October 25, 2024 • 42 mins
70 percent of technology projects fail. Why? And how do you prevent it? Peter walks us through major challenges that AI uniquely presents and frameworks to overcome them.
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Episode Transcript
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Welcome to Artificial Insights, the podcast where we learn to build AI people need and
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use by interviewing product leaders who have launched AI products.
I'm your host, Daniel Manary, and today I'm joined by Peter Carr from the University of
Waterloo.
At the University of Waterloo, Peter works with the Department of Management Sciences
as well as the Conrad School of Entrepreneurship and Business.
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And across those functions, he's worked with dozens, possibly more, of businesses and helped
them adapt to this fourth industrial revolution that we are experiencing now.
Peter, could you introduce yourself to our audience?
Sure.
My name is Peter Connor.
I'm an associate professor at the University of Waterloo in the Department of Management
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Sciences, which is part of the Faculty of Engineering.
I am also the author and instructor for the digital transformation program that we've
offered for the past couple of years now from Watt Speed at the University of Waterloo.
And so I work with a lot of companies around what they're doing around digital transformation.
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The focus of what I do is really on achieving results, on making change successful that
involves technology, including, of course, artificial intelligence.
That's really what I'm concerned with.
We know that 70% of technology projects fail.
And of course, it's really important to try and increase the probability that they'll
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be successful.
So that's really what I'm interested in and what I try to help with.
Awesome.
And I think the big question that's on everyone's mind, Peter, is, are you an AI?
Are I an AI?
No, but apparently I'm going to be.
Waterloo are creating some material for courses which will involve an avatar or an AI version
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of me as part of their experimentation with this technology.
So it will be with some trepidation that I engage in this activity.
I have no idea what it's going to look like yet, but it should be interesting and hopefully
will have value too.
So that'll help teach the courses in some way?
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Yeah, I think answer student questions perhaps and present material.
We're in a really early stage.
I'd be wrong to suggest that this is something that is going to be a significant part of
something the students might see in the near future, but I think we'll try it as we should.
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That's very neat.
And this would be potentially an example of a digital transformation in a project that
might go nowhere, might go somewhere.
So what would you suggest for a project like this to get it off the ground?
For a project like this, I think companies may do the finding an area where this can
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be tried with people who are going to be receptive to it and going to contribute to it being
successful.
For this type of project, I think is a relatively straightforward and obvious way to approach
it.
I think the big challenge is taking it further after that.
I think that initial phase of piloting technologies, seeing what it can do and gaining some familiarity
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with it is relatively straightforward.
It's something a lot of companies have done, but we also know a lot of them stop at that
point.
So the challenge is really moving beyond pilots, moving beyond the trial stage of using technologies.
So could you give us an example of a project that you've worked with or seen that has tried
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to bridge that gap between pilot and larger availability?
Yes, I think there are many examples of where companies have tried to do it, to expand beyond
their initial use of the technology.
Particularly, what we've seen since COVID is that during COVID, there were a lot of
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implementations that were relatively ad hoc, may have happened in one part of a company.
In some cases, they learned from that and they wanted to take it further, whereas in
others they may have decided not to proceed.
So that period of consolidation is what we're seeing a lot now.
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Part of the work that we've done to develop materials at Waterloo, we've spoken to people
that have been doing these things.
Then BDC was one of those in the financial services company, I guess, particularly for
startups.
Perhaps the approach they've taken is to say, okay, how do we move this forward?
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Their emphasis particularly is good on the cultural challenge that is necessary to ensure
effective adoption inside the organization.
The other that comes through is Interac, that company we all use pretty much.
All of a sudden?
Absolutely.
And they also went through this process of reviewing what they had done.
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Their emphasis was on the establishment of processes that would enable the technologies
to be adopted effectively.
But there are two examples that come quickly to mind.
Of course, there are many more, unfortunately, that don't go beyond that pilot phase.
There are many reasons for that, including that most senior management teams do not have
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the confidence or the experience to implement revolutionary change inside their organizations.
So it's one thing to undertake a pilot that is in a small part of your organization and
to manage the change that requires, but to then spread a radical change throughout an
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organization requires the management of a revolution to some degree, which most senior
managers have never been trained to do and have actually never done.
And that's a huge challenge for organizations to address.
It's very clear, it's fairly easy to imagine scenarios and implementations of technology
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or applications of them that can have a wonderful impact on an organization.
But to get people who can make that happen effectively is a challenge.
Basically what we've got in most companies are people whose capability that they were
selected for in the job is based on their ability to make the organization run effectively
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every day, doing much the same, if not exactly, usually exactly the same tomorrow as they
did today.
They're not necessarily people with aptitude for radical change.
Some of them do, some of them are going to be capable of making that change, but it is
a big change.
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And our job at Waterloo is to try to help them make that change as easily, quickly as
possible to get to a point where they can be confident to do it.
And you mentioned two big topics here, I think.
And one of those is that internal change management and leading a revolution.
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And the other one you mentioned earlier with BDC, I think, which is getting greater adoption
of the product outside of the company.
So there's that kind of outside change management and inside.
Would you say that's accurate?
Yes, I think so.
They're both huge challenges, absolutely.
And they are also significant reasons for why we don't get beyond the 30% success rate
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we have at the moment.
And in your role now, which side of that do you find yourself participating in more?
Well, a good question.
I would say equally.
I wouldn't say one was more than the other.
What we try and do in our digital transformation program is to take a holistic approach that
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combines the main elements that we understand are the challenges that organizations face.
We did a study to look at the research that others had done and also to do some of our
own where we went and talked to companies, surveyed those visited people, and basically
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created a framework for understanding the common challenges in digital transformation.
And then built our program around that with guidance on how these challenges might be
addressed, but also by developing tools that make it easier for organizations to address
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the various areas where the challenges existed.
Because that's basically our main goal is to help most organizational leaders more easily
and confidently adopt new technologies.
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That's what we need them to do and in most cases, that's where they're lacking.
They don't have that confidence to be able to do this.
I have seen this addressed in other ways, one of which is where organizations are told
you need to be more of a risk taker.
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The future is uncertain.
The outcomes may be uncertain for what you're going to do with technology, but you really
can't worry about that right now.
You've just got to go for it.
I think that's really bad advice.
I don't think any organization leader should ever do that.
I think you can only make change if you are comfortable and confident that change is going
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to benefit the organization.
You shouldn't do otherwise.
You're being basically irresponsible.
Closing that gap to help people get to a point where they can have that confidence is... there
are many areas we need to tackle.
Is that framework you mentioned developing through all those interviews, is that a tool
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designed to help give leaders the confidence?
Yes.
Basically in each of the areas that we identified as being important, and we looked at a number.
We looked at factors around people and culture and the need for change there, usually identified
as being important.
We looked at integrating technology and processes.
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We looked at leadership.
We looked at innovation of organizations, become more innovative, become much more capable
in terms of continuous improvement.
We looked at the influence of privacy and security and regulation and things like that
too.
In each of these areas, we haven't done it for all of them yet, but in most of them,
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we developed instruments which organizations can apply that will help them simplify and
plan the activity that they're going to undertake in each of these areas successfully.
One of the things that we've emphasized in the development and selection of tools is
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that they should be tools that enable collaboration within the organization so that the integration
that's necessary of activity in all of the areas I've just mentioned and more can happen
effectively and happen easily.
Organizations aren't always used to collaborating well.
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We've talked about it for a long time.
Teamwork has been very important, but at the same time, we still find organizations are
very siloed.
Management teams don't work well with each other either at mid or senior level often.
Often there's a culture of competition, then this is not good.
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So it's enabling organizations to get to a point where they can't be thieve in the ways
that are necessary for digital transformation to be successful.
I've definitely worked even small companies that would have problems collaborating on
something.
So that sounds super useful.
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Just at a high level, could you outline the components of the framework?
Yeah, fairly simply.
We found a larger number of areas, but we categorized those into four main areas.
Developmental and culture, technology and process integration, moving the needle on
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innovation we called it, which combined innovation, leadership, and continuous improvement.
And then privacy, security, and regulation, which are always important and are likely
to become more important because we are seeing more regulation taking place.
We're seeing more concerns about privacy and security.
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So those are the main categories.
One thing I should emphasize about the categories though is that they do, we did have some items
that we've included in categories where the inclusion may be a little bit of a stretch.
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So if you feel something was excluded, that we've not considered something because it
wouldn't easily fit in one of the categories, there's a fair chance it's there somewhere.
Yes, all frameworks have their limitations, but it's very helpful to use them to talk
at a high level.
And you're targeting executives primarily.
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And so in that case, could you tell us a bit about a time where a company had to change
internally in order to implement something in AI or a new type of technology like that,
and you use this framework?
The framework's been used alongside the other tools that we've developed.
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One of the tools that the people who are in the program at the moment are using is our
roadmap integration matrix, which basically is a matrix of the categories that the main
categories that we think should be included inside consideration of the roadmap that an
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organization was using to plan the detail of its implementation.
So many companies have used that layer based on their work inside the program, ranging
from retail to manufacturing to banking to many others.
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And are there any that you specifically advised?
Yes, as part of the program that we have, there is a consulting element to it.
So everyone who participates in our program as part of the fees they pay receives a minimum
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of one hour of consulting time.
And so I meet with the companies individually.
They may include other people from their company.
And we discuss the challenges they face, the work that they're doing, and try and help
them do that.
So basically, I work with all of the companies that we have inside our program, ever so from
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government to manufacturing to oil and gas to mining to many different sectors here.
And it's fascinating to see the challenges the different sectors face in the way that
they're approaching their digital transformation activity.
Could you give us an example of maybe one of the more exciting ones to you personally
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and how that worked out?
Okay.
I mean, I think we see many in manufacturing, we've seen many examples of automation where
they've been considering particularly around maintenance, where they're looking at the
application of AI to their maintenance activity, where I think the work that we've done with
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them often has been around, how do you put this into practice?
So it's relatively straightforward to imagine or to achieve a situation where you're gathering
lots of data from your equipment and you want to implement a preventative maintenance program.
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But it's another one to put that into practice.
How do you challenge from being a firefighting type of maintenance operation where you're
responding when things go wrong to one where your resources are moved over to preventative?
That's often a huge struggle.
Obviously it may require changes in the roles of the people in maintenance and the work
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we'll need to do to make that transition.
But it's also around what do you do when fires break out?
Which during that transitory period and how do you ensure you've got the resources you
need to be able to make that change?
It would be nice to be able to imagine that you could reduce the amount of resources you've
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got allocated to maintenance because you've implemented a good preventative program.
But the reality is that's often not the case.
Certainly through the transitional period, you may require additional resources and there
are difficult decisions to be made around about these things.
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So that's an example of a real world challenge that exists from the application of AI.
I think more broadly, the application of AI to process improvement has huge potential
and will enable operations to be transformed.
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But at the same time, having the resources to be able to make that happen, it's one
thing to be able to understand your world and your company better through the information
that you'll gain from AI.
But quite another to implement the decisions that follow from that.
That's often much more difficult than simply getting the advice.
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Organizations have lots of advice already around things that they could improve.
The question is always around the resources and the allocation of time that are necessary
in order to actually make the changes.
And that's the type of challenge we often have to deal with.
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I think you said something there that was beautiful actually, because taking AI as a
means to then think of, I'm not just fighting fires, I am using AI to look ahead and be
proactive.
And I feel like AI is a unique tool in that way, because it'll try and give you information
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about what might happen.
And thinking of that as what I hear from people sometimes is that AI is just, it solves things.
But what you've explained is it doesn't just solve something, it gives you a new capability.
It shifts your focus from now to the future.
And that transition period is difficult.
It probably takes more resources.
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It probably takes upskilling.
It probably takes even deciding at an executive level, is this the advice that I want to listen
to now?
Is it something I want to act on?
So I think there are maybe two big topics in there.
And one is that AI is not taking my jobs.
Probably if I'm a maintenance guy, I can do something that's still needed and useful.
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It might be a bit different, but it's still a maintenance role.
And then that how do I as an executive make a decision that this is a project I should
pursue now?
I'd love to hear about those.
Yeah, you mentioned a number of things there.
The question of AI and jobs, I think is obviously important and is one that many people are
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worried about.
But I've dealt with technological change and jobs for a very long time, particularly dealt
with it when we're moving into an era where there was more robotics, electronics, hydraulics,
pneumatics, and computer controlled equipment a long time ago.
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On a bank time, the argument with workers around this really has two possibilities.
One is resist and hope that it won't happen.
And of course, that is completely unrealistic.
It is going to happen.
If it doesn't happen in your company, it's going to happen in another one, and your company
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will not compete and you will lose your job.
No question.
That is the unfortunate, perhaps, truth.
But on the other side of that, there is the possibility of adapting, gaming with skills
that the new technologies require, understanding how your job can change and the type of role
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you can play in the future.
And that may be within your own company.
It may be somewhere else.
But as far as the price for workers is concerned, that is the best, for me, I think is the best
advice.
It is the future is changing.
And your ability to adapt to that and take advantage of it is how you're going to survive.
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There isn't another option.
As hard as that might sound, there really isn't another option.
And so our job as educators, trainers, as companies should be to help people do that.
So that said, I did see a study, I can't remember the precise statistic at the moment, but it
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basically said that most workers are not confident in their ability to do this.
So that is the challenge that we have today around this, I think, is what can we do to
make it easier for workers to change and to feel comfortable about the future?
We know, you know, statistics tell us this, but there is also anecdotal personal evidence
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that people are scared about what the future might bring.
It's one of the main factors that's underlie of how people all vote in the election in
America in a couple of months' time.
That is a problem we really have to address, that people are basically scared about the
future.
They're scared by AI.
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Many of them.
And we've got you overcome the last few years.
We've got to do it because our economies depend on it, but also not just making sure we're
economically successful.
But the world can be a better place if we apply technologies well.
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There are many things we've got to mitigate that are around negative consequences, but
our focus should be on mitigating those negative factors and trying to ensure that we take
advantage of the huge opportunities that technology provides.
Yeah, I even just spoke this morning to a business owner of a 40-person agency, and
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he's wondering what the future of his agency and business are.
It's on everybody's mind.
Is there something that you would recommend that people look into for looking to upscale
into the future?
I think there are many opportunities around upscaling at the moment.
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There are many different places you can get upscaling training of one kind or another.
Many organizations growing up to do this.
I think the area that perhaps people need help around is making that selection from
a possible wide range of choices.
There's a lot of good training out there.
There's also a lot of bad training.
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I think selecting what's good and getting the support that you need can be difficult.
There are various ways to do that.
To look at reviews, to talk to your friends, to look to established providers like the
University of Waterloo.
But basically to be very careful.
I think the only education market today, because of the disruption which is taking place, which
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is only really just a guy, and that's a whole other area of discussion.
But because within that disruption, it becomes harder for the consumer or the purchaser of
training to really understand what they're buying.
And that, I think, is a significant difficulty.
The other thing that I think is really important around this is what organizations do with
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training themselves.
And what they do to continue to develop their organizational knowledge around technology.
This has come to the fore as an issue, I think, primarily around AI.
And where we talk to so many organizations, I do, you probably do too, Daniel, where people
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are saying, we've got to get on board with AI.
It's moving so fast, we can't get left behind.
What do we do?
And that is very interesting that they say this to me.
The reason I think it's interesting is that in an ideal world, organizations would have
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their radar, or whatever you want to call it, where they were monitoring developments
in their world and being ready for them.
Instead, with AI, AI has been around, being developed slowly for 20 years, 30 years, longer,
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but they're not aware of it.
And that really is a comment, or an indicator of their external knowledge as an organization.
The world we're in now requires them to be much more aware of these things and ready
for them.
And so that, I think, is something they need to pay more attention to.
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But there's also the continuous within them.
If they were aware of the way the world is changing quickly, that would result, if they
deal with it well, in the development of their employees in response to this, which requires
that training will be a much more important element in what they do than it is today.
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All in the future, beyond the fourth industrial revolution, which we're in now, one of the
characteristics of that revolution is that skills will change more quickly, technology
will change more quickly, and people can't stay the same while that happens.
They've got to change with it, and companies have got to make it easy for them to do that.
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Going back to a previous point, it's a challenge for organizations that are not designed to
change, that are deliberately designed not to change, to be repeatable, to have high
repeatable, reliable quality in what they do.
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That's a huge transition for them to make.
In Canada's economy, in developed nations, in any nation's economy, our ability to adapt
to this new world is going to determine our future economic success, the future welfare
of our citizens, the future services that governments can offer, and the future standards
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of living that people have.
We've got to get this right.
To me, it is the biggest challenge facing us as a country at the moment.
So would you recommend then that small, medium, large, all organizations put more effort into
training and staying up to date?
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Yes.
I mean, there is no other way.
There is no other answer.
Of course, yes.
They do need to do that.
And organizations have been reluctant to train in the past, partly because of the money that
it might cost them, but also because of the time it takes people away from what they would
consider to be productive activity.
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But it is essential.
Automation might help to make that easier, reduce the impact on productivity.
At the same time, there isn't another option.
They've got to do it.
They've got to find a way to do it.
But this debate over that training takes people away from productive work has gone on for
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a very long time.
Companies have used this as an excuse not to train for a very long time.
And we see the consequences.
Unless they do it now, they will disappear.
Yeah.
I think my favorite example of that is back in the 70s, IBM decided, you know, we've got
billions of dollars.
We're really far ahead of our competitors.
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Interest rates are really high.
Let's put it in the bank.
So instead of investing in research, they put it in the bank and they lost their lead
by three years.
This should be fairly obvious to people by now in the world that we're living in.
But yes, that's a great example.
And we know from research that's been done fairly widely established that when things
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get tough, when the economy declines or is not so strong, one of the first things organizations
cut back on is research and training.
So that's the wrong time to do that.
I want to go back to the other part that I mentioned before about how executives can
make sense of which signals they should be paying attention to.
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Because like you mentioned, there's always been opportunity for training.
When you've got a potential project using AI, process improvement or something new,
what advice would you give someone who needs to make sense of the noise?
I guess there are two parts to the answer.
One is having that external view is very important to know what's going on in your environment
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because that's going to inform your priorities as far as technology is concerned.
What are the things that your competitors are doing?
What is happening inside your market?
What's happening with your customers?
And then more broadly, what's happening with the world?
There are many instruments.
We use some of them in our programming, which are standard tools for analyzing the external
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environment to your company.
They'd be included in SWOT analysis and other things.
But also of course your internal capabilities are going to influence your selection as well.
But basically, it's your business plan and your strategic objectives based on your analysis
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are what are going to inform your technology decisions.
Time and time again, this doesn't happen within organizations.
I see example after example of companies that say, no, we've got this good idea for this
technology, which is going to improve productivity.
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And it will.
Their idea, if successfully implemented, will improve their productivity, but it's not got
anything to do with their future strategic development as an organization and with what's
happening in their market and what should be most important to them.
So this question of what is most important really has to include a strong business element.
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But then this raises other questions, including how do we effectively combine the business
and technology parts of the organization with business people, the tech people, how do we
put them together?
That question I've been dealing with for at least 30 years has been a topic of discussion
or how do we effectively combine technology and the business?
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I love that question.
Yes.
Well, I mean, it never goes away.
And there is no simple there is no quick answer to it.
I think often when we discuss this question, people would like an answer which says, ah,
there's an easy way to do this.
There isn't.
The only way to do it is to, well, there are a few elements.
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The business people need to understand a bit more about the technology and they need to
work into doing that.
The tech people need to do the other side of that and learn more about business.
And then they have to be effectively combined in an organization as good collaboration.
That's all we can learn.
But as an organization to enable this to happen, everything we've already talked about in
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terms of becoming a learning organization that is continually learning about technology.
And I think the one specific piece of advice around this would be you need a plan for it.
This is not going to happen automatically.
You need to consciously sit down and say, this is what we're going to do this year
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to do this.
And this is our plan going into the future.
These elements will exist.
A senior management team are going to go to tech conferences.
Maybe we're going to bring these people in this year.
We're going to have a program of education around this.
It's got to be consciously done.
It's not going to happen any other way.
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So that's part of it.
One other part that I need to include is that within organizations, one of the things I
often see is technology being adopted that is not consistent with the operating model
that the organization has.
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So there are various operating models that companies have.
There are traditional ones that might often be referred to as scientific management, where
they're focused particularly on minimizing costs, where there is a centralization of
control, various aspects.
We don't need to go into them now.
Like an automaker.
Sometimes, yes, because we also have lean, which might also be an automaker.
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So lean manufacturing and lean operations, they're applied in many different types,
from government to McDonald's to elsewhere.
But lean, or another operating system model, and agile maybe being another.
And often today we're seeing hybrids of agile and lean as well.
Some organizations do often, or too often, is they'll bring in technology that is not
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consistent with their operating system model.
So this creates problems.
We were more concerned about operating system models before the rise of the presence of
technology within decision-making on companies, or within consideration of how companies would
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approve.
That still has to be considered, operating system models, or operating models.
You can call them whatever you want, but that's still important.
I'm sure there's consistency there.
It's not something that's given enough attention.
We've developed a simulation teaching tool that focuses on this aspect.
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It's very important and needs also to be considered.
Sounds around what your priorities are as far as technological change goes.
Sounds like technology has to be considered as a business priority, like a strategic priority
for the business.
And maybe that's different than it used to be even 10 years ago.
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Yes.
Technology is absolutely strategically important for most organizations, if not all.
I think all have got to consider the impact of technology in their industry and their
business and determine what their response should be.
There may be some where the response is, okay, we don't need to do anything.
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I haven't found many of those yet, but there are.
But there really are.
Shoeshine or maybe, I don't know.
But this is, in fact, with Shoeshine, that would need to be considered because Shoeshine
must be impacted by the fact that people are working for the whole more.
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So there is a technological impact on that impact of technology on Shoeshine.
Therein is.
That's a digression.
But they've got to look at it and say, what is our response?
Where do we need to go?
Every company has to do that.
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And last question from me, is there anything that you'd like to share about what you're
working on now?
Working on a lot of things at the moment, considering a very, just completely removed,
to some degree removed from what we're talking about here.
I'm working on a case study on the impact of technology on world trade.
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And the process of convergence between rich and poor countries and how that might develop
in the future.
One of the interesting aspects of that is automation and the application of AI as part
of that, of course.
And whether that's going to lead to the gap widening between rich and poor countries.
(39:53):
It had been narrowing, partly because of global, largely because of globalization.
But COVID caused the gap to widen a little bit.
And now we're moving into a period where automation is rising, calls for protection is arising
for all sorts of reasons.
(40:17):
And so the consequence there is uncertain at the moment.
So that's an area that I think is very important as far as technology goes.
It is one that I'm working on today.
We are continuing to develop our simulation, but we're going to develop it further.
It's fairly basic at the moment.
(40:37):
We've got resources to develop it a lot further, which should be interesting.
Those are two of the areas, I guess, that I might mention just now.
There are many others.
Things move very slowly in academia.
And so survival requires that there are a number of things cooking at the same time.
(40:59):
And if people wanted to connect with you or learn more, how can they do that?
Here my email pdcar at uwaterloo.ca is usually the easiest way to do that.
Or they can find me on LinkedIn, which is on publicly visible rare.
I'll link that after too.
Thank you very much, Peter.
This was an awesome discussion.
(41:20):
I'm sure people will get a lot out of it.
And for me especially, that connecting the business people with the tech people is something
I feel very passionate about.
So I'm glad you do too.
Yes, extremely important.
And it's been great talking with you, Daniel, and anytime.
(41:41):
Thanks for listening.
I made this podcast because I want to be the person at the city gate to talk to every person
coming in and out doing great things with AI and find out what and why.
And then share the learnings with everyone else.
It would mean a lot if you could share the episode with someone that you think would
like it.
(42:01):
And if you know someone who would be a great person for me to talk to, let me know.
Please reach out to me at Daniel Manary on LinkedIn or shoot an email to daniel@manary
.haus, which is Daniel at M-A-N-A-R-Y dot H-A-U-S.
(42:21):
Thanks for listening.
Welcome to Artificial Insights, the podcast where we learn to build AI people need and
(00:06):
use by interviewing product leaders who have launched AI products.
I'm your host, Daniel Manary, and today I'm joined by Peter Carr from the University of
Waterloo.
At the University of Waterloo, Peter works with the Department of Management Sciences
as well as the Conrad School of Entrepreneurship and Business.
(00:26):
And across those functions, he's worked with dozens, possibly more, of businesses and helped
them adapt to this fourth industrial revolution that we are experiencing now.
Peter, could you introduce yourself to our audience?
Sure.
My name is Peter Connor.
I'm an associate professor at the University of Waterloo in the Department of Management
(00:50):
Sciences, which is part of the Faculty of Engineering.
I am also the author and instructor for the digital transformation program that we've
offered for the past couple of years now from Watt Speed at the University of Waterloo.
And so I work with a lot of companies around what they're doing around digital transformation.
(01:14):
The focus of what I do is really on achieving results, on making change successful that
involves technology, including, of course, artificial intelligence.
That's really what I'm concerned with.
We know that 70% of technology projects fail.
And of course, it's really important to try and increase the probability that they'll
(01:37):
be successful.
So that's really what I'm interested in and what I try to help with.
Awesome.
And I think the big question that's on everyone's mind, Peter, is, are you an AI?
Are I an AI?
No, but apparently I'm going to be.
Waterloo are creating some material for courses which will involve an avatar or an AI version
(02:05):
of me as part of their experimentation with this technology.
So it will be with some trepidation that I engage in this activity.
I have no idea what it's going to look like yet, but it should be interesting and hopefully
will have value too.
So that'll help teach the courses in some way?
(02:27):
Yeah, I think answer student questions perhaps and present material.
We're in a really early stage.
I'd be wrong to suggest that this is something that is going to be a significant part of
something the students might see in the near future, but I think we'll try it as we should.
(02:49):
That's very neat.
And this would be potentially an example of a digital transformation in a project that
might go nowhere, might go somewhere.
So what would you suggest for a project like this to get it off the ground?
For a project like this, I think companies may do the finding an area where this can
(03:16):
be tried with people who are going to be receptive to it and going to contribute to it being
successful.
For this type of project, I think is a relatively straightforward and obvious way to approach
it.
I think the big challenge is taking it further after that.
I think that initial phase of piloting technologies, seeing what it can do and gaining some familiarity
(03:43):
with it is relatively straightforward.
It's something a lot of companies have done, but we also know a lot of them stop at that
point.
So the challenge is really moving beyond pilots, moving beyond the trial stage of using technologies.
So could you give us an example of a project that you've worked with or seen that has tried
(04:09):
to bridge that gap between pilot and larger availability?
Yes, I think there are many examples of where companies have tried to do it, to expand beyond
their initial use of the technology.
Particularly, what we've seen since COVID is that during COVID, there were a lot of
(04:33):
implementations that were relatively ad hoc, may have happened in one part of a company.
In some cases, they learned from that and they wanted to take it further, whereas in
others they may have decided not to proceed.
So that period of consolidation is what we're seeing a lot now.
(04:53):
Part of the work that we've done to develop materials at Waterloo, we've spoken to people
that have been doing these things.
Then BDC was one of those in the financial services company, I guess, particularly for
startups.
Perhaps the approach they've taken is to say, okay, how do we move this forward?
(05:13):
Their emphasis particularly is good on the cultural challenge that is necessary to ensure
effective adoption inside the organization.
The other that comes through is Interac, that company we all use pretty much.
All of a sudden?
Absolutely.
And they also went through this process of reviewing what they had done.
(05:39):
Their emphasis was on the establishment of processes that would enable the technologies
to be adopted effectively.
But there are two examples that come quickly to mind.
Of course, there are many more, unfortunately, that don't go beyond that pilot phase.
There are many reasons for that, including that most senior management teams do not have
(06:04):
the confidence or the experience to implement revolutionary change inside their organizations.
So it's one thing to undertake a pilot that is in a small part of your organization and
to manage the change that requires, but to then spread a radical change throughout an
(06:29):
organization requires the management of a revolution to some degree, which most senior
managers have never been trained to do and have actually never done.
And that's a huge challenge for organizations to address.
It's very clear, it's fairly easy to imagine scenarios and implementations of technology
(06:57):
or applications of them that can have a wonderful impact on an organization.
But to get people who can make that happen effectively is a challenge.
Basically what we've got in most companies are people whose capability that they were
selected for in the job is based on their ability to make the organization run effectively
(07:21):
every day, doing much the same, if not exactly, usually exactly the same tomorrow as they
did today.
They're not necessarily people with aptitude for radical change.
Some of them do, some of them are going to be capable of making that change, but it is
a big change.
(07:41):
And our job at Waterloo is to try to help them make that change as easily, quickly as
possible to get to a point where they can be confident to do it.
And you mentioned two big topics here, I think.
And one of those is that internal change management and leading a revolution.
(08:03):
And the other one you mentioned earlier with BDC, I think, which is getting greater adoption
of the product outside of the company.
So there's that kind of outside change management and inside.
Would you say that's accurate?
Yes, I think so.
They're both huge challenges, absolutely.
And they are also significant reasons for why we don't get beyond the 30% success rate
(08:30):
we have at the moment.
And in your role now, which side of that do you find yourself participating in more?
Well, a good question.
I would say equally.
I wouldn't say one was more than the other.
What we try and do in our digital transformation program is to take a holistic approach that
(08:56):
combines the main elements that we understand are the challenges that organizations face.
We did a study to look at the research that others had done and also to do some of our
own where we went and talked to companies, surveyed those visited people, and basically
(09:18):
created a framework for understanding the common challenges in digital transformation.
And then built our program around that with guidance on how these challenges might be
addressed, but also by developing tools that make it easier for organizations to address
(09:40):
the various areas where the challenges existed.
Because that's basically our main goal is to help most organizational leaders more easily
and confidently adopt new technologies.
(10:00):
That's what we need them to do and in most cases, that's where they're lacking.
They don't have that confidence to be able to do this.
I have seen this addressed in other ways, one of which is where organizations are told
you need to be more of a risk taker.
(10:20):
The future is uncertain.
The outcomes may be uncertain for what you're going to do with technology, but you really
can't worry about that right now.
You've just got to go for it.
I think that's really bad advice.
I don't think any organization leader should ever do that.
I think you can only make change if you are comfortable and confident that change is going
(10:43):
to benefit the organization.
You shouldn't do otherwise.
You're being basically irresponsible.
Closing that gap to help people get to a point where they can have that confidence is... there
are many areas we need to tackle.
Is that framework you mentioned developing through all those interviews, is that a tool
(11:07):
designed to help give leaders the confidence?
Yes.
Basically in each of the areas that we identified as being important, and we looked at a number.
We looked at factors around people and culture and the need for change there, usually identified
as being important.
We looked at integrating technology and processes.
(11:31):
We looked at leadership.
We looked at innovation of organizations, become more innovative, become much more capable
in terms of continuous improvement.
We looked at the influence of privacy and security and regulation and things like that
too.
In each of these areas, we haven't done it for all of them yet, but in most of them,
(11:51):
we developed instruments which organizations can apply that will help them simplify and
plan the activity that they're going to undertake in each of these areas successfully.
One of the things that we've emphasized in the development and selection of tools is
(12:14):
that they should be tools that enable collaboration within the organization so that the integration
that's necessary of activity in all of the areas I've just mentioned and more can happen
effectively and happen easily.
Organizations aren't always used to collaborating well.
(12:35):
We've talked about it for a long time.
Teamwork has been very important, but at the same time, we still find organizations are
very siloed.
Management teams don't work well with each other either at mid or senior level often.
Often there's a culture of competition, then this is not good.
(12:56):
So it's enabling organizations to get to a point where they can't be thieve in the ways
that are necessary for digital transformation to be successful.
I've definitely worked even small companies that would have problems collaborating on
something.
So that sounds super useful.
(13:18):
Just at a high level, could you outline the components of the framework?
Yeah, fairly simply.
We found a larger number of areas, but we categorized those into four main areas.
Developmental and culture, technology and process integration, moving the needle on
(13:39):
innovation we called it, which combined innovation, leadership, and continuous improvement.
And then privacy, security, and regulation, which are always important and are likely
to become more important because we are seeing more regulation taking place.
We're seeing more concerns about privacy and security.
(14:01):
So those are the main categories.
One thing I should emphasize about the categories though is that they do, we did have some items
that we've included in categories where the inclusion may be a little bit of a stretch.
(14:22):
So if you feel something was excluded, that we've not considered something because it
wouldn't easily fit in one of the categories, there's a fair chance it's there somewhere.
Yes, all frameworks have their limitations, but it's very helpful to use them to talk
at a high level.
And you're targeting executives primarily.
(14:43):
And so in that case, could you tell us a bit about a time where a company had to change
internally in order to implement something in AI or a new type of technology like that,
and you use this framework?
The framework's been used alongside the other tools that we've developed.
(15:06):
One of the tools that the people who are in the program at the moment are using is our
roadmap integration matrix, which basically is a matrix of the categories that the main
categories that we think should be included inside consideration of the roadmap that an
(15:27):
organization was using to plan the detail of its implementation.
So many companies have used that layer based on their work inside the program, ranging
from retail to manufacturing to banking to many others.
(15:48):
And are there any that you specifically advised?
Yes, as part of the program that we have, there is a consulting element to it.
So everyone who participates in our program as part of the fees they pay receives a minimum
(16:09):
of one hour of consulting time.
And so I meet with the companies individually.
They may include other people from their company.
And we discuss the challenges they face, the work that they're doing, and try and help
them do that.
So basically, I work with all of the companies that we have inside our program, ever so from
(16:31):
government to manufacturing to oil and gas to mining to many different sectors here.
And it's fascinating to see the challenges the different sectors face in the way that
they're approaching their digital transformation activity.
Could you give us an example of maybe one of the more exciting ones to you personally
(16:56):
and how that worked out?
Okay.
I mean, I think we see many in manufacturing, we've seen many examples of automation where
they've been considering particularly around maintenance, where they're looking at the
application of AI to their maintenance activity, where I think the work that we've done with
(17:20):
them often has been around, how do you put this into practice?
So it's relatively straightforward to imagine or to achieve a situation where you're gathering
lots of data from your equipment and you want to implement a preventative maintenance program.
(17:43):
But it's another one to put that into practice.
How do you challenge from being a firefighting type of maintenance operation where you're
responding when things go wrong to one where your resources are moved over to preventative?
That's often a huge struggle.
Obviously it may require changes in the roles of the people in maintenance and the work
(18:07):
we'll need to do to make that transition.
But it's also around what do you do when fires break out?
Which during that transitory period and how do you ensure you've got the resources you
need to be able to make that change?
It would be nice to be able to imagine that you could reduce the amount of resources you've
(18:28):
got allocated to maintenance because you've implemented a good preventative program.
But the reality is that's often not the case.
Certainly through the transitional period, you may require additional resources and there
are difficult decisions to be made around about these things.
(18:49):
So that's an example of a real world challenge that exists from the application of AI.
I think more broadly, the application of AI to process improvement has huge potential
and will enable operations to be transformed.
(19:11):
But at the same time, having the resources to be able to make that happen, it's one
thing to be able to understand your world and your company better through the information
that you'll gain from AI.
But quite another to implement the decisions that follow from that.
That's often much more difficult than simply getting the advice.
(19:37):
Organizations have lots of advice already around things that they could improve.
The question is always around the resources and the allocation of time that are necessary
in order to actually make the changes.
And that's the type of challenge we often have to deal with.
(19:57):
I think you said something there that was beautiful actually, because taking AI as a
means to then think of, I'm not just fighting fires, I am using AI to look ahead and be
proactive.
And I feel like AI is a unique tool in that way, because it'll try and give you information
(20:18):
about what might happen.
And thinking of that as what I hear from people sometimes is that AI is just, it solves things.
But what you've explained is it doesn't just solve something, it gives you a new capability.
It shifts your focus from now to the future.
And that transition period is difficult.
It probably takes more resources.
(20:39):
It probably takes upskilling.
It probably takes even deciding at an executive level, is this the advice that I want to listen
to now?
Is it something I want to act on?
So I think there are maybe two big topics in there.
And one is that AI is not taking my jobs.
Probably if I'm a maintenance guy, I can do something that's still needed and useful.
(21:01):
It might be a bit different, but it's still a maintenance role.
And then that how do I as an executive make a decision that this is a project I should
pursue now?
I'd love to hear about those.
Yeah, you mentioned a number of things there.
The question of AI and jobs, I think is obviously important and is one that many people are
(21:25):
worried about.
But I've dealt with technological change and jobs for a very long time, particularly dealt
with it when we're moving into an era where there was more robotics, electronics, hydraulics,
pneumatics, and computer controlled equipment a long time ago.
(21:45):
On a bank time, the argument with workers around this really has two possibilities.
One is resist and hope that it won't happen.
And of course, that is completely unrealistic.
It is going to happen.
If it doesn't happen in your company, it's going to happen in another one, and your company
(22:07):
will not compete and you will lose your job.
No question.
That is the unfortunate, perhaps, truth.
But on the other side of that, there is the possibility of adapting, gaming with skills
that the new technologies require, understanding how your job can change and the type of role
(22:29):
you can play in the future.
And that may be within your own company.
It may be somewhere else.
But as far as the price for workers is concerned, that is the best, for me, I think is the best
advice.
It is the future is changing.
And your ability to adapt to that and take advantage of it is how you're going to survive.
(22:55):
There isn't another option.
As hard as that might sound, there really isn't another option.
And so our job as educators, trainers, as companies should be to help people do that.
So that said, I did see a study, I can't remember the precise statistic at the moment, but it
(23:18):
basically said that most workers are not confident in their ability to do this.
So that is the challenge that we have today around this, I think, is what can we do to
make it easier for workers to change and to feel comfortable about the future?
We know, you know, statistics tell us this, but there is also anecdotal personal evidence
(23:47):
that people are scared about what the future might bring.
It's one of the main factors that's underlie of how people all vote in the election in
America in a couple of months' time.
That is a problem we really have to address, that people are basically scared about the
future.
They're scared by AI.
(24:07):
Many of them.
And we've got you overcome the last few years.
We've got to do it because our economies depend on it, but also not just making sure we're
economically successful.
But the world can be a better place if we apply technologies well.
(24:27):
There are many things we've got to mitigate that are around negative consequences, but
our focus should be on mitigating those negative factors and trying to ensure that we take
advantage of the huge opportunities that technology provides.
Yeah, I even just spoke this morning to a business owner of a 40-person agency, and
(24:52):
he's wondering what the future of his agency and business are.
It's on everybody's mind.
Is there something that you would recommend that people look into for looking to upscale
into the future?
I think there are many opportunities around upscaling at the moment.
(25:13):
There are many different places you can get upscaling training of one kind or another.
Many organizations growing up to do this.
I think the area that perhaps people need help around is making that selection from
a possible wide range of choices.
There's a lot of good training out there.
There's also a lot of bad training.
(25:34):
I think selecting what's good and getting the support that you need can be difficult.
There are various ways to do that.
To look at reviews, to talk to your friends, to look to established providers like the
University of Waterloo.
But basically to be very careful.
I think the only education market today, because of the disruption which is taking place, which
(26:01):
is only really just a guy, and that's a whole other area of discussion.
But because within that disruption, it becomes harder for the consumer or the purchaser of
training to really understand what they're buying.
And that, I think, is a significant difficulty.
The other thing that I think is really important around this is what organizations do with
(26:27):
training themselves.
And what they do to continue to develop their organizational knowledge around technology.
This has come to the fore as an issue, I think, primarily around AI.
And where we talk to so many organizations, I do, you probably do too, Daniel, where people
(26:52):
are saying, we've got to get on board with AI.
It's moving so fast, we can't get left behind.
What do we do?
And that is very interesting that they say this to me.
The reason I think it's interesting is that in an ideal world, organizations would have
(27:13):
their radar, or whatever you want to call it, where they were monitoring developments
in their world and being ready for them.
Instead, with AI, AI has been around, being developed slowly for 20 years, 30 years, longer,
(27:34):
but they're not aware of it.
And that really is a comment, or an indicator of their external knowledge as an organization.
The world we're in now requires them to be much more aware of these things and ready
for them.
And so that, I think, is something they need to pay more attention to.
(27:56):
But there's also the continuous within them.
If they were aware of the way the world is changing quickly, that would result, if they
deal with it well, in the development of their employees in response to this, which requires
that training will be a much more important element in what they do than it is today.
(28:22):
All in the future, beyond the fourth industrial revolution, which we're in now, one of the
characteristics of that revolution is that skills will change more quickly, technology
will change more quickly, and people can't stay the same while that happens.
They've got to change with it, and companies have got to make it easy for them to do that.
(28:46):
Going back to a previous point, it's a challenge for organizations that are not designed to
change, that are deliberately designed not to change, to be repeatable, to have high
repeatable, reliable quality in what they do.
(29:07):
That's a huge transition for them to make.
In Canada's economy, in developed nations, in any nation's economy, our ability to adapt
to this new world is going to determine our future economic success, the future welfare
of our citizens, the future services that governments can offer, and the future standards
(29:32):
of living that people have.
We've got to get this right.
To me, it is the biggest challenge facing us as a country at the moment.
So would you recommend then that small, medium, large, all organizations put more effort into
training and staying up to date?
(29:54):
Yes.
I mean, there is no other way.
There is no other answer.
Of course, yes.
They do need to do that.
And organizations have been reluctant to train in the past, partly because of the money that
it might cost them, but also because of the time it takes people away from what they would
consider to be productive activity.
(30:14):
But it is essential.
Automation might help to make that easier, reduce the impact on productivity.
At the same time, there isn't another option.
They've got to do it.
They've got to find a way to do it.
But this debate over that training takes people away from productive work has gone on for
(30:36):
a very long time.
Companies have used this as an excuse not to train for a very long time.
And we see the consequences.
Unless they do it now, they will disappear.
Yeah.
I think my favorite example of that is back in the 70s, IBM decided, you know, we've got
billions of dollars.
We're really far ahead of our competitors.
(30:58):
Interest rates are really high.
Let's put it in the bank.
So instead of investing in research, they put it in the bank and they lost their lead
by three years.
This should be fairly obvious to people by now in the world that we're living in.
But yes, that's a great example.
And we know from research that's been done fairly widely established that when things
(31:22):
get tough, when the economy declines or is not so strong, one of the first things organizations
cut back on is research and training.
So that's the wrong time to do that.
I want to go back to the other part that I mentioned before about how executives can
make sense of which signals they should be paying attention to.
(31:45):
Because like you mentioned, there's always been opportunity for training.
When you've got a potential project using AI, process improvement or something new,
what advice would you give someone who needs to make sense of the noise?
I guess there are two parts to the answer.
One is having that external view is very important to know what's going on in your environment
(32:10):
because that's going to inform your priorities as far as technology is concerned.
What are the things that your competitors are doing?
What is happening inside your market?
What's happening with your customers?
And then more broadly, what's happening with the world?
There are many instruments.
We use some of them in our programming, which are standard tools for analyzing the external
(32:36):
environment to your company.
They'd be included in SWOT analysis and other things.
But also of course your internal capabilities are going to influence your selection as well.
But basically, it's your business plan and your strategic objectives based on your analysis
(32:57):
are what are going to inform your technology decisions.
Time and time again, this doesn't happen within organizations.
I see example after example of companies that say, no, we've got this good idea for this
technology, which is going to improve productivity.
(33:17):
And it will.
Their idea, if successfully implemented, will improve their productivity, but it's not got
anything to do with their future strategic development as an organization and with what's
happening in their market and what should be most important to them.
So this question of what is most important really has to include a strong business element.
(33:43):
But then this raises other questions, including how do we effectively combine the business
and technology parts of the organization with business people, the tech people, how do we
put them together?
That question I've been dealing with for at least 30 years has been a topic of discussion
or how do we effectively combine technology and the business?
(34:07):
I love that question.
Yes.
Well, I mean, it never goes away.
And there is no simple there is no quick answer to it.
I think often when we discuss this question, people would like an answer which says, ah,
there's an easy way to do this.
There isn't.
The only way to do it is to, well, there are a few elements.
(34:31):
The business people need to understand a bit more about the technology and they need to
work into doing that.
The tech people need to do the other side of that and learn more about business.
And then they have to be effectively combined in an organization as good collaboration.
That's all we can learn.
But as an organization to enable this to happen, everything we've already talked about in
(34:55):
terms of becoming a learning organization that is continually learning about technology.
And I think the one specific piece of advice around this would be you need a plan for it.
This is not going to happen automatically.
You need to consciously sit down and say, this is what we're going to do this year
(35:18):
to do this.
And this is our plan going into the future.
These elements will exist.
A senior management team are going to go to tech conferences.
Maybe we're going to bring these people in this year.
We're going to have a program of education around this.
It's got to be consciously done.
It's not going to happen any other way.
(35:39):
So that's part of it.
One other part that I need to include is that within organizations, one of the things I
often see is technology being adopted that is not consistent with the operating model
that the organization has.
(36:00):
So there are various operating models that companies have.
There are traditional ones that might often be referred to as scientific management, where
they're focused particularly on minimizing costs, where there is a centralization of
control, various aspects.
We don't need to go into them now.
Like an automaker.
Sometimes, yes, because we also have lean, which might also be an automaker.
(36:25):
So lean manufacturing and lean operations, they're applied in many different types,
from government to McDonald's to elsewhere.
But lean, or another operating system model, and agile maybe being another.
And often today we're seeing hybrids of agile and lean as well.
Some organizations do often, or too often, is they'll bring in technology that is not
(36:48):
consistent with their operating system model.
So this creates problems.
We were more concerned about operating system models before the rise of the presence of
technology within decision-making on companies, or within consideration of how companies would
(37:09):
approve.
That still has to be considered, operating system models, or operating models.
You can call them whatever you want, but that's still important.
I'm sure there's consistency there.
It's not something that's given enough attention.
We've developed a simulation teaching tool that focuses on this aspect.
(37:32):
It's very important and needs also to be considered.
Sounds around what your priorities are as far as technological change goes.
Sounds like technology has to be considered as a business priority, like a strategic priority
for the business.
And maybe that's different than it used to be even 10 years ago.
(37:55):
Yes.
Technology is absolutely strategically important for most organizations, if not all.
I think all have got to consider the impact of technology in their industry and their
business and determine what their response should be.
There may be some where the response is, okay, we don't need to do anything.
(38:20):
I haven't found many of those yet, but there are.
But there really are.
Shoeshine or maybe, I don't know.
But this is, in fact, with Shoeshine, that would need to be considered because Shoeshine
must be impacted by the fact that people are working for the whole more.
(38:43):
So there is a technological impact on that impact of technology on Shoeshine.
Therein is.
That's a digression.
But they've got to look at it and say, what is our response?
Where do we need to go?
Every company has to do that.
(39:05):
And last question from me, is there anything that you'd like to share about what you're
working on now?
Working on a lot of things at the moment, considering a very, just completely removed,
to some degree removed from what we're talking about here.
I'm working on a case study on the impact of technology on world trade.
(39:31):
And the process of convergence between rich and poor countries and how that might develop
in the future.
One of the interesting aspects of that is automation and the application of AI as part
of that, of course.
And whether that's going to lead to the gap widening between rich and poor countries.
(39:53):
It had been narrowing, partly because of global, largely because of globalization.
But COVID caused the gap to widen a little bit.
And now we're moving into a period where automation is rising, calls for protection is arising
for all sorts of reasons.
(40:17):
And so the consequence there is uncertain at the moment.
So that's an area that I think is very important as far as technology goes.
It is one that I'm working on today.
We are continuing to develop our simulation, but we're going to develop it further.
It's fairly basic at the moment.
(40:37):
We've got resources to develop it a lot further, which should be interesting.
Those are two of the areas, I guess, that I might mention just now.
There are many others.
Things move very slowly in academia.
And so survival requires that there are a number of things cooking at the same time.
(40:59):
And if people wanted to connect with you or learn more, how can they do that?
Here my email pdcar at uwaterloo.ca is usually the easiest way to do that.
Or they can find me on LinkedIn, which is on publicly visible rare.
I'll link that after too.
Thank you very much, Peter.
This was an awesome discussion.
(41:20):
I'm sure people will get a lot out of it.
And for me especially, that connecting the business people with the tech people is something
I feel very passionate about.
So I'm glad you do too.
Yes, extremely important.
And it's been great talking with you, Daniel, and anytime.
(41:41):
Thanks for listening.
I made this podcast because I want to be the person at the city gate to talk to every person
coming in and out doing great things with AI and find out what and why.
And then share the learnings with everyone else.
It would mean a lot if you could share the episode with someone that you think would
like it.
(42:01):
And if you know someone who would be a great person for me to talk to, let me know.
Please reach out to me at Daniel Manary on LinkedIn or shoot an email to daniel@manary
.haus, which is Daniel at M-A-N-A-R-Y dot H-A-U-S.
(42:21):
Thanks for listening.
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