February 13, 2024 • 27 mins
AI and autonomous technology are showing up in more and more industries. From banking to racing to emergency medicine, AI is giving humans a serious leg up in their work. This week Sarah Burnett, renowned technology industry analyst and author of the book The Autonomous Enterprise: Powered by AI, joins us to preach the AI gospel and explain how most industries are utilizing some form of it – even if they don’t realize it.
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Episode Transcript
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Speaker 1 (00:00):
My aim was to get as many women into AI
as possible, because the more diversity there is, the better
products we get, and I was very aware that there
weren't many women in the field of AI. Unfortunately, I
think it's changing anyway.
Speaker 2 (00:19):
The world around us is changing faster than ever before.
Ideas once only imagined in science fiction are becoming a reality.
Throughout the course of our amazing twenty three episode season,
we'll speak to some of the greatest minds in robotics
and artificial intelligence to discuss the groundbreaking work that's fueling
it all. I'm your host Ryan Marine joined me and
my co host Paul Mitchell, the president of the Indie
(00:41):
Autonomous Challenge, and see why we call this the inside track.
While the super fast cars of the Indie Autonomous Challenge
certainly make headlines, the ultimate goal of the IAC is
to still find the best ways to practically apply that
cutting edge technology to our daily lives. This is the
idea that Sarah Burnett wrote about in her recent book,
(01:03):
The Autonomous Enterprise Powered by AI. Sarah, our guest this week,
is a renowned technology industry analyst and intelligent automation specialist,
and the current chief Technology evangelist at KYP dot Ai.
She joins us to preach the AI gospel and explain
how most industries are utilizing some form of it, even
(01:25):
if they don't realize it. Even the classic customer service
chatbot was fortified with AI during the pandemic to share
rapidly changing health information updates via mobile app. Automation, Sarah says,
is already changing the world for the better. So glad
(01:46):
to have you here on the inside track, and I'd
love to chat with you a little bit about that
book and the AI role in industry as a whole.
So let's start with the book without giving too much away,
of course, because we want folks to go out and
pick it up and read it for themselves. But what
is the general premise of what you put down on paper.
Speaker 1 (02:06):
Well, first of all, thank you very much for inviting me.
The book is about the premise of it is the
autonomous enterprise, and that is an enterprise that those most
of its work either automated or digitally. Anyway, in terms
of technology, this is already possible. In fact, there are
(02:27):
companies that I would categorize as autonomous in existence today.
These are companies that you can deal with and the
processes that they go through to provide you a service
or a product barely ever goes through a human because
they've got back office integration. They've got some robots doing
(02:49):
some of the work. And in my role as an
industry analyst working very closely with leading edge technologies, I
can see that more and more organizations are heading that way.
And the question is what does that mean. As we
are on this journey of more and more enterprises becoming autonomous,
(03:12):
what does that mean for the future of work? How
will work be completed? What role will humans play? So
I address a lot of these in the book. And
then there's the bigger picture, because it's not just about automation. Firstly,
because they've highly digitalized, they have an awful lot of
(03:33):
data and they can tap that data to innovate, to
do things faster and better as well.
Speaker 3 (03:40):
And then having become extremely.
Speaker 1 (03:44):
Innovative, they basically pull away from the pack. They are differentiated.
There is this opportunity to mix and match technologies to
create new things. So it's about a whole bigger world
at their world of opportunities, and I explore all of
these in the book.
Speaker 4 (04:05):
How and when did the premise of this book occur
to you.
Speaker 1 (04:09):
Well, for many years now, I've worked as an industry analyst,
and in that role, I work with leading edge technologies,
and I see what they're doing, what they're bringing. They're
bringing new versions of their products, each better than the
previous version, with more capabilities.
Speaker 3 (04:29):
And so it was very clear to me.
Speaker 1 (04:34):
It has been very clear to me for years now
that we are on this journey towards autonomous enterprises, and
I wanted, you know, to speak about it and tell
this story.
Speaker 2 (04:43):
We'll talk more about the journey and where things are headed,
but I do want to take a brief step backwards
as well and learn a bit more about you. How
you got interested in this topic. Was there's something that
you set out to learn more about actively or is
it something that you stumbled upon over the course of
your work.
Speaker 1 (05:01):
Well, so, I've always been a sort of science and
computer kind of person.
Speaker 3 (05:09):
I studied science.
Speaker 1 (05:10):
At university and then that allowed me to get into
computing and many in many roles, I was working in
some form of automation, and then later I moved into
e government, which was all about enabling you know, on
the fly, real time services provided by local authorities. One
(05:34):
of my most recent roles as an industry analyst was
setting up the intelligent Automation practice for Everest Group, which
is an industry analyst firm, And so one way or another,
I got to know an awful lot about automation technologies.
And now I have a portfolio of jobs and one
(05:54):
of them is being chief technology evangelists for a process
intelligence company called k Ai.
Speaker 2 (06:01):
You mentioned earlier that there are already some industries that
are operating in some fashion as an autonomous enterprise. What
are some of these early adopters and what can we
learn from their experience at the forefront, at the cutting
edge that might apply to other industries that maybe initially
you wouldn't think that this could apply quite so readily.
Speaker 3 (06:22):
Yeah, so I would say they are.
Speaker 1 (06:27):
You know, there are industries that are using it extensively
and the most the one that comes to mind the
most is e commerce. Of course, you know, we're all
used to ordering something having it delivered very quickly. Behind
the scenes, there's an awful lot of automation going on,
you know, from whether a robot picks something up from
(06:48):
a shelf as you order that product and takes it
to a station where it gets packed, packed and dispatched
to all the back office functions like invoices and status
of your order, these kinds of things being sent to
you and then for the company itself. Behind the scenes,
you'll find that they are checking stocks, they are then
(07:11):
seeing if they need to order more of that product.
So a huge amount of work is going on behind
the scenes. So and a lot of those processes are
today automated, but it's sort of gone way beyond e commerce.
You know, if you look at insurance companies, there are
insurance companies that are now wholly automated. From the moment
(07:34):
that you ask for a quote, your risks are assessed
by AI and then the offer is made and then
the onboarding process. The same with banking. In fact, a
friend of mine opened a new bank account while on
a taxi.
Speaker 3 (07:49):
Right in a black cab in London.
Speaker 1 (07:52):
All he had to do was, you know, take a
picture of his passport photo ID, you know, post it
via the app and then show himself on the camera
and you know, he was approved.
Speaker 3 (08:03):
So this is the pace of change.
Speaker 1 (08:07):
This is the pace of business today, enabled by automation
by AI.
Speaker 3 (08:13):
And you know, a few years ago I was what I.
Speaker 1 (08:16):
Talked to South African Bank that had changed its customer
on boarding process from something that took fifteen to sixteen
days to fifteen to sixteen minutes.
Speaker 4 (08:27):
It's a really eye opening example.
Speaker 2 (08:30):
Actually, So when you're making the rounds as a public
speaker like you do, when you can boil it down
to someone in such simple terms, something so relatable to
opening a bank account, we've all done that, to writing
it a taxi, we've all done that. Does that help
to demonstrate just how game changing this technology, this marriage
between automation and AI is going to be in the
(08:52):
near future.
Speaker 1 (08:53):
Absolutely absolutely, And I go back to the point that
I was making earlier. It's the common nation of technology.
So before the pandemic, I visited a doctor's app provider,
and you know, you could have the app on your
phone and no matter where you were in the world,
you could then arrange to have a consultation with a doctor.
(09:16):
And behind the scenes, it was also capturing the knowledge
from you know, the symptoms to the treatment and what worked,
what didn't work, of course anonymized without sort of damaging
patient privacy. And of course the pandemic then made it
a kind of you know, go to device, Otherwise many
(09:37):
of us would have never had any doctor's consultations in lockdown,
so it.
Speaker 3 (09:41):
Was a huge help.
Speaker 1 (09:44):
And you know, now it's quite commonplace. A lot of
healthcare insurance companies offer it as an alternative to any
other kind of arrangements that you might have.
Speaker 3 (09:55):
But back then it was.
Speaker 1 (09:56):
Really new, So you know, it was again this commu
of technologies that enables that kind of innovation.
Speaker 2 (10:03):
And to that end, automation is nothing new. I mean,
you could go back to things like a water wheel, right,
I mean, going back in history, automation has always been
driving towards the future, and more in recent terms, something
like manufacturing where you can see automation. But it seems
to be the game changer here is AI and how
all of a sudden automation becomes a reality for industries
(10:27):
that previously you would have thought, well, this is always
going to have to have human involvement. One thing I
also found interesting is your focus on incorporating ethics at
every step of the way in integrating AI and integrating
autonomy into business as we look towards the future. And
I bring that up because with autonomy, with automation, certainly
(10:52):
in the manufacturing realm. We know how this had a
trickle down effect on changing people's lives, changing demographics of cities,
this nature. How important is it to be thinking about
the knock on effects of integration of AI and of
automation into these other industries to learn from the pitfalls
of implementing automation at previous times in our past.
Speaker 1 (11:15):
I think it's very important, and I do want to
see governments getting together and putting some guardrails around what
can be done, what can't be done, how humans can
be treated, and what shouldn't happen. But I just I'm
really worried that we'll just jump into it, and I
think we do need to think about the consequences of
(11:38):
it and put some guardrails in place for that.
Speaker 2 (11:42):
Yeah, there's a couple different ways we could go with this,
and actually, kind of to your point just a moment ago,
I'm curious who bears the burden, who bears the responsibility
for setting those guardrails. Can business leaders be counted on
to implement this the right way or does this need
to have, as you say, some kind of of government involvement,
And then how do you standardize that if it is
(12:04):
from the government side. It's very difficult to get international
governments to agree on much of anything, So why can
we expect that they would be able to be able
to agree on these guardrails here and implement them in
a way that is universally for the best.
Speaker 1 (12:19):
Well, they have come together, and governments have come together
in other areas and agreed made agreements, you know, And
I think that needs to happen. And I believe there
is a AI safety conference into governmental conference planned to
happen in London in the autumn.
Speaker 3 (12:36):
So I think it is going to happen.
Speaker 1 (12:38):
But I just wish they were a bit quicker, you know,
the government's always a bit slow to catch up.
Speaker 2 (12:42):
Well, this is one of many conversations that stems from
what you've written about trying to forecast the future. You
say that you'd like to be a guide into this
automated future, and reading that, it struck me how difficult
of a task that would be. It makes me think
of say Arco Polo right leaving to go on his
journey to China.
Speaker 4 (13:02):
He might have some notion about what's going to await
him on the other.
Speaker 2 (13:05):
Side, but to truly be a guide, it's going to
be quite difficult if you don't quite know what the
destination is going to be. So what are the challenges
that you've encountered as you try to forecast the future
and help to nudge industry and government and the rest
of us, for that matter, the consumer in the right direction.
Speaker 3 (13:24):
Well, that's a very good question.
Speaker 1 (13:26):
I really had to sit and think about what does
it mean for the world, what does it mean for enterprises?
And I was very lucky because through my work I
got to know how automation technologies worked and what they
were capable of, and how AI work some what is
capable of.
Speaker 3 (13:46):
I had to do.
Speaker 1 (13:49):
Lots of interviews with enterprises, some of them didn't want
to be named, but I managed to get four really
good case studies into the book I give.
Speaker 2 (13:58):
We don't want to give away too much because we
want our audience to pick the book up and read
it for themselves. But would you be willing to share
at least one of the case studies what you were
able to glean from that, and then how that allowed
you to think about the direction things are headed?
Speaker 3 (14:13):
Yes, certainly.
Speaker 1 (14:14):
The one that I particularly like is about the deployment
of a chat bot at a local government and The
reason I like it is because it started small, so
it was to basically handle queries to do with one function.
I think it was the sort of local tax as
(14:36):
payments where they got the most queries, and it was just.
Speaker 3 (14:41):
So well done.
Speaker 1 (14:43):
Because there are an awful lot of chatbots that we
come across, don't we when we were in contact centers,
and they're completely useless. With this one, they had actually
worked really closely with the contact center team, so the
kind of questions, every type of question that they were
likely to be asked, they had prepared for and they'd
(15:03):
prepared the chatbot for. So it wasn't that kind of
dumb chatbot that fails after a couple of questions. It's
that it could actually cope with a lot. And then
they ran it, tested it among themselves, they asked their
partner organizations to test it, and they regularly check it
(15:24):
and get feedback from their customers. And as things changed,
particularly during the pandemic, when sometimes the government would hear
in the UK would announce changes to the way things
had to be done because of the pandemic, literally changing
things overnight, the staff could go and retrain that chatbot
to be able to answer questions from the next day onwards.
Speaker 2 (15:45):
Something that struck me in that response was your discussion
about the chatbot, and everybody knows that first reaction to
seeing the chatbot up pop up on the screen because
you've tried to use it, it just can't do what
a human can do. It got me thinking, how important
is it to implement new technology like this in a
(16:05):
way that is capable immediately so that we don't have
this instant perception of an aptitude like unfortunately I think
follows the chatbot around. I mean, it would take me
a little bit of time to be convinced that this
chatbot can actually do the job, because we've all encountered
so many that have struggled.
Speaker 1 (16:23):
That's a really sad story, isn't it, Because it's the
most visible part of AI, and then it's the one
that's often so badly deployed. It gives everything a really
bad name. But look at chat GPT that is effectively
a chatbot, and look at how successful that is and
the number and variety of topics it can handle is
(16:44):
just phenomenal. But within the context of business, you can
train your chatbots to do a lot better than what
we see typically what we experience.
Speaker 2 (16:54):
This podcast is associated with the in the Autonomous Challenge.
So our case study are these high speeds AI driven
autonomous race cars. And what we have found is to
have such a visible case study working on the edges
of what is expected from autonomous driving technology. This is
eye opening for the general public to say, Okay, well,
(17:17):
I'm never going to need an autonomous car that can
drive at one hundred and eighty miles an hour side
by side with another car. But if this technology can
do that, imagine what it could do in a road
car kind of scenario. So, with that in mind, how
important is it to have a high profile like a
chat GPT, a high profile case study that can then
(17:40):
sway the general public, grab their attention, show them what
it's capable of.
Speaker 1 (17:45):
I think it's incredibly important because it engages an awful
lot of people and it also influences the opinion form,
as you know, and so it's also so easily accessible
that almost makes it ubiquitous, you know, because it's there
(18:09):
and you know, people can use it whenever they wish to.
Occasionally there's a queue to get in if you're not subscribing,
but you know, it's incredibly powerful tool at your fingertips
and it just shows you because it's not just about
automation in this case, because you know it has some knowledge,
but you have to know exactly what task you at
(18:30):
the prompt to get into any deep level, but to
get you started on something.
Speaker 3 (18:35):
It's brilliant.
Speaker 1 (18:37):
And I think it's all about helping productivity, people being
able to do their work faster, people who are into
in the business.
Speaker 3 (18:45):
Of content creation. But you know, to draw.
Speaker 1 (18:48):
Similarities between the Indie Autonomous Car challenge and the rest
of the world and all their business applications of AI.
I think the lessons from the autonomous challenge is that
AI working really fast in real time, capturing loads of information,
(19:08):
processing and making decisions shows us that it's possible for
it to do more complicated things at work as well
in business as well. At the moment, there's a lot
of very simplistic use of AI, but in future I
expect there'll be many more complex processes that are helped
(19:29):
by AI as well.
Speaker 2 (19:31):
Any examples of those that you might see implemented here
in the short.
Speaker 1 (19:34):
Term, well, I think one example is probably another case
study in my book, which is it helping understand CT
scan images so actually understanding in this case, the case
study that I've done, it's about patients that present with
(19:55):
strokes at a hospital here near me. What happens is
that sometimes they present out of ours and there aren't
specialists available, They're only emergency doctors. And with the help
of AI, they can now actually diagnose they damaged onto
the brain much quicker, and they can actually engage a
(20:16):
specialist remotely as well, because the solution that they've deployed
not only helps diagnose what the damage is in the
CT scan, it's actually then transmitting the image to handheld
devices to the specialists, and the specialists can take over
very quickly, even though they might be remote.
Speaker 3 (20:34):
And that was during the pandemic. That was a huge,
huge bonus.
Speaker 2 (20:38):
Really interesting and amazing what that could mean for the
future of healthcare. Back to maybe your role when you're
going out and you're speaking to business leaders, you almost
have the role of an evangelist right going out and
preaching the gospel of automation and AI. And again back
to our example of the Indie autonomous challenge, especially in
(20:59):
automo of which and in motorsports even more specifically, it
is a maybe an industry that is slow to adapt.
Speaker 4 (21:08):
In some cases, there is a.
Speaker 2 (21:11):
Fraction of this segment of the population that is very
rooted in the history of this and struggle to see
what the benefits of something like this might be.
Speaker 4 (21:23):
But then they see it on the track.
Speaker 2 (21:25):
They see this example, this tangible example of what is possible,
and it's eye opening. So I'm curious about your experience
going out and trying to convince business leaders who might
be rooted in the way that has worked for decades,
perhaps even longer, and proving to them you know, actually, yes,
(21:45):
this has been successful, but times are changing, and this
is why you need to be looking towards the future.
Speaker 3 (21:50):
Yes, I think.
Speaker 1 (21:53):
I think you have to present it in a way
that resonates with the person you're presenting to, Otherwise it
will just be incredible amount of skepticism. And I think
you have to help them see the benefits of maybe
running a trial, maybe a proof of concept, and seeing
(22:14):
the benefits and then building from there that I've spoken
to some incredible business leaders who say they're not afraid
of failure, so they trial new technologies, fail fast, and
learn from it. So my job is really to inspire
decision makers to take those kinds of decisions.
Speaker 2 (22:34):
One other elements that you're involved in, and you seem
to have your your fingers in a lot of different pies,
but I think this one's very interesting, the AI Accelerator program.
Can you tell us about it, how it functions, and
who you're targeting with it.
Speaker 1 (22:48):
Yes, Unfortunately I've had to put that on hold, so
let me tell you the history of it please. I'm
a member of the British Institute for It, which is
called BCS for short, and BCS Women is the women's
group of that organization, and I was. I became chair
of it for a few years back in twenty sixteen seventeen,
(23:12):
and I started the AI Accelerator then, and my aim
was to get as many women into AI as possible,
because the more diversity there is, the better products we get.
And I was very aware that there weren't many women
in the field of AI. Unfortunately, I think it's changing anyway.
But so I started running these ad hockey events, basically
(23:37):
grabbing any good speaker I could find, and begging and
borrowing facilities from big companies like you know, Development systems,
so we could run workshops and we could run seminars
on you know what is AI on the science of it,
about what education opportunities were available and so on.
Speaker 4 (24:00):
Well, we'll be on the lookout for that, that's for sure.
Speaker 2 (24:03):
But something you said really got me thinking there about
this is AI. So it's an artificial intelligence, but ultimately
it is a human created artificial intelligence. So to your
point about diversity, if it is only one segment of
the population that is by and large writing this artificial intelligence,
it's going to be lacking the well roundedness that we
(24:25):
would expect from artificial intelligence, is there right?
Speaker 3 (24:29):
Yes.
Speaker 1 (24:30):
A lot of it is to do with the data
that it is used to train it. So if it's
not representative enough because you know, you can't use you
need huge volumes of data, but that it's got to
be a representative sample of the user group that we'll
end up using it in the future. For example, those
(24:50):
banking apps that identify your picture from your video and
match it to your say, passport or ID card, you know,
could be far worse at recognizing women and matching them
than men because if they might have been trained or
more men photos than women. So these are the kind
of issues that we would end up with, and that's
(25:12):
why I think we need diversity in the development teams.
Speaker 2 (25:16):
Let's close with this, then let's take a look into
the future. You seem very polish about what the implementation
of this technology is going to mean for industry and
for society as a whole. Where do you see the
biggest benefit coming and affecting the most people in the
next five to ten years. As we see an increased
(25:39):
level of autonomation in the workplace, as we start to
see the integration of the autonomous enterprise as you call it.
Speaker 1 (25:46):
Well, I think there'll be I don't think there'll be
one thing. I think there'll be several things. One is
that human augmentation that I was talking about, helping people
do things faster and better and getting more satisfaction out
of their job. I think that's definitely one of the benefits.
The other is I'm starting to see developments that are
(26:09):
to do with the environment and with sustainability, and my goodness,
don't we need to do something about that So that
you know, it's increasingly now being used to manage energy
consumption in heavy industries. It is being used to make
sure that renewable energy sources run longer and provide more
(26:32):
energy By just looking at weather patterns and being able
to predict when they can be used. So I hope
there'll be a lot of those kinds of AI for
good uses as well, and I believe I'm seeing the
beginning of that as well.
Speaker 2 (26:48):
Sarah Burnett is the author of The Autonomous Enterprise Powered
by AI. This has been a wide ranging and fascinating conversation.
We really appreciate your time and your insight.
Speaker 3 (26:58):
Thank you for asking me, Thank you very much.
Speaker 2 (27:05):
Thanks for joining us this week on the inside track.
That was Sarah Burnett, renowned technology industry analyst and author
of the book The Autonomous Enterprise Powered by AI, sharing
her insights into how soon we can expect advanced automation
to be running more and more aspects of our daily lives.
Next week, we'll continue exploring youth focused tech when we
(27:27):
speak with Andy Saba about his band of undergraduate engineering
students actively going head to head with pH D teams
in this year's Indie Autonomous Challenge. Thanks to the IE
ed C and the IAC for keeping us on the
inside track.
My aim was to get as many women into AI
as possible, because the more diversity there is, the better
products we get, and I was very aware that there
weren't many women in the field of AI. Unfortunately, I
think it's changing anyway.
Speaker 2 (00:19):
The world around us is changing faster than ever before.
Ideas once only imagined in science fiction are becoming a reality.
Throughout the course of our amazing twenty three episode season,
we'll speak to some of the greatest minds in robotics
and artificial intelligence to discuss the groundbreaking work that's fueling
it all. I'm your host Ryan Marine joined me and
my co host Paul Mitchell, the president of the Indie
(00:41):
Autonomous Challenge, and see why we call this the inside track.
While the super fast cars of the Indie Autonomous Challenge
certainly make headlines, the ultimate goal of the IAC is
to still find the best ways to practically apply that
cutting edge technology to our daily lives. This is the
idea that Sarah Burnett wrote about in her recent book,
(01:03):
The Autonomous Enterprise Powered by AI. Sarah, our guest this week,
is a renowned technology industry analyst and intelligent automation specialist,
and the current chief Technology evangelist at KYP dot Ai.
She joins us to preach the AI gospel and explain
how most industries are utilizing some form of it, even
(01:25):
if they don't realize it. Even the classic customer service
chatbot was fortified with AI during the pandemic to share
rapidly changing health information updates via mobile app. Automation, Sarah says,
is already changing the world for the better. So glad
(01:46):
to have you here on the inside track, and I'd
love to chat with you a little bit about that
book and the AI role in industry as a whole.
So let's start with the book without giving too much away,
of course, because we want folks to go out and
pick it up and read it for themselves. But what
is the general premise of what you put down on paper.
Speaker 1 (02:06):
Well, first of all, thank you very much for inviting me.
The book is about the premise of it is the
autonomous enterprise, and that is an enterprise that those most
of its work either automated or digitally. Anyway, in terms
of technology, this is already possible. In fact, there are
(02:27):
companies that I would categorize as autonomous in existence today.
These are companies that you can deal with and the
processes that they go through to provide you a service
or a product barely ever goes through a human because
they've got back office integration. They've got some robots doing
(02:49):
some of the work. And in my role as an
industry analyst working very closely with leading edge technologies, I
can see that more and more organizations are heading that way.
And the question is what does that mean. As we
are on this journey of more and more enterprises becoming autonomous,
(03:12):
what does that mean for the future of work? How
will work be completed? What role will humans play? So
I address a lot of these in the book. And
then there's the bigger picture, because it's not just about automation. Firstly,
because they've highly digitalized, they have an awful lot of
(03:33):
data and they can tap that data to innovate, to
do things faster and better as well.
Speaker 3 (03:40):
And then having become extremely.
Speaker 1 (03:44):
Innovative, they basically pull away from the pack. They are differentiated.
There is this opportunity to mix and match technologies to
create new things. So it's about a whole bigger world
at their world of opportunities, and I explore all of
these in the book.
Speaker 4 (04:05):
How and when did the premise of this book occur
to you.
Speaker 1 (04:09):
Well, for many years now, I've worked as an industry analyst,
and in that role, I work with leading edge technologies,
and I see what they're doing, what they're bringing. They're
bringing new versions of their products, each better than the
previous version, with more capabilities.
Speaker 3 (04:29):
And so it was very clear to me.
Speaker 1 (04:34):
It has been very clear to me for years now
that we are on this journey towards autonomous enterprises, and
I wanted, you know, to speak about it and tell
this story.
Speaker 2 (04:43):
We'll talk more about the journey and where things are headed,
but I do want to take a brief step backwards
as well and learn a bit more about you. How
you got interested in this topic. Was there's something that
you set out to learn more about actively or is
it something that you stumbled upon over the course of
your work.
Speaker 1 (05:01):
Well, so, I've always been a sort of science and
computer kind of person.
Speaker 3 (05:09):
I studied science.
Speaker 1 (05:10):
At university and then that allowed me to get into
computing and many in many roles, I was working in
some form of automation, and then later I moved into
e government, which was all about enabling you know, on
the fly, real time services provided by local authorities. One
(05:34):
of my most recent roles as an industry analyst was
setting up the intelligent Automation practice for Everest Group, which
is an industry analyst firm, And so one way or another,
I got to know an awful lot about automation technologies.
And now I have a portfolio of jobs and one
(05:54):
of them is being chief technology evangelists for a process
intelligence company called k Ai.
Speaker 2 (06:01):
You mentioned earlier that there are already some industries that
are operating in some fashion as an autonomous enterprise. What
are some of these early adopters and what can we
learn from their experience at the forefront, at the cutting
edge that might apply to other industries that maybe initially
you wouldn't think that this could apply quite so readily.
Speaker 3 (06:22):
Yeah, so I would say they are.
Speaker 1 (06:27):
You know, there are industries that are using it extensively
and the most the one that comes to mind the
most is e commerce. Of course, you know, we're all
used to ordering something having it delivered very quickly. Behind
the scenes, there's an awful lot of automation going on,
you know, from whether a robot picks something up from
(06:48):
a shelf as you order that product and takes it
to a station where it gets packed, packed and dispatched
to all the back office functions like invoices and status
of your order, these kinds of things being sent to
you and then for the company itself. Behind the scenes,
you'll find that they are checking stocks, they are then
(07:11):
seeing if they need to order more of that product.
So a huge amount of work is going on behind
the scenes. So and a lot of those processes are
today automated, but it's sort of gone way beyond e commerce.
You know, if you look at insurance companies, there are
insurance companies that are now wholly automated. From the moment
(07:34):
that you ask for a quote, your risks are assessed
by AI and then the offer is made and then
the onboarding process. The same with banking. In fact, a
friend of mine opened a new bank account while on
a taxi.
Speaker 3 (07:49):
Right in a black cab in London.
Speaker 1 (07:52):
All he had to do was, you know, take a
picture of his passport photo ID, you know, post it
via the app and then show himself on the camera
and you know, he was approved.
Speaker 3 (08:03):
So this is the pace of change.
Speaker 1 (08:07):
This is the pace of business today, enabled by automation
by AI.
Speaker 3 (08:13):
And you know, a few years ago I was what I.
Speaker 1 (08:16):
Talked to South African Bank that had changed its customer
on boarding process from something that took fifteen to sixteen
days to fifteen to sixteen minutes.
Speaker 4 (08:27):
It's a really eye opening example.
Speaker 2 (08:30):
Actually, So when you're making the rounds as a public
speaker like you do, when you can boil it down
to someone in such simple terms, something so relatable to
opening a bank account, we've all done that, to writing
it a taxi, we've all done that. Does that help
to demonstrate just how game changing this technology, this marriage
between automation and AI is going to be in the
(08:52):
near future.
Speaker 1 (08:53):
Absolutely absolutely, And I go back to the point that
I was making earlier. It's the common nation of technology.
So before the pandemic, I visited a doctor's app provider,
and you know, you could have the app on your
phone and no matter where you were in the world,
you could then arrange to have a consultation with a doctor.
(09:16):
And behind the scenes, it was also capturing the knowledge
from you know, the symptoms to the treatment and what worked,
what didn't work, of course anonymized without sort of damaging
patient privacy. And of course the pandemic then made it
a kind of you know, go to device, Otherwise many
(09:37):
of us would have never had any doctor's consultations in lockdown,
so it.
Speaker 3 (09:41):
Was a huge help.
Speaker 1 (09:44):
And you know, now it's quite commonplace. A lot of
healthcare insurance companies offer it as an alternative to any
other kind of arrangements that you might have.
Speaker 3 (09:55):
But back then it was.
Speaker 1 (09:56):
Really new, So you know, it was again this commu
of technologies that enables that kind of innovation.
Speaker 2 (10:03):
And to that end, automation is nothing new. I mean,
you could go back to things like a water wheel, right,
I mean, going back in history, automation has always been
driving towards the future, and more in recent terms, something
like manufacturing where you can see automation. But it seems
to be the game changer here is AI and how
all of a sudden automation becomes a reality for industries
(10:27):
that previously you would have thought, well, this is always
going to have to have human involvement. One thing I
also found interesting is your focus on incorporating ethics at
every step of the way in integrating AI and integrating
autonomy into business as we look towards the future. And
I bring that up because with autonomy, with automation, certainly
(10:52):
in the manufacturing realm. We know how this had a
trickle down effect on changing people's lives, changing demographics of cities,
this nature. How important is it to be thinking about
the knock on effects of integration of AI and of
automation into these other industries to learn from the pitfalls
of implementing automation at previous times in our past.
Speaker 1 (11:15):
I think it's very important, and I do want to
see governments getting together and putting some guardrails around what
can be done, what can't be done, how humans can
be treated, and what shouldn't happen. But I just I'm
really worried that we'll just jump into it, and I
think we do need to think about the consequences of
(11:38):
it and put some guardrails in place for that.
Speaker 2 (11:42):
Yeah, there's a couple different ways we could go with this,
and actually, kind of to your point just a moment ago,
I'm curious who bears the burden, who bears the responsibility
for setting those guardrails. Can business leaders be counted on
to implement this the right way or does this need
to have, as you say, some kind of of government involvement,
And then how do you standardize that if it is
(12:04):
from the government side. It's very difficult to get international
governments to agree on much of anything, So why can
we expect that they would be able to be able
to agree on these guardrails here and implement them in
a way that is universally for the best.
Speaker 1 (12:19):
Well, they have come together, and governments have come together
in other areas and agreed made agreements, you know, And
I think that needs to happen. And I believe there
is a AI safety conference into governmental conference planned to
happen in London in the autumn.
Speaker 3 (12:36):
So I think it is going to happen.
Speaker 1 (12:38):
But I just wish they were a bit quicker, you know,
the government's always a bit slow to catch up.
Speaker 2 (12:42):
Well, this is one of many conversations that stems from
what you've written about trying to forecast the future. You
say that you'd like to be a guide into this
automated future, and reading that, it struck me how difficult
of a task that would be. It makes me think
of say Arco Polo right leaving to go on his
journey to China.
Speaker 4 (13:02):
He might have some notion about what's going to await
him on the other.
Speaker 2 (13:05):
Side, but to truly be a guide, it's going to
be quite difficult if you don't quite know what the
destination is going to be. So what are the challenges
that you've encountered as you try to forecast the future
and help to nudge industry and government and the rest
of us, for that matter, the consumer in the right direction.
Speaker 3 (13:24):
Well, that's a very good question.
Speaker 1 (13:26):
I really had to sit and think about what does
it mean for the world, what does it mean for enterprises?
And I was very lucky because through my work I
got to know how automation technologies worked and what they
were capable of, and how AI work some what is
capable of.
Speaker 3 (13:46):
I had to do.
Speaker 1 (13:49):
Lots of interviews with enterprises, some of them didn't want
to be named, but I managed to get four really
good case studies into the book I give.
Speaker 2 (13:58):
We don't want to give away too much because we
want our audience to pick the book up and read
it for themselves. But would you be willing to share
at least one of the case studies what you were
able to glean from that, and then how that allowed
you to think about the direction things are headed?
Speaker 3 (14:13):
Yes, certainly.
Speaker 1 (14:14):
The one that I particularly like is about the deployment
of a chat bot at a local government and The
reason I like it is because it started small, so
it was to basically handle queries to do with one function.
I think it was the sort of local tax as
(14:36):
payments where they got the most queries, and it was just.
Speaker 3 (14:41):
So well done.
Speaker 1 (14:43):
Because there are an awful lot of chatbots that we
come across, don't we when we were in contact centers,
and they're completely useless. With this one, they had actually
worked really closely with the contact center team, so the
kind of questions, every type of question that they were
likely to be asked, they had prepared for and they'd
(15:03):
prepared the chatbot for. So it wasn't that kind of
dumb chatbot that fails after a couple of questions. It's
that it could actually cope with a lot. And then
they ran it, tested it among themselves, they asked their
partner organizations to test it, and they regularly check it
(15:24):
and get feedback from their customers. And as things changed,
particularly during the pandemic, when sometimes the government would hear
in the UK would announce changes to the way things
had to be done because of the pandemic, literally changing
things overnight, the staff could go and retrain that chatbot
to be able to answer questions from the next day onwards.
Speaker 2 (15:45):
Something that struck me in that response was your discussion
about the chatbot, and everybody knows that first reaction to
seeing the chatbot up pop up on the screen because
you've tried to use it, it just can't do what
a human can do. It got me thinking, how important
is it to implement new technology like this in a
(16:05):
way that is capable immediately so that we don't have
this instant perception of an aptitude like unfortunately I think
follows the chatbot around. I mean, it would take me
a little bit of time to be convinced that this
chatbot can actually do the job, because we've all encountered
so many that have struggled.
Speaker 1 (16:23):
That's a really sad story, isn't it, Because it's the
most visible part of AI, and then it's the one
that's often so badly deployed. It gives everything a really
bad name. But look at chat GPT that is effectively
a chatbot, and look at how successful that is and
the number and variety of topics it can handle is
(16:44):
just phenomenal. But within the context of business, you can
train your chatbots to do a lot better than what
we see typically what we experience.
Speaker 2 (16:54):
This podcast is associated with the in the Autonomous Challenge.
So our case study are these high speeds AI driven
autonomous race cars. And what we have found is to
have such a visible case study working on the edges
of what is expected from autonomous driving technology. This is
eye opening for the general public to say, Okay, well,
(17:17):
I'm never going to need an autonomous car that can
drive at one hundred and eighty miles an hour side
by side with another car. But if this technology can
do that, imagine what it could do in a road
car kind of scenario. So, with that in mind, how
important is it to have a high profile like a
chat GPT, a high profile case study that can then
(17:40):
sway the general public, grab their attention, show them what
it's capable of.
Speaker 1 (17:45):
I think it's incredibly important because it engages an awful
lot of people and it also influences the opinion form,
as you know, and so it's also so easily accessible
that almost makes it ubiquitous, you know, because it's there
(18:09):
and you know, people can use it whenever they wish to.
Occasionally there's a queue to get in if you're not subscribing,
but you know, it's incredibly powerful tool at your fingertips
and it just shows you because it's not just about
automation in this case, because you know it has some knowledge,
but you have to know exactly what task you at
(18:30):
the prompt to get into any deep level, but to
get you started on something.
Speaker 3 (18:35):
It's brilliant.
Speaker 1 (18:37):
And I think it's all about helping productivity, people being
able to do their work faster, people who are into
in the business.
Speaker 3 (18:45):
Of content creation. But you know, to draw.
Speaker 1 (18:48):
Similarities between the Indie Autonomous Car challenge and the rest
of the world and all their business applications of AI.
I think the lessons from the autonomous challenge is that
AI working really fast in real time, capturing loads of information,
(19:08):
processing and making decisions shows us that it's possible for
it to do more complicated things at work as well
in business as well. At the moment, there's a lot
of very simplistic use of AI, but in future I
expect there'll be many more complex processes that are helped
(19:29):
by AI as well.
Speaker 2 (19:31):
Any examples of those that you might see implemented here
in the short.
Speaker 1 (19:34):
Term, well, I think one example is probably another case
study in my book, which is it helping understand CT
scan images so actually understanding in this case, the case
study that I've done, it's about patients that present with
(19:55):
strokes at a hospital here near me. What happens is
that sometimes they present out of ours and there aren't
specialists available, They're only emergency doctors. And with the help
of AI, they can now actually diagnose they damaged onto
the brain much quicker, and they can actually engage a
(20:16):
specialist remotely as well, because the solution that they've deployed
not only helps diagnose what the damage is in the
CT scan, it's actually then transmitting the image to handheld
devices to the specialists, and the specialists can take over
very quickly, even though they might be remote.
Speaker 3 (20:34):
And that was during the pandemic. That was a huge,
huge bonus.
Speaker 2 (20:38):
Really interesting and amazing what that could mean for the
future of healthcare. Back to maybe your role when you're
going out and you're speaking to business leaders, you almost
have the role of an evangelist right going out and
preaching the gospel of automation and AI. And again back
to our example of the Indie autonomous challenge, especially in
(20:59):
automo of which and in motorsports even more specifically, it
is a maybe an industry that is slow to adapt.
Speaker 4 (21:08):
In some cases, there is a.
Speaker 2 (21:11):
Fraction of this segment of the population that is very
rooted in the history of this and struggle to see
what the benefits of something like this might be.
Speaker 4 (21:23):
But then they see it on the track.
Speaker 2 (21:25):
They see this example, this tangible example of what is possible,
and it's eye opening. So I'm curious about your experience
going out and trying to convince business leaders who might
be rooted in the way that has worked for decades,
perhaps even longer, and proving to them you know, actually, yes,
(21:45):
this has been successful, but times are changing, and this
is why you need to be looking towards the future.
Speaker 3 (21:50):
Yes, I think.
Speaker 1 (21:53):
I think you have to present it in a way
that resonates with the person you're presenting to, Otherwise it
will just be incredible amount of skepticism. And I think
you have to help them see the benefits of maybe
running a trial, maybe a proof of concept, and seeing
(22:14):
the benefits and then building from there that I've spoken
to some incredible business leaders who say they're not afraid
of failure, so they trial new technologies, fail fast, and
learn from it. So my job is really to inspire
decision makers to take those kinds of decisions.
Speaker 2 (22:34):
One other elements that you're involved in, and you seem
to have your your fingers in a lot of different pies,
but I think this one's very interesting, the AI Accelerator program.
Can you tell us about it, how it functions, and
who you're targeting with it.
Speaker 1 (22:48):
Yes, Unfortunately I've had to put that on hold, so
let me tell you the history of it please. I'm
a member of the British Institute for It, which is
called BCS for short, and BCS Women is the women's
group of that organization, and I was. I became chair
of it for a few years back in twenty sixteen seventeen,
(23:12):
and I started the AI Accelerator then, and my aim
was to get as many women into AI as possible,
because the more diversity there is, the better products we get.
And I was very aware that there weren't many women
in the field of AI. Unfortunately, I think it's changing anyway.
But so I started running these ad hockey events, basically
(23:37):
grabbing any good speaker I could find, and begging and
borrowing facilities from big companies like you know, Development systems,
so we could run workshops and we could run seminars
on you know what is AI on the science of it,
about what education opportunities were available and so on.
Speaker 4 (24:00):
Well, we'll be on the lookout for that, that's for sure.
Speaker 2 (24:03):
But something you said really got me thinking there about
this is AI. So it's an artificial intelligence, but ultimately
it is a human created artificial intelligence. So to your
point about diversity, if it is only one segment of
the population that is by and large writing this artificial intelligence,
it's going to be lacking the well roundedness that we
(24:25):
would expect from artificial intelligence, is there right?
Speaker 3 (24:29):
Yes.
Speaker 1 (24:30):
A lot of it is to do with the data
that it is used to train it. So if it's
not representative enough because you know, you can't use you
need huge volumes of data, but that it's got to
be a representative sample of the user group that we'll
end up using it in the future. For example, those
(24:50):
banking apps that identify your picture from your video and
match it to your say, passport or ID card, you know,
could be far worse at recognizing women and matching them
than men because if they might have been trained or
more men photos than women. So these are the kind
of issues that we would end up with, and that's
(25:12):
why I think we need diversity in the development teams.
Speaker 2 (25:16):
Let's close with this, then let's take a look into
the future. You seem very polish about what the implementation
of this technology is going to mean for industry and
for society as a whole. Where do you see the
biggest benefit coming and affecting the most people in the
next five to ten years. As we see an increased
(25:39):
level of autonomation in the workplace, as we start to
see the integration of the autonomous enterprise as you call it.
Speaker 1 (25:46):
Well, I think there'll be I don't think there'll be
one thing. I think there'll be several things. One is
that human augmentation that I was talking about, helping people
do things faster and better and getting more satisfaction out
of their job. I think that's definitely one of the benefits.
The other is I'm starting to see developments that are
(26:09):
to do with the environment and with sustainability, and my goodness,
don't we need to do something about that So that
you know, it's increasingly now being used to manage energy
consumption in heavy industries. It is being used to make
sure that renewable energy sources run longer and provide more
(26:32):
energy By just looking at weather patterns and being able
to predict when they can be used. So I hope
there'll be a lot of those kinds of AI for
good uses as well, and I believe I'm seeing the
beginning of that as well.
Speaker 2 (26:48):
Sarah Burnett is the author of The Autonomous Enterprise Powered
by AI. This has been a wide ranging and fascinating conversation.
We really appreciate your time and your insight.
Speaker 3 (26:58):
Thank you for asking me, Thank you very much.
Speaker 2 (27:05):
Thanks for joining us this week on the inside track.
That was Sarah Burnett, renowned technology industry analyst and author
of the book The Autonomous Enterprise Powered by AI, sharing
her insights into how soon we can expect advanced automation
to be running more and more aspects of our daily lives.
Next week, we'll continue exploring youth focused tech when we
(27:27):
speak with Andy Saba about his band of undergraduate engineering
students actively going head to head with pH D teams
in this year's Indie Autonomous Challenge. Thanks to the IE
ed C and the IAC for keeping us on the
inside track.
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