August 27, 2025 • 41 mins
AI in Business: How Dojo is Turning Proof of Concept into Real Impact
This week on the Tech on Toast Podcast, we’re live at Dojo HQ with Rob Howes, Senior Vice President of Technology, to explore what AI in business really looks like beyond the hype.
Rob breaks down how Dojo are embedding AI across payments, customer service, and fraud prevention — moving from proof of concept experiments to production-grade impact.
We cover:
🔄 Automation, augmentation, amplification — the spectrum of AI adoption
📊 Why many businesses stall at proof of concept (and how to push through)
🧩 Grassroots vs. top-down adoption — how ChatGPT accelerated uptake
💡 The use cases delivering ROI fastest: ops, customer service, and cybersecurity
⚖️ Transparency, ethics, and building trust in AI-driven decisions
🚀 Dojo’s AI Trailblazer programme with Imperial College and Google
🔮 What’s next: hyper-personalisation, agentic AI, and explainability
If you’re wondering where to start with AI in your business — from building a roadmap to picking the right use cases — this episode is packed with insights you can take straight back to your team.
👉 Learn more about Dojo at dojo.tech.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:01):
Welcome to the next episode of the Tech and Toes podcast.
And it's a special. Today we're joined live in
Dojo's offices and with Rob House, Senior Vice President of
Techno. That right?
That's right. That's right.
Very nice to meet you. How are you?
I'm very well. Thank you.
Looking forward to this conversation.
No, thank you. You've picked my specialist
subject not, but no, I'm really,I mean, I think everybody's
interested in the area right nowand we're going to have a chat
around AI and business. But before we get into that,
(00:23):
just tell us a little bit about your role at Dojo and what do
you do here? Sure.
So I've been with the business alittle over 2 years now as SVP
of Technology. That means I work with and look
after the broader engineering and data communities here.
I have had a career that is beenfintech focused throughout
before. Before Dojo I was in one of Yan
(00:45):
and George's other payments businesses as their CTO there.
And before that I spent about 15plus years in the capital market
space. So I've always been drawn to
industries which have high amounts of data within them.
So an opportunity for from from kind of AII suppose presented
itself throughout that that journey.
And I've really very much been drawn to, I, I really kind of
(01:08):
fallen in love with, with technical problems that have big
impacts. And I, I think that that's been
a recurring theme throughout. And, and before, before payments
in that capital market space, I Co founded a couple of
businesses building proprietary trading platform.
So, so kind of innovating with data has been something that's
been part of my, my career storyso far.
And I and I think it's really interesting that now we land in
(01:30):
this for us normal folk, we landin the world of AI and AI in
business I think is something that is over the last year,
particularly in, in my world in hostilities has suddenly started
to become a talking point. I would say maybe not been
action, but what does it actually mean AI in business?
How can you kind of like broadenthat for us?
If we take a little step back, II think it's, it's so much more
than thinking about computers mimicking I'm sort of humans
(01:53):
and, and I think that that's really a lot how in the last
couple of years has been a lot of attention focusing on that
element. But for me, it's, it's, it's a,
it's a spectrum of possibility where we're using this
technology to, and places like Dojo, for example, really expand
our art of the possible. And the way I think about that
and the way we're starting to think about it more and more at
Dojo is, is sort of across 3 levels really of sort of
(02:19):
automation, augmentation and amplification.
Now I'll talk a little bit more about this.
So automation has been somethingto be honest, we've been doing
an industry has been doing for awhile with, with AI and that's
really kind of looking at those repetitive tasks and more menial
tasks. I think what's more exciting
more recently is the augmentation element.
That's the AI in human combination.
How do we start to do things faster?
(02:41):
How do we start to do things better than before?
And I think where this is going on that spectrum for me is, is
that, that amplification pieces,how do, how does that tool AI
tooling with that human, how do we start to elevate and reinvent
the ways that we do things? So as I said, at the moment, I
think we're really moving from that automation to augmentation
(03:02):
basis, doing things better at greater scale than before.
But I'm really excited about howdo we start to completely
eliminate some of those barriers, whether they're
technical expertise or processesthat frankly get in the way of
our people doing their jobs. And I think that's what's still
to come. And that's, that's the
potential, that's the prize for me.
Yeah, it lose back to what you talked about the style of big
(03:23):
data and big impact, right. And the OS reported recently
that around one in six UK businesses are now using at
least one our AI technology and 70% are using up to improve
cybersecurity. Why do you think it's still, I
mean it's still relatively low in terms of uptake if you think
about some of the trends that have come before this, Why?
Why do you think that is? Yeah.
I mean, I, I, I think, I believethat the numbers, the adoption
(03:45):
numbers have moved probably quite a bit more since, since
that sort of ONS report came in.I, I think that was probably
from the, from maybe maybe a couple of years ago.
But what I would still say is whilst I think adoption of the
technology is probably moved on quite a bit in, in a more
positive direction since that report, what I'm still seeing is
(04:06):
a, is a gap between businesses that are using AI at a proof of
concept EOC stage versus those that have been able to convert
those PO CS into production grade instances.
And, and I think there's a variety of reasons or why that
uptake perhaps is still not as great as we would like it to be.
I think 1 is looking at the dataquality, the data governance
(04:30):
that, that, that businesses havehaving to really utilize this
technology to its fullest potential.
You need to have very clean, structured data.
You need to make it easier for AI to consume that data.
And, and that's still something that I think many businesses are
not yet well prepared or set up to do.
I think you've got ethical and trust concerns that come into
(04:51):
play here. I think that comes from both
people being more fearful about things that they don't
understand and worrying about how ethical and, and how
trustworthy is the data that's feeding this.
But, but also, if I look at businesses like Dojo, we're a
regulated business. And that means, you know, and
that, and those regulations and our compliance with those are
there to protect our customers and, and to protect people.
(05:13):
So making sure that this new technology is, is clearly
complying with that is, is, is also something that I can see
stopping many businesses from fully being able to go beyond
the, the kind of the lab sort ofcontext.
The other thing is, you know, sort of still a skill gap.
I, I think this is relatively new and they're not that many
(05:36):
people out there that are, that are proficient and there are no
real rule books yet on how to dothis at scale.
And that, and that is also another barrier.
But probably one of the, the, the biggest things I, I, I see
is in terms of tripping up businesses at that POC stage,
trying to go beyond is, you know, I talked about at the
beginning, 1 narrative for my career has always been trying to
(05:57):
fall in love with problems. And I think in this case,
there's a lot of examples out there where I think people have
fall in love with the solution. And so they, they, they, they've
got this AI solution and they, well, yes, it's it, they're,
they're looking for a problem, it's going to fix.
And, and, and I think that meansthat you get these underwhelming
moments where these PO CS then don't maybe have the impact that
(06:17):
they could have or should have. And that's not because the
technology doesn't have promise,but because we probably not pick
the right use case to pursue. That it's very interesting and
and I think I did a speak, I dida talk the other night with
about 100 restaurant operators and asked them to show show of
hands who was using ChatGPT in their business, all of
everybody. And I think if I'd asked that
maybe even 18 months ago, you'd probably have. 10 to be changed.
(06:39):
Yeah, Deloitte said there's 7 million people in the UK have
used some kind of generative AI at work.
Do you think as you said, maybe the is the tech early as in do
you think that people are it's like a grasp ground level kind
of movement or do you think it'sa top down strategy coming from
the amount of people using it? So people self discovery with
ChatGPT and all the other stuff out there?
Or is it the other way around? I think it's a bit of both like
(06:59):
many trends, you've, you're going to have your portion of
early adopters that, that, that,that, I guess start to form
those grass roots, you know, movements.
And I think the more, you know, those grass roots communities
then start to increase pressure in, in terms of, you know, at a
top level leadership point of view, they start to be more
intentional about what should orcould their strategy be.
(07:21):
So you sort of get that top down, then starting to so almost
like a feedback loop. And, and here at Dojo, there's
a, there's a bit of a combination of both.
I I I mean. The the the.
Tools. People like Open AI, ChatGPT,
they've incredibly friendly userinterfaces.
It's much more ubiquitous now. It's also quite easy to get
going with with it, which means that I think that that's helped
(07:43):
that grassroots adoption become quite, as you said, quite
widespread. I'm not surprised to hear that,
that you were getting that kind of response rate from it.
And what we're doing here is, isboth from the top.
So getting we have senior leadership buy in that this is
important. This is something that we must
get behind and and are investingin, but we're also tapping into
those grassroots communities. We formed sort of the like an AI
(08:05):
champions group and how do we tap into that to make sure that
our communication is more effective that any gaps in
skills and feedback on that is is happening in a tight way and
ultimately leveraging that top down endorsement with that sort
of bottom up evangelism. Yeah, and I think all of it's
down as you talked before about solution versus problem.
(08:26):
And sorry, you know, people are kind of almost trying to find
the problem, not have the problem first, Excuse my words
get mixed up there, but what arewhat are the common use cases
where we're looking at when it comes to implementing IO,
solving problems with it? Can you give us some examples of
what's happening? Yeah, sure.
I, I, I think there's, there's probably, there's maybe 3 core
(08:46):
dimensions that I think people are, are, are pursuing with
this. You've got efficiency gains.
So how can we save money? I could be more cost effective,
save time, free up people's time, you've got effectiveness.
So how, how can we use the technology to help people do a
better job, not necessarily justfaster, but but actually get a
more accurate results. You have have bigger, bigger
(09:08):
impact, you know. And then the third one would be
looking at maybe the, the innovation side of things.
Can we come up with new products, new services that that
we can offer or sell as a business?
I think that last one is, is happening, but perhaps on, on a
smaller sort of scale. I'd say what we're really seeing
quite a lot of success in is in the first two camps that
efficiency and effectiveness point points.
(09:31):
And when I look at, at examples,I mean, I think there's now an
increasingly wide variety of, ofindustries and, and, and sort of
domains within those industries where we're using this
technology. But there's a few, I, I would
say common established good examples for this customer
service is, is definitely a hugeone of things like AI chat bots
(09:53):
being able to do things like intelligent routing of, of, of
queries and calls to the right department.
You know, things like, for example, sentiment analysis, you
know, to enrich the customer service agents experience with
the quality of, of support that they're giving their customers.
So I think that's a huge area inseeing that across multiple
industries quite well established.
Now you're also got opportunities in things like
(10:15):
marketing and sales and how can we do much better sort of
predictive lead scoring, for example, content generation,
particularly looking at maybe personalized content generation,
how can we tailor campaigns and and optimize ad campaigns to
reach the right audience and speak and use relative sort of
language. I, I would say operations
(10:36):
functions in, in general what, whatever that, that may be that
if I look at Dojo, for example, that can range from things like
our on boarding and and sort of KYB process.
How do we how do we streamline that and how do we make it more
effective as a regulated business?
This scheme and regulatory compliance use cases there's a
lot of procedural operational divisions within a company that
(10:59):
I think lend themselves very, very well to this cybersecurity
something you know you touched on I'm.
Well informed on. That is another one, whether
that's fraud prevention or whether that's sort of threat
detection, anomaly detection and, and, and a, and a big one
is software engineering. And I'm not talking about vibe
coding. And that's almost a forbidden
(11:20):
term. I hear that's a, that's a very,
very different thing. It's great for rapid
prototyping, but it's not how you build engineering solutions
at scale. But, but absolutely looking at,
at and, and sort of how do we complement our engineering force
with this kind of tooling? We, we talk about sort of AI
native engineering and, and that's from the code it
(11:40):
generates to the test cases thatit produces to the documentation
that that can accompany that code.
So those are some pretty well established areas where we're
finding this technology being used successfully and at scale.
Consistency and operations in inhostiles, particularly where I'm
I'm really interested in it. It's been hard to achieve for
many, many years. You need every managers and.
Do it. Do you think that unlocking AI
(12:01):
in that field or not just in operations, but around so the
payments PCI is, is going to really support these industries
going forward? Because I feel like they're,
they're in a moment when they'rein between and they're they're
struggling with obviously finding people going people, you
know, there's a high churn on people.
Do you think that AI is going toplug a bit of a gap there?
I think it can create a level playing field as I mentioned
earlier, how do we use this technology to better maybe
(12:22):
eliminate barriers to getting inthe way of our people.
And so I think a lot of what you've described there are
they're, they're areas, domains of expertise, but they're quite
established. There's a lot of documentation,
there's a lot of material on them.
And all of that feeds incrediblywell into the world of AI
tooling. So how do we augment those kinds
of industries and those kinds ofskill sets with, with not having
(12:43):
to go and become PCI experts and, and and so forth.
So absolutely. And, and I wanted to talk about
one particular case study. Oh, Polly, you know, about this.
So I just, it's a really clever use of AI for discount delivery,
but also shows the tension, I suppose, between personalisation
and fairness. Where, where's the line?
You know how should we business balance innovation versus you
(13:04):
know customer trust and not and not cross that line?
Yeah, I think that is a really interesting example.
And I'm and I'm not surprised that there was some mixed sort
of feedback from people as to kind of how that feature landed
on the, on the one hand, I, I dosee the innovation, you know,
changing the, the, the kind of the, the paradigm of, of how one
does sort of discounting and so on.
(13:26):
But, but I think for me, a big, a big issue is, is I think
maybe, maybe business is not fully considering that
transparency. We talked a little bit before
about how I, I, I think the, the, the trust or the, or the
perceived lack of trust or, or worry about the ethics side of
things can be a barrier to adoption.
And I think it's a barrier to perhaps a, a, a seamless
(13:49):
experience or a, or a good feeling from a consumer point of
view when you're interacting with AI in, in, in this way.
And I think businesses to get that balance right.
I think key for me is that they really identify and focus on
that transparency, being able toexplain how and why the, the,
the, the AI solution arrived at the conclusion that that it did.
(14:10):
And in this case, if we, if we want to look at it from a
discounting point of view, you know, you don't want the, the
user to feel that, that perhaps there's an element of sort of
profit driving here. You know, I'm only going to
offer you a discount if you think to ask.
But for one, I think it can leadto this sort of cynical points
(14:31):
of view. And I, and I think in this case,
they, they were actually genuinely looking to find a way
to positively engage with their customers and, and, and truly be
innovative. But I think there is this danger
that that can come across without some more explanation
that can come across as potentially sort of exploitative
of those, let's say, let's say, are less savvy of the technology
or less inclined to think or askthese things.
(14:52):
And, and again, why am I gettinga discount?
Does that mean that, you know, there was just fat margin lying
around there that you're hoping I wouldn't get to?
And, and so I think again, thinking about the decision
process that frames the AI, the guy, you know, is this because,
for example, this is a repeat customer.
So actually as a repeat customer, we see that you, you
buy with us regularly, we'd liketo offer you a loyalty discount
(15:15):
on top of that or, or because you know, there's an
opportunity, you know, to buy more things and we'll give you
an additive sort of discount. I think otherwise it feels a
little bit opaque and that blackbox can be, I think, a real
barrier to getting that trust element.
And I suppose that quite a bit of education still to be dozen
around AI because I think as I said, ChatGPT is a commonly used
(15:35):
tooling now. But the reality of what you're
talking about there is just doing something and putting it
out and not really fully explaining how that what's you
know, ending up when it's decision making that that
education piece quite big, isn'tit, what's gonna have you for
the next 5-10 years. Huge.
I, I, I mean, I, I think you've,you've got education in, in
general to kind of the, the masses.
I, I think I'm, I'm tomorrow, for example, I'm actually going
(15:58):
to, to Imperial College as part of their Advisory Board to talk
about the future of, of computereducation and how AI can and
might need to shape that. But you've also got it within
companies as as well. So a big part of what we're
doing here at Dojo is, is starting to think much more
intentionally about how do we drive that AI literacy.
Something that actually worries me more broadly about this.
(16:19):
I, I believe in the power of, ofthis, of this tool.
It's only getting more and more capable.
Still not great at everything necessarily, but but there are
plenty. Oh yes, it, it, it does.
And I, and I think being also, that's the part of the education
piece, being honest and, and, and being vocal about, yes, it's
really strong at lots of things,but it's also not yet good at,
(16:39):
at a lot of things. And, and sharing those things I
think is so important because from an education point of view,
otherwise you're getting these warped points of view as to what
you know you, you see, if you see only the bad, then you, you
walk away thinking and disillusioned with what it can
do. Equally you, you, you, you
potentially succumb to the hype too much.
If if you don't recognise, if you're only seeing the the
absolute amazing success storiesI.
(17:01):
Think we're really in that moment now it feels like anyway
in business I think we're reallyin that moment where it's kind
of everyone's in between some are on the fence and some aren't
as what are the biggest reasons to invest in AI.
So if AI and R So if you're sitting there thinking this is
great, sounds clever, it sounds like you can fix a lot of our
problem. But why now?
Why would you get into it now and not wait another five years?
I, I, I mean, I think you're as a business, you're, we're in an
(17:22):
incredible sweet spot right now in, in terms of, I think we've
had a, it's still new, but I think we've had enough success
and enough interest and investment in this that it's
gone beyond being niche and, anda fad.
I, I, I think there's a, there'sa growing consensus that in some
way, shape or form, this is now here to, to stay.
And, and so I think as a business, you've got that
(17:44):
benefit of more tooling, there'sthis more, more infrastructure
available with which you can start to use this.
So I think the barriers to entryare, are lower, but you've still
got that, as I said, sweet spot where you're early enough as a
business to still be considered.I think an early adopter,
particularly if you set the groundwork in place that you're
(18:06):
not just spinning up your, your,your next POC lab project that
doesn't go anywhere. So if you're intentional about
these things, I think you have an opportunity as a business to
really start to set the playbookfor what great looks like and,
and, and really be establish yourself as a thought leader.
And I think that's going to do lots of things, not just to your
business, but in terms of your customers or the product that
(18:28):
you're offering, but also your, your, your, your talent.
I think it's going to be a hugely powerful tool for
attracting and retaining talent.I, I think on the flip side, I
talked about maybe a few carrotsabout how it can elevate your
business there and, and why hopefully those would be
motivators enough. But, but I think there is also
that, that opportunity cost of not getting involved now I think
is also rapidly growing. And so I think if you're not
(18:51):
doing it, your competition almost certainly are.
And I, and I mention that that kind of talent point as well,
you're going to struggle, I think to retain people are
increasingly looking to work in roles and within companies that
give them access to this toolingthat are upscaling them in this.
And, and I think there's a real danger that between your
employees, between versus your competitors, I mean, customer
(19:15):
expectation is also growing. I think they're seeing this in
other aspects of their life, youknow, whether that's video
streaming services like Netflix or, or whether it's Amazon, that
they're increasingly seeing AI used to help personalize their
experience. And so that expectation, I think
is broadening. I, I think there's a real, real
danger that businesses become irrelevant if they don't get
(19:38):
involved. I'm sort of in this, in this
kind of technology. I was, I, I was, I was kind of
looking at a quote. I'm at a quote shared with me
from, from an author called Peter Henson.
He's written a book called The New Normal a number of years ago
now. And, and he said BC no longer
stands for before Christ. It stands for Before ChatGPT.
(20:00):
And, and I think for me it's it's, it's a great illustration
of the fact that this is a movement that's.
It's a moment. It's a moment in time and it's a
material moment in time, much inthe way of the Internet or
smartphones change the paradigm or engaging with with consumers
out there. And I and I think businesses
need to be relevant if they're or they, they won't be relevant
if they're not acknowledging. And I liken it.
(20:20):
I mean, I'm old enough to remember the Internet and and in
phones and all that. Likewise.
And I, and I remember it being websites particularly actually
remember people saying why wouldI want?
Why would I put my on a on a on Internet?
But, and obviously we've we've gone since then, right, and it's
obviously was a good idea. And same with social media and
all the other stuff that's fallen.
I feel like this is the first time since that I feel like this
(20:40):
kind of there's a big shift. It is for business as well as
also in our day-to-day lives. Hugely transformative and
businesses have an opportunity. As I said, we're still early
enough that you have an opportunity to be that thought
leader to help shape what this is.
I think that's tremendously exciting for businesses.
And we and we talked about some efficiency gains, but
particularly in supply chain, you know, cybersecurity, which I
(21:02):
spoke to naveed about four, which is really eye opening in
terms of what Dojo are doing. Which of these are kind of are
you getting the quickest ROI? You know, people are impatient,
want to see where can I get my quickest game, but where are you
finding a Is producing that gamequick enough?
I mean, I think, I mean cybersecurity is, is, is is a
great one. I think demonstrating or, or, or
talking about the value proposition in terms of
(21:24):
protecting our customers, you know, protecting or preventing
fraudulent transactions at, at the scale that we're at, you
know, this is, this can be millions and millions, 10s of
millions of pounds. So I, I think that there,
there's a great narrative there,but it's probably things that
I've touched on already really in terms of I, I think customer
service is, is a fantastic area from an ROI point of view.
(21:46):
Just the sheer, you know, you know, we have 150,000 businesses
and, and so, and we pride ourselves on 1st class service
and, and a lot of that it requires kind of high touch
engagement from our customer service agents.
And so if I take a step back, it's the broad customer
operations piece, I think is a fantastic area from an ROI point
of view because it's, it's, it'snot just what a customer might
(22:09):
directly experience, it's what happens behind the scenes.
How do we empower our customer service agents to have more free
time or the time that they do spend with our customers to be
that much richer? So I'm going to ask you a
question. How do you start?
Because I think a lot of people are looking at this opportunity
thinking they agree with you. And I think as I said, the show
of hands the other night kind ofsays people are ready.
How do they kind of get started on that journey?
(22:30):
They don't know what to do. How do they go about building in
a road map for AI? That's a great question.
What we've done here and, and, and my advice to, to, to, to
listeners on that would be to take a step back and really
think about what is it that you want as a business AI to unlock
or achieve? But for you, you know, in, in
our case, we, you know, I believe in, you know, we've
(22:52):
talked about a lot, but I reallybelieve in the transformative
potential, not just potential, actually the transformative
effect it's already having with businesses and there's more
potential out there. And so for me, it was this
recognition that this is going to transform every role, every
business to some to a greater orlesser extent.
And so that really very, you know, sort of led us to, to, to
(23:15):
kind of crafting a vision statement for AI, which was to
be the model workplace where AI is unlocking the full potential
of our people. And that's really to see them be
the happiest, the most innovative and the most
productive professionals shapingthe future of in person
payments. We're talking about transforming
our business to being an AI native, a business.
(23:38):
And that's what it, that's what it was for us.
That's what the ambition is. And so that was hugely important
in a, in a, in, in defining whatthat vision is for your
business, because that's the framing for everything that then
comes as part of then identifying that vision.
We then moved on to the next step, which is so OK, what,
what, what's our, what's going to be our next 12 month mission
(23:59):
towards that vision. And, and for Dojo, that was
looking at establishing those, those fundamentals on which we
were going to build an AI nativebusiness.
And that included something we talked about earlier, AI
literacy. Yeah.
And that is with our exec team all the way through to the rest
of the company, making sure we understand what this is and what
its potential is. And keep that I'm going as this
(24:20):
is rapidly changing. It's about making sure that we
have the right data foundations and the right data assets and
that we have the right governance in place.
This is still a relatively nascent field.
So from a, I'm sure Naveed wouldhave talked about it, but from a
security point of view, you know, making sure that we are
old but grounded and informed. And, and a big part of that is
(24:44):
not doing this as a tech silo initiative.
But this is, you know, us working with Naveed's team in
security. It's working with the legal team
as well to make sure that all ofthose are, are aligned and, and,
and working together to set us up for that success.
We didn't want us to hit those proof of concept fancy demos
(25:05):
that then can't go anywhere because it doesn't comply from
a, from a regulatory or legal point of view or from a security
standpoint. We, we, we, I would then sort
of, you know, going back to this, don't be that solution
finding a problem, you know, sort of situation.
You don't want to find yourself in that situation.
And So what we did was we started canvassing across the
(25:26):
business for what are your top problem statements?
I, I, I, I kind of like, yes, weframed it as we're doing and
we're asking this question because we're excited to see
where AI could play a role. But the brief was, but don't
tell us about an AI solution. Don't assume AI or, or can or
can't fix your problem. Tell us what is your number one
(25:47):
problem? I, I often frame this in the, in
the, in the form of, you know, if you know, what would you get
your next person or your next 10people to do?
You know what, what are you looking to hire for?
That's probably a good indication of where your biggest
problems are. And we had in, in the matter of
a few weeks, we had over 100 usecases submitted across our
business, which we then spent time creating.
(26:07):
And that's the next step, which is really be intentional about
which ones you start with. And it's not starting with 100.
I mean, it's great to get so many, but first of all, not all
of them were great AI fits. But but it's really being
intentional. What are those high impact ones,
picking two or three of those, making sure that they're
achievable, thinking about success metrics, what does great
(26:29):
look like if we, if we work on this and then getting a, a team
of people that you're properly supporting working on those.
And, and so I think those, thosesteps for us have worked really
well. And I think I, I think they
could really be useful for framing how another business
might want to get started on this if they haven't already.
Very useful. I think.
I think just clearly kind of pushing everything aside from it
(26:50):
and looking at the objective, like you said at the start there
almost having a mission statement or what you want to
achieve is crucial to any project you start on any journey
you're going to go on. So it makes real sense and
obviously you guys have a certain resource here at Dojo
and how do you, if you're smaller or maybe even if you're
a bigger enterprise, how do you kind of balance internal
capability versus using an external partner when it comes
(27:10):
to this, this kind of work? Is it?
How much do you want to keep it,I suppose in house?
Great question. I I mean, look, I think the size
of your organization will play arole in this.
But, but, but if I take again, if I'm, if I'm sort of agnostic
of the company size, I think howprecious you might want to be
about whether this is in house or not, I think really depends
on exactly what is the nature ofyour business.
(27:32):
I, I mean, if I'm, if I'm building AILLM models, then I'm
probably going to be fairly precious about, about who I have
working on that, that, that, that's, that's very proprietary
IP that that I want to protect for a business like Dojo.
That's not really the layer that, that, that, that is our
USP or that I'm seeking to sort of expand our thought
(27:53):
leadership. And I'm, I'm actually mindful,
I'm there was a concern really about how much energy we invest
to low level at that stage. These are areas that are
increasingly being commoditized.If I think of AI infrastructure
tools or AI platform services, I'm mindful that we are very
limited in how much we want to build at that layer rather than
(28:15):
consume. I'm much more interested in in,
in how do we use that, that tooling, that infrastructure,
that capability to unlock betterpayments, better customer
service and that the the bits that make Dojo special.
And there isn't a playbook for how to do this well.
So I think there is ausp in businesses that know how to
(28:35):
unlock this potential. And so that is something that is
perhaps a little bit more sensitive and I'm certainly keen
to invest in that in house. But even on that journey, we
don't need to go it alone and we're not going it alone.
So how do we, how do we stand onthe shoulders of giants?
So we're working with, you know,now we have a number of
technology partners and collaborators, but we're working
very heavily with Google, for example, and whether that's
(28:58):
there Gemini. So, so the model, you know, we,
we, whether that's the, the Vertex AI platform on which we
build the agents. If if I take Gemini for example,
we've we've rolled it out acrossour entire real estate here
again, it's that, it's that adoption, it's that training,
it's that putting it in front ofpeople.
So the, the tooling is much morereadily available than it was
(29:20):
before. And so I think that that helps
you do certain things you can say in house in in a way more
more readily. But we are also working with
partners to help us validate theway we're thinking about some of
these solutions. We're a very big sales force
partner for example. And so we've invested a lot in
preparing data to be consumed across the various sales force
(29:41):
offerings, which really kind of made it a natural place for us
to start exploring synergies with Agentforce, which is their
their AI agent platform built ontop of Salesforce.
And so we're definitely looking at various partnerships as well.
A really interesting one that I'm quite proud of is we, we
started a collaboration with Imperial College where we have
(30:05):
10 Imperial MH computing AI students with us and they're
part of a, a newly formed AI trailblazer program.
And, and, and here the idea behind this is we've got some
investment from Google as well from a training point of view.
But the idea is how do we get intensive training, almost like
an sort of an immersive boot camp, not just on the
(30:25):
technology, but on what Dojo is what it, what does it mean to
work in customer service? What are those problem
statements there or in risk and underwriting?
It's a really, really immersive deep in house that kind of
knowledge which we then use to see those use cases I mentioned
earlier and start to explore different streams in a really
rapid, iterative, incremental way.
(30:46):
How do we start working in, how do we start working in sort of,
if not, if not days, weeks to, to sort of test things to get
that, you know, sort of to validate whether or not we're,
we're, we're, we're getting the results we expect and we will
stumble. We will, we will find hit a few,
you know, sort of dead ends, butthat's OK if the feedback cycle
is, is, is relatively short. So it's a it's an in house
(31:09):
curated program, but it's augmented and supported by
partnerships. And it almost goes back to our
grassroots question. Let's start right.
You're actually involving these people who are involved in the
movement or young got that firstfor it, but kind of taking part,
it's fantastic. And bringing it back to
payments, how is it trapped intoAI and is it keeping up?
Yeah, definitely more to go. No, I mean it is, it is, it is
(31:31):
I'm aware in a very highly competitive.
So instead of industry. And so you know, you're seeing
that across our competition and and Dojo is certainly not not
kind of I'm resting on that front.
So you know, we talk about customer service.
I, I, I think that that's something Joe just been using AI
for quite some time in that space.
(31:51):
Over a year ago, we launched a call summarization piece, for
example, that that really tries to, to provide our customer
service agents with an automatedway of generating their call
summaries, generating actions, freeing them up to to to take,
to take the next call. But also more recently with
Salesforce, again, it's the speed with which things are
moving. That was AUK English based
(32:13):
solution. And as we become over the last
year a more international business with operations in
Spain, Italy as well. You know, we, we we're sort of
always on the backlog on our howdo we expand this to multi or
other languages? But actually with the with the
kind of evolution of the AI techstack, we we don't really need
to do much more tech investment to to again, is that art of the
(32:34):
possible now? It's just another language.
Is is almost like carrier. Removal, as you discussed
before, right normal a business two years ago would probably
struggle to. Absolutely, it'd be a huge
investment to think about makingthis sort of a, a multi language
capability that that AI trailblazer group are are
looking at a customer service agent Chapel that's, you know,
they're they're they've got so many information sources with
(32:58):
which to try and offer support to our customers.
And so how do we consolidate lots and lots of disparate
information sources and in real time for our customer service
agents so that they have that context where real time sort of
information around the customer that they're speaking to a much
richer history about that customer.
But also as we become sort of just said multi geographic
(33:22):
based, but also multi product, you've now got much more sort of
cognitive load that those customer service agents need to
bear in mind to do an effective job.
So how do we turbocharge that is, is an area that that we're
looking at. And I, and I'm, I'm sure other,
other businesses will be as well.
Fraud is, is a big one. I mean, we, we use AI tooling
(33:43):
to, to, to help us with more accurate sort of fraud detection
and, and with fewer false positives.
And, and, and that's, I mean, the speed with which fraudsters
move mean that a traditional rule based system simply can't
keep up with, with those. So, so it's, it's, it's sort of
fighting that game at a on, on amore equal level playing field
(34:04):
on that front. Chargebacks is an, is another
area typically very bureaucraticprocess driven, not necessarily
maybe the first domain you mightthink of when it comes to
innovation, but but we're looking at how can AI look at
sort of automating large aspectsof that, particularly the
information gathering process. I mean, you know, some
(34:27):
chargeback and, and, and for thereaders or the listeners, I
guess to this, you know, chargeback is a dispute
mechanism. So this is where, where a
consumer disputes that a chart whether a transaction was really
made by, by them and that that's, that's quite an involved
process in terms of backwards and forwards.
Do I need CCTV evidence that, that the merchant supplies?
(34:48):
So it can be quite a, a, an expensive and, and, and sort of
laborious process both for us and our customers.
So AI we, we, we, we've got a stream at the moment building a,
a sort of an automated charge back flow powered by AI.
And that really massively speedsup the the, the summarisation,
the analysis of that informationand, and, and, and really tries
(35:10):
to get to solving this quite painful problem for our
customers. Another great example, right, of
that, of that lengthy process where previously, well when you
were talking about the 100 problems that come to the 100
common use cases, you could use.It's a great example of where
backwards and forwards happens alot doing or maybe 3 different
parties for the customer, your customer and you guys.
That's right, that's right. And you know, so piercing all
(35:30):
those bridging all that togetherwith AI that shortcut to kind of
success. I mean, when we saw that that
use case, it, it, it shot up the, you know, we, we for the
reasons you just described it, it was an it was a clear pain
point for those three parties involved, but but also one where
we saw this AI could actually bea really great fit for and, and
early results certainly indicate.
And you got I remember, right. Don't you have the wall of pain?
(35:52):
Is that right? You guys have this that I was,
yes, when I first met you guys, I was told about it, but that
it's almost that in effect, right?
Isn't it going and back to, you know, when you first built over,
you got the pain points, but nowlooking at it from an AI
standpoint, what can you achieve?
So look, it's very interesting. I can talk to you for now, I'm
not allowed. So I need you to give me 3
emerging, I suppose, AI tech trends that you, that you, we
(36:13):
think we've covered a little bitthat we could watch out for in
the next 12 months. Do you think would be they're
going to make some movement? Sure, I, I, I think one, one
that comes to mind would be sortof hyper personalization and,
and doing so at scale. And, and what I mean by that is,
is really starting. We touched a little bit on on,
on on this, but it's starting tolook at in real time, providing
(36:33):
truly uniquely relevant sort of personalized feedback to, to to
a customer. I, I think loyalty, for example,
for me is a great stand out. I'm I'm one here really, really
doing that well, of course, you know, making sure you're doing
that with consent and, and, and,and, and this comes back to
that, that trust, you know, where are you coming at this
(36:54):
from a decision process? Why are you offering the the
services? But, but I think it's a really
exciting opportunity to really delight the end consumer, you
know, with things that are, as Isaid, relevant to them that
they're, they're expecting more and more of these kinds of
things. But for me, it's that
opportunity to really wow and delight, you know, for example,
recognizing, well, it's great that you've got an offer on
(37:14):
burgers, But, but I'm a vegan, let's say, and, and, and so you
don't have, you know, I'm not interested in your in, in your
beef burger offer, you know, so,so being able to, to, to, to
recognize what's right for this particular consumer.
Or maybe is, you know, my early riser, you know, get your
coffee, you know, get a pound off your coffee because you're
coming in. I'm super early.
So I, I think this is a, a really exciting space because I
(37:38):
think with this technology, you can really do it at scale.
I think that that's what's the challenge for is, is sort of
computationally and, and, and sort of labour intensively kind
of infeasible really kind of kind of to look at that before.
Another one for me would be the rise of agentic AI of the last
sort of six months. You're hearing that that phrase
kind of cropping up more and more and biogenic AI.
(38:00):
What we're really talking about is, is, is, is systems where you
have multiple agents and I thinkof them as sort of like digital
workers and, and, and, and the difference here is starting to
join those workers together. And, and, and now together they
can start to tackle much more complex work flows and use
cases. And so rather than than having a
(38:22):
summarizer or an analyzer, it's being able to group those things
together to be much more proactive.
And, and I think about it from a, let's say, a sort of a fraud
point of view. It's not just detecting or
flagging that a transaction might be fraudulent.
It might also be starting to initiate actions to, to warn the
customer that there that, that there's a growing threat in
(38:43):
other respects. Or for example, starting to
automate gathering some information or even automate the
the the chargeback the dispute process.
Almost like triggering behaviourlike you would in a marketing
campaign if like a nurture. Campaign yes, you know so so you
can start to do far more complexyou know I, I think on the
customer support side again, which which is a a recurring
(39:03):
theme of I think of ROI that we talked about earlier.
You know, how can you have multiple agency and looking on
behalf of a customer account, you know maybe identifying, you
know opportunities. Maybe there's a there's an agent
that tracks our terminal telemetry data and maybe
recognizes that oh what you knowone of your terminals is a bit
old now maybe the battery life you know sort of health isn't as
(39:24):
good on that one. Maybe it can tell you about that
or even in advance in a sort of order you a replacement
terminal. So it's, it's really starting to
get much more sophisticated. So I'm I'm I'm very excited
about the potential of taking a step up.
I'm here and, and looking at thecomposition of multiple agents
and you're seeing a lot of technology trends supporting
(39:46):
that things like Google's agent to agent protocol.
So I'm going to really think about how do we make it easier
for these agents to talk to eachother, irrespective of how those
individual agents have been built.
So I think that's going to be a super interesting place.
And I think the third area for me would be the sort of explain
ability of AI and, and I think that last use case on on a
(40:07):
gentic, it's very, very powerful, but you also need to
have those guard rails in place to make sure that they are
working as intended within sort of their their appropriately
bounded contexts. And that's and and then of
course, even on the hyper personalization piece, again,
you know, making sure that we'reusing information that is.
We're allowed to use, it's appropriate that we're not
(40:28):
perpetuating bias, that we can explain why it is that we're
reaching out to you with a particular offering or with a,
you know, with a particular service.
I think all of that's becoming very, very relevant.
You've got, you know, legislation that's looking at
this. You've got the EUAI Act for for
example. So transparency, fairness.
I think the technology I, I think will need to start
(40:50):
evolving much in the same way that you've got things like the
agent, agent protocol. I, I believe that you will start
to see standards form around howa model and LLM can prove where
it's, you know, arrived or how it's arrived at.
It's it's decision process and standardizing how we do that.
I think it's a direction that I see the, the, the, the tech sort
(41:11):
of AI tech enablers really investing some energy in.
Look, it's, it's fascinating. I think anybody listening has
been on a journey the last 45 minutes.
I know across all the different factions what AI can do or is
doing and also how it could be treated in business.
That was Rob Howes everybody. Thank you very much, Rob, for
coming on. If you were having me, no.
You're welcome. You'll find out more about Rob
or Dojo. Head over to Dojo dot Tech and
(41:32):
we shall see you all next week. Thanks.
Thank you.
Welcome to the next episode of the Tech and Toes podcast.
And it's a special. Today we're joined live in
Dojo's offices and with Rob House, Senior Vice President of
Techno. That right?
That's right. That's right.
Very nice to meet you. How are you?
I'm very well. Thank you.
Looking forward to this conversation.
No, thank you. You've picked my specialist
subject not, but no, I'm really,I mean, I think everybody's
interested in the area right nowand we're going to have a chat
around AI and business. But before we get into that,
(00:23):
just tell us a little bit about your role at Dojo and what do
you do here? Sure.
So I've been with the business alittle over 2 years now as SVP
of Technology. That means I work with and look
after the broader engineering and data communities here.
I have had a career that is beenfintech focused throughout
before. Before Dojo I was in one of Yan
(00:45):
and George's other payments businesses as their CTO there.
And before that I spent about 15plus years in the capital market
space. So I've always been drawn to
industries which have high amounts of data within them.
So an opportunity for from from kind of AII suppose presented
itself throughout that that journey.
And I've really very much been drawn to, I, I really kind of
(01:08):
fallen in love with, with technical problems that have big
impacts. And I, I think that that's been
a recurring theme throughout. And, and before, before payments
in that capital market space, I Co founded a couple of
businesses building proprietary trading platform.
So, so kind of innovating with data has been something that's
been part of my, my career storyso far.
And I and I think it's really interesting that now we land in
(01:30):
this for us normal folk, we landin the world of AI and AI in
business I think is something that is over the last year,
particularly in, in my world in hostilities has suddenly started
to become a talking point. I would say maybe not been
action, but what does it actually mean AI in business?
How can you kind of like broadenthat for us?
If we take a little step back, II think it's, it's so much more
than thinking about computers mimicking I'm sort of humans
(01:53):
and, and I think that that's really a lot how in the last
couple of years has been a lot of attention focusing on that
element. But for me, it's, it's, it's a,
it's a spectrum of possibility where we're using this
technology to, and places like Dojo, for example, really expand
our art of the possible. And the way I think about that
and the way we're starting to think about it more and more at
Dojo is, is sort of across 3 levels really of sort of
(02:19):
automation, augmentation and amplification.
Now I'll talk a little bit more about this.
So automation has been somethingto be honest, we've been doing
an industry has been doing for awhile with, with AI and that's
really kind of looking at those repetitive tasks and more menial
tasks. I think what's more exciting
more recently is the augmentation element.
That's the AI in human combination.
How do we start to do things faster?
(02:41):
How do we start to do things better than before?
And I think where this is going on that spectrum for me is, is
that, that amplification pieces,how do, how does that tool AI
tooling with that human, how do we start to elevate and reinvent
the ways that we do things? So as I said, at the moment, I
think we're really moving from that automation to augmentation
(03:02):
basis, doing things better at greater scale than before.
But I'm really excited about howdo we start to completely
eliminate some of those barriers, whether they're
technical expertise or processesthat frankly get in the way of
our people doing their jobs. And I think that's what's still
to come. And that's, that's the
potential, that's the prize for me.
Yeah, it lose back to what you talked about the style of big
(03:23):
data and big impact, right. And the OS reported recently
that around one in six UK businesses are now using at
least one our AI technology and 70% are using up to improve
cybersecurity. Why do you think it's still, I
mean it's still relatively low in terms of uptake if you think
about some of the trends that have come before this, Why?
Why do you think that is? Yeah.
I mean, I, I, I think, I believethat the numbers, the adoption
(03:45):
numbers have moved probably quite a bit more since, since
that sort of ONS report came in.I, I think that was probably
from the, from maybe maybe a couple of years ago.
But what I would still say is whilst I think adoption of the
technology is probably moved on quite a bit in, in a more
positive direction since that report, what I'm still seeing is
(04:06):
a, is a gap between businesses that are using AI at a proof of
concept EOC stage versus those that have been able to convert
those PO CS into production grade instances.
And, and I think there's a variety of reasons or why that
uptake perhaps is still not as great as we would like it to be.
I think 1 is looking at the dataquality, the data governance
(04:30):
that, that, that businesses havehaving to really utilize this
technology to its fullest potential.
You need to have very clean, structured data.
You need to make it easier for AI to consume that data.
And, and that's still something that I think many businesses are
not yet well prepared or set up to do.
I think you've got ethical and trust concerns that come into
(04:51):
play here. I think that comes from both
people being more fearful about things that they don't
understand and worrying about how ethical and, and how
trustworthy is the data that's feeding this.
But, but also, if I look at businesses like Dojo, we're a
regulated business. And that means, you know, and
that, and those regulations and our compliance with those are
there to protect our customers and, and to protect people.
(05:13):
So making sure that this new technology is, is clearly
complying with that is, is, is also something that I can see
stopping many businesses from fully being able to go beyond
the, the kind of the lab sort ofcontext.
The other thing is, you know, sort of still a skill gap.
I, I think this is relatively new and they're not that many
(05:36):
people out there that are, that are proficient and there are no
real rule books yet on how to dothis at scale.
And that, and that is also another barrier.
But probably one of the, the, the biggest things I, I, I see
is in terms of tripping up businesses at that POC stage,
trying to go beyond is, you know, I talked about at the
beginning, 1 narrative for my career has always been trying to
(05:57):
fall in love with problems. And I think in this case,
there's a lot of examples out there where I think people have
fall in love with the solution. And so they, they, they, they've
got this AI solution and they, well, yes, it's it, they're,
they're looking for a problem, it's going to fix.
And, and, and I think that meansthat you get these underwhelming
moments where these PO CS then don't maybe have the impact that
(06:17):
they could have or should have. And that's not because the
technology doesn't have promise,but because we probably not pick
the right use case to pursue. That it's very interesting and
and I think I did a speak, I dida talk the other night with
about 100 restaurant operators and asked them to show show of
hands who was using ChatGPT in their business, all of
everybody. And I think if I'd asked that
maybe even 18 months ago, you'd probably have. 10 to be changed.
(06:39):
Yeah, Deloitte said there's 7 million people in the UK have
used some kind of generative AI at work.
Do you think as you said, maybe the is the tech early as in do
you think that people are it's like a grasp ground level kind
of movement or do you think it'sa top down strategy coming from
the amount of people using it? So people self discovery with
ChatGPT and all the other stuff out there?
Or is it the other way around? I think it's a bit of both like
(06:59):
many trends, you've, you're going to have your portion of
early adopters that, that, that,that, I guess start to form
those grass roots, you know, movements.
And I think the more, you know, those grass roots communities
then start to increase pressure in, in terms of, you know, at a
top level leadership point of view, they start to be more
intentional about what should orcould their strategy be.
(07:21):
So you sort of get that top down, then starting to so almost
like a feedback loop. And, and here at Dojo, there's
a, there's a bit of a combination of both.
I I I mean. The the the.
Tools. People like Open AI, ChatGPT,
they've incredibly friendly userinterfaces.
It's much more ubiquitous now. It's also quite easy to get
going with with it, which means that I think that that's helped
(07:43):
that grassroots adoption become quite, as you said, quite
widespread. I'm not surprised to hear that,
that you were getting that kind of response rate from it.
And what we're doing here is, isboth from the top.
So getting we have senior leadership buy in that this is
important. This is something that we must
get behind and and are investingin, but we're also tapping into
those grassroots communities. We formed sort of the like an AI
(08:05):
champions group and how do we tap into that to make sure that
our communication is more effective that any gaps in
skills and feedback on that is is happening in a tight way and
ultimately leveraging that top down endorsement with that sort
of bottom up evangelism. Yeah, and I think all of it's
down as you talked before about solution versus problem.
(08:26):
And sorry, you know, people are kind of almost trying to find
the problem, not have the problem first, Excuse my words
get mixed up there, but what arewhat are the common use cases
where we're looking at when it comes to implementing IO,
solving problems with it? Can you give us some examples of
what's happening? Yeah, sure.
I, I, I think there's, there's probably, there's maybe 3 core
(08:46):
dimensions that I think people are, are, are pursuing with
this. You've got efficiency gains.
So how can we save money? I could be more cost effective,
save time, free up people's time, you've got effectiveness.
So how, how can we use the technology to help people do a
better job, not necessarily justfaster, but but actually get a
more accurate results. You have have bigger, bigger
(09:08):
impact, you know. And then the third one would be
looking at maybe the, the innovation side of things.
Can we come up with new products, new services that that
we can offer or sell as a business?
I think that last one is, is happening, but perhaps on, on a
smaller sort of scale. I'd say what we're really seeing
quite a lot of success in is in the first two camps that
efficiency and effectiveness point points.
(09:31):
And when I look at, at examples,I mean, I think there's now an
increasingly wide variety of, ofindustries and, and, and sort of
domains within those industries where we're using this
technology. But there's a few, I, I would
say common established good examples for this customer
service is, is definitely a hugeone of things like AI chat bots
(09:53):
being able to do things like intelligent routing of, of, of
queries and calls to the right department.
You know, things like, for example, sentiment analysis, you
know, to enrich the customer service agents experience with
the quality of, of support that they're giving their customers.
So I think that's a huge area inseeing that across multiple
industries quite well established.
Now you're also got opportunities in things like
(10:15):
marketing and sales and how can we do much better sort of
predictive lead scoring, for example, content generation,
particularly looking at maybe personalized content generation,
how can we tailor campaigns and and optimize ad campaigns to
reach the right audience and speak and use relative sort of
language. I, I would say operations
(10:36):
functions in, in general what, whatever that, that may be that
if I look at Dojo, for example, that can range from things like
our on boarding and and sort of KYB process.
How do we how do we streamline that and how do we make it more
effective as a regulated business?
This scheme and regulatory compliance use cases there's a
lot of procedural operational divisions within a company that
(10:59):
I think lend themselves very, very well to this cybersecurity
something you know you touched on I'm.
Well informed on. That is another one, whether
that's fraud prevention or whether that's sort of threat
detection, anomaly detection and, and, and a, and a big one
is software engineering. And I'm not talking about vibe
coding. And that's almost a forbidden
(11:20):
term. I hear that's a, that's a very,
very different thing. It's great for rapid
prototyping, but it's not how you build engineering solutions
at scale. But, but absolutely looking at,
at and, and sort of how do we complement our engineering force
with this kind of tooling? We, we talk about sort of AI
native engineering and, and that's from the code it
(11:40):
generates to the test cases thatit produces to the documentation
that that can accompany that code.
So those are some pretty well established areas where we're
finding this technology being used successfully and at scale.
Consistency and operations in inhostiles, particularly where I'm
I'm really interested in it. It's been hard to achieve for
many, many years. You need every managers and.
Do it. Do you think that unlocking AI
(12:01):
in that field or not just in operations, but around so the
payments PCI is, is going to really support these industries
going forward? Because I feel like they're,
they're in a moment when they'rein between and they're they're
struggling with obviously finding people going people, you
know, there's a high churn on people.
Do you think that AI is going toplug a bit of a gap there?
I think it can create a level playing field as I mentioned
earlier, how do we use this technology to better maybe
(12:22):
eliminate barriers to getting inthe way of our people.
And so I think a lot of what you've described there are
they're, they're areas, domains of expertise, but they're quite
established. There's a lot of documentation,
there's a lot of material on them.
And all of that feeds incrediblywell into the world of AI
tooling. So how do we augment those kinds
of industries and those kinds ofskill sets with, with not having
(12:43):
to go and become PCI experts and, and and so forth.
So absolutely. And, and I wanted to talk about
one particular case study. Oh, Polly, you know, about this.
So I just, it's a really clever use of AI for discount delivery,
but also shows the tension, I suppose, between personalisation
and fairness. Where, where's the line?
You know how should we business balance innovation versus you
(13:04):
know customer trust and not and not cross that line?
Yeah, I think that is a really interesting example.
And I'm and I'm not surprised that there was some mixed sort
of feedback from people as to kind of how that feature landed
on the, on the one hand, I, I dosee the innovation, you know,
changing the, the, the kind of the, the paradigm of, of how one
does sort of discounting and so on.
(13:26):
But, but I think for me, a big, a big issue is, is I think
maybe, maybe business is not fully considering that
transparency. We talked a little bit before
about how I, I, I think the, the, the trust or the, or the
perceived lack of trust or, or worry about the ethics side of
things can be a barrier to adoption.
And I think it's a barrier to perhaps a, a, a seamless
(13:49):
experience or a, or a good feeling from a consumer point of
view when you're interacting with AI in, in, in this way.
And I think businesses to get that balance right.
I think key for me is that they really identify and focus on
that transparency, being able toexplain how and why the, the,
the, the AI solution arrived at the conclusion that that it did.
(14:10):
And in this case, if we, if we want to look at it from a
discounting point of view, you know, you don't want the, the
user to feel that, that perhaps there's an element of sort of
profit driving here. You know, I'm only going to
offer you a discount if you think to ask.
But for one, I think it can leadto this sort of cynical points
(14:31):
of view. And I, and I think in this case,
they, they were actually genuinely looking to find a way
to positively engage with their customers and, and, and truly be
innovative. But I think there is this danger
that that can come across without some more explanation
that can come across as potentially sort of exploitative
of those, let's say, let's say, are less savvy of the technology
or less inclined to think or askthese things.
(14:52):
And, and again, why am I gettinga discount?
Does that mean that, you know, there was just fat margin lying
around there that you're hoping I wouldn't get to?
And, and so I think again, thinking about the decision
process that frames the AI, the guy, you know, is this because,
for example, this is a repeat customer.
So actually as a repeat customer, we see that you, you
buy with us regularly, we'd liketo offer you a loyalty discount
(15:15):
on top of that or, or because you know, there's an
opportunity, you know, to buy more things and we'll give you
an additive sort of discount. I think otherwise it feels a
little bit opaque and that blackbox can be, I think, a real
barrier to getting that trust element.
And I suppose that quite a bit of education still to be dozen
around AI because I think as I said, ChatGPT is a commonly used
(15:35):
tooling now. But the reality of what you're
talking about there is just doing something and putting it
out and not really fully explaining how that what's you
know, ending up when it's decision making that that
education piece quite big, isn'tit, what's gonna have you for
the next 5-10 years. Huge.
I, I, I mean, I, I think you've,you've got education in, in
general to kind of the, the masses.
I, I think I'm, I'm tomorrow, for example, I'm actually going
(15:58):
to, to Imperial College as part of their Advisory Board to talk
about the future of, of computereducation and how AI can and
might need to shape that. But you've also got it within
companies as as well. So a big part of what we're
doing here at Dojo is, is starting to think much more
intentionally about how do we drive that AI literacy.
Something that actually worries me more broadly about this.
(16:19):
I, I believe in the power of, ofthis, of this tool.
It's only getting more and more capable.
Still not great at everything necessarily, but but there are
plenty. Oh yes, it, it, it does.
And I, and I think being also, that's the part of the education
piece, being honest and, and, and being vocal about, yes, it's
really strong at lots of things,but it's also not yet good at,
(16:39):
at a lot of things. And, and sharing those things I
think is so important because from an education point of view,
otherwise you're getting these warped points of view as to what
you know you, you see, if you see only the bad, then you, you
walk away thinking and disillusioned with what it can
do. Equally you, you, you, you
potentially succumb to the hype too much.
If if you don't recognise, if you're only seeing the the
absolute amazing success storiesI.
(17:01):
Think we're really in that moment now it feels like anyway
in business I think we're reallyin that moment where it's kind
of everyone's in between some are on the fence and some aren't
as what are the biggest reasons to invest in AI.
So if AI and R So if you're sitting there thinking this is
great, sounds clever, it sounds like you can fix a lot of our
problem. But why now?
Why would you get into it now and not wait another five years?
I, I, I mean, I think you're as a business, you're, we're in an
(17:22):
incredible sweet spot right now in, in terms of, I think we've
had a, it's still new, but I think we've had enough success
and enough interest and investment in this that it's
gone beyond being niche and, anda fad.
I, I, I think there's a, there'sa growing consensus that in some
way, shape or form, this is now here to, to stay.
And, and so I think as a business, you've got that
(17:44):
benefit of more tooling, there'sthis more, more infrastructure
available with which you can start to use this.
So I think the barriers to entryare, are lower, but you've still
got that, as I said, sweet spot where you're early enough as a
business to still be considered.I think an early adopter,
particularly if you set the groundwork in place that you're
(18:06):
not just spinning up your, your,your next POC lab project that
doesn't go anywhere. So if you're intentional about
these things, I think you have an opportunity as a business to
really start to set the playbookfor what great looks like and,
and, and really be establish yourself as a thought leader.
And I think that's going to do lots of things, not just to your
business, but in terms of your customers or the product that
(18:28):
you're offering, but also your, your, your, your talent.
I think it's going to be a hugely powerful tool for
attracting and retaining talent.I, I think on the flip side, I
talked about maybe a few carrotsabout how it can elevate your
business there and, and why hopefully those would be
motivators enough. But, but I think there is also
that, that opportunity cost of not getting involved now I think
is also rapidly growing. And so I think if you're not
(18:51):
doing it, your competition almost certainly are.
And I, and I mention that that kind of talent point as well,
you're going to struggle, I think to retain people are
increasingly looking to work in roles and within companies that
give them access to this toolingthat are upscaling them in this.
And, and I think there's a real danger that between your
employees, between versus your competitors, I mean, customer
(19:15):
expectation is also growing. I think they're seeing this in
other aspects of their life, youknow, whether that's video
streaming services like Netflix or, or whether it's Amazon, that
they're increasingly seeing AI used to help personalize their
experience. And so that expectation, I think
is broadening. I, I think there's a real, real
danger that businesses become irrelevant if they don't get
(19:38):
involved. I'm sort of in this, in this
kind of technology. I was, I, I was, I was kind of
looking at a quote. I'm at a quote shared with me
from, from an author called Peter Henson.
He's written a book called The New Normal a number of years ago
now. And, and he said BC no longer
stands for before Christ. It stands for Before ChatGPT.
(20:00):
And, and I think for me it's it's, it's a great illustration
of the fact that this is a movement that's.
It's a moment. It's a moment in time and it's a
material moment in time, much inthe way of the Internet or
smartphones change the paradigm or engaging with with consumers
out there. And I and I think businesses
need to be relevant if they're or they, they won't be relevant
if they're not acknowledging. And I liken it.
(20:20):
I mean, I'm old enough to remember the Internet and and in
phones and all that. Likewise.
And I, and I remember it being websites particularly actually
remember people saying why wouldI want?
Why would I put my on a on a on Internet?
But, and obviously we've we've gone since then, right, and it's
obviously was a good idea. And same with social media and
all the other stuff that's fallen.
I feel like this is the first time since that I feel like this
(20:40):
kind of there's a big shift. It is for business as well as
also in our day-to-day lives. Hugely transformative and
businesses have an opportunity. As I said, we're still early
enough that you have an opportunity to be that thought
leader to help shape what this is.
I think that's tremendously exciting for businesses.
And we and we talked about some efficiency gains, but
particularly in supply chain, you know, cybersecurity, which I
(21:02):
spoke to naveed about four, which is really eye opening in
terms of what Dojo are doing. Which of these are kind of are
you getting the quickest ROI? You know, people are impatient,
want to see where can I get my quickest game, but where are you
finding a Is producing that gamequick enough?
I mean, I think, I mean cybersecurity is, is, is is a
great one. I think demonstrating or, or, or
talking about the value proposition in terms of
(21:24):
protecting our customers, you know, protecting or preventing
fraudulent transactions at, at the scale that we're at, you
know, this is, this can be millions and millions, 10s of
millions of pounds. So I, I think that there,
there's a great narrative there,but it's probably things that
I've touched on already really in terms of I, I think customer
service is, is a fantastic area from an ROI point of view.
(21:46):
Just the sheer, you know, you know, we have 150,000 businesses
and, and so, and we pride ourselves on 1st class service
and, and a lot of that it requires kind of high touch
engagement from our customer service agents.
And so if I take a step back, it's the broad customer
operations piece, I think is a fantastic area from an ROI point
of view because it's, it's, it'snot just what a customer might
(22:09):
directly experience, it's what happens behind the scenes.
How do we empower our customer service agents to have more free
time or the time that they do spend with our customers to be
that much richer? So I'm going to ask you a
question. How do you start?
Because I think a lot of people are looking at this opportunity
thinking they agree with you. And I think as I said, the show
of hands the other night kind ofsays people are ready.
How do they kind of get started on that journey?
(22:30):
They don't know what to do. How do they go about building in
a road map for AI? That's a great question.
What we've done here and, and, and my advice to, to, to, to
listeners on that would be to take a step back and really
think about what is it that you want as a business AI to unlock
or achieve? But for you, you know, in, in
our case, we, you know, I believe in, you know, we've
(22:52):
talked about a lot, but I reallybelieve in the transformative
potential, not just potential, actually the transformative
effect it's already having with businesses and there's more
potential out there. And so for me, it was this
recognition that this is going to transform every role, every
business to some to a greater orlesser extent.
And so that really very, you know, sort of led us to, to, to
(23:15):
kind of crafting a vision statement for AI, which was to
be the model workplace where AI is unlocking the full potential
of our people. And that's really to see them be
the happiest, the most innovative and the most
productive professionals shapingthe future of in person
payments. We're talking about transforming
our business to being an AI native, a business.
(23:38):
And that's what it, that's what it was for us.
That's what the ambition is. And so that was hugely important
in a, in a, in, in defining whatthat vision is for your
business, because that's the framing for everything that then
comes as part of then identifying that vision.
We then moved on to the next step, which is so OK, what,
what, what's our, what's going to be our next 12 month mission
(23:59):
towards that vision. And, and for Dojo, that was
looking at establishing those, those fundamentals on which we
were going to build an AI nativebusiness.
And that included something we talked about earlier, AI
literacy. Yeah.
And that is with our exec team all the way through to the rest
of the company, making sure we understand what this is and what
its potential is. And keep that I'm going as this
(24:20):
is rapidly changing. It's about making sure that we
have the right data foundations and the right data assets and
that we have the right governance in place.
This is still a relatively nascent field.
So from a, I'm sure Naveed wouldhave talked about it, but from a
security point of view, you know, making sure that we are
old but grounded and informed. And, and a big part of that is
(24:44):
not doing this as a tech silo initiative.
But this is, you know, us working with Naveed's team in
security. It's working with the legal team
as well to make sure that all ofthose are, are aligned and, and,
and working together to set us up for that success.
We didn't want us to hit those proof of concept fancy demos
(25:05):
that then can't go anywhere because it doesn't comply from
a, from a regulatory or legal point of view or from a security
standpoint. We, we, we, I would then sort
of, you know, going back to this, don't be that solution
finding a problem, you know, sort of situation.
You don't want to find yourself in that situation.
And So what we did was we started canvassing across the
(25:26):
business for what are your top problem statements?
I, I, I, I kind of like, yes, weframed it as we're doing and
we're asking this question because we're excited to see
where AI could play a role. But the brief was, but don't
tell us about an AI solution. Don't assume AI or, or can or
can't fix your problem. Tell us what is your number one
(25:47):
problem? I, I often frame this in the, in
the, in the form of, you know, if you know, what would you get
your next person or your next 10people to do?
You know what, what are you looking to hire for?
That's probably a good indication of where your biggest
problems are. And we had in, in the matter of
a few weeks, we had over 100 usecases submitted across our
business, which we then spent time creating.
(26:07):
And that's the next step, which is really be intentional about
which ones you start with. And it's not starting with 100.
I mean, it's great to get so many, but first of all, not all
of them were great AI fits. But but it's really being
intentional. What are those high impact ones,
picking two or three of those, making sure that they're
achievable, thinking about success metrics, what does great
(26:29):
look like if we, if we work on this and then getting a, a team
of people that you're properly supporting working on those.
And, and so I think those, thosesteps for us have worked really
well. And I think I, I think they
could really be useful for framing how another business
might want to get started on this if they haven't already.
Very useful. I think.
I think just clearly kind of pushing everything aside from it
(26:50):
and looking at the objective, like you said at the start there
almost having a mission statement or what you want to
achieve is crucial to any project you start on any journey
you're going to go on. So it makes real sense and
obviously you guys have a certain resource here at Dojo
and how do you, if you're smaller or maybe even if you're
a bigger enterprise, how do you kind of balance internal
capability versus using an external partner when it comes
(27:10):
to this, this kind of work? Is it?
How much do you want to keep it,I suppose in house?
Great question. I I mean, look, I think the size
of your organization will play arole in this.
But, but, but if I take again, if I'm, if I'm sort of agnostic
of the company size, I think howprecious you might want to be
about whether this is in house or not, I think really depends
on exactly what is the nature ofyour business.
(27:32):
I, I mean, if I'm, if I'm building AILLM models, then I'm
probably going to be fairly precious about, about who I have
working on that, that, that, that's, that's very proprietary
IP that that I want to protect for a business like Dojo.
That's not really the layer that, that, that, that is our
USP or that I'm seeking to sort of expand our thought
(27:53):
leadership. And I'm, I'm actually mindful,
I'm there was a concern really about how much energy we invest
to low level at that stage. These are areas that are
increasingly being commoditized.If I think of AI infrastructure
tools or AI platform services, I'm mindful that we are very
limited in how much we want to build at that layer rather than
(28:15):
consume. I'm much more interested in in,
in how do we use that, that tooling, that infrastructure,
that capability to unlock betterpayments, better customer
service and that the the bits that make Dojo special.
And there isn't a playbook for how to do this well.
So I think there is ausp in businesses that know how to
(28:35):
unlock this potential. And so that is something that is
perhaps a little bit more sensitive and I'm certainly keen
to invest in that in house. But even on that journey, we
don't need to go it alone and we're not going it alone.
So how do we, how do we stand onthe shoulders of giants?
So we're working with, you know,now we have a number of
technology partners and collaborators, but we're working
very heavily with Google, for example, and whether that's
(28:58):
there Gemini. So, so the model, you know, we,
we, whether that's the, the Vertex AI platform on which we
build the agents. If if I take Gemini for example,
we've we've rolled it out acrossour entire real estate here
again, it's that, it's that adoption, it's that training,
it's that putting it in front ofpeople.
So the, the tooling is much morereadily available than it was
(29:20):
before. And so I think that that helps
you do certain things you can say in house in in a way more
more readily. But we are also working with
partners to help us validate theway we're thinking about some of
these solutions. We're a very big sales force
partner for example. And so we've invested a lot in
preparing data to be consumed across the various sales force
(29:41):
offerings, which really kind of made it a natural place for us
to start exploring synergies with Agentforce, which is their
their AI agent platform built ontop of Salesforce.
And so we're definitely looking at various partnerships as well.
A really interesting one that I'm quite proud of is we, we
started a collaboration with Imperial College where we have
(30:05):
10 Imperial MH computing AI students with us and they're
part of a, a newly formed AI trailblazer program.
And, and, and here the idea behind this is we've got some
investment from Google as well from a training point of view.
But the idea is how do we get intensive training, almost like
an sort of an immersive boot camp, not just on the
(30:25):
technology, but on what Dojo is what it, what does it mean to
work in customer service? What are those problem
statements there or in risk and underwriting?
It's a really, really immersive deep in house that kind of
knowledge which we then use to see those use cases I mentioned
earlier and start to explore different streams in a really
rapid, iterative, incremental way.
(30:46):
How do we start working in, how do we start working in sort of,
if not, if not days, weeks to, to sort of test things to get
that, you know, sort of to validate whether or not we're,
we're, we're, we're getting the results we expect and we will
stumble. We will, we will find hit a few,
you know, sort of dead ends, butthat's OK if the feedback cycle
is, is, is relatively short. So it's a it's an in house
(31:09):
curated program, but it's augmented and supported by
partnerships. And it almost goes back to our
grassroots question. Let's start right.
You're actually involving these people who are involved in the
movement or young got that firstfor it, but kind of taking part,
it's fantastic. And bringing it back to
payments, how is it trapped intoAI and is it keeping up?
Yeah, definitely more to go. No, I mean it is, it is, it is
(31:31):
I'm aware in a very highly competitive.
So instead of industry. And so you know, you're seeing
that across our competition and and Dojo is certainly not not
kind of I'm resting on that front.
So you know, we talk about customer service.
I, I, I think that that's something Joe just been using AI
for quite some time in that space.
(31:51):
Over a year ago, we launched a call summarization piece, for
example, that that really tries to, to provide our customer
service agents with an automatedway of generating their call
summaries, generating actions, freeing them up to to to take,
to take the next call. But also more recently with
Salesforce, again, it's the speed with which things are
moving. That was AUK English based
(32:13):
solution. And as we become over the last
year a more international business with operations in
Spain, Italy as well. You know, we, we we're sort of
always on the backlog on our howdo we expand this to multi or
other languages? But actually with the with the
kind of evolution of the AI techstack, we we don't really need
to do much more tech investment to to again, is that art of the
(32:34):
possible now? It's just another language.
Is is almost like carrier. Removal, as you discussed
before, right normal a business two years ago would probably
struggle to. Absolutely, it'd be a huge
investment to think about makingthis sort of a, a multi language
capability that that AI trailblazer group are are
looking at a customer service agent Chapel that's, you know,
they're they're they've got so many information sources with
(32:58):
which to try and offer support to our customers.
And so how do we consolidate lots and lots of disparate
information sources and in real time for our customer service
agents so that they have that context where real time sort of
information around the customer that they're speaking to a much
richer history about that customer.
But also as we become sort of just said multi geographic
(33:22):
based, but also multi product, you've now got much more sort of
cognitive load that those customer service agents need to
bear in mind to do an effective job.
So how do we turbocharge that is, is an area that that we're
looking at. And I, and I'm, I'm sure other,
other businesses will be as well.
Fraud is, is a big one. I mean, we, we use AI tooling
(33:43):
to, to, to help us with more accurate sort of fraud detection
and, and with fewer false positives.
And, and, and that's, I mean, the speed with which fraudsters
move mean that a traditional rule based system simply can't
keep up with, with those. So, so it's, it's, it's sort of
fighting that game at a on, on amore equal level playing field
(34:04):
on that front. Chargebacks is an, is another
area typically very bureaucraticprocess driven, not necessarily
maybe the first domain you mightthink of when it comes to
innovation, but but we're looking at how can AI look at
sort of automating large aspectsof that, particularly the
information gathering process. I mean, you know, some
(34:27):
chargeback and, and, and for thereaders or the listeners, I
guess to this, you know, chargeback is a dispute
mechanism. So this is where, where a
consumer disputes that a chart whether a transaction was really
made by, by them and that that's, that's quite an involved
process in terms of backwards and forwards.
Do I need CCTV evidence that, that the merchant supplies?
(34:48):
So it can be quite a, a, an expensive and, and, and sort of
laborious process both for us and our customers.
So AI we, we, we, we've got a stream at the moment building a,
a sort of an automated charge back flow powered by AI.
And that really massively speedsup the the, the summarisation,
the analysis of that informationand, and, and, and really tries
(35:10):
to get to solving this quite painful problem for our
customers. Another great example, right, of
that, of that lengthy process where previously, well when you
were talking about the 100 problems that come to the 100
common use cases, you could use.It's a great example of where
backwards and forwards happens alot doing or maybe 3 different
parties for the customer, your customer and you guys.
That's right, that's right. And you know, so piercing all
(35:30):
those bridging all that togetherwith AI that shortcut to kind of
success. I mean, when we saw that that
use case, it, it, it shot up the, you know, we, we for the
reasons you just described it, it was an it was a clear pain
point for those three parties involved, but but also one where
we saw this AI could actually bea really great fit for and, and
early results certainly indicate.
And you got I remember, right. Don't you have the wall of pain?
(35:52):
Is that right? You guys have this that I was,
yes, when I first met you guys, I was told about it, but that
it's almost that in effect, right?
Isn't it going and back to, you know, when you first built over,
you got the pain points, but nowlooking at it from an AI
standpoint, what can you achieve?
So look, it's very interesting. I can talk to you for now, I'm
not allowed. So I need you to give me 3
emerging, I suppose, AI tech trends that you, that you, we
(36:13):
think we've covered a little bitthat we could watch out for in
the next 12 months. Do you think would be they're
going to make some movement? Sure, I, I, I think one, one
that comes to mind would be sortof hyper personalization and,
and doing so at scale. And, and what I mean by that is,
is really starting. We touched a little bit on on,
on on this, but it's starting tolook at in real time, providing
(36:33):
truly uniquely relevant sort of personalized feedback to, to to
a customer. I, I think loyalty, for example,
for me is a great stand out. I'm I'm one here really, really
doing that well, of course, you know, making sure you're doing
that with consent and, and, and,and, and this comes back to
that, that trust, you know, where are you coming at this
(36:54):
from a decision process? Why are you offering the the
services? But, but I think it's a really
exciting opportunity to really delight the end consumer, you
know, with things that are, as Isaid, relevant to them that
they're, they're expecting more and more of these kinds of
things. But for me, it's that
opportunity to really wow and delight, you know, for example,
recognizing, well, it's great that you've got an offer on
(37:14):
burgers, But, but I'm a vegan, let's say, and, and, and so you
don't have, you know, I'm not interested in your in, in your
beef burger offer, you know, so,so being able to, to, to, to
recognize what's right for this particular consumer.
Or maybe is, you know, my early riser, you know, get your
coffee, you know, get a pound off your coffee because you're
coming in. I'm super early.
So I, I think this is a, a really exciting space because I
(37:38):
think with this technology, you can really do it at scale.
I think that that's what's the challenge for is, is sort of
computationally and, and, and sort of labour intensively kind
of infeasible really kind of kind of to look at that before.
Another one for me would be the rise of agentic AI of the last
sort of six months. You're hearing that that phrase
kind of cropping up more and more and biogenic AI.
(38:00):
What we're really talking about is, is, is, is systems where you
have multiple agents and I thinkof them as sort of like digital
workers and, and, and, and the difference here is starting to
join those workers together. And, and, and now together they
can start to tackle much more complex work flows and use
cases. And so rather than than having a
(38:22):
summarizer or an analyzer, it's being able to group those things
together to be much more proactive.
And, and I think about it from a, let's say, a sort of a fraud
point of view. It's not just detecting or
flagging that a transaction might be fraudulent.
It might also be starting to initiate actions to, to warn the
customer that there that, that there's a growing threat in
(38:43):
other respects. Or for example, starting to
automate gathering some information or even automate the
the the chargeback the dispute process.
Almost like triggering behaviourlike you would in a marketing
campaign if like a nurture. Campaign yes, you know so so you
can start to do far more complexyou know I, I think on the
customer support side again, which which is a a recurring
(39:03):
theme of I think of ROI that we talked about earlier.
You know, how can you have multiple agency and looking on
behalf of a customer account, you know maybe identifying, you
know opportunities. Maybe there's a there's an agent
that tracks our terminal telemetry data and maybe
recognizes that oh what you knowone of your terminals is a bit
old now maybe the battery life you know sort of health isn't as
(39:24):
good on that one. Maybe it can tell you about that
or even in advance in a sort of order you a replacement
terminal. So it's, it's really starting to
get much more sophisticated. So I'm I'm I'm very excited
about the potential of taking a step up.
I'm here and, and looking at thecomposition of multiple agents
and you're seeing a lot of technology trends supporting
(39:46):
that things like Google's agent to agent protocol.
So I'm going to really think about how do we make it easier
for these agents to talk to eachother, irrespective of how those
individual agents have been built.
So I think that's going to be a super interesting place.
And I think the third area for me would be the sort of explain
ability of AI and, and I think that last use case on on a
(40:07):
gentic, it's very, very powerful, but you also need to
have those guard rails in place to make sure that they are
working as intended within sort of their their appropriately
bounded contexts. And that's and and then of
course, even on the hyper personalization piece, again,
you know, making sure that we'reusing information that is.
We're allowed to use, it's appropriate that we're not
(40:28):
perpetuating bias, that we can explain why it is that we're
reaching out to you with a particular offering or with a,
you know, with a particular service.
I think all of that's becoming very, very relevant.
You've got, you know, legislation that's looking at
this. You've got the EUAI Act for for
example. So transparency, fairness.
I think the technology I, I think will need to start
(40:50):
evolving much in the same way that you've got things like the
agent, agent protocol. I, I believe that you will start
to see standards form around howa model and LLM can prove where
it's, you know, arrived or how it's arrived at.
It's it's decision process and standardizing how we do that.
I think it's a direction that I see the, the, the, the tech sort
(41:11):
of AI tech enablers really investing some energy in.
Look, it's, it's fascinating. I think anybody listening has
been on a journey the last 45 minutes.
I know across all the different factions what AI can do or is
doing and also how it could be treated in business.
That was Rob Howes everybody. Thank you very much, Rob, for
coming on. If you were having me, no.
You're welcome. You'll find out more about Rob
or Dojo. Head over to Dojo dot Tech and
(41:32):
we shall see you all next week. Thanks.
Thank you.
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