Fresh Voices on Legal Tech with Amy Conroy

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Got the world turning as fast as it can?

Hear how technology can help, legally speaking,

with two of the top legal technology experts,

authors, and lawyers, Dennis Kennedy and Tom Myle.

Welcome to the Kennedy-Myle Report

here on the Legal Talk Network.

And welcome to the Legal Talk Network.

Welcome to episode 373 of the Kennedy-Myle Report.

I'm Dennis Kennedy in Ann Arbor.

And I'm Tom Myle in Dallas.

In our last episode, we had another guest

in our Fresh Voices in Legal Tech series,

Amanda Brown of Lanyap Law Lab.

So must listen.

In this episode, we have another very special guest

in our Fresh Voices series.

In Fresh Voices, we want to showcase different

and compelling perspectives on legal tech and much more.

We have another fabulous guest, Tom.

Tom, what's all on our agenda?

What's on our agenda for this episode?

Well, Dennis, in this edition of the Kennedy-Myle Report,

we are thrilled to continue our Fresh Voices

in Legal Tech interview series with Amy Conroy,

who, among other things, is Principal Data Scientist

at Mishkondorea LLP in London,

the co-founder and director of Law School 2.0,

and a visible and insightful contributor

in the legal tech world.

We want our Fresh Voices series to not only introduce you

to terrific leaders in the legal tech space,

but to provide you with their perspective

on the things you should be doing in the legal tech world.

And as usual, we'll finish up with our parting shots,

that one-tip website or observation

that you can start to use the second that this podcast is over.

But first up, we are so pleased to welcome Amy Conroy

to our Fresh Voices series.

Amy, welcome to the Kennedy-Myle Report.

Thank you for having me.

Before we get started, can you tell our audience

a little bit more about you, about Mishkondorea,

about Law School 2.0,

what your audience should know to get started?

Yeah, of course.

So at Mishkondorea,

I am a data scientist,

and here I'm responsible for a few different strands of work.

The first is our litigation data strand of work

within our data science team.

And that's really looking at how, as a firm,

we can collect more information around our litigation work

and in the future do predictive analytics on that work.

So the more exciting, can we predict outcomes,

things like that.

But in the first instance,

we need to start collecting that information.

So I've developed a strategy around that

within our data team.

The second strategy is to collect more information

within our data science team.

The third strand of work is our MDR research strand of work.

So we collaborate with a few different universities

and other institutions on different research papers

and studies as well,

which we've published a few articles on,

really focusing on how we can make legal tech more accessible.

We've done some research on predicting outcomes,

things like that.

And then the final strand of work,

which takes up most of my time at Mishkondorea,

is our automation strand of work.

So that's primarily working with power apps

and power apps,

so the power platform to build out different applications

for data collection and really automate

a lot of the processes that we have.

So it's not what a traditional data scientist does.

If you think about, you know,

big tech companies such as Google, et cetera,

but within Mishkondorea,

it's really around the initial stages

of a data scientist journey.

So we're looking at the collection

and centralization of data

so that we can make more informed decisions

and serve our clients better.

Then the other hat that I wear, as you mentioned,

is as the co-founder of Law School 2.0.

So Law School 2.0 was founded by myself

and Annabel Pemberton and Nathan Core

a few years ago now,

because we were fresh out of law school

into the world of legal tech.

We realized there was no traditional path

to get into legal tech.

So we decided to build out this organization

to bring people together to help them understand

what they need to know about legal tech to work,

whether they want to work in a law firm,

work for a legal tech company, et cetera.

And so we started doing what we call,

the Legal Tech Summer Vacation Scheme.

So each summer so far,

we've run a one week long program

where we teach people problem framing,

solution creation and solution adoption.

So really a tech agnostic approach to legal tech,

which has now gone on to really underpin the curriculum

in our course,

which is the Legal Service Innovation Course.

So those are kind of two key offerings,

which are both available for free.

That's great.

Amy, first of all, it's so awesome for us to have you

as a guest on the podcast.

You're doing so many cool things.

And I can already tell that Tom is going to be

your new best friend after this podcast.

So sometimes I get a little frustrated with how difficult

it still is to explain technology old and new

and its benefits to those in the legal profession.

You're in an even harder area in some ways in data science.

So would you talk about your own approach

to communicating with lawyers and others

in the legal profession about technology

and topics like data science that,

that lawyers often find complex, intimidating,

and as lawyers often say, too much like math?

Yeah, of course.

It's something that always pops into mind

when I think about communicating with lawyers.

In my last role at a legal tech company,

we were helping one of the users troubleshoot an issue

that they were having and they had to reset their password.

So we were on the phone with them.

They received the email to reset their password

and it had a button there that said,

click here to reset your password.

And this user copy and pasted the words,

click here to reset your password,

and then put that into Google and searched,

click here to reset your password in Google.

And so that was kind of my first experience being like,

you have to really appreciate the fact

that people won't approach things or think about things

the way that you do.

And you have to think about what's the simplest way

to approach this.

So when I'm speaking to lawyers,

especially about something as complex

as data science,

I like to think about it from the what's in it for me

approach.

So rather than starting with the side of it

that I really enjoy,

which is here's everything we could do

with all of your data.

Think about, well, if we're gonna build out

this new application for collecting data,

actually it's gonna save you a lot of time

because you're not having to go into Excel spreadsheets

and free type.

We can pull in other information and speed up this process.

So making them understand the value of what's in it for them.

And I have both a legal and computer science background

from university.

So I find it, I could get really swept away

with the technicalities of explaining different sides

of what we're doing in data science.

But I think it's really important to,

rather than focusing on the how we do certain things

within data science,

it's really the why side of it.

So it's all about avoiding jargon

when explaining what our team does.

You know, it's funny when you mentioned that story

that takes me a way back

to when I was still working at the law firm

and I was helping the other lawyers with legal technology.

And I believe that we had just gotten our very first

Windows computers and it was in the 90s at some point.

And I walked by the name partner's office

and he had taken the mouse

and he was holding it up in the air

and pointing it at the computer screen like a remote control.

And I thought then this is what job security looks like.

And it was for a while,

but that kind of leads into the next question,

which is something that we talk about a lot on the podcast,

which is technology competence.

Here in the United States,

we have a lot of discussion

about it.

It's been written into the ethical rules

of most of the states in the nation,

but we see very little enforcement of it.

And to be honest,

Dennis and I are a little skeptical

about the level of technology competence.

What are you seeing on your side of the pond

in terms of technology competence

in the lawyers that you come in contact with?

Do you feel like it's improving?

It's staying the same?

How does it look like from where you sit?

So I think generative AI has really changed things

in the past couple of years.

So from when I first started working in legal tech,

it felt like it was a lot more of a struggle

to get people to understand why you wanted them

to implement certain types of technologies.

And then all of a sudden,

what opening AI and what they did with ChatGPT

with their marketing strategy was just amazing

because they made such a complex technology so simple to use.

And what really excited me was how many people

kind of came to our team and they're like,

how can I get my hands on this?

Can I use it to draft documents?

Things like that.

So as a firm, it was a really interesting time to be like,

well, no, let's not use ChatGPT to do that.

We have these other tools that you can use.

So it was kind of a pivoting point

that where you could start to direct them

to the other legal tech that they have.

But I think as a whole across the UK,

there is this increasing awareness

about the importance of technology.

A lot of this is stemmed from generative AI,

but there is still a lag in the expertise that we have.

So there's sometimes a gap speaking to people

in other firms as well,

between the lawyers and the innovation teams in a firm.

So the innovation teams who are there

to implement these legal tech tools.

But I think lawyers can be quite keen to get involved

and to be like, I really want to use this cool tool

that I saw on LinkedIn,

or a friend at another firm is using.

But they jump straight into the solution stage

and one firm is gonna have a very different problem

than a different firm.

So that's the gap that I think we're seeing now is,

now we need to understand as a whole,

how we implement new tools,

how they should be addressing a specific problem.

There is of course, still a resistance to change.

I think there's always gonna be,

the people who've been at the firm for 40, 50 years,

who were like, I don't want any process to change,

this is the way I've always done it.

But now with a new generation of graduates

coming into the work stream,

who have been using these technologies

through the entire time that they've been studying,

it would be quite a shock for them to get into a law firm

and then have to go back and do it again.

So I think there's a lot of work to writing things down.

Like I did work experience when I was in university,

it was working for a small firm back in Canada

and they were drafting wills.

And I remember I had to go through,

they didn't even have square brackets,

they just had things highlighted.

So I had to go through and type everything in.

And at that point I had no idea about document automation,

but I was still like,

surely there's a better way to be doing this.

And I think we're gonna have that same approach now

where people will be using generative AI

or similar tools in university,

because some universities are starting to adopt that.

And then they're gonna come into firms

and expect there to be this technology.

So now it's about really having those innovation teams

and the lawyers identifying the different problems

within their firm,

and then deciding which solutions they should implement

to actually address those problems.

I think they're in a better position now to do that

than they were in the past.

When I was still working at the law firm,

the younger lawyers,

what I would call digital natives

rather than digital immigrants,

they were comfortable with technology,

but they just sort of accepted

the way that it was being used at the firm

because they didn't wanna make waves.

They didn't know better,

but I think that they're starting to know better.

So I hope that evolution is taking place.

So classes at Michigan State Law School start next week,

and I'm looking forward to it.

But one of the things I'm really looking forward to

is seeing the experience of the students

when they come back with the technology they've had,

especially AI during their work experience.

And I always tell people, they say,

what do you students think of technology?

And I say, they all feel

like they're taking a giant step backwards

when they go into law firms.

So what's interesting is I try to figure out ways

to teach them and to come up with projects and things

to help them move forward.

Like what's the best way to do that?

And I like what you're saying about,

there is this tendency among lawyers and firms

to say, to go solution first and then problem.

What problem? What's the problem?

Do we even need to think about problems?

And I think that causes a lot of different issues.

I was talking with somebody yesterday who asked me like,

how would you use generative AI to analyze litigation data?

And I was like, well, I would want to use better tools

for one thing I said, but I think it's useful

in that it lowers a barrier to entry.

And I could try some things that maybe help point me

to the right tools

and make decisions and get some directionality.

So I've talked to students about data, data usage,

data protection a lot.

And I kind of want to get some suggestions

of how do you think you help?

What are the best ways to help lawyers get some level

of competence or literacy about the technology they're using?

And is part of the answer what you're doing

with Law School 2.0?

So one of the first things that came to mind was

in our legal service innovation course,

we actually had Dan Hoadley.

He was at MishCon, he is now at VLex,

but he did a module on data-driven thinking

and the importance of that for law students

and lawyers as well, which covered on that ethics point too.

But I think data underpins everything.

Like all of these generative AI models have been trained

on data and if that's bad data,

then you're going to get bad outputs.

And it's the same thing in a law firm.

So what I always like to say when we're talking

about the importance of collecting structured data,

so everything is rather than having dates

that are typed 10 different ways,

we have some sort of structure there,

or rather than calling a court

that you're in 10 different names,

you have a list that you use across the firm,

so everyone's using the same name.

So having that kind of structured data is so important

so that our lawyers, rather than going to a client

and saying, well, listen, I think you'll probably win

this case because I've done a bunch

and I'm tend to be really good

at this and I'm going to get them.

Instead, they can go to their clients and say,

well, actually, you know, I've done 20,

I've succeeded at 18 of these applications.

And we did it in three to five months on average,

because you have that structured data.

And right now, most firms aren't really collecting

that litigation data.

But this is something that people can start to do

when they're in university too,

that you're structuring your notes

so that you can really easily search that information,

which you don't need generative AI for.

You can structure everything, you know,

in whatever tool that you use.

And that's the thing, when they come to a firm,

you can start to use Excel to track this type of information.

And then you can just be a much better lawyer,

because I always think about it, if I was a client

and I was spending a lot of money on a lawyer,

I would want them to give me data-driven insights

into like why they think they're going to be successful

or why they think I need to spend 200,000 on a case

to get X amount.

So you want to understand why they're

approaching it that way.

So I think that's the real importance of why we should be

doing it in a data-driven way.

And then the ethics side of it is,

it really comes back to generative AI.

I don't think people fully appreciated that

with all these tools that you're using,

if you're putting client information in there,

suddenly you have a breach of, you know,

all the agreements that you have with your clients.

So it's really important to understand

where this data is going, you know,

the obligations that you have to protect personal information.

And in the same way,

if you're going to be tracking the outcomes of your cases,

if you have personal information in there,

you do have to be checking with your firm

to make sure it's okay that you're collecting that

wherever you are collecting that, you know,

if you're going to try and put it in a tool

that you've signed up to on your own,

your firm needs to make sure that that

and complies with their policies.

So there's all of these different considerations

that in the long run,

I think will just make them better lawyers really,

because I do think we're heading towards a data-driven,

well, I'd like to think we're heading towards

a data-driven legal practice in the future.

Let's take a right turn and talk about a different topic.

And that is,

is collaboration.

We love to talk about collaboration tools

and technologies on this podcast.

And so we're always interested to learn about

what our guests use for collaboration.

What are the tools that are most effective for you,

either working with people at Mishcon

or with Law School 2.0,

or just the people out in the world that you deal with?

Yeah, so I'm a very old fashioned,

like I like to take pen and paper notes.

I have my pen and paper agenda that I do.

And when we're in person,

as a team, we really like to get a whiteboard out,

get some sort of like big old paper

and put sticky notes down so that everyone's interacting.

You're actually drawing out things together.

I think people have a real tendency of,

you know, speaking and speaking and speaking.

And I'm the type of person I'll zone out after five minutes,

if you're just talking at me.

But when you have a meeting where everyone's engaging

and they're using sticky notes, they're providing ideas,

they're writing things down,

it's just gonna be a lot more interactive.

And with the shift to hybrid working at Law School 2.0,

we tend to use Mural.

So, and we all live in different, Nathan lives in Scotland,

I live in London and Annabelle's currently in Prague.

So we're running Law School 2.0 across different countries.

So we use Mural to work together

and then we use Slack to communicate.

And then at MishCon,

we tend to use Teams' built-in whiteboard feature as well,

so that you can mimic some of the things

that you do in person online.

All good tools, all good tools.

We have lots more questions for Amy Conroy

and Amy.

Amy Conroy at MishCon Derea and Law School 2.0.

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And we're back with Amy Conroy,

Principal Data Scientist at Mishkondorea in London.

We found it in the Fresh Voices series

that we love to hear about our guests' career paths

and our audience does as well.

Would you talk about your career path

and what kinds of things you've done

to get you into your current role and focus?

I'm especially interested,

when I look back at my time at MasterCard,

given your background,

it'd be really tempting for me to try to talk you

into becoming a lawyer for our whole data group

or to have you into some of our info governance

or the whole data team.

So how did you make the choice

and could you talk about your career path

maybe as a way to illustrate to students and others

how they might be able to get into your career path?

Yeah, of course.

So like I mentioned, I'm actually from Canada originally

and I came over to study law at the University of Bristol

fully with the full intention of going back to Canada

and practising as a lawyer there.

But while I was in Bristol, I fell in love with the UK

and during my undergrad degree,

so during my law degree, I did an IT law module.

And so at that point within the EU,

you have the right to be forgotten.

So you have the right to contact, you know, Facebook

or any tech company, any company really,

and ask for them to erase your personal data.

And I wrote my thesis on how compatible is that

with machine learning algorithms?

So if they're anonymising your data,

is that still protecting your personal data rights?

I found that incredibly interesting,

but I had no idea how machine learning algorithms worked.

So then I went on to do a Master's of Computer Science.

So this was a conversion programme that Bristol offered.

So it was a one-year programme,

that kind of takes you up to speed

as if you'd done a full computer science undergraduate degree.

Again, found it really interesting coming from,

I hadn't done math in like five years, I think at that point.

So it was quite the transition then to jump into computer science

where you need quite a mathsy brain,

especially switching from doing my law degree.

But again, really enjoyed it.

Didn't fully want to be a software engineer.

I wanted to still use my law degree.

And I did my thesis during my computer science degree

on creating a automatic computer.

So I did my thesis during my computer science degree

on creating a automatic computer science degree

before it's time with generative AI.

But this was just using supervised machine learning.

So it's quite simple in that we had annotators go through

and annotate different judgments for their outcome.

They animated sentences based on the rhetorical role

that that sentence played within the judgment as well.

So whether that was a fact sentence, these were appeals.

So whether that discussed the proceedings, things like that.

So that was the kind of data that we were using.

And then we had a lot of questions about,

And then the annotators compared actual summaries

of the judgments, so manual written summaries

against the judgment itself.

And they went through and said,

yeah, this sentence corresponds to one from the summary.

And that's how we decided

whether a sentence was relevant or not.

And then we used all that information

to create a system that outputted summaries at the end.

Again, really enjoyed that.

We wrote a couple of papers on that with Bristol.

And then I took the dive kind of into working

at a legal tech company.

They're a document automation startup

as a legal engineer.

Did that for about a year and a half.

And then I decided that I still wanted to use

some of the stuff that I had done in computer science.

And this opportunity popped up at MishCon.

And I realized it was the perfect way

to combine both of my degrees

and still be a bit technical, but also work within law.

So for me, it was a number of different things

that made me realize what I was passionate about.

And I really love taking my kind of joy

within my job.

I love is taking a problem that's presented to me

and thinking within the tools that we have at our firm,

whether that's coding something new,

whether that's Power Apps, Power Automate,

how can we design a solution that solves that problem

as best as possible?

So working within the constraints

of what we have at the firm,

but definitely not the path that I expected to go on,

which I think for most people in legal tech,

it's a very similar story.

You just kind of have ended up where you are,

but it's definitely been an interesting one.

So I want to go back to something you said

at the beginning of that.

That answer, which is I am incredibly interested

to know what your conclusion was from the paper

on whether machine learning

can appropriately anonymize personal information.

Can you give me a short answer?

Yes or no?

At that point, I said, yes,

it would comply with the regulations

as long as you took it basically as far as possible

in the anonymization and took out their data

at the point that it's not anonymized.

Awesome.

Okay, that's good.

That's good, good, good.

All right, we're going to talk now

about sort of our obligatory chat GPT question

because it is the only thing

that people are talking about generative AI.

There is the hype cycle that is obviously still in play,

but probably has morphed a little since last year.

I think it feels different this year than it did last year.

But we wanted to talk about what you're seeing

about how generative AI, chat GPT,

and similar tools, Claude, other things,

are playing in legal technology

and how you feel like that's going to

mesh with data science,

with information governance, knowledge management,

anything that you might be working on with Mishkan

or even Law School 2.0.

Yeah, and I think the way that it's affected legal tech so far,

and I'm sure you've seen this as well,

is on the one side of it,

we have existing legal tech tools

that are now incorporating generative AI.

So lots of document automation startups, for example,

trying to pull in some form of generative AI.

And the other side is we have a whole other

kind of field of tools that have popped up

that are essentially,

chat GPT for law firms.

And from speaking with a few different firms,

I think how most of them are approaching it now

is they've realized that they need to have

some form of generative AI for their lawyers.

And it comes back to what we talked about before,

where you have this new generation coming in

who are using these tools on a day-to-day basis.

I use chat GPT personally all the time for different things.

And so to not have that at work

kind of feels like a real blocker.

And I think there will be a wave of talent

who will just kind of look for the first thing

that they're going to be able to do.

And I think that's going to be one of the things

that we're going to be able to do.

And I think that's going to be one of the things

that we're going to be able to do.

So then there's the firms who are either piloting

these kind of legal tech tools

who are doing these generative AI platforms for legal.

We have firms who are building their own in-house tools.

So they're building chat GPTs for their firms.

And then we do have firms who are waiting

and seeing what happens with everything else.

For me, what I find really interesting

is going back to what I said before,

where I felt like generative AI has kind of opened the door

for people being really excited about legal tech.

And I think that's going to be a really big part of that.

And actually being interested in using technology

on a day-to-day basis.

These tools can really start to provide a real hub

for all of the information,

all of the data that sits across a firm.

So right now, for instance,

and this comes back to the litigation data strategy as well.

But if I wanted to know if a colleague

had worked on a similar case before,

the best way to do that really in a firm

is to go and talk to that colleague.

Because it's going to take you a long time

to search across your time recording tools,

your document.

It's going to take you a long time

to search across your time recording tools,

wherever you keep all of this different information.

There's no centralized location.

So what we're really looking at

is how can you start to pull all of these databases together

and make it accessible

so that our lawyers can kind of chat

with their assistant really.

And that's what our internal tool will do.

But yeah, everything really comes back to this data point.

So how can I easily access the data

so that I can help my clients?

And it comes back to those questions.

How can I give them the information that they want?

So whether that's past budgets

and their cases, precedents, et cetera,

how do we pull all of that together

and make it more accessible?

And also extracting information way faster.

But I think especially in London,

having spoken to different firms,

what it's coming down to is that

a lot of clients are using generative AI technologies

in their own work as well.

So now they're starting to really ask

whether law firms are doing it

when they're putting their pitches out to the firms.

Real quick follow-up.

What are you doing?

What do you see in terms of law firms using Copilot?

Good, bad, ugly?

What are you thinking?

I think it's been hit or miss.

And it really depends on how well

you've structured your data up until that point.

We ran a really small trial with it,

but it really does come down to,

again, it's making sure

that you have everything structured and in place

because it's only what you get out of any of these tools

is only as good as what you're putting in.

So that's what we've been really focused on

is how can we make sure that we're doing it right?

How can we actually structure

all of this information that we already have

to make it more accessible first?

Information governance.

Yep, absolutely.

It always comes back to that, doesn't it, Tom?

It does.

So I've been kind of thinking around the topic

of that we have, you know, with the structured data,

the tools that we have, tools that you've mentioned,

we sort of have this layer of really mature,

robust, powerful tools that are hard to,

you know, for the average,

the average person to access in a really useful way.

And so I'm intrigued with generative AI

as helping on the input side to make that easier

and on the output side to pull out information

in the ways we need.

And I've been doing a lot of thinking about that.

And I sort of think it comes out to this notion

that law firms need to figure out,

and I don't know how they're doing it, actually,

because I'm not sure they have the right talent

to do this yet, to decide what should generative AI

be used for, what is it good at, what is it not good at,

and can we kind of, you know, assign it to the right roles?

So that's an observation.

My question for you is that I have a habit of,

and I have for a long time, of saying, you know,

the European firms, especially the London firms,

are like way ahead of U.S. firms in terms of legal technology,

and AI, and I just routinely say that.

So I like to ask people who are in Europe

whether I'm still right in saying that

or sort of how do you rate what's happening in Europe

to the U.S. and to the rest of the world?

Where do you look for the coolest things happening in legal tech?

It's a good question, and I would say that we often look

to the American firms now when we're looking at what's happening,

but I wouldn't, it's hard with generative AI,

and AI in general now because everyone's kind of been fighting

the same battle for the past year, year and a half,

and when you look at the press releases that are coming out

from different firms, you kind of sit back and go,

oh, well, we're all building the same tool, aren't we,

internally, when you're building internally.

So I think it's a new ballgame in terms of figuring out

who's actually ahead, and I imagine it will settle

in the next year or so, and it will start to become

a lot more evident how successful certain things have been

because it's one thing to put press release out,

saying you're piloting a tool.

It's another thing to actually have had a successful pilot

with that tool.

We've got more to talk about with Amy Conroy,

but we need to take a quick break for a message

from our sponsors.

And now let's get back to the Kennedy Mile Report.

I'm Dennis Kennedy.

And I'm Tom Mile, and we are joined by our special guest,

Amy Conroy, Principal Data Scientist at Mishkondorea

and co-founder at Law School 2.0.

We've got time for just a few more questions.

Amy, I would like to ask what I consider

our best advice question, which is,

can you give our listeners kind of what's the best advice

you've ever been given by somebody,

or what's the best advice you have for our listeners,

or maybe if you've got time for both, then both.

Yeah, of course.

I'll start with the best advice given,

which I think also feeds into advice for listeners.

But this one, I wouldn't say it's necessarily advice

that was given so much as something that I learned.

So at my past role, we were trying to sell a tool,

this document automation tool.

And I remember,

how keen I was to just get this law firm to use it,

even though I didn't take,

I was doing the shiny things syndrome,

where I was so excited and so swept up

in them using this tool.

I didn't think about the problem that they had.

And it was when I took a step back

and we kind of crunched the numbers and we're like,

oh, the document that you're interested in automating,

you'll do maybe four, you'll draft four of them a month,

which just isn't worth the licensing fees.

You may as well stick with what you have, basically,

just drafting manually.

And that was when I realized,

again, it comes back to that idea of making sure

that you're solving a real problem

with the tool that you're implementing.

Otherwise, you're going to spend so much more money

on a legal tech tool,

just for the sake of feeling like you're using

really advanced technology.

So there is a lot that you can do,

whether that's with a PDF form,

if it's simple enough, something like that,

where you're still doing some layer of automation,

but it's not costing you a lot.

So the real focus there,

whether you're on the vendor side

or whether you're coming from the law firm

is making sure that you're using a tool that's really advanced.

That you're solving a problem with that solution.

And especially if you're coming from the vendor side,

that's going to earn you a lot of trust in this client.

They're going to be more likely to recommend you

to other law firms

because you were the one who said,

actually, I don't think our tool is right for you.

But then you can recommend other tools as well.

And again, that's what we've really tried to focus on

with Law School 2.0

and Legal Service Innovation course

is making sure that,

like we took a really tech agnostic approach with it

to make sure that people understood

to start with the problem

because,

sometimes you'll realize

that the solution to your problem

is something that you never even thought of

when you actually sit down

and frame your problem.

And then the other side,

in terms of advice,

I would say,

stay curious and keep learning.

A lot of what I've learned about technology

has come from other industries

and looking at how they're implementing technology as well.

Law can be really quite far behind other industries.

So keeping an eye on new technologies

that are coming out,

like ChatGPT,

being curious

and thinking as a new tech,

or you see a new tech tool

or you see another industry doing something,

think about how that can apply

to the legal services world as well

because things are rapidly evolving.

So you have to keep up to date with new developments.

So just, yeah, keep that curiosity going.

You know, it's interesting

how many of our guests talk about,

look to the problems first,

ask like fundamental questions.

And I find myself going to more and more,

you know, saying the simpler the question,

the better.

So people ask me like,

how might I use AI?

I can't, I can't even imagine any way I would do it.

I would say,

look at the place,

you know, where do you write off bills right now?

Where do, you know,

where do you write off time?

Where do you give discounts?

That is a really fertile place

to start looking for experiments to try.

But I want to wrap up with two questions.

So first is,

as I approach this next year,

I'm going to ask you a question.

How would you encourage today's law students

and new lawyers to find career paths

like yours in legal tech

and other non-traditional careers in law?

And then the second question we'd like to wrap up with,

which is our lazy web question,

is who are the other fresh voices in legal tech

you would like to signal out

that you like to hear from

and maybe see as part of our fresh voices series?

The one piece of advice that I always give to people

who ask, you know,

how to find untraditional career paths

or how to find out more about legal tech

is really reaching out to people in the industry.

So finding them on LinkedIn.

I was shocked when I was a fresh graduate

to find out just how receptive people were

to giving you 10, 15 minutes

to chat through what they were seeing in the industry.

And that's how you're going to learn

what problems the industry is facing as well.

Because it's one thing to be like,

I want to go into a firm and work in innovation.

It's another thing to be like,

I want to go into another to kind of get a bit of an edge

and say, oh, you know, this firm,

they're really struggling to figure out

how to use generative AI.

Let me see if there's any companies out there

who are doing things.

And then you can kind of start to weave down

these less traditional paths.

And that's really how I got my first role

is I followed Catherine Bamford,

amazing legal engineer on Twitter.

And she was the one who actually reached out to me

and she's like, I'd love to have a coffee.

You have such an untraditional career path

going from law school to doing your computer science

to great.

And then she introduced me to Annabelle and Nathan.

And then we founded Law School 2.0

and she introduced me to Dan Hoadley

who brought me into MishCon.

So everything happens for a reason.

And I really think it's making those connections

and finding the less traditional people

within the legal industry.

And your second bit on fresh voices,

I'd really have to recommend one of my friends

and former colleagues, Caitlin McCabe.

So she is now working at Barbary,

Legal Tech.

Or a legal education company, sorry.

And she has some really fresh takes

on how we can transform legal technology

or legal education rather

in light of legal technology

and how we can use different tech tools

when we're delivering legal education.

And I just love speaking to her about it

because she does just have a really fresh perspective on that.

And Catherine Bamford is cool, of course.

We had Catherine on

and we could have her on like a hundred times

and keep learning new things.

Yeah, she's amazing to speak to.

And legal education is something

that we haven't done a lot of.

So, except when Dennis talks about his own.

So that's something we need to think about.

All right.

We want to thank Amy Conroy,

Principal Data Scientist at MishCon Derea LLP

and Law School 2.0 for being a guest on the podcast.

Amy, tell us where our listeners can get in touch with you

or learn more about what you're doing.

Yeah, of course.

I mean, you can get in touch with me on LinkedIn

under Amy Conroy.

And I'd also check out Law School 2.0

on our website.

Our website is LawSchool2-0.com

and our Legal Service Innovation course

is LegalServiceInnovation.com.

That course is available completely for free.

It takes about 68 hours of your own time on demand.

So I would really recommend checking it out

as kind of like a starting point

into the world of legal tech, if you're interested.

Well, thank you so much, Amy.

You were a fantastic guest.

Great information, great advice for our listeners.

And I think you might've found a new friend in Tom.

But now it's time for,

our parting shots,

that one tip website or observation

you can use the second this podcast ends.

Amy, take it away.

Yeah, and I think you kind of touched on it a bit before,

but the one tip I have is if you're looking to innovate,

you don't have to wait for a big problem to pop up.

You can start now.

So if you think about one routine task that you have

that you do daily,

whether that's spending 10 minutes,

summarizing meeting notes, something like that.

Think about how you can frame that problem.

Think about what's causing that problem.

And then look at the solution,

solutions that you probably already have access to

that you can use to innovate and solve that problem.

So you don't have to wait for a whole firm wide problem

to come and land on your feet before you can start innovating.

I have so many of those.

I don't know where to even start with that.

I, it has been a while since I have had a headphone slash earbud tip,

but I will bring up one this time.

And I've been doing this in a slightly different way

as I've been looking for earbuds that I can use while I'm sleeping

to whether either I'm using it to,

to push out the noise or listen to some soothing rain or water,

or if I want to listen to soothing voices or something when I'm sleeping,

the problem with earbuds are they are not made for side sleepers.

It is very painful to wear earbuds.

If you are a side sleeper,

which I am,

I am trying the anchor sound core sleep,

a 20 and they are amazing.

They are fantastic for,

uh, for side sleepers.

You don't feel them at all.

They have great battery life,

so they're going to last through the night.

If you want them,

if that matters to you,

if, uh,

if it doesn't matter,

then,

uh,

then just,

you know,

they're,

they're perfect for that.

But,

um,

a great option if you,

uh,

if you're looking for earbuds for the purpose of sleeping,

these are great.

If you're a side sleeper,

you know,

you remind me time on the,

the,

if you're using like Bluetooth earbuds for sleep,

I can say this from experience experience.

If it says like a five hour battery life and it beeps when the battery's going out,

it is like the worst thing you can use for sleeping.

And these do.

Not beep.

They just turn off with the battery life.

That's I agree.

Cause I've had,

I've had,

there've been a bunch of sleep.

I've tried a bunch of sleep head headsets and things,

and these are the best by far.

There's no beeping.

So my,

uh,

parting shot is so Amy is in London.

Tom just vacation in the London.

So I had to up my game.

And so we've been watching reruns of this fantastic show on,

on,

uh,

Netflix,

but,

but it's out of a BBC originally called time team.

It's about archeology.

It's like,

you know,

an hour show,

they go dig up things.

You learn a ton of stuff.

I can now identify certain types of,

uh,

Roman pottery.

And I've learned a ton of things and it's just a great,

one of these great things that you need to do.

If you want to innovate is just kind of immerse yourself a little bit in a

non-threatening way with,

with some new area.

And,

and it's,

it's really been interesting.

Some of the insights that watching the show has,

has thrown for me that I'm using in unexpected places.

And so that wraps it up for this edition of the Kennedy mile report.

Thanks for joining us on the podcast.

You can find show notes for this episode on the legal talk networks page for

our show.

You can find all of our previous podcasts along with transcripts on the legal

talk network website.

If you'd like to subscribe to our podcast,

you can do so again on the legal talk network website,

uh,

or on your favorite podcast app.

If you'd like to get in touch with us,

remember you can always reach out to us on LinkedIn,

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Uh,

so leave us a voicemail at 7 2 0 4 4 1 6 8 2 0.

So until the next podcast,

I'm Tom mile.

And I'm Dennis Kennedy.

And you've been listening to the Kennedy mile report,

a podcast on legal technology with an internet focus.

We wanted to remind you to share the podcast with a friend or two that helps us

out as always a big thank you to the legal talk network team for producing and

distributing this podcast.

And we'll see you next time.

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