Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
Transcript
Marlene Gebauer (00:08)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gabauer.
Greg Lambert (00:14)
I’m Greg Lambert.
Marlene Gebauer (00:16)
So this week we are joined by Wendy Jephson CEO and founder of Let’s Think, a behavioral science technology company focused on improving decision making and knowledge transfer within organizations. Wendy, welcome to The Geek in Review.
Wendy Jephson (00:29)
Great to be here.
Greg Lambert (00:30)
All right, before we jump in, we did have a little bit of banter that we wanted to do with you. And this should be right down your alley, because we’re going to be talking a lot more about this, about your company, Let’s Think. And that is, let’s talk a little bit about leveraging behavioral science when it comes to businesses, to organizations. And do you mind just kind of talking about
how behavioral science can benefit an organization. And quite frankly, the thing I want to know is how the hell do you even bring that up to an organization’s leader?
Marlene Gebauer (01:10)
How do you bring it up? do you get it in? It’s like all of those.
Wendy Jephson (01:13)
That’s a really good question. So the way I did it was I got NASDAQ to buy the company and they bought it.
Marlene Gebauer (01:22)
Okay. Easy. That’s how do it.
Wendy Jephson (01:25)
But you’re right. It’s, I mean, I, it’s my favorite subject. think it massively can benefit companies in many areas because companies are basically made up of people, aren’t they? So understanding how people are interacting with the systems within the company and their clients and other parts of the companies interact with. Super important can make all the difference between how well things work and how well they don’t work. It is a new topic relatively speaking within organizations though.
Everyone loves it, thinks it sounds really cool. And they kind of go, yeah, I love it. I want to hear more about it. But you carry on with it over there and I’ll keep doing what I’m doing.
Marlene Gebauer (02:07)
So not on my lawn!
Wendy Jephson (02:09)
I mean, within Nasdaq it was, and I love solving problems that are hard. That’s what it’s great for. It’s unpacking something fresh and looking at it anew and using behavioral science methodology to do that. So actually it’s, you pick the problems that people go, yeah, you won’t be able to solve that. We had a go, you know, good luck. And we’ve had some success with that because we start off with the experts unpack what they’re doing.
and then figure out what the real problems are that need solving. And then you come up with solutions that they didn’t know they needed, but absolutely won’t when you’ve done it that way.
Greg Lambert (02:48)
I know there’s a lot of integration, what we called in the nineties, the law and it was like law and economics. And here we have law and behavior science issues. I know there’s probably some pushback, but are there also some times where people may over anticipate how it will change the way an organization works?
Wendy Jephson (03:11)
suppose the way I think about it, there’s a lot of pop psychology out there. So there’s a lot of kind of assumptions of what it is. Plus there’s also a lot of people who think, I know people so I think I could do what you do. So you get some of that attitude. There’s a lot of literature out there about kind of the biases side of work. But actually what we know is also from looking at experts at work is
They can be really brilliant as well. stop a lot of bad things happening just because of what they know and they intervene early and know how to work a system. there’s lots of different, there’s, behavioral sciences are broad. Lots of the work that we’ve learned from is used in high risk industries where people die if they get it wrong and they’re designing systems like in nuclear and aviation and medicine. So it matters when you’re designing a system that people are interacting with. So really understanding how work gets done.
not how it’s described as done which two very different things and that’s where a lot of the methodology comes from understanding that and unpacking how people really navigate complex environments with uncertain information.
Marlene Gebauer (04:18)
It’s often times people can’t really explain what they do and how they do it.
Wendy Jephson (04:23)
And that’s just where we are with Let’s Think, so we can all put that in a short throughout this.
Marlene Gebauer (04:28)
Yes we will.
Greg Lambert (04:29)
I guess a good way to know if you can explain what you do is go ask your mom to explain what it is that you do and if you’ve explained it well enough to her and I think that you know what you do. Because my mom still doesn’t know what I do.
Marlene Gebauer (04:41)
I I failed.
Wendy Jephson (04:46)
Well my youngest daughter, we can cut this bit out, my youngest, every time you start telling me I just start going la la la in my head. Yeah, okay.
Marlene Gebauer (04:56)
Yeah, I think my kids too. So Wendy, I had the pleasure of meeting you at the TLTF conference last year and I got to know you a little bit. You you have a very impressive background with significant experience in both law and behavioral science. What motivated you to start another venture with Let’s Think after your previous successes, including your role at NASDAQ? What lessons from your past experience have you applied to this new start?
Wendy Jephson (05:23)
So firstly, thank you for that. And really it’s it’s kind of a passion and a frustration. I think actually lots of advisors, professional services, advisors, lawyers, actually both scientists as well. Quite often the product is advice, isn’t it? But actually building tech is really fun building a product. So a real passion from, as I described before, understanding problems really that people really have in the workplace. And then, then you get out your sharpie pen and start designing what that
solution could look like, and then seeing people build it and it moves and people use it and they love it. actually, love the whole process from beginning to end of that. So I left and thought, yeah, I’d really like to do some more of that. Plus the frustration of lot of tech, particularly to date has been workflows and trying to think for you. And there’s a huge narrative around that at the moment, isn’t there, with AI actually trying to do the work for you.
Well, actually, I love thinking and I like technology that helps me think and that’s what I want more of. Technology that really starts to help me improve my critical thinking and figure out complex things that are difficult and that there’s less of that. So that’s really what is, you know, I’m excited about trying to achieve.
Marlene Gebauer (06:37)
going to say that that’s almost the opposite kind of of what you’re hearing right now. It’s more about like automating and having, you know, having technology do things for you. But I wonder if sort of that allows sort of more of the critical thinking. Like if it’s doing some of these, you know, baseline type of things, that allows the end user to actually do more of the critical thinking in terms of the content they’re working with.
Wendy Jephson (07:02)
So that’s a really good point. there’s literally some, Microsoft research just come out on this. So I think absolutely it’s going to free you up more time to do that more complex thinking. But there’s a couple of challenges around that, which is if you’ve never seen it done before and never done it yourself, how do you know how to do it? And actually there’s a recent research we’re saying it depends what level of expertise you’ve already got. So people who’ve got expertise can definitely, they found increased critical thinking.
And if they have a lower kind of confidence in the AI and a greater confidence in their own abilities, greater critical thinking, because they are analyzing what they’re seeing and thinking carefully and referring to their own knowledge base. But those people who don’t have that level of expertise, they saw less critical thinking going on because they were more accepting of what was being given to them. So they weren’t exercising that muscle.
and developing it for themselves and that’s the real challenge that is potentially coming. This thing’s been released to the world and everyone’s using it merrily but not necessarily understanding all the unintended consequences that could be happening here.
Marlene Gebauer (08:10)
Kind of goes back to the question about the juniors, you how do we make sure that they are learning in this world of automation?
Greg Lambert (08:17)
Yeah. There was one thing in your work that you did before you did Let’s Think. And that was, and I think this was by the time you got to NASDAQ, you also did, I think, a study or wrote about kind of bad behavior and identifying when people would try to manipulate the market or, you know, kind of do bad things and the motivations behind that.
You know, I think a lot of times when we look at behavioral science in the organization, we look at truly identifying, you know, ways to make good behavior, but you kind of turn that around in finding ways to identify bad behavior. Can you just talk to me a little bit? I want to just know just a little bit more about what was the motivation behind that.
Wendy Jephson (09:07)
Yeah, so where that originally came from was looking at fund managers decision-making to understand, you know, in order to kind of design decision support for them to make better decisions. So you take all their kind of trading decisions and you can feed back to them so they could learn from it. And yeah, so we started building that system and it’s an individual sell to each fund manager and there’s a challenge there of showing them back data they’ve not seen on themselves before. It doesn’t always look as good as they.
like to remember that it did, but you know, there, there was some, progress there, but at the same time, it was the head of compliance at Mangrove. She’s now the CEO of Mangrove who spotted this back in 2011, who said, look, if you’re understanding how my firm managed to making decisions, I want that from a compliance perspective because I want to be able to absolutely sposted any of them are doing something they shouldn’t be doing because I’m not having that in the firm. But equally.
actually they’re paid to make good decisions and be ahead of the market and make money and I want to be able to defend them if that’s actually what they’ve been doing with having some data to do that. So we built that system first off to have a look at that and start to spot the patterns and we use the same methodologies that we’re bringing into let’s think to understand both how far managers are making complex decisions but also how the compliance officers were looking at.
those fund managers making decisions with an understanding of what does market abuse look like, what are the high risk factors in that, what’s going on in the market and that allows you to understand what data points to put into your technology and how to risk score it in a way that both experienced people and junior compliance officers could look at it and get better understanding of the behavior and that led to actually really good conversations between
front office and back office, which is something you would be surprised to hear about, with my managers going to say, let me tell you what I’m about to do because you’ll come and talk to me about this because it might be a bit different to what I normally do. And that’s kind of behavior, but that behind the behavior you want in an organization that’s functioning really well. And that’s a product that NASDAQ bought and, you know, because it had done such a good job in designing the technology, it was an acqui hire.
It was, let’s buy the tech, retire our own version of this and have the team doing that work, which we continued at NASDAQ because they are the market leaders in detecting market abuse.
Greg Lambert (11:39)
Yeah, that is absolutely fascinating. So, well, let’s talk about Let’s Think. Can you kind of give us a broad overview of what Let’s Think’s mission is or the knowledge exchange? And if I’m a potential customer, what needs do I have that’s going to make me want to reach out to you for help?
Wendy Jephson (12:00)
Okay, so our purpose is to help the people of the world think brilliantly because when they do think brilliantly, they do great things. So that’s the kind of lofty goal, which sounds great. And then you’ll say, well, great, but what does that mean? So you’re my potential buyer. So I think one thing that we’d all agree on is that the best people in our organizations create the most value for us in terms of our people, our productivity and profits of the firm. You know, they’re brilliant actually at bringing up other people and working alongside them and getting value out of them.
they’ve productive themselves because they’ve got so much experience, they know what to do when and how to do it and how to get it done in the most impactful manner. And both of those things feed into the profits of the organization. But lots of that knowledge that they have or know-how, and we call it know what, when, why, who, is locked in their heads. And lots of that knowledge is not written down and it’s not shared particularly well before they leave. It’s obviously lost when they leave, but it’s not shared particularly well that, you know,
quite often on the job because they just do it. They just know how to do it. As you’ve already said, we think we can improve on that because that’s the problem that’s been exacerbated by hybrid working and this upcoming challenge of AI eating what Jyn is doing. So we have been working on pulling tacit knowledge out of experts’ heads. As I’ve said, we’ve used that to design technologies and understand what data is required for algorithms and how to get people to use technologies that we’re designing for them.
Then along comes GenAI and we think, wow, actually for us to do it, it creates an incredibly valuable data set, but it’s really time consuming. It’s quite, you know, quite the level of expertise you need to be able to a conversation going with another expert and pull that information out. And then it takes a lot of to analyze the data you’ve got. How we automate ourselves in doing that. And GenAI gives us that capability to do that. So we can.
bake ourselves into a conversational interface that can have a conversation with an expert in the way that we do that activates memory to kind of pull back what was going on in complex matters and capture the tacit knowledge in a conversational form. That kind of results in a fairly rambling story that goes all around. No disrespect to experts, that’s the way the interviewing process works. It takes you on a journey that goes backwards and forwards and up and down.
But the way we analyze, we’ve trained models that can analyze that output the way that we do to put structure and labels around it. And then it can be fed back and visualized so that people can see the story of what happened in the strategic decision making that went on and why people, you know, made the calls that they did at specific particular points in time so they can learn from it. That’s the concept of the Nolidic Shanes.
Greg Lambert (14:49)
Yeah, Marlene and I are kind of, we grew up in the knowledge management sector. That’s been one of the things that KM has been missing is this tacit knowledge that’s, we usually call it the information that’s just between the ears of the partner. Can this information that you’re gathering be used to augment the KM systems or does it kind of…
run separately. Is it its own vertical or does it merge with the KM system?
Wendy Jephson (15:21)
So to my mind, it’s creating a new data set that I think is complimentary to all the KM systems that are out there. Cause to my mind, as you also seem to agree, it’s a bit that’s missing. There is lots of great stuff that does get written down, but actually this is something that can tie together and help people actually pull out the most important. Cause we’re looking for the key decisions that happened that changed the course of events and how the people deal with them. So you can highlight the most important matters.
and strategic decisions, etc. in a case more easily and then you tag in everything else so you can drill down and get more detail as and you want it. That’s how I see it. You guys are the experts in this space so I’d rather ask you lot questions.
Greg Lambert (16:09)
Well, we talk a lot. Experts is, I’m not sure about that.
Marlene Gebauer (16:15)
Exactly. How do you ensure the quality of the knowledge that’s being captured by AI? I know we had Dr. Megan Ma on the show a few months back, it sounds similar to some of the things that she’s doing.
Wendy Jephson (16:30)
Yeah, she’s great. had the pleasure of an afternoon with her when she was over in London in the summer. She’s fantastic. So obviously we’re speaking to the experts and the AI is asking questions of the experts. So the knowledge is coming from them. They have the ability because it’s presented back to them to also edit it if it’s not right. And we have shown it back to the people that we’ve been doing this with.
to make sure that it is telling the story that they remember telling and then they can drill into it. And actually it’s interesting watching them do that because it again elicits more information than seeing the stories themselves and activates memories as they do it. Because it creates a knowledge graph, a huge knowledge graph that can be represented. Forming that is great because it can make you sound super smooth in the way that often when we talk we’re not. This is good example of that.
And then the text, what they actually said is still there. And actually that’s what I find really powerful is that you can drill down to somebody, senior partner saying that part was individually difficult. that just really brings it to life as well. Hearing their words. Yeah. That’s how we make sure that what we captured is actually just coming from the source of truth.
Marlene Gebauer (17:48)
And how and where is it transferred once you do have this information?
Wendy Jephson (17:54)
So this is for a law firm for themselves. I mean, it’s their most valuable data set, as we see it, is their competitive advantage of what’s in their best people’s heads. the model isn’t trained on it. We train the model in order to pull out the information and organize it and visualize it. And therefore the data is the law firms own with the securities you would want to have around it to ensure things like protection of privilege and ethical walls and that sort of thing that we’re mindful that we…
you need to build too.
Marlene Gebauer (18:24)
I’m just wondering if you have any examples of, you know, once the data is provided to the firm, you know, where are they putting it? How are they using it?
Wendy Jephson (18:32)
So we’re still super early stages, but what we see is that it will be used for self-reflection for experts. You know, they’re going to have a quick look back and they’re doing something. And there was somebody we saw again in action. they initially said, I’m not sure I’ve used it. You’ve captured it well. You can see why it’s good for other people. And then he said, well, actually I’m just about to start on one just like this. And that, is a good reminder that authority didn’t move very quickly last time and we need to be, you know, have a different strategy for them this time.
So that’s the perfect use case for somebody. And there are other reasons why they would want to create the dataset in the first place. But as a reminder, a reflective learning tool that it needs to be quick for somebody who already knows their stuff. For more junior people, them quite often they dropped into something that’s been running for a long time already. So how do they get up to speed quickly? And they spend time figuring out who’s who in it and what’s been going on before. And what were the decisions that led up to that point?
which is quite hard to see quite often just from the paper trail. So they’ve already said this would be amazing for a time saver for getting an understanding of who the different actors are and how they influence each other and what were those strategic decisions that led up to this point. And also I’ve been asked to do this little thing that’s a small part of this huge matter. Now I understand a bit of why I’m doing it and what it relates to and contributes to and that’s a bit more engaging.
given that it’s hard to get hold of supervisory time now, actually do some legwork first to go and ask a better question of a busy, experienced person. Also remove some of that psychological fear factor of, loyza, there’s an industry where you’re paid to know, isn’t it? It’s really hard to go and ask questions and say you don’t know things. So being able to go in with a better understanding of how that partner has done work in the past.
and ask a small question about something that you’re trying to figure out how to do, can tool them up for that sort of purpose.
Greg Lambert (20:34)
to look at the outcomes that you produce. So once you’ve gone through your process and you’ve elicited, you’ve captured and you’ve transferred the information to the platform, how do you assess the effectiveness that the platform is providing, the knowledge sharing with the organizations that are using Let’s Think? Do you have metrics? How do you measure success on this?
Wendy Jephson (21:04)
This was definitely a question I was going to ask you two back, given that you work with knowledge of how you do it. know, we’re thinking carefully about how we are going to measure the impact it’s going to have. Clearly there’s actually, much time are you saving? Non-billable time for people getting up to speed on matters, having a better understanding. For people who are creating these stories of events that they’ve worked on, we think that there’s a great opportunity to improve conversations with clients.
because we know that lawyers love lawyering, but not so much explaining what they’ve done in order to get paid. And that ends up with, I think there was something like 72 % of firms had an increase of write-offs last year, according to a big hand report, with up to 20 % of fees being written off because of clients not understanding the complexity of the work that’s done. So actually, there’s a version of this which can get across, and that’s some of the feedback we’ve had.
you can really see the complexity of what happened, the invisible thinking of why it was complicated to make calls because the next steps weren’t necessarily obvious and you’re trying to weigh up the pros and cons of different tactics you might take. And a reminder of the Swiss in terms of the things that come out the blue, which always come out the blue and what had to be dealt with in that.
having a reminder of that, you begin to understand the complexity of what lawyers are really doing and how they’re dealing with it. As one of my colleagues said after one of the interviews that we’ve done, he said, submitting how little all there is in being a lawyer, which is all that know what, why, who, how, isn’t it? So I think improving those conversations around actually the work that was done in order to improve that fee recovery rate, which can also demonstrate your value, which you can look at career progression and ideally,
billable hour I don’t think is going away anytime soon yep you can justify that more but you could also explain the value of the people you’ve put on them on the matter why you’ve got different types of partners who are very expensive why you’ve had a junior on there and the value that they had you can begin to showcase that in a better way
Greg Lambert (23:15)
So it’s not just about showing the value internally, it’s actually showing the value to your clients as well.
Wendy Jephson (23:23)
Yeah, because I mean, that’s what I love actually, when you talk to experts is you get such a rich insight into the world that they live in in their heads and all the things that they’re noticing, but they’re really bad at explaining that, if you ask them. They don’t even notice that they’ve got it. So it’s actually really interesting to see it laid out in various kind of visualizations in that way and the story that unfolds.
Marlene Gebauer (23:49)
Yeah, and I would say improving client understanding and client satisfaction is probably the number one metric that we want to be tracking. So switching gears for a little bit now, like let’s think integrates behavioral science into technology solutions. And we’ve been talking about that. How do you think that this integration is enhancing user engagement and knowledge retention among employees? Like how is it impacting them?
Wendy Jephson (24:17)
Yeah, so using behavioral science, as I said, we use it to unpack the problem and understand what they’re already doing. So you can then design technology that fits into their day and they want to use.
Marlene Gebauer (24:31)
And that’s the key, they want to use it.
Wendy Jephson (24:33)
that they want to use it. It’s really interesting at the moment, isn’t it? Because it’s, think, even a year ago, anyone saying about legal tech is, you need to design something that fits into Word, because that’s where they live. And I don’t disagree with that at all, because I think it’s really important to understand the systems and the tech they’re already using. But OpenAI is not built into Word. I mean, those are the tools that people will get. We all know all the things that we go and use.
that are not connected to anything just because we really want to and they deliver some sort of value to us. So understanding the underlying need and making sure you’ve hit that first and then you think about the integration as well. think those two things are super important in tandem for sure. But if you can get something that people actually want to engage with because it’s giving them something that, and this is what we think carefully about.
One of those can we improve that client engagement conversation? Because that’s a pain point, whatever level you’re at potentially. Actually, ideally, once you start seeing your own knowledge and because it’s a reflection tool, ideally we want to move this into, this is something you do regularly. You do it as part of your chargeable work. So again, because it’s improving your own strategic thinking as you’re doing it. So it’s a way of…
Having a thinking partner, which is the first part of the platform, is the thinking partner that can help you test your own thinking and brainstorm with you and strengthens your own expertise, which can always continue to develop. Ideally, that’s where we’re headed to. But you have to kind of take people on a journey, isn’t it? Well, what does this knowledge even look like? You can get this stuff out people’s heads. Well, what’s that look like? So it’s starting with that journey. This is what it can look like. This is how you can engage with it.
This is when you might engage it, what you might use it for. It’s that sort of journey that we’re going on and leveraging the archive understanding of human behavior to try and each step of the way, give us some tools to do that.
Marlene Gebauer (26:39)
So, I mean, are there particular behavioral principles that have been particularly effective in fostering a culture of knowledge sharing?
Wendy Jephson (26:47)
What’s really helpful is understanding barriers to it. would like for at the junior end, psychological safety, as I say, being able to, that stops people from going even asking the questions. At a more senior level, you still have elements of this fear factor of, well, if I should tell you everything I know. So there’s one of them, which is, it’s really important to recognize why people wouldn’t want to do these things.
Marlene Gebauer (27:06)
the trust factor.
Wendy Jephson (27:14)
And actually, you what we know from the development of expertise and how it comes about is you could absolutely accelerate it by showing them how experts think, but you still have to put it into practice and do it for yourself. And what’s really clear from seeing, you know, what some of these experts that we’ve been working with is, wow, I now understand the relationships they have from previous places they’ve worked that they pull on sometimes, these matters that they work on. understand why they’re thinking about getting something done.
today in not in six weeks time, but I can’t be them. I don’t have the confidence that they’ve got or necessarily the grammar tests to go and do it yet or the skills to go and do it yet. But I can start looking at the things that they’re looking at and noticing what matters, that signal in the noise. So it’s to get across to people, this isn’t going to mean you’re
If anything, it will showcase why you’re so valuable and can put your number up even more. People are going to eat away at you quite immediately. But the other big challenge is they don’t know what they know. Quite often, this is where the imposter syndrome comes from. And again, the way expertise develops is through lots of exposure to multiple examples of something in your domain is domain specific.
and the brain takes that on, there’s that model that you have unconscious incompetence that moves across to unconscious competence, which is your expert, which you kind of start learning what you know and then you automate it. So we have our own system to automate our own intelligence because that makes it really quick and then it just comes immediately to mind and you recognize that you just know what to do, you don’t know how you knew what to do. So that problem is what we have to unpack.
because the way that we ask the questions is to activate memories and allow people to look deeply into what it is that they know and how they know it and pull that out. So those are two key barriers at that expert end that we’re using our science understanding to address.
Greg Lambert (29:22)
Yeah, we talked about some of the challenges there. I’m just wondering, do you face any kind of misconceptions about what you’re trying to do with Let’s Think and extracting the knowledge from the folks? So how do you kind of clarify so that people don’t think you’re trying to kind of replace them, I would think is maybe something that fear that people may have.
Wendy Jephson (29:46)
Yeah, so actually it’s really interesting that particular because I think in law, I’m as surprised as anyone that I’m back in law. I didn’t expect to be, I thought I’d left.
Greg Lambert (29:57)
Kind of like the mob. You it’s like you think you’re out and they drag you back in.
Wendy Jephson (30:03)
Brilliant. So we started this in financial services and this is a problem that exists in many places. And what was really interesting is that Sarah Dippertus meeting at an innovation lab with a head of innovation for, with Sarah, I think Sarah Harris from Kingsley Napoli, we got put together and lawyers get it.
So in other industries, they quite quickly are going and they’re on that, AI is automating, replacing bandwagon and they go, that’s what you’re doing. And it’s, there’s a whole expert, no, that’s not what we’re doing. Lawyers get the problem of tacit knowledge. They get the challenges of getting it from senior lawyers. They just are on board with the idea that you can capture this to make lawyers better. So it’s interesting the differences, which is why this is a domain that
I think, you know, it’s great to be in and it’s a brilliant time to be in it because even in the space of a year, the transformation in it has been extraordinary, I think, and the willingness to listen to it. And when we have these conversations, because we start talking about the problem, and I think the problem is being talked about a lot, people come up to us and say, that is a challenge that we’ve got and really want to hear about how you’re addressing it. We’ve had a lot of interest, even in the last week or two, of people coming up.
And I’ve noticed that they don’t say, and have you seen what such and such is doing? Which is common if there is a competitor. They aren’t saying that. I’m not saying there aren’t necessarily people who are going to be doing something that will be analogous. Just haven’t heard it yet. So I think that’s quite interesting.
Greg Lambert (31:46)
Yeah, it’s definitely not boring, I can tell you that.
Wendy Jephson (31:49)
We don’t think so, but you know, we love it.
Marlene Gebauer (31:52)
Pulling out your crystal ball, what do you think are going to be the… Actually, I’ll do this. I’ll give you an option on the question. So what do you think is going to be the biggest challenges or opportunities for legal tech and women-led startups in the next few years? Or what do you think are going to be the challenges and opportunities for using behavioral science in law firms?
Wendy Jephson (32:14)
Yeah, that’s interesting.
Greg Lambert (32:16)
Or could answer both.
Marlene Gebauer (32:17)
Or you could just pick your own thing that you want to answer. You don’t even have to answer. We’re very chill about that here.
Wendy Jephson (32:24)
that’s really-
We haven’t got long enough for the second one, so I’ll go with the first one. think the challenges for legal tech, well, the opportunities for legal tech are quite extraordinary. I think the challenges are on the buyer’s side because they’ve got so many people coming at them, haven’t they? And trying to figure out who’s the one to pick. But I was at a conference last week with the master of the roles over here. He’s kind of…
number two in the legal justice system were saying AI isn’t a choice for lawyers anymore. It’s about how to use it and get effective benefit from it. This move from skepticism to optimism has been amazing in the last year. And that’s what you need if you’re going to transform an industry, isn’t it? need, and that’s not to say that lawyers aren’t going to remain the most skeptical, challenging testers of your product, which actually is a great place.
to be, especially we want to carry on into all knowledge work. Let’s start with the toughest gang a lot. We’ll, you know, aren’t quite a lot of the challenges if we can answer their problems. So it’s a really interesting time, I think. And in terms of women led startups, think, in legal, the other thing that I’ve noticed coming back into law, it feels slightly like coming home, which is rather nice. But there’s also an awful lot of women, I mean, compared to the natural services.
There’s a lot of very smart women and there’s a lot of women in these functions that didn’t exist when I was lawyering around the periphery and in the, and I don’t say that in a light way, they’re very important in a firm, but around knowledge and innovation and technology. There’s a lot of women in those roles who I can see taking significant, playing significant parts in the whole legal tech industry.
So perhaps that’s going to be the area where we take off more. It’ll be interesting to see.
Marlene Gebauer (34:25)
Yeah, I hope so.
Greg Lambert (34:26)
Me too. Well, Wendy Jeffson, co-founder of Let’s Think, or founder of Let’s Think, thank you very much for joining us on the Geek in Review.
Wendy Jephson (34:36)
Thank you very much, pleasure, great to be here, the dog, dog’s gonna start barking. He’s a great friend, and I have got great co-founders as well, so.
Marlene Gebauer (34:42)
Join in.
And Wendy, again, thank you so much. And thanks to you, all our listeners, for taking the time to listen to the Geek in Review podcast. If you enjoy the show, share it with a colleague. We’d love to hear from you, so reach out to us on LinkedIn. And Blue Sky, is that right?
Greg Lambert (35:01)
Yeah, Blue
Sky. Geek Law Blog. So Wendy, we’ll make sure that we put links in the show notes, but if people want to learn more about Let’s Think or reach out to you, connect with you, what’s the best place for them to go?
Wendy Jephson (35:18)
I would say LinkedIn is a great place. Do drop me a there. That’d be great.
Marlene Gebauer (35:23)
And as always, the music you hear is from Jerry David DeSica. Thank you, Jerry.
Greg Lambert (35:28)
Thanks, Jerry. All right. Talk to you later.
Marlene Gebauer (35:30)
Okay, bye bye.
Wendy Jephson (35:42)
Hey!