elaine, I want to return to a, uh, phrase you used earlier. Going slow to go fast. We had a guest a couple of weeks ago now who really intelligently, I think talked through that same concept. There's a pace of change now that feels very rapid and there's just a sense that if I'm not staying up to speed, then I'm falling behind. I kind of want you to unpack that as well. I'm interested in this because there are companies that when we were slow to get on the cloud and then they got eaten up. But so there was a speed there that was slow. When is slow wisdom versus when is slow decay?

Speaker A: So when I say go slow to go fast, I don't mean moving timidly, I just mean doing the work in the right order. So I'll start with three questions. Where is repetitive work slowing people down? Like in the legal use case, Is it the intake? Is it the summarization? Is it the drawing up Second, where is like knowledge trapped across like documents, contrast systems, tickets, where's all that knowledge system or teams? And then third is where is human judgment really being stretched by either speed or volume or complexity? So going slow to go fast is really like can we answer those three questions? Can we do it in the right order? And then you have to pressure test the use case against the value and against the readiness that we spoke about for. So like I said with legal NDA, review matter, triage playbook, outside counsel, policy questions, for a small business, it might be just the drafting, might be the client follow up. So these are very important questions that uh, you need to go through in the beginning to go fast to answer first. So you can go fast.

Speaker B: At Silicon Foundry, when you take on a new project or work with a new company, what does success, how do you measure success at the end of that project?

Speaker A: Yeah, first of all, I mean I'm a venture partner so they bring me in as a subject matter expert, particularly on AI transformations. But they work with some of the largest corporates on their innovation strategy. And right now a lot of it is centered around AI and AI transformation. But unlocking human capital, setting up the corporate venture capital or their innovation teams internally, success is very much. And what I really love about Silicon Foundry is their partnership with corporates. It's an extension of their own team. They come in there and they need to implement a new transformation and they want the outside thinking and they will do everything from organize the competitive landscape, what are other people doing, ensure they have a pulse on it, to bringing in startups and partners, to evaluating their strategy. So deliver pending the actual client relationship, it depends will determine the outcome for that.

Speaker C: You mentioned a little bit ago too, this idea of you're talking about these transformations and the reskilling and you mentioned fear. I am interested in exploring that more too because it does feel pervasive across a number of sectors right now, number of industries. Some fear obviously is irrational and some is rational. Is there any kind of fear in this space that is correct that people should be afraid of? Is there anything that feels really overblown

Speaker A: to you that is correct or is feels overblown?

Speaker C: Sort of both. I mean both ends.

Speaker A: We're undergoing a massive change and I don't think any of us know. I think all of us see the rapid acceleration of the tools and of AI and change management. So I think for all of us, we're all prized by uh, one day the announcements OpenAI is a new model and anthropic another model and so everybody's just trying to keep up. So I think that's very natural because we're undergoing major changes in the economy and the transformation so. Absolutely. And then I've seen people at all levels adopt AI and so I would say it allows the person, the business person that me that wasn't trained as an engineer to actually become more and learn about engineering which I get excited about because I'm a very curious person. But so I would just encourage everybody to just dip your toe and and ask questions, be curious and not resist what's in front of us because the change is here and we're all going to have to adopt to a new norm just like we did back then in the E commerce. We've had to do this numerous times again so it could be exciting and that's how I view it.

Speaker B: Very exciting. We've been talking about workflows and a lot of different implementations that are really done with larger corporations. I'm curious, what is your perspective on AI affecting small and medium sized businesses? Are you seeing that it's actually driving real change? What is your viewpoint on this?

Speaker A: I think it's actually very exciting time for small to medium sized businesses and the reason why is they often are encumbered by maybe the technology debt or the decision rights or the workflows or there's a clear line between work workflow improvement in the actual outcome manual admins and I think of a small business that I'm working with doesn't uh, even have legal counsel and all of a sudden they can do it themselves so to speak or use AI to check contracts. So the benefit is so tremendous to actually transform these small business and unlocking massive amounts of human capital and creation. So they often feel the pain directly but but then they can often feel the outcome pretty quickly as well whether from serving clients or growing their business. And so it's incredible to see how much value you can unlock through some of these workflow redesign very easily the small businesses or solopreneur that now build their own website just like simple things to that matter.

Speaker B: So speeding everything up.

Speaker A: Yeah. Where can we save time without damaging trust? Isn't that what we should always. What should we never automate and it's the relationship part of it. There's an accounting firm that I'm working with and what are they in the business of serving clients, of um, establishing trusted relationships. Can the other parts of their manual data entry work be somewhat automated? Yes. What does that allow for? Serving more clients, taking on more clients, spending more time with Them, particularly during these busy seasons where every single new client means that I have to hire more people to serve that client and that's more resources. That's costly and eventually it doesn't make sense from a profitability standpoint, from a value creation standpoint.

Speaker C: Elaine, with these smaller businesses, the opportunity is obviously very real. Do they have their own version of AI mandate problem? Do they do things? Do you see trends with smaller companies as well, that they feel like they just need to do something?

Speaker A: I think that certainly what I see is that they're being sold a lot of tools and it's much harder to implement an AI strategy when you're small and you're just, you're in the work every day, the CEOs in the work. So they're all about getting time, focus and capacity back. And so breaking through the noise is a little bit more challenging, I find, for the small to medium sized business. And where, if I only have limited dollars, what should I invest in if this tool is promising me more capacity, more time and more focus? So unfortunately it could be a smaller version, but they do need to go through the similar discovery process of okay, where's knowledge stuck? What can I unlock? Where's the knowledge retrieval? Where are the areas where I have to get my data ready, even for a small company like that? So a lot of repeatable friction in small businesses. And so there's a lot of need for human judgment that's not necessarily solved with just one tool.

Speaker B: It is, you have a very fine tuned discovery process. It sounds like you got to ask the right questions to really identify some of these problems where you can fit in proper recommendations. What are some of your favorite questions to ask some of these businesses that kind of get you those answers you're looking for?

Speaker A: Oh, uh, gosh, I have a whole diagnostic of, uh, questions that I ask. I guess some of the more interesting ones, particularly to the employees, is what are you scared most about if you were to implement AI tomorrow? And what excites you the most if you implement AI tomorrow? There's so much within those questions that you, uh, unlock about where their fears are, but also where you can actually, where's the leverage on their curiosity? So on the upside is what am I most excited about? Oh gosh, this process that takes me forever, that I spend eight hours a day. And if they suddenly realize that you can actually help them with their jobs, that's exciting. And then you can turn a skeptic into a champion. And that's the biggest unlock that I've seen within small, uh, businesses and within organizations and stuff. And also, would you like to be a part of the rewiring of this whole process? Do you have ideas and suddenly you're more of an ideation phase than you are in just like a diagnostic phase, if that makes sense.

Speaker B: Oh, it does. And it sounds like these questions also allow you to implement the process a lot easier. You can identify the champions, understand their fears, helps you understand the friction, bring that up beforehand. Now it's easier to implement.

Speaker A: Yeah. And turn them into champions. All of a sudden you're not just deploying a tool, you're actually rewiring a whole system. And you'd be surprised by 8 out of 10 people are always excited about, oh my gosh, how can I make this system better that I'm working in? That's so manual and repetitive and my knowledge gets stuck. And if you ask questions, would you love an internal knowledge system where you can just ask it questions and it can provide you some answers and people get excited? I've never heard somebody say, no, I'm not excited about that.

Speaker C: That's amazing. Elaine. Also, their buy in means they're sort of thought partners and there's no competition there or it doesn't have to be, I guess related to that is this whole build versus buy question. And obviously the answer probably is almost always, it depends. But is there meaningful framework that you go through on that?

Speaker A: I do have a framework around it that includes everything from speed, control, urgency, but as well as the human infrastructure. Really, what is your competitive, what's core to your competitive advantage? If the data is proprietary, you know, customer service is deeply differentiated than you may want to build. If you're really just looking to go fast and you're not worried about that and you've got all the integration in place, then you know, you buy. So most of the times there's some hybrid component of, uh, the process that I go through that ends up with the company. But for some, they can only buy for smaller organization. For others, it's so important to build for. We see the technologies, the technology, organization, but they do have the infrastructure, they do have the investment, they do have the resources, they have the talent. So you can't only think about it for today, but when you think about build versus buy, you have to think about it for three years from now because who's going to maintain that? Who's going to update it? What talent do you need for that? And do you have that right now? Are you building for the future? We're not just building for today. So these are the things that you really need to go through. And, and it's very much of a workshop that the engineers, the business owners, the change management that everybody really needs to come through to come together. And we used to do this at Nike is bring everybody in the room and we'd go through hours of asking questions and doing a scorecard and revisiting and the risk that's associated with it as well.

Speaker B: I love how you're actually compiling real data from your own employees and being like, all right, let's actually map this out. When you got all those responses, how did you know where to go next? How did you take what uh, are those responses were? And then actually put them into actionable items.

Speaker A: Most of the time we had done, I would say, the scouting work before. So if it's kind of a partner, we'd look at the market. For example, legal is a very interesting use case. There are so many vendor specific to companies out there that are just trying to focus on the legal use case. That's a more mature use case or customer service is than some other ones back I would say two years ago, content creation, video creation, it was not at the fidelity where it was now. So it's also evaluating where the competitive landscape is and where it's going and what the tools are to actually bring in solve that problem. It could be that we have such a differentiated production process that there are no real tools out there. So we've got to really create that IP internally in house. So. Or you may have to wait. We're not ready yet. It's not. The tools aren't ready yet there. So it's evaluating it uh, against those dimensions and having that information beforehand before going through that process so that you can actually make the right decisions and score it. And sometimes it's leadership that makes the call. So it's not, it isn't, um, it's not always the teams coming together. Sometimes it's truly is the CEO or the senior leader said this is where I'd like to go. Really?

Speaker C: No, truly, sometimes that's what it takes staying in the legal space for a second. Elaine, are there any areas that come to mind for you where you feel like very excited about legal AI adoption or do you there areas that you feel like are overhyped or underhyped? Do you, what do you, how do you sort of position yourself here?

Speaker A: Yeah, you know what I'm really excited about is there's so much knowledge in legal lawyers, pattern recognition and often you start like you're on a new client, you're on a new case, and you're starting all over again. So how can all of that knowledge, data, not knowledge retrieval, that can be whether for a legal firm or an internal legal team from years upon end that, oh yeah, if we're working like on a sports contract or we've actually done this and we can take some of the clause language from a, uh, previous from like 10 years ago that we didn't know, or actually compare side by side. I know it sounds, it might sound small, but that whole life cycle and that data is what I get most excited about with legal, and then it actually frees up the legal team to actually do what they do best in serving clients, negotiation and ensuring they're operating with like, plenty of, you know, the knowledge at their fingertips and stuff.

Speaker B: So you're one of, uh, many guests who just mentioned the data aspect of it and why it's so exciting. It sounds like one of the most important things you can have as an organization is keeping good tabs of your data and then being able to feed it into a model because that is yours and not all out there publicly. Are you finding that's the next treasure or gold mine for the future? Gold rush? It's owned data.

Speaker A: Yes. I think it's unlocking maybe the data that exists. And sometimes it's not in a platform, it's not in a system. And this is why I really believe in the human potential, it's in the humans itself. So really unlocking that human capacity to actually take that tribal knowledge from years upon years, which is not a small task, and really incorporating that, uh, into making your company compounding your advantage because you can apply the tools and the tools are becoming commoditized. But where the real competitive advantage is those 10,000 decisions that your humans made regarding your contracts or regarding how we make a shoe, or regarding how this particular product and those 10,000 decisions come with the AI, that's what compounds your advantage and that's really what differentiates and provides the competitive advantage.

Speaker C: Well, Elaine, this. I don't know if this is a perfect segue into the last question that we ask at the end of every one of our podcasts.

Speaker D: But.

Speaker C: But what is your hottest take on the industry right now?

Speaker A: My hottest take that most companies do not have an AI technical problem or M strategy. They have a readiness problem. And so they have tools, they have pilots, they have smart people, but they've not done the work to redesign the decision rights and the rewiring of people. And that requires Incentives, government incentives, and leadership behaviors that allow AI to scale. And that's where the work transformation is. That's where the real work is.

Speaker C: Well, that is. Yeah.

Speaker B: Truly.

Speaker C: What a fantastic hot take. Um, it's always. I love getting to the end of.

Speaker A: Quiet rewiring is what I call it.

Speaker C: I always love getting to the end of these because it almost reflectively becomes a theme of the entire episode. And yours is definitely fantastic, Elaine. And with that's a wrap on today's episode of between the Briefs. A big thank you to Elaine Barsoom for sharing all of her insights here today. Adrian, what do you have to add anything before we wrap up?

Speaker B: Love this conversation. If you're listening, really go back and listen, because you dropped some gems, Elaine. You really did. About the way we got to think about AI, uh, how they change workflows, and actually the questions we need to be asking ourselves before we just jump right in. It's so important. So thank you so much for joining us here today.

Speaker A: Thank you. Thank you, Adrian. Thank you, Joe. It's been such a pleasure to be here today.

Speaker B: Of course. And to all of you listening, be sure to subscribe so you don't miss future episodes. Thanks for listening. Stay curious, stay inspired, and we'll see you next time

Speaker D: up.

Speaker B: Uh, next on between the Briefs.

Speaker D: Certainly lawyers are understanding that AI is imperfect. I think that is something now in terms of sort of the broader tension of. Of being risk averse and the need to adapt. I think that that risk aversion is now evolving into something on the lines of cautiousness and sort of how cautious folks need to be in terms of adopting AI. Uh, they want to do it, but they don't know exactly how fast they need to do it or the right way to do it, which I think is a different kind of expression of being risk averse than the past when it was kind of just, oh, well, I'm not. Just not gonna. I don't want to use this at all. It's too risky. A lot of lawyers are willing to accept a greater degree of risk to some degree, but by accepting that greater degree of risk, their expectations for what they're getting out of the tour higher.

Speaker B: Stay tuned for the full interview. Coming to you soon. Between the Briefs is brought to you by Steno. Um, to find out more about Steno and how we combine find exceptional court reporting and litigation support services to deliver a superior litigation experience, visit steno.com, that's S-T-E-N-O.com and then make sure to search for between the briefs in Apple Podcasts, Spotify or anywhere else. You get your podcasts and click subscribe so you don't miss any future episodes. On behalf of the team here at Steno, thanks for listening.