July 5, 2025 • 49 mins
Jim Love hosts Krish Banerjee, Canadian Managing Director at Accenture for AI and Data, in a discussion that spans the rapid evolution of AI, enterprise adoption, and the interplay between data and innovation. They tackle the transformation of industry practices, the growing role of AI in everyday life, and the significance of responsible AI development. Krish emphasizes the need for focusing on tangible value and the transformation of existing processes through AI, while touching on the future implications for Canada’s digital sovereignty and productivity advances.
00:00 Introduction and Guest Welcome
01:06 AI Evolution and Market Shifts
02:31 Data's Crucial Role in AI
04:53 Enterprise AI: Challenges and Opportunities
13:28 Global AI Landscape and Canada's Position
24:10 Innovative AI Projects and Passionate Pursuits
25:59 Reinventing Healthcare with AI
26:52 Commercializing AI for Canadian Businesses
28:41 The Responsibility of AI Development
29:13 Economic Impact and Future Predictions of AI
33:42 Agentic AI: The Next Frontier
39:24 Democratization and Open Source AI
41:34 Advice for Executives on AI Adoption
45:23 Encouraging AI Learning in the Next Generation
47:20 Final Thoughts and Future Conversations
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:04):
Welcome to hashtag Trending in the summer.
My guest today is K Banerjee.
He's a thought leader,a technology leader.
He's the Canadian managing directorat Accenture for AI and data , he
leads a team of over 500 professionalswho deliver solutions for clients
across industries across the country.
(00:26):
And did I mention he's alsoan incredibly nice guy and I'm
glad to have him on the show.
Krish was last on thepodcast in March, 2024.
That was amazing for two reasons.
One, there's still a podcast.
I forgot to retire.
But I didn't even have to rememberthat I went, when was Krish here?
And I went, I can ask AI that.
(00:47):
So I asked PerplexityI've outsourced my memory.
March, 2024 went, wow.
That in AI years.
That's decades.
So Krish, welcome Jim.
Thank you.
And always a pleasure to be withyou and your podcast, and thoroughly
enjoyed our last conversation.
Looking forward to this one.
Yeah.
So what's been happeningrecently since we'd last talked?
(01:08):
Nothing much, right?
Not very exciting.
There's nothing going on.
As you said, in in AI times or AIyears, it's probably probably just
a few few blips, if you will, interms of, the micro kind of units
of time that you were measuring.
But the change has beensignificant, obviously.
The market has shifted quite a bitin terms of how we have seen new
(01:32):
opportunities exist and emerge.
And I'm not going to shareanything new for people who knows
about Agent Tech now, which.
We were not talking as much whenwe met last time, but now there
is no conversation that can happenwithout talking about Agent Tech
So we will obviously talk about that.
But I, I am also seeing an uptickin terms of how our Canadian
(01:55):
organizations and community isactually moving towards adopting.
There's quite a bit of workstill needs to happen, but there
is an intent is very inspiring.
Yeah, and I was being facetious.
It a year is like adecade in AI right now.
There's been so much happening, butthe one place where I think you were
prescient was you've always kept dataand AI together in, in our conversation
(02:20):
last time, I think you, you reallyconsidered that and I think that's.
Somebody woke up the rest of the world,woke up to that at one point, said,
wait a minute, this is important.
Yeah, exactly.
Exactly.
And I think the most interestingquote that I heard on that topic
is that we have everything readyfor AI except for the data.
Everything course is ready toactually launch into AI and get
(02:45):
their hands on AI, on the platformsor the agents and all the.
Human beings except for data.
So I think we are turning towards data nowto give its right full share of attention.
And I can see there's already quite a bitof work happening in the space of whether
it's privacy or whether it's integration,whether we are actually building some data
(03:07):
products which are specific to industries.
I can see that the next, few decades,which will be few months in this case.
Yeah.
Will be spent on focusing on data.
Yeah.
I was watching it because we startedto talk about access to data.
And I come out of my, my, I'm an old guy.
(03:29):
I'm from the old data warehouse dayswhen, remember when you, if you had a,
somebody sent you a query or somethingthey wanted done, I had a three month
project to reorganize the data and pullit all in and do all that sort of stuff.
Now we're talking about.
Conversations with data warehouses.
And I, we may not totally be thereyet, but we're, this is gonna, this
(03:49):
is gonna be something that comesthat's gotta be fairly exciting
from your whole background.
It is very exciting and weat Accenture have always been
focused on data and making surethat the fundamentals of it are.
Are secured and then everything ontop of it, whether, to your point,
at some point it was reportingand business intelligence, which
(04:10):
was an extremely important aspectof what we can build on data.
From there, we went on to analytics andusing some of the models and algorithms
and how we can drive value out of it.
And then from that now to ai.
But we have always been true to thefundamentals and the core of it.
And that is coming back again.
Like we have seen the shift of how thingsmove towards more of a shiny object at
(04:34):
the time which has changed over differenterrors of technology advancements, whether
it's reporting, whether it's analytics,whether it's now ai, but the core
fundamentals of what you need to build.
True insights out of remains thesame, which is in this case is data.
Yeah.
But your practice covers, mostof Canada's large enterprises.
(04:57):
So you are, you're seeing thisfrom the enterprise version of
AI, which is an exciting piece.
My feeling is that we're finally grapplingwith that, and that is, I think people
were dipping their toe in the water.
They were doing things, but we'renow starting to confront the, I. I
wouldn't wanna call 'em the issues,but the things that you have to
(05:19):
put in place to have enterprise AI.
Do you have the same sense?
Yeah I think enterprise AI isan interesting topic, right?
And we have few examples of that.
I would say in Canada we are probablywhere there is an opportunity,
there's a lot of, pockets of ai.
There's a lot of like functional AIthat is happening in very specific
(05:41):
areas, whether it's HR, whether it'sfinance, whether it's in areas of
performance management or whetherit's in areas of supply chain.
But true enterprise AI has.
Has been the area of interestfor us, which is where you can
drive value, which is where youcan actually drive reinvention.
There's quite a bit of workhappening globally on that.
(06:04):
However, I do believe that in Canadawe, we are a little behind when it
comes to driving true productivityand value and generating value
out of a true enterprise AI.
So not sure if I answered your question,Jim but that's how I'm looking at it.
But.
Double click on that.
What exactly you wanted to know about it?
No, I think that's, I think that's fair.
I think your perspective is that, we'vebeen building sort of point solutions.
(06:27):
And now bringing thattogether is the next task.
I think.
I totally agree with you.
I think I. The world is further ahead ofus in many cases, and we need to fix that.
I think that's an important thing tofix, but it, but I'm starting to see
the discussions take on a new maturity.
When you start to talk aboutwhat are we gonna do about data?
What are we gonna do about security?
(06:48):
And that, those are the pointsthat, that need the foundation
points because we've been.
I don't think that's unfair.
I think we always do this in technology.
We're led by the bright, shinyobject and, and we're led by,
by the promise of something new.
We always say you should buildthe data and the security in as
you're going, but we never do that.
(07:08):
We always get.
For far advance say, we bettergo back and get control of this.
And I think that's the place I see us.
I see not just Canada butacross the entire world.
We start to see people grapplingwith saying this is serious.
It's enterprise level.
We need to figure out howwe're going to run it now.
It can't be just somethingthe employees bring in.
(07:28):
Yeah.
And I think we have spent thelast two plus years proving that.
This is real.
This is what I try to explain toa number of my clients and friends
here is that I don't think AIneeds to prove itself anymore.
Like it has done its jobof proving that it works.
And it's real.
It's really for us to make sure thatwe are engaging that capability and
(07:51):
power to the right use of whether it'sa, country or community or a specific.
Organization.
That's where our job is, like AI hasdone its job in the past couple of years.
Yeah.
And I think o in particularly in thepast three to six months, I think
we've hit some levels where we can saythis, we no longer talk about if we're
(08:12):
talking about when, and I think that'sa, that's an important distinction
we make, but also I think, and I've.
And I've been happy tonot have the conversation.
Every conversation I had about AI startedwith when are we gonna get to AGI?
When are we gonna get to,when is it gonna be Sentient?
And I'm going, you know somethingguys, you got enough tools right now
in the toolkit to do astonishing thingslike you, AGI, nice conversation.
(08:39):
Let's have a beer and talk aboutthat on the on, on the dock.
But let's get to workwith the tools we have.
Do you get the same sense thatwe're not really using everything
that we've got available for us?
For us?
Yeah, for sure.
I think back to the conversation we werejust having that there are definitely very
pockets of AI and pockets of brilliancethat are happening, but there's also a
(09:01):
lot of opportunity that are like whitespace for us where if we stitch the
things that are happening, whether youconnect the dots between, opportunities
that exist across your enterprise,there is an opportunity to multiplex the
value that you are already unlocking.
So a hundred percent.
There is things that are right nowavailable that can unlock two x
(09:25):
three x of opportunity value savingsfor any organization, whether
it's public, private, federal,commercial, whichever flavor of it is.
And then AGI is an interesting concept,but AGI to me is also relative.
If you roll back 10 years,what we are doing today could
have been considered as AGI.
(09:47):
So the boundary of AGI is alsowhat we keep shifting, right?
And I think that's like almost chasinga target that keeps changing on us.
So let's just focus on the currentand think about the possibilities
that we will work towards.
But not cheese.
Something that almost likeragio or chasing in the dessert.
(10:07):
Yeah, it's like we keepmoving the goalposts.
I don't know how, I don't know, you'renot as old as me, but there was a show in
the 1980s I think, where this guy had atalking car and it would drive itself and
it had a great voice and would chat withyou and give you all this information.
We could do that now.
Exactly.
(10:28):
Yeah.
And you and that's so we are living inour fantasies from 20 years ago, but
we're sitting there going, that all youcan do, make a talking car, exactly.
I grew up watching and animationat that time called the Johnny
Soko and his flying robot.
Oh yeah.
And it it used to have a watch whereyou can talk to, that was one of
(10:50):
the most fascinating thing at thattime, mean that's Apple Watch or
any other smart watch at this point.
I think that's anexample of how AGI is in.
Is in target that we set up for ourselvesto achieve something and then we say,
okay, now I'm moving the boundary.
Another, 20 years further because thenyou can actually have more brilliance in
(11:12):
terms of human thinking and innovation,which may explain this story we did a
couple days ago and we picked it up.
It was, I think it was a TDBank, did a survey and said that
Canadians gave themselves a C attheir understanding and use of AI.
And I found that astonishingbecause even today I don't think
(11:35):
people realize how much they'redoing with AI and don't realize it.
Yeah.
There's al already a lot of AI evenI. In our lives whether we realize
it, whether we acknowledge it.
There's already a lot of AI in ourlives and I can only see that we'll
become increasingly more embeddedand more blended into our lives.
(11:57):
But there is an aspect of beingdeliberate, and I think that's the
part that we need more tension.
Like how can I be more deliberateabout unlocking things that are not
just happening to me, whether it'sin my world, whether it's in how.
How I'm looking for information.
How am I searching information?
How am I working on mynext vacation planning?
(12:20):
I think we will be working on thingslike Airbnb and Expedia and others
that are building AI, which is somebodyelse is doing AI to me, versus I am
going out and seeking the power anduse of AI for my own productivity.
I think that's to me is the kind ofthe bottom up difference that we need
(12:42):
to build as a community, as a society.
If we all can be deliberate aboutwhat everything, what we can do in
our lives, that can change even, a10, 2%, 5% productivity improvements
that will build up an snowballeffect of what it could mean for the.
For the country, for the community ina whole, because if you look at our
(13:05):
situation like Bank of Canada said,we are in a productivity emergency.
We are number 18 in OECD, whichis not a great place to be.
No.
And that productivity emergency is,will not change unless we become better
at investing in and using technology.
Jim Balsillie I've had him on theprogram, is passionate about this.
(13:28):
We need to become a hub and we'lltalk about Canada in a second.
I do want to get there, I just want togo back to this idea of, you're dealing
with enterprises and with their customers.
I. This is maybe just my perspective andI'm close to 70 man I'm not supposed to
be on the bleeding edge of this stuff.
I am so impatient with companiesthat give me the old interfaces and
(13:52):
already I want nothing to do with it.
Maybe that's becoming a cranky old guy,but if I can't just type in, tell me
how I do this, and it comes back withthe answer, I'm not, I just move on.
And so I think if, I think we maythink we're not as advanced in AI,
but I suspect that we're impatientlywaiting for what it can actually do.
(14:16):
I agree.
And I was watching my 13-year-olddaughter the other day trying to find
some information, and I think the defaultnow is to go to publicly available.
Copilot, Gemini or any of those platformsinstead of going to the actual search.
So if you go by that shiftthere itself, they're growing
(14:39):
up as an AI native generation.
Yeah.
And I think that's, that's something that.
Customers will start to demand.
I'm sorry, but I'm immensely criticalof government interfaces for the
most part because I don't thinkthey really understand that people
who come to deal with governmentknow nothing about government.
We don't know the policies,we don't know the procedures.
(15:01):
We just want an answer and, and somany of the interfaces are set up.
To deal with and we had thisin corporate life a while ago.
I think it's changed a lot.
A lot of interfaces were set up sothat the corporation could manage
the transaction with, in otherwords, according to their rules.
Consumers rebelled againstthat and I think most.
(15:23):
Good or competitive.
Corporations have realized thatand have changed their attitude.
I don't think government has quitefigured that out yet, that when
citizens come, they don't, they're notinterested in dealing with the rules.
They're interested in getting an answer.
And I think that's a challengethat AI allows us to address.
I hope.
I think there's a lot of positive intentand momentum and conversations happening
(15:45):
if we collect everything around us from.
What we just announced at G7 towhat what we can expect out of the
appointment of our first AI minister.
There's a lot of good intent and Iwould like to take the optimism and the
positivity out of that and obviouslythere's a lot to be proven and a
(16:09):
lot to be opportunity to be walking.
The talk needs to happen.
Yep.
But there, there's some good signsof the first couple of steps of
the talk that needs to be walked.
I'm very optimistic.
I just hope we don't be, andI think things have changed.
And I don't think, I don't thinkit's a political statement.
I think you, if you looked at any,you talked to any of the leaders
(16:33):
in the, people have woken up andsaid, we have to be better at this.
And the productivity crisis may be thedriver, it may be the trade issues that
we have, but people are starting torealize we need to get a handle of this.
And I think the appointment of anEvan Solomon interesting appointment.
Do you remember Evan Solomonfrom when he was a broadcaster?
(16:53):
He's, I do.
I do.
Yeah.
And it's definitely interesting and Ithink it shows that AI is a topic that
is pretty, everywhere and it doesn't getlimited to people who might be academic
or might have in policy background ormight have a very specific background
but it's an interesting kind of messagethat I think I'm taking away from that.
(17:16):
Yeah.
Yeah.
And don't get me wrong, you can bein I am, I guess I am in the media.
You, there's some veryintelligent people in the media.
There you go.
Jim.
That's your next career path.
Maybe.
Maybe.
Yeah.
Yeah, I could get it.
So talk to me about this.
So what do you have you,I. Heard, heard much of.
I, I've been digging into, I was surprisedthere was already a great deal of activity
(17:39):
going on in the federal government interms of AI that, that was kept hidden, I
think, or maybe I just didn't notice it.
I think there is a lot of interestright now in the few specific areas,
obviously in the areas of service.
And I know that there'sconversations around how.
Citizen service could be improvedusing AI and using more front
(18:02):
office capabilities of AI.
There's also conversations aroundhow could we use AI for more
productive collaboration in business?
How could we reduce all the red tapethat we might have make business
easier for us to do whether it's for.
Individuals or from a businessto business relationship.
(18:25):
And there's also a conversation thatis publicly known now about what it
means from, in sovereign perspective.
And there's a lot of focus from a federalgovernment, which is I think is also
another good step in terms of what do, howshould Canada think about AI as a federal,
as a, so sorry, a sovereign topic.
(18:46):
Doesn't need to be sovereign by.
By physical kind of definition, likeobviously there's a physical definition
of sovereign, but I think there isan, to me there is a more broader,
probably an philosophical, thinkingabout sovereign it, it is a Canadian
thinking, Canadian priority Canadianinvestment and sovereign investment.
(19:08):
And then everything else works togetherin order to bring that to life.
Yeah.
And I don't think we're the only ones.
I think the world's woken up to thefact that we sat back and watched
the Americans develop AI and now theChinese have entered in a big way.
And I think a lot of countriesare starting to say, wait a
minute how does this fit withour operations, our sovereignty?
(19:33):
Can we sit on the sidelinesand just wait for.
America to develop everything for us.
What should we be doing?
I know Europe is go grappling with this.
I presume the same thing is happeningin India and definitely China
Canada's another player in there.
It's almost like the world woke cupsaid we can't be a spectator anymore.
Yeah, and you're right, likethere, there's different degrees
(19:56):
of interest and speed and.
An investment fromdifferent jurisdictions.
So some others are probably further along.
And it's also true that everyonehas a slightly different angle.
Some are taking more of ancompliance and regulation angle
versus some are taking more ofopen and free and innovation angle.
(20:17):
Some would be taking this morefrom an, let's use this as
our world dominance, an angle.
So we know those flavors are, withoutbeing more specific, but I think the
opportunity for me, for Canada is tothink sovereign for Canadian purpose.
And I. Objectives, right?
(20:39):
What do we need to do to help ourCanadian challenges that are in
front of us from innovation, fromeverything that is happening in
the whole geopolitical situation.
And how can we use that to our advantageand use that to come out at the other
end of this stronger and more resilient.
Yeah, and I think it's forced us.
(21:00):
To actually have a discussion aboutwhat digital sovereignty means.
We're actually gonna do a whole programon this because it's we've thought
of sovereignty as a physical thing.
Yeah.
And even with the whole digitaltransformation of our economies, we
never actually sat down and said, wait aminute, sovereignty's a digital thing too.
And we haven't we don't, we haven'tthought through what we even mean by that.
(21:24):
Yeah.
No, I think it's a great point.
And that.
What I was referring to that the defaultdirection of sovereignty is physical.
Let's have a Canadian data center.
Let's make sure that thedata stays in the country.
The network is Canadian, and how we canconnect to Canadian network on that.
However to your point, like there isa whole digital angle to that and how
(21:46):
we can use a digital and the nationaland the nation building side of it.
Like how we can use sovereign tobuild the nation and to come out to my
point of, all of these uncertaintiesthat are going around come out on
the other end of this stronger.
And I think there's a great opportunity,we've seen that during COVID.
There are countries, thereare organizations who came out
(22:09):
stronger at the other end of COVID.
And I almost feel like there's this.
There's a similar kind of a challenge,may not be as prominent at this point,
but definitely an opportunity ahead of us.
Yeah.
I honestly think it isprominent at this point.
I think people have started to realize,and part of it was people banging
on the drum about productivity and,productivity could be a sterile thing.
(22:32):
And I've, people have talked aboutthis output GDP , per capita,
and why does that really matter?
Why does it really matter?
Because if you don't create wealth,you've got nothing to distribute, and
wealth creation is going to change.
I, I. Maybe a whole notherprogram on this, but we will not
recognize our economy in 10 years.
(22:53):
And that's a short periodof time for an economy.
The, we talk about mining and oiland gas and the industries that
Canada's been classically goodat, these are 10 and 20 year.
Projects.
People talk about a pipeline.
A pipeline, if you get it approved intwo years is still a 10 year project.
(23:14):
Heck, a having a a new subwayline is a 10 year project.
But we're gonna have to move ata faster speed than that because
technology won't slow down for us.
So it's, I think we have gottento that realization, I hope.
I would like to believe so, and I'm seeingsome signs of it and I'm very po positive
and optimistic about what I'm seeing.
(23:36):
But I, I also believe that thereis a lot more that we could do
and to the conversations we werehaving in the earlier part of this
chat that we have the tools nowwe have the opportunity now with.
Proof of concepts and things that wehave done over the past two years.
We just need to now take a stepto say, I'm going to now try
(23:59):
and do this for a broader thanjust building a point solution.
Cause let me reinvent the entirevalue chain of my industry.
Let me think about how I can sell.
Consumer packaged goods to mycustomers very differently than what
I have done in the last 50 years.
I think that is a binary pointin my opinion, that where we are
(24:23):
in front of and what, let me askyou, just, let me just start with
one so we could have an example.
What's the project you'd most like to do?
If I came in and said,Krish, I got a budget.
Don't worry about that.
What's the project you'd most like to do?
It's a great question and Iwould say there's probably
two, two parts of the answer.
(24:43):
One is there's a passion side ofthe answer, which is I would like to
do something which is more tangiblefrom an I. Community perspective.
Like at Accenture, we doquite a bit of pro bono work.
We have done some of the work withuniversities and and mental health
organizations and others, and somethingaround that would be of interest.
So if we can use the power thatwe have been given with AI to the
(25:06):
cause of something better, likeAI for good would be an area of
interest and something around that.
And there could be many flavors of it,whether it's mental health, whether it's
human lives, whether it's tra transportand traffic, which is very predominant
problem for Toronto and my city.
Anything can do therewould be of interest.
(25:27):
The other answer would be like somethingaround the whole by Canada and for Canada
from a sovereign perspective, like if Ican be part of something that will build
a. Canada stronger and build us as theai destination, which we should be given
everything that we have from our lineageand history and bring back kind of our
(25:50):
legitimate and rightful supremacy in AI
I think that would be the other project.
Do you know the two projects I wanna do?
Yeah.
One and I won't get to play in this'cause I don't think I'm, I don't
think I'm really, I don't think I'mreally a full-time employee ever again
for the I'm keep trying to retire.
But the, but I'm just excited about, I'dlove to take AI and reinvent healthcare,
(26:16):
starting with outcomes, not processes.
And say, what are the outcomeswe wanna bring about and how
could we use AI for them?
My example is, I wanna follow mydoctor around in his office all
day, and I want to take all of theadministration stuff that he's got to
do and make that disappear so that allhe's got to do is talk to patients.
(26:42):
And that is doable.
That would excite the heck out me.
I think that's a great one.
And it's in my first bucket of somethingthat will touch human lives like this.
Yeah.
I think we agree on that.
One other one I'd love to do, andthis one I may actually participate
in 'cause we're, we started ourown institute, believe it or not
to do this commercialization of ai.
(27:03):
I just want to see.
Our efforts go towards commercializingai, not necessarily even to own it,
but just to say, let's push thesethings that will make productivity so
much greater for Canadian businesses.
And I, that, that's, I was, I would, Iused to teach at Waterloo and I was having
a discussion with another pro friend ofmine who's also retiring and looked at it
(27:27):
went, that's, I'd love to see that happen.
I'd just love to see the mechanismof taking that wonderful.
Enthusiasm and energy and knowledge thatwe have, and it's, there's tons of it.
And driving that to a small businessthat can now be much more profitable.
Those are my two.
No, I think that's great.
(27:47):
And the second one you mentioned isis really very interesting because
it's about how we are thinking aboutreinvention, like in our Accenture.
World and how we think about helpingour clients reinvent, whether
it's in the mid-market or in theenterprise level, the G two thousands.
There is an opportunity for everyoneto think about the reinvention and I
(28:10):
think what you're saying is how do I andcreate a path of value opportunity for
organizations to reinvent and build thatcommercial kind of pathway for them.
Yeah.
Yeah.
And it, and when that sparkstarts, it's exciting.
Yeah.
I remember in the early daysof it, that's how we felt.
(28:31):
We thought we were magicians, wewere building, no, we were building
these systems, barely holdingtogether with bailing wire, but we
were doing incredible things, yeah.
The, yeah.
So I think we, we talked about in thelast one, which is about, with great
power comes great responsibilities, right?
Yes, absolutely.
Mentioned this in thelast session as well.
So can lose sight of that, can lose sightof how we need to think about building
(28:56):
AI with with the right responsiblemindset and thinking and framework and,
we call it responsible ai, ethical ai.
There's all different names to it,but essentially to make sure that we
are doing it with the right intent.
Yeah.
Yeah.
And the right tools.
Speaking of responsibility, the, we'restarting to have the conversation
(29:18):
about the economy and where,and the changes in the economy.
I was interviewing someone today andwe were talking about the almost, it's
not a total disappearance, but entrylevel jobs are disappearing faster than.
Than kids can.
There, there are far fewer enterentry level jobs than there ever
were, and that's not just economics.
(29:41):
That's something that we'restarting to see the impact.
I don't think we fully I don't thinkwe fully imagined the impact of AI.
We know, I know ultimately, atone point or another, most of
what we can do can be automated.
That's, it's just a questionof how many years, decades,
weeks, months, whatever it is.
Prob but.
The change in the economy.
(30:01):
How do you see that?
What do you, what are you lookingforward at in, in terms of what
I think there will be few things.
If I have to hazard a predictionor a guess in terms of the
next maybe two to three years.
Because anything beyond that is,is almost unknown at this point.
Yeah.
So I wouldn't go beyond three years.
The first one is we will certainlysee a point where we will have to make
(30:25):
a decision as an individual, as anorganization which I was referring to
as that binary decision point wherewe have choices in front of us, which
is, am I going to be an AI leaderor am I going to be still following?
And we have seen from.
From the past, similar kind of itpivots or other moments that there
(30:50):
is a two to three X differencebetween those who will make the
choice of leading versus following.
So I think we are there, like that binarykind of big bang point is already there.
So the second one willbe about differentiation.
At some point, AI willbecome commoditized.
(31:11):
Like we all will have some agents, we willall have some kind of an AI capability.
It might be six months, it might be 12months, 18 months for some, but there
will be a point where you will drawthe line and say it's like digital.
Having an app on the cell phoneis no longer a differentiation.
Used to be in 20 13, 20 14, and somebodywill say, this bids a company has
(31:34):
an app the other one doesn't have.
It's only on website andI will go and use it.
The one that has an app.
But it's no longer a differentiation.
So what is going to differentiate?
What is going to be your face inthe future of your organization?
How are you going to lookdifferently than others?
Is going to be important.
And the third thing, I'll wrap up withwhat I believe is, we will see more of
(31:58):
an interlock between the physical worldand the soft world or the AI world, like
whether it's in robotics, whether it'sin how we are looking at manufacturing.
We are already seeing humanoids.
We in Accenture, we may investmentin humanoid research and are, and
firms that are building those.
I think that's a little bit on theout yet, but if you look two to three
(32:22):
years, it's not going to be too far.
When we would actually see robots in ourhouses doing stuff like we already have
Roomba and other stuff doing things,it has quite a bit of AI built in it.
Having a humanoid do other stuffis probably not too far away.
Yeah I'm more optimistic than you are.
I think we're gonna see commercialrobots in 18 months that are, that
(32:44):
it will be at the high end Yeah.
Of the of purchase.
And most of 'em are estimatingcoming in at 30 grand or so.
Us.
And I have to say, not it, notimmediately, when you look at the future
world I'm looking, I live in a placein the country difficult to maintain.
I'm 70.
How many years more can Iactually look at doing this?
(33:06):
I'm thinking long enoughto get a robot man.
Yeah.
And then the point would be like,when does the price comes from 30
to 20 to 10 to five, so that itbecomes more of a commodity and and
the necessity that everyone willfeel like they will have and are
restricted to only those who can afford.
And then at point, at that point, it'llbe how do you differentiate, right?
(33:27):
Yeah.
Yeah, everyone has some kind ofa mechanism to clean their house,
whether it's an AI or not, butthe differentiation will be how
intelligent it'll be going forward.
Yeah.
And robots being the outside of ourtwo to three year prediction, the.
I I was remiss in not allowing youto get, give more of an explanation
of agentic ai, because I think thatis what's gonna power the next two to
(33:52):
three years is AI that can take action.
Have I got that correctin your definition?
For sure.
And it's great that we talked about AIand this topic for 45 minutes without
coming to agrentic, which is good inmy opinion because to some extent I
am thinking that we are over indexingon agentic and I'll explain why.
(34:14):
And I think there's a ton ofvalue in thinking agentic.
We have the tools we can buildagents that can make decisions.
You provide them withpurpose and the goal.
And it solves towards that goal.
Instead of LLMs, which is prompt, like youask a question, it gives you an answer in
an agent, you give them a goal, you go,it goes and, does, its all kind of things.
(34:37):
Even if it talks to another agent,does orchestration comes back.
However, if it's only as successfuland as relevant as the process
that you are trying to reinvent.
Whether it's simple in your life, whetherit's what you do in your day to day,
whether your vacation plan or planningfor your weekend to and supply chain
(35:02):
company, who's trying to think about theirsource to pay process and how are you
using Agentic to actually get the value?
And that's what I am, I'm more interestedin having the conversation because we
have an natural kind of, opportunityto use some of these shiny hammers,
(35:23):
if you will, and look for the nailsand there's a bit of that happens.
Whenever there is in something new,and I think there's something like that
happening with Agentic, which is great.
It's getting all the attention andit should get all the attention,
but let's use the opportunityto think in my insurance.
Organization.
What is that?
We can drive in claims.
What can I do in underwriting?
(35:45):
If I'm a bank, what can I do withKYC and fraud and a ML? Or if I'm
a retail organization, what does itmean for my inventory optimization?
What is my full process flow of that, thatI can then start picking and dissecting
and say, where does an agent play?
And I'm going to get 20, 30% of animprovement in terms of productivity.
(36:05):
That to me is more interesting and tome is the topic of the next six months,
12 months when it comes to Agentic.
Agent tick for sure,but agent tick for what?
And to do what and how is whatI would like to lead us to.
Yeah.
And I think the interesting piece ofthat I think, I hope we'll get to, is to
(36:26):
not look at it as something that has to.
Automate everything.
It, I think we need to collaborate withour AI and realize that many of the things
that we think of as barriers to ag agenticare just failures to us, us of us thinking
we need to be involved in the process.
(36:48):
We need to collaborate, and I thinkthat's a more mature way to look at it
because AI always gets judged way worsethan humans or by bigger standards.
And you look at this and you say,I'm gonna look through all these
lists and banking's a great example.
We look through all these,I'm gonna find fraud.
Or indicated fraud.
You're gonna get false positives.
(37:09):
I'm gonna get false positivesif I put a human on it.
And as a matter of fact, if I puta human on it, they're not gonna
be as fast and they're probablygonna fall asleep during this.
So we, we accept a certain amountof false positives or errors.
We, we can't seem to get to that withAI in, like we al we always raised the
bar so high that you could never do it.
(37:29):
I booked a flight the other day.
The system double booked me.
It wasn't AI.
I dealt with it, and I think we, wehave to get to that some somehow to that
idea that we're not, we're collaboratingwith something that is that is new.
Yeah.
I think it's a great point.
And the way I think about it is thatsimple example let's say I'm going from
(37:51):
point A to point B and that's my goal.
I want to travel frompoint A to point B. And.
And the only way I can go there is byusing different modes of transportation.
I need to take a car, I need to flysome distance, maybe walk to a ferry
(38:12):
and take a ferry and cross the river.
Now, if at the beginning of the travel Igive you the condition that you can only
use one mode of transportation, which isfly, then how would your travel look like?
You will try to use an airbornemechanism of travel every single step,
(38:32):
which you could have either walkedor you could have taken a car or you
could have taken a train or a ferry.
That's ai, like you are tryingto use AI for everything
rather than, to your example.
There's pure automation,there's probably human need.
It's in some parts of it, andthat's why I always say take the
value angle, lead with the value.
Look at the processyou're trying to reinvent.
(38:52):
Maybe 40% of this is AI and agenticand everything else is probably
just pure human efficiency.
Yeah.
It's interesting when you think from theoutcomes the Google has now released,
its new Google Maps in Europe and itwill now do exactly what you said.
And are you gonna take a car?
Would you like to bike?
(39:13):
Would you like to walk andoffer you those choices?
And I thought.
That's why I always keep saying there'smore AI in our lives than we think.
And it hundred percentit, it's already there.
Can we talk about a little bit aboutjust the democratization of AI and open.
Source AI has become a big thing.
(39:34):
Certainly one of the studies Isaw, we did a story on it was
from the Linux Foundation said 89%of organizations are using open
source AI in one form or another.
What's your position?
What is your thoughts on that?
I think there, there area couple of things there.
There's value in how we.
Use open ai or rather ai, which isopen 'cause sometime it becomes,
(40:00):
yeah, we have to be careful on that.
Yeah.
And careful about that.
So let's say ai, which is moreopen for use, there's definitely
an advantage to using that.
If you know what you wanna do, right?
If you want to.
Use that to actually go under the hoodand make adjustments that will serve
(40:21):
your purpose, then sure, go for it.
But to the same conversation we werejust having that not every single
nail needs the same hammer or thesame problem is the same solution.
You don't need.
Ai, which is open for everything.
You are completely okay with thingsthat has been built for you, and you can
(40:42):
use them now, if you wanna go under thehood and adjust some of the parameters
for a specific use case, then obviouslyyou need that, like you need that.
But try to understand whereyou need and why you need it.
I often find that people just withoutunderstanding the real need of it.
(41:02):
Says, oh this AI is not open.
But the question is, why?
But like, why does it need to be open?
It has given you the APIs, ithas given you the opportunity to
go and make those adjustments.
But certainly for the right type of usecases, it is a value that you can have,
but you need to be able to use that power.
Because if you're given an ai,which is open, but all you can, all
(41:25):
you are using that for is askingquestions and it gives you an answer.
You might as well do that witha black box in front of you.
Yeah.
Yeah.
Just want to turn it around andbecause we're coming to the end of our
time here, and this time goes by soquickly when you're talking about this
stuff two things I want to ask you.
One is, if.
(41:47):
A lot of people watch the show or listento the show are our executives and they're
trying to figure out where they go.
With ai, and I think one of thethings I always wanted to know
is, what are the questions thatexecutives should be asking?
Three things.
I. I think a lot of that wediscussed on this call already.
(42:08):
The first and foremost to me iswhat value am I trying to drive?
What is the problem I'm trying tosolve, which I think is, hasn't
changed in my 25 years in, inthis space and consulting, right?
What problem am I trying to solveand what value am I going to drive?
So that's question number one.
The number two would be, do I havethe right tools in my toolkit?
(42:31):
Whether it's the right digital core,whether it's the right data platform,
whether it's the right AI tools, you needto understand, do I have the right things?
Because if you are going to doa renovation in your backyard,
you need to know whether you havethe right tools or your kitchen.
Do I have the right tools that Ineed for the next few years that
(42:53):
will get me to the end point?
And the third, and probably one of themost important ones is about talent.
Where my people are they ready?
How are they going to acceptwhat training what kind of and
skill upliftment do they need?
Because the first two can be true, andI've seen so many that has not gone to
(43:15):
the extent of delivering what they shouldhave to or should be because of the third
point, which is about talent and changeand how my organization is going to react.
How am I going to change the ways ofworking because I can put in great
agent solution for my supply chain.
How am I going to make surethat my organization is going to
adopt this new ways of working?
(43:37):
So those are the three thingslike problem I'm trying to solve.
Make sure that I know exactly what myNorth Star is, what am I driving, leading
with value, understanding the toolkitthat you have and have the right tools or
not, and then making sure that you havethe people angle covered and you have
the people who can lead with this change.
(43:58):
And as a coach and I'm, I don'tknow how you work, but I suspect
you're a pretty good one.
I'm feeling behind.
I'm feeling a little overwhelmed.
What can I do?
I always say, and I told this to mymy daughter who's 13, and also to
my father who's, old enough to beworried about you getting his hands
(44:20):
on AI and say, just get started.
Get your hands on something like playaround with within simple interface
make some small incremental changeslike the way Jim, you mentioned that
you were starting to use some aspectsof ai more from search to synthesizing.
Use AI to build some of the thingsthat we do in our lives, whether it's a
(44:44):
vacation planning, whether it's planningyour weekend getaway or how you're
going to plan your daughter's birthday.
These are simple things that areavailable and then there further
advance things that we can always getpeople exposed to, depending on their
interest and skill level and all.
Taking that first step of adoptionis important, so get started.
(45:06):
There's not much you can breakby getting started for yourselves
and for ai, so don't worry.
Yeah.
It's big enough to take theabuse you're gonnAGIve it.
Yeah.
Yeah.
And as a, and I get this a lot,this is, I think we all talk
about it as a family person.
And you've got your children.
And growing up I've heard allkinds of advice from teach them to
(45:27):
code to what do, what's your, howare you trying to help your kids
move forward into this new world?
Yeah, so I have two daughters.
One just graduated from university,other one going into high school.
I think both have degrees of interestin this space and trying to get their
hands on some kind of an, ai education.
(45:48):
My, my older one took cognitivescience as an discipline, so
she's already exposed to that.
And the younger one is gettingpython lessons in summer.
So that's one way to get them interested.
I couldn't have imagined learningPython when I was 13 or going to grade
nine, but that's what she's going todo for for a good part of her summer.
(46:10):
And see how she likes it andbuild some app or build some
kind of a, decision engine.
I think it's important to buildstuff like how we used to learn by
playing with Legos, like our kindof thinking about structures and
building and all the mechanics ofhow things connect with each other.
That grew when I was youngwith playing with Lo Legos.
(46:32):
I think for this generation,it's about playing with ai as you
rightly said, like AI is bold andbig enough to be abused by anyone.
So go and play with it, and I think it, itwill have the patience to to be with you.
Yeah.
If anybody's gonna break it, it would'vebeen me and I would've done it by now.
But I love your answer because Ithink that's, as a parent and my
(46:53):
kids are now, are grown and havetheir careers and I hope that even
pre ai, that I left them with theidea that be passionate and learn.
It's no matter what people tell, don'tlearn to don't learn this, don't learn
and be passionate and enjoy learning.
(47:14):
And because nothing I'veever learned has been wasted.
Yeah.
And I think that's a, yeah.
So here's your chance to evaluateme as your interviewer here.
What didn't I ask you about that youwere excited about talking about?
I think we had a great conversationacross very different topics.
You covered things that I would'vewanted to talk about this, like more
(47:35):
than a q and a. I think these arethe topics that I'm passionate about.
And as you probably heard me talking aboutthe value and the reinvention for Canada.
These are topics that I'm very passionateand very close to, so I. Happy to come
back to your show anytime, to double clickon any of these topics if interested.
(47:55):
But I'm hoping that the next time when wespeak, we would assess ourselves against
some of these dimensions and say we havemoved the needle on, on whichever way
we are trying to, do it in our lives.
I think that's excellent.
And I think that's, I'mlooking forward to that.
I always look forwardto our conversations.
Just been, it's been great talking.
(48:16):
We won't make it so long Next time.
We won't make it decadesin AI time between.
Love it.
I would be happy to be back, Jen.
Thank you.
Great.
Thank you very much.
Thanks everyone Closely.
. My guest today is Krish Banerjee.
He's the Canada Managing Directorat Accenture for AI and data.
And thank you very much, Krish.
Great to have you on the show.
Thank you, Jim.
(48:37):
I. And all of you out there listening tothis, I hope you're on the dock, having
a nice time and enjoying the podcast.
But you had lots of places youcould have been on your weekend
and you spent it with us.
So thank you very much.
I'm your host, Jim Love.
Thanks for listening.
Welcome to hashtag Trending in the summer.
My guest today is K Banerjee.
He's a thought leader,a technology leader.
He's the Canadian managing directorat Accenture for AI and data , he
leads a team of over 500 professionalswho deliver solutions for clients
across industries across the country.
(00:26):
And did I mention he's alsoan incredibly nice guy and I'm
glad to have him on the show.
Krish was last on thepodcast in March, 2024.
That was amazing for two reasons.
One, there's still a podcast.
I forgot to retire.
But I didn't even have to rememberthat I went, when was Krish here?
And I went, I can ask AI that.
(00:47):
So I asked PerplexityI've outsourced my memory.
March, 2024 went, wow.
That in AI years.
That's decades.
So Krish, welcome Jim.
Thank you.
And always a pleasure to be withyou and your podcast, and thoroughly
enjoyed our last conversation.
Looking forward to this one.
Yeah.
So what's been happeningrecently since we'd last talked?
(01:08):
Nothing much, right?
Not very exciting.
There's nothing going on.
As you said, in in AI times or AIyears, it's probably probably just
a few few blips, if you will, interms of, the micro kind of units
of time that you were measuring.
But the change has beensignificant, obviously.
The market has shifted quite a bitin terms of how we have seen new
(01:32):
opportunities exist and emerge.
And I'm not going to shareanything new for people who knows
about Agent Tech now, which.
We were not talking as much whenwe met last time, but now there
is no conversation that can happenwithout talking about Agent Tech
So we will obviously talk about that.
But I, I am also seeing an uptickin terms of how our Canadian
(01:55):
organizations and community isactually moving towards adopting.
There's quite a bit of workstill needs to happen, but there
is an intent is very inspiring.
Yeah, and I was being facetious.
It a year is like adecade in AI right now.
There's been so much happening, butthe one place where I think you were
prescient was you've always kept dataand AI together in, in our conversation
(02:20):
last time, I think you, you reallyconsidered that and I think that's.
Somebody woke up the rest of the world,woke up to that at one point, said,
wait a minute, this is important.
Yeah, exactly.
Exactly.
And I think the most interestingquote that I heard on that topic
is that we have everything readyfor AI except for the data.
Everything course is ready toactually launch into AI and get
(02:45):
their hands on AI, on the platformsor the agents and all the.
Human beings except for data.
So I think we are turning towards data nowto give its right full share of attention.
And I can see there's already quite a bitof work happening in the space of whether
it's privacy or whether it's integration,whether we are actually building some data
(03:07):
products which are specific to industries.
I can see that the next, few decades,which will be few months in this case.
Yeah.
Will be spent on focusing on data.
Yeah.
I was watching it because we startedto talk about access to data.
And I come out of my, my, I'm an old guy.
(03:29):
I'm from the old data warehouse dayswhen, remember when you, if you had a,
somebody sent you a query or somethingthey wanted done, I had a three month
project to reorganize the data and pullit all in and do all that sort of stuff.
Now we're talking about.
Conversations with data warehouses.
And I, we may not totally be thereyet, but we're, this is gonna, this
(03:49):
is gonna be something that comesthat's gotta be fairly exciting
from your whole background.
It is very exciting and weat Accenture have always been
focused on data and making surethat the fundamentals of it are.
Are secured and then everything ontop of it, whether, to your point,
at some point it was reportingand business intelligence, which
(04:10):
was an extremely important aspectof what we can build on data.
From there, we went on to analytics andusing some of the models and algorithms
and how we can drive value out of it.
And then from that now to ai.
But we have always been true to thefundamentals and the core of it.
And that is coming back again.
Like we have seen the shift of how thingsmove towards more of a shiny object at
(04:34):
the time which has changed over differenterrors of technology advancements, whether
it's reporting, whether it's analytics,whether it's now ai, but the core
fundamentals of what you need to build.
True insights out of remains thesame, which is in this case is data.
Yeah.
But your practice covers, mostof Canada's large enterprises.
(04:57):
So you are, you're seeing thisfrom the enterprise version of
AI, which is an exciting piece.
My feeling is that we're finally grapplingwith that, and that is, I think people
were dipping their toe in the water.
They were doing things, but we'renow starting to confront the, I. I
wouldn't wanna call 'em the issues,but the things that you have to
(05:19):
put in place to have enterprise AI.
Do you have the same sense?
Yeah I think enterprise AI isan interesting topic, right?
And we have few examples of that.
I would say in Canada we are probablywhere there is an opportunity,
there's a lot of, pockets of ai.
There's a lot of like functional AIthat is happening in very specific
(05:41):
areas, whether it's HR, whether it'sfinance, whether it's in areas of
performance management or whetherit's in areas of supply chain.
But true enterprise AI has.
Has been the area of interestfor us, which is where you can
drive value, which is where youcan actually drive reinvention.
There's quite a bit of workhappening globally on that.
(06:04):
However, I do believe that in Canadawe, we are a little behind when it
comes to driving true productivityand value and generating value
out of a true enterprise AI.
So not sure if I answered your question,Jim but that's how I'm looking at it.
But.
Double click on that.
What exactly you wanted to know about it?
No, I think that's, I think that's fair.
I think your perspective is that, we'vebeen building sort of point solutions.
(06:27):
And now bringing thattogether is the next task.
I think.
I totally agree with you.
I think I. The world is further ahead ofus in many cases, and we need to fix that.
I think that's an important thing tofix, but it, but I'm starting to see
the discussions take on a new maturity.
When you start to talk aboutwhat are we gonna do about data?
What are we gonna do about security?
(06:48):
And that, those are the pointsthat, that need the foundation
points because we've been.
I don't think that's unfair.
I think we always do this in technology.
We're led by the bright, shinyobject and, and we're led by,
by the promise of something new.
We always say you should buildthe data and the security in as
you're going, but we never do that.
(07:08):
We always get.
For far advance say, we bettergo back and get control of this.
And I think that's the place I see us.
I see not just Canada butacross the entire world.
We start to see people grapplingwith saying this is serious.
It's enterprise level.
We need to figure out howwe're going to run it now.
It can't be just somethingthe employees bring in.
(07:28):
Yeah.
And I think we have spent thelast two plus years proving that.
This is real.
This is what I try to explain toa number of my clients and friends
here is that I don't think AIneeds to prove itself anymore.
Like it has done its jobof proving that it works.
And it's real.
It's really for us to make sure thatwe are engaging that capability and
(07:51):
power to the right use of whether it'sa, country or community or a specific.
Organization.
That's where our job is, like AI hasdone its job in the past couple of years.
Yeah.
And I think o in particularly in thepast three to six months, I think
we've hit some levels where we can saythis, we no longer talk about if we're
(08:12):
talking about when, and I think that'sa, that's an important distinction
we make, but also I think, and I've.
And I've been happy tonot have the conversation.
Every conversation I had about AI startedwith when are we gonna get to AGI?
When are we gonna get to,when is it gonna be Sentient?
And I'm going, you know somethingguys, you got enough tools right now
in the toolkit to do astonishing thingslike you, AGI, nice conversation.
(08:39):
Let's have a beer and talk aboutthat on the on, on the dock.
But let's get to workwith the tools we have.
Do you get the same sense thatwe're not really using everything
that we've got available for us?
For us?
Yeah, for sure.
I think back to the conversation we werejust having that there are definitely very
pockets of AI and pockets of brilliancethat are happening, but there's also a
(09:01):
lot of opportunity that are like whitespace for us where if we stitch the
things that are happening, whether youconnect the dots between, opportunities
that exist across your enterprise,there is an opportunity to multiplex the
value that you are already unlocking.
So a hundred percent.
There is things that are right nowavailable that can unlock two x
(09:25):
three x of opportunity value savingsfor any organization, whether
it's public, private, federal,commercial, whichever flavor of it is.
And then AGI is an interesting concept,but AGI to me is also relative.
If you roll back 10 years,what we are doing today could
have been considered as AGI.
(09:47):
So the boundary of AGI is alsowhat we keep shifting, right?
And I think that's like almost chasinga target that keeps changing on us.
So let's just focus on the currentand think about the possibilities
that we will work towards.
But not cheese.
Something that almost likeragio or chasing in the dessert.
(10:07):
Yeah, it's like we keepmoving the goalposts.
I don't know how, I don't know, you'renot as old as me, but there was a show in
the 1980s I think, where this guy had atalking car and it would drive itself and
it had a great voice and would chat withyou and give you all this information.
We could do that now.
Exactly.
(10:28):
Yeah.
And you and that's so we are living inour fantasies from 20 years ago, but
we're sitting there going, that all youcan do, make a talking car, exactly.
I grew up watching and animationat that time called the Johnny
Soko and his flying robot.
Oh yeah.
And it it used to have a watch whereyou can talk to, that was one of
(10:50):
the most fascinating thing at thattime, mean that's Apple Watch or
any other smart watch at this point.
I think that's anexample of how AGI is in.
Is in target that we set up for ourselvesto achieve something and then we say,
okay, now I'm moving the boundary.
Another, 20 years further because thenyou can actually have more brilliance in
(11:12):
terms of human thinking and innovation,which may explain this story we did a
couple days ago and we picked it up.
It was, I think it was a TDBank, did a survey and said that
Canadians gave themselves a C attheir understanding and use of AI.
And I found that astonishingbecause even today I don't think
(11:35):
people realize how much they'redoing with AI and don't realize it.
Yeah.
There's al already a lot of AI evenI. In our lives whether we realize
it, whether we acknowledge it.
There's already a lot of AI in ourlives and I can only see that we'll
become increasingly more embeddedand more blended into our lives.
(11:57):
But there is an aspect of beingdeliberate, and I think that's the
part that we need more tension.
Like how can I be more deliberateabout unlocking things that are not
just happening to me, whether it'sin my world, whether it's in how.
How I'm looking for information.
How am I searching information?
How am I working on mynext vacation planning?
(12:20):
I think we will be working on thingslike Airbnb and Expedia and others
that are building AI, which is somebodyelse is doing AI to me, versus I am
going out and seeking the power anduse of AI for my own productivity.
I think that's to me is the kind ofthe bottom up difference that we need
(12:42):
to build as a community, as a society.
If we all can be deliberate aboutwhat everything, what we can do in
our lives, that can change even, a10, 2%, 5% productivity improvements
that will build up an snowballeffect of what it could mean for the.
For the country, for the community ina whole, because if you look at our
(13:05):
situation like Bank of Canada said,we are in a productivity emergency.
We are number 18 in OECD, whichis not a great place to be.
No.
And that productivity emergency is,will not change unless we become better
at investing in and using technology.
Jim Balsillie I've had him on theprogram, is passionate about this.
(13:28):
We need to become a hub and we'lltalk about Canada in a second.
I do want to get there, I just want togo back to this idea of, you're dealing
with enterprises and with their customers.
I. This is maybe just my perspective andI'm close to 70 man I'm not supposed to
be on the bleeding edge of this stuff.
I am so impatient with companiesthat give me the old interfaces and
(13:52):
already I want nothing to do with it.
Maybe that's becoming a cranky old guy,but if I can't just type in, tell me
how I do this, and it comes back withthe answer, I'm not, I just move on.
And so I think if, I think we maythink we're not as advanced in AI,
but I suspect that we're impatientlywaiting for what it can actually do.
(14:16):
I agree.
And I was watching my 13-year-olddaughter the other day trying to find
some information, and I think the defaultnow is to go to publicly available.
Copilot, Gemini or any of those platformsinstead of going to the actual search.
So if you go by that shiftthere itself, they're growing
(14:39):
up as an AI native generation.
Yeah.
And I think that's, that's something that.
Customers will start to demand.
I'm sorry, but I'm immensely criticalof government interfaces for the
most part because I don't thinkthey really understand that people
who come to deal with governmentknow nothing about government.
We don't know the policies,we don't know the procedures.
(15:01):
We just want an answer and, and somany of the interfaces are set up.
To deal with and we had thisin corporate life a while ago.
I think it's changed a lot.
A lot of interfaces were set up sothat the corporation could manage
the transaction with, in otherwords, according to their rules.
Consumers rebelled againstthat and I think most.
(15:23):
Good or competitive.
Corporations have realized thatand have changed their attitude.
I don't think government has quitefigured that out yet, that when
citizens come, they don't, they're notinterested in dealing with the rules.
They're interested in getting an answer.
And I think that's a challengethat AI allows us to address.
I hope.
I think there's a lot of positive intentand momentum and conversations happening
(15:45):
if we collect everything around us from.
What we just announced at G7 towhat what we can expect out of the
appointment of our first AI minister.
There's a lot of good intent and Iwould like to take the optimism and the
positivity out of that and obviouslythere's a lot to be proven and a
(16:09):
lot to be opportunity to be walking.
The talk needs to happen.
Yep.
But there, there's some good signsof the first couple of steps of
the talk that needs to be walked.
I'm very optimistic.
I just hope we don't be, andI think things have changed.
And I don't think, I don't thinkit's a political statement.
I think you, if you looked at any,you talked to any of the leaders
(16:33):
in the, people have woken up andsaid, we have to be better at this.
And the productivity crisis may be thedriver, it may be the trade issues that
we have, but people are starting torealize we need to get a handle of this.
And I think the appointment of anEvan Solomon interesting appointment.
Do you remember Evan Solomonfrom when he was a broadcaster?
(16:53):
He's, I do.
I do.
Yeah.
And it's definitely interesting and Ithink it shows that AI is a topic that
is pretty, everywhere and it doesn't getlimited to people who might be academic
or might have in policy background ormight have a very specific background
but it's an interesting kind of messagethat I think I'm taking away from that.
(17:16):
Yeah.
Yeah.
And don't get me wrong, you can bein I am, I guess I am in the media.
You, there's some veryintelligent people in the media.
There you go.
Jim.
That's your next career path.
Maybe.
Maybe.
Yeah.
Yeah, I could get it.
So talk to me about this.
So what do you have you,I. Heard, heard much of.
I, I've been digging into, I was surprisedthere was already a great deal of activity
(17:39):
going on in the federal government interms of AI that, that was kept hidden, I
think, or maybe I just didn't notice it.
I think there is a lot of interestright now in the few specific areas,
obviously in the areas of service.
And I know that there'sconversations around how.
Citizen service could be improvedusing AI and using more front
(18:02):
office capabilities of AI.
There's also conversations aroundhow could we use AI for more
productive collaboration in business?
How could we reduce all the red tapethat we might have make business
easier for us to do whether it's for.
Individuals or from a businessto business relationship.
(18:25):
And there's also a conversation thatis publicly known now about what it
means from, in sovereign perspective.
And there's a lot of focus from a federalgovernment, which is I think is also
another good step in terms of what do, howshould Canada think about AI as a federal,
as a, so sorry, a sovereign topic.
(18:46):
Doesn't need to be sovereign by.
By physical kind of definition, likeobviously there's a physical definition
of sovereign, but I think there isan, to me there is a more broader,
probably an philosophical, thinkingabout sovereign it, it is a Canadian
thinking, Canadian priority Canadianinvestment and sovereign investment.
(19:08):
And then everything else works togetherin order to bring that to life.
Yeah.
And I don't think we're the only ones.
I think the world's woken up to thefact that we sat back and watched
the Americans develop AI and now theChinese have entered in a big way.
And I think a lot of countriesare starting to say, wait a
minute how does this fit withour operations, our sovereignty?
(19:33):
Can we sit on the sidelinesand just wait for.
America to develop everything for us.
What should we be doing?
I know Europe is go grappling with this.
I presume the same thing is happeningin India and definitely China
Canada's another player in there.
It's almost like the world woke cupsaid we can't be a spectator anymore.
Yeah, and you're right, likethere, there's different degrees
(19:56):
of interest and speed and.
An investment fromdifferent jurisdictions.
So some others are probably further along.
And it's also true that everyonehas a slightly different angle.
Some are taking more of ancompliance and regulation angle
versus some are taking more ofopen and free and innovation angle.
(20:17):
Some would be taking this morefrom an, let's use this as
our world dominance, an angle.
So we know those flavors are, withoutbeing more specific, but I think the
opportunity for me, for Canada is tothink sovereign for Canadian purpose.
And I. Objectives, right?
(20:39):
What do we need to do to help ourCanadian challenges that are in
front of us from innovation, fromeverything that is happening in
the whole geopolitical situation.
And how can we use that to our advantageand use that to come out at the other
end of this stronger and more resilient.
Yeah, and I think it's forced us.
(21:00):
To actually have a discussion aboutwhat digital sovereignty means.
We're actually gonna do a whole programon this because it's we've thought
of sovereignty as a physical thing.
Yeah.
And even with the whole digitaltransformation of our economies, we
never actually sat down and said, wait aminute, sovereignty's a digital thing too.
And we haven't we don't, we haven'tthought through what we even mean by that.
(21:24):
Yeah.
No, I think it's a great point.
And that.
What I was referring to that the defaultdirection of sovereignty is physical.
Let's have a Canadian data center.
Let's make sure that thedata stays in the country.
The network is Canadian, and how we canconnect to Canadian network on that.
However to your point, like there isa whole digital angle to that and how
(21:46):
we can use a digital and the nationaland the nation building side of it.
Like how we can use sovereign tobuild the nation and to come out to my
point of, all of these uncertaintiesthat are going around come out on
the other end of this stronger.
And I think there's a great opportunity,we've seen that during COVID.
There are countries, thereare organizations who came out
(22:09):
stronger at the other end of COVID.
And I almost feel like there's this.
There's a similar kind of a challenge,may not be as prominent at this point,
but definitely an opportunity ahead of us.
Yeah.
I honestly think it isprominent at this point.
I think people have started to realize,and part of it was people banging
on the drum about productivity and,productivity could be a sterile thing.
(22:32):
And I've, people have talked aboutthis output GDP , per capita,
and why does that really matter?
Why does it really matter?
Because if you don't create wealth,you've got nothing to distribute, and
wealth creation is going to change.
I, I. Maybe a whole notherprogram on this, but we will not
recognize our economy in 10 years.
(22:53):
And that's a short periodof time for an economy.
The, we talk about mining and oiland gas and the industries that
Canada's been classically goodat, these are 10 and 20 year.
Projects.
People talk about a pipeline.
A pipeline, if you get it approved intwo years is still a 10 year project.
(23:14):
Heck, a having a a new subwayline is a 10 year project.
But we're gonna have to move ata faster speed than that because
technology won't slow down for us.
So it's, I think we have gottento that realization, I hope.
I would like to believe so, and I'm seeingsome signs of it and I'm very po positive
and optimistic about what I'm seeing.
(23:36):
But I, I also believe that thereis a lot more that we could do
and to the conversations we werehaving in the earlier part of this
chat that we have the tools nowwe have the opportunity now with.
Proof of concepts and things that wehave done over the past two years.
We just need to now take a stepto say, I'm going to now try
(23:59):
and do this for a broader thanjust building a point solution.
Cause let me reinvent the entirevalue chain of my industry.
Let me think about how I can sell.
Consumer packaged goods to mycustomers very differently than what
I have done in the last 50 years.
I think that is a binary pointin my opinion, that where we are
(24:23):
in front of and what, let me askyou, just, let me just start with
one so we could have an example.
What's the project you'd most like to do?
If I came in and said,Krish, I got a budget.
Don't worry about that.
What's the project you'd most like to do?
It's a great question and Iwould say there's probably
two, two parts of the answer.
(24:43):
One is there's a passion side ofthe answer, which is I would like to
do something which is more tangiblefrom an I. Community perspective.
Like at Accenture, we doquite a bit of pro bono work.
We have done some of the work withuniversities and and mental health
organizations and others, and somethingaround that would be of interest.
So if we can use the power thatwe have been given with AI to the
(25:06):
cause of something better, likeAI for good would be an area of
interest and something around that.
And there could be many flavors of it,whether it's mental health, whether it's
human lives, whether it's tra transportand traffic, which is very predominant
problem for Toronto and my city.
Anything can do therewould be of interest.
(25:27):
The other answer would be like somethingaround the whole by Canada and for Canada
from a sovereign perspective, like if Ican be part of something that will build
a. Canada stronger and build us as theai destination, which we should be given
everything that we have from our lineageand history and bring back kind of our
(25:50):
legitimate and rightful supremacy in AI
I think that would be the other project.
Do you know the two projects I wanna do?
Yeah.
One and I won't get to play in this'cause I don't think I'm, I don't
think I'm really, I don't think I'mreally a full-time employee ever again
for the I'm keep trying to retire.
But the, but I'm just excited about, I'dlove to take AI and reinvent healthcare,
(26:16):
starting with outcomes, not processes.
And say, what are the outcomeswe wanna bring about and how
could we use AI for them?
My example is, I wanna follow mydoctor around in his office all
day, and I want to take all of theadministration stuff that he's got to
do and make that disappear so that allhe's got to do is talk to patients.
(26:42):
And that is doable.
That would excite the heck out me.
I think that's a great one.
And it's in my first bucket of somethingthat will touch human lives like this.
Yeah.
I think we agree on that.
One other one I'd love to do, andthis one I may actually participate
in 'cause we're, we started ourown institute, believe it or not
to do this commercialization of ai.
(27:03):
I just want to see.
Our efforts go towards commercializingai, not necessarily even to own it,
but just to say, let's push thesethings that will make productivity so
much greater for Canadian businesses.
And I, that, that's, I was, I would, Iused to teach at Waterloo and I was having
a discussion with another pro friend ofmine who's also retiring and looked at it
(27:27):
went, that's, I'd love to see that happen.
I'd just love to see the mechanismof taking that wonderful.
Enthusiasm and energy and knowledge thatwe have, and it's, there's tons of it.
And driving that to a small businessthat can now be much more profitable.
Those are my two.
No, I think that's great.
(27:47):
And the second one you mentioned isis really very interesting because
it's about how we are thinking aboutreinvention, like in our Accenture.
World and how we think about helpingour clients reinvent, whether
it's in the mid-market or in theenterprise level, the G two thousands.
There is an opportunity for everyoneto think about the reinvention and I
(28:10):
think what you're saying is how do I andcreate a path of value opportunity for
organizations to reinvent and build thatcommercial kind of pathway for them.
Yeah.
Yeah.
And it, and when that sparkstarts, it's exciting.
Yeah.
I remember in the early daysof it, that's how we felt.
(28:31):
We thought we were magicians, wewere building, no, we were building
these systems, barely holdingtogether with bailing wire, but we
were doing incredible things, yeah.
The, yeah.
So I think we, we talked about in thelast one, which is about, with great
power comes great responsibilities, right?
Yes, absolutely.
Mentioned this in thelast session as well.
So can lose sight of that, can lose sightof how we need to think about building
(28:56):
AI with with the right responsiblemindset and thinking and framework and,
we call it responsible ai, ethical ai.
There's all different names to it,but essentially to make sure that we
are doing it with the right intent.
Yeah.
Yeah.
And the right tools.
Speaking of responsibility, the, we'restarting to have the conversation
(29:18):
about the economy and where,and the changes in the economy.
I was interviewing someone today andwe were talking about the almost, it's
not a total disappearance, but entrylevel jobs are disappearing faster than.
Than kids can.
There, there are far fewer enterentry level jobs than there ever
were, and that's not just economics.
(29:41):
That's something that we'restarting to see the impact.
I don't think we fully I don't thinkwe fully imagined the impact of AI.
We know, I know ultimately, atone point or another, most of
what we can do can be automated.
That's, it's just a questionof how many years, decades,
weeks, months, whatever it is.
Prob but.
The change in the economy.
(30:01):
How do you see that?
What do you, what are you lookingforward at in, in terms of what
I think there will be few things.
If I have to hazard a predictionor a guess in terms of the
next maybe two to three years.
Because anything beyond that is,is almost unknown at this point.
Yeah.
So I wouldn't go beyond three years.
The first one is we will certainlysee a point where we will have to make
(30:25):
a decision as an individual, as anorganization which I was referring to
as that binary decision point wherewe have choices in front of us, which
is, am I going to be an AI leaderor am I going to be still following?
And we have seen from.
From the past, similar kind of itpivots or other moments that there
(30:50):
is a two to three X differencebetween those who will make the
choice of leading versus following.
So I think we are there, like that binarykind of big bang point is already there.
So the second one willbe about differentiation.
At some point, AI willbecome commoditized.
(31:11):
Like we all will have some agents, we willall have some kind of an AI capability.
It might be six months, it might be 12months, 18 months for some, but there
will be a point where you will drawthe line and say it's like digital.
Having an app on the cell phoneis no longer a differentiation.
Used to be in 20 13, 20 14, and somebodywill say, this bids a company has
(31:34):
an app the other one doesn't have.
It's only on website andI will go and use it.
The one that has an app.
But it's no longer a differentiation.
So what is going to differentiate?
What is going to be your face inthe future of your organization?
How are you going to lookdifferently than others?
Is going to be important.
And the third thing, I'll wrap up withwhat I believe is, we will see more of
(31:58):
an interlock between the physical worldand the soft world or the AI world, like
whether it's in robotics, whether it'sin how we are looking at manufacturing.
We are already seeing humanoids.
We in Accenture, we may investmentin humanoid research and are, and
firms that are building those.
I think that's a little bit on theout yet, but if you look two to three
(32:22):
years, it's not going to be too far.
When we would actually see robots in ourhouses doing stuff like we already have
Roomba and other stuff doing things,it has quite a bit of AI built in it.
Having a humanoid do other stuffis probably not too far away.
Yeah I'm more optimistic than you are.
I think we're gonna see commercialrobots in 18 months that are, that
(32:44):
it will be at the high end Yeah.
Of the of purchase.
And most of 'em are estimatingcoming in at 30 grand or so.
Us.
And I have to say, not it, notimmediately, when you look at the future
world I'm looking, I live in a placein the country difficult to maintain.
I'm 70.
How many years more can Iactually look at doing this?
(33:06):
I'm thinking long enoughto get a robot man.
Yeah.
And then the point would be like,when does the price comes from 30
to 20 to 10 to five, so that itbecomes more of a commodity and and
the necessity that everyone willfeel like they will have and are
restricted to only those who can afford.
And then at point, at that point, it'llbe how do you differentiate, right?
(33:27):
Yeah.
Yeah, everyone has some kind ofa mechanism to clean their house,
whether it's an AI or not, butthe differentiation will be how
intelligent it'll be going forward.
Yeah.
And robots being the outside of ourtwo to three year prediction, the.
I I was remiss in not allowing youto get, give more of an explanation
of agentic ai, because I think thatis what's gonna power the next two to
(33:52):
three years is AI that can take action.
Have I got that correctin your definition?
For sure.
And it's great that we talked about AIand this topic for 45 minutes without
coming to agrentic, which is good inmy opinion because to some extent I
am thinking that we are over indexingon agentic and I'll explain why.
(34:14):
And I think there's a ton ofvalue in thinking agentic.
We have the tools we can buildagents that can make decisions.
You provide them withpurpose and the goal.
And it solves towards that goal.
Instead of LLMs, which is prompt, like youask a question, it gives you an answer in
an agent, you give them a goal, you go,it goes and, does, its all kind of things.
(34:37):
Even if it talks to another agent,does orchestration comes back.
However, if it's only as successfuland as relevant as the process
that you are trying to reinvent.
Whether it's simple in your life, whetherit's what you do in your day to day,
whether your vacation plan or planningfor your weekend to and supply chain
(35:02):
company, who's trying to think about theirsource to pay process and how are you
using Agentic to actually get the value?
And that's what I am, I'm more interestedin having the conversation because we
have an natural kind of, opportunityto use some of these shiny hammers,
(35:23):
if you will, and look for the nailsand there's a bit of that happens.
Whenever there is in something new,and I think there's something like that
happening with Agentic, which is great.
It's getting all the attention andit should get all the attention,
but let's use the opportunityto think in my insurance.
Organization.
What is that?
We can drive in claims.
What can I do in underwriting?
(35:45):
If I'm a bank, what can I do withKYC and fraud and a ML? Or if I'm
a retail organization, what does itmean for my inventory optimization?
What is my full process flow of that, thatI can then start picking and dissecting
and say, where does an agent play?
And I'm going to get 20, 30% of animprovement in terms of productivity.
(36:05):
That to me is more interesting and tome is the topic of the next six months,
12 months when it comes to Agentic.
Agent tick for sure,but agent tick for what?
And to do what and how is whatI would like to lead us to.
Yeah.
And I think the interesting piece ofthat I think, I hope we'll get to, is to
(36:26):
not look at it as something that has to.
Automate everything.
It, I think we need to collaborate withour AI and realize that many of the things
that we think of as barriers to ag agenticare just failures to us, us of us thinking
we need to be involved in the process.
(36:48):
We need to collaborate, and I thinkthat's a more mature way to look at it
because AI always gets judged way worsethan humans or by bigger standards.
And you look at this and you say,I'm gonna look through all these
lists and banking's a great example.
We look through all these,I'm gonna find fraud.
Or indicated fraud.
You're gonna get false positives.
(37:09):
I'm gonna get false positivesif I put a human on it.
And as a matter of fact, if I puta human on it, they're not gonna
be as fast and they're probablygonna fall asleep during this.
So we, we accept a certain amountof false positives or errors.
We, we can't seem to get to that withAI in, like we al we always raised the
bar so high that you could never do it.
(37:29):
I booked a flight the other day.
The system double booked me.
It wasn't AI.
I dealt with it, and I think we, wehave to get to that some somehow to that
idea that we're not, we're collaboratingwith something that is that is new.
Yeah.
I think it's a great point.
And the way I think about it is thatsimple example let's say I'm going from
(37:51):
point A to point B and that's my goal.
I want to travel frompoint A to point B. And.
And the only way I can go there is byusing different modes of transportation.
I need to take a car, I need to flysome distance, maybe walk to a ferry
(38:12):
and take a ferry and cross the river.
Now, if at the beginning of the travel Igive you the condition that you can only
use one mode of transportation, which isfly, then how would your travel look like?
You will try to use an airbornemechanism of travel every single step,
(38:32):
which you could have either walkedor you could have taken a car or you
could have taken a train or a ferry.
That's ai, like you are tryingto use AI for everything
rather than, to your example.
There's pure automation,there's probably human need.
It's in some parts of it, andthat's why I always say take the
value angle, lead with the value.
Look at the processyou're trying to reinvent.
(38:52):
Maybe 40% of this is AI and agenticand everything else is probably
just pure human efficiency.
Yeah.
It's interesting when you think from theoutcomes the Google has now released,
its new Google Maps in Europe and itwill now do exactly what you said.
And are you gonna take a car?
Would you like to bike?
(39:13):
Would you like to walk andoffer you those choices?
And I thought.
That's why I always keep saying there'smore AI in our lives than we think.
And it hundred percentit, it's already there.
Can we talk about a little bit aboutjust the democratization of AI and open.
Source AI has become a big thing.
(39:34):
Certainly one of the studies Isaw, we did a story on it was
from the Linux Foundation said 89%of organizations are using open
source AI in one form or another.
What's your position?
What is your thoughts on that?
I think there, there area couple of things there.
There's value in how we.
Use open ai or rather ai, which isopen 'cause sometime it becomes,
(40:00):
yeah, we have to be careful on that.
Yeah.
And careful about that.
So let's say ai, which is moreopen for use, there's definitely
an advantage to using that.
If you know what you wanna do, right?
If you want to.
Use that to actually go under the hoodand make adjustments that will serve
(40:21):
your purpose, then sure, go for it.
But to the same conversation we werejust having that not every single
nail needs the same hammer or thesame problem is the same solution.
You don't need.
Ai, which is open for everything.
You are completely okay with thingsthat has been built for you, and you can
(40:42):
use them now, if you wanna go under thehood and adjust some of the parameters
for a specific use case, then obviouslyyou need that, like you need that.
But try to understand whereyou need and why you need it.
I often find that people just withoutunderstanding the real need of it.
(41:02):
Says, oh this AI is not open.
But the question is, why?
But like, why does it need to be open?
It has given you the APIs, ithas given you the opportunity to
go and make those adjustments.
But certainly for the right type of usecases, it is a value that you can have,
but you need to be able to use that power.
Because if you're given an ai,which is open, but all you can, all
(41:25):
you are using that for is askingquestions and it gives you an answer.
You might as well do that witha black box in front of you.
Yeah.
Yeah.
Just want to turn it around andbecause we're coming to the end of our
time here, and this time goes by soquickly when you're talking about this
stuff two things I want to ask you.
One is, if.
(41:47):
A lot of people watch the show or listento the show are our executives and they're
trying to figure out where they go.
With ai, and I think one of thethings I always wanted to know
is, what are the questions thatexecutives should be asking?
Three things.
I. I think a lot of that wediscussed on this call already.
(42:08):
The first and foremost to me iswhat value am I trying to drive?
What is the problem I'm trying tosolve, which I think is, hasn't
changed in my 25 years in, inthis space and consulting, right?
What problem am I trying to solveand what value am I going to drive?
So that's question number one.
The number two would be, do I havethe right tools in my toolkit?
(42:31):
Whether it's the right digital core,whether it's the right data platform,
whether it's the right AI tools, you needto understand, do I have the right things?
Because if you are going to doa renovation in your backyard,
you need to know whether you havethe right tools or your kitchen.
Do I have the right tools that Ineed for the next few years that
(42:53):
will get me to the end point?
And the third, and probably one of themost important ones is about talent.
Where my people are they ready?
How are they going to acceptwhat training what kind of and
skill upliftment do they need?
Because the first two can be true, andI've seen so many that has not gone to
(43:15):
the extent of delivering what they shouldhave to or should be because of the third
point, which is about talent and changeand how my organization is going to react.
How am I going to change the ways ofworking because I can put in great
agent solution for my supply chain.
How am I going to make surethat my organization is going to
adopt this new ways of working?
(43:37):
So those are the three thingslike problem I'm trying to solve.
Make sure that I know exactly what myNorth Star is, what am I driving, leading
with value, understanding the toolkitthat you have and have the right tools or
not, and then making sure that you havethe people angle covered and you have
the people who can lead with this change.
(43:58):
And as a coach and I'm, I don'tknow how you work, but I suspect
you're a pretty good one.
I'm feeling behind.
I'm feeling a little overwhelmed.
What can I do?
I always say, and I told this to mymy daughter who's 13, and also to
my father who's, old enough to beworried about you getting his hands
(44:20):
on AI and say, just get started.
Get your hands on something like playaround with within simple interface
make some small incremental changeslike the way Jim, you mentioned that
you were starting to use some aspectsof ai more from search to synthesizing.
Use AI to build some of the thingsthat we do in our lives, whether it's a
(44:44):
vacation planning, whether it's planningyour weekend getaway or how you're
going to plan your daughter's birthday.
These are simple things that areavailable and then there further
advance things that we can always getpeople exposed to, depending on their
interest and skill level and all.
Taking that first step of adoptionis important, so get started.
(45:06):
There's not much you can breakby getting started for yourselves
and for ai, so don't worry.
Yeah.
It's big enough to take theabuse you're gonnAGIve it.
Yeah.
Yeah.
And as a, and I get this a lot,this is, I think we all talk
about it as a family person.
And you've got your children.
And growing up I've heard allkinds of advice from teach them to
(45:27):
code to what do, what's your, howare you trying to help your kids
move forward into this new world?
Yeah, so I have two daughters.
One just graduated from university,other one going into high school.
I think both have degrees of interestin this space and trying to get their
hands on some kind of an, ai education.
(45:48):
My, my older one took cognitivescience as an discipline, so
she's already exposed to that.
And the younger one is gettingpython lessons in summer.
So that's one way to get them interested.
I couldn't have imagined learningPython when I was 13 or going to grade
nine, but that's what she's going todo for for a good part of her summer.
(46:10):
And see how she likes it andbuild some app or build some
kind of a, decision engine.
I think it's important to buildstuff like how we used to learn by
playing with Legos, like our kindof thinking about structures and
building and all the mechanics ofhow things connect with each other.
That grew when I was youngwith playing with Lo Legos.
(46:32):
I think for this generation,it's about playing with ai as you
rightly said, like AI is bold andbig enough to be abused by anyone.
So go and play with it, and I think it, itwill have the patience to to be with you.
Yeah.
If anybody's gonna break it, it would'vebeen me and I would've done it by now.
But I love your answer because Ithink that's, as a parent and my
(46:53):
kids are now, are grown and havetheir careers and I hope that even
pre ai, that I left them with theidea that be passionate and learn.
It's no matter what people tell, don'tlearn to don't learn this, don't learn
and be passionate and enjoy learning.
(47:14):
And because nothing I'veever learned has been wasted.
Yeah.
And I think that's a, yeah.
So here's your chance to evaluateme as your interviewer here.
What didn't I ask you about that youwere excited about talking about?
I think we had a great conversationacross very different topics.
You covered things that I would'vewanted to talk about this, like more
(47:35):
than a q and a. I think these arethe topics that I'm passionate about.
And as you probably heard me talking aboutthe value and the reinvention for Canada.
These are topics that I'm very passionateand very close to, so I. Happy to come
back to your show anytime, to double clickon any of these topics if interested.
(47:55):
But I'm hoping that the next time when wespeak, we would assess ourselves against
some of these dimensions and say we havemoved the needle on, on whichever way
we are trying to, do it in our lives.
I think that's excellent.
And I think that's, I'mlooking forward to that.
I always look forwardto our conversations.
Just been, it's been great talking.
(48:16):
We won't make it so long Next time.
We won't make it decadesin AI time between.
Love it.
I would be happy to be back, Jen.
Thank you.
Great.
Thank you very much.
Thanks everyone Closely.
. My guest today is Krish Banerjee.
He's the Canada Managing Directorat Accenture for AI and data.
And thank you very much, Krish.
Great to have you on the show.
Thank you, Jim.
(48:37):
I. And all of you out there listening tothis, I hope you're on the dock, having
a nice time and enjoying the podcast.
But you had lots of places youcould have been on your weekend
and you spent it with us.
So thank you very much.
I'm your host, Jim Love.
Thanks for listening.
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