Latest / C-Suite Perspectives / What the AI Acceleration Curve Means for Corporate Strategy

Transcript

What the AI Acceleration Curve Means for Corporate Strategy · C-Suite Perspectives

0:00
Welcome to C-Suite Perspectives, a signature series by The Conference Board.
0:04
I'm Steve Odland from The Conference Board and the host of this podcast series.
0:08
And in today's conversation we're going to talk about where we are with AI,
0:12
more importantly, where we're headed.
0:14
Joining me today is John Metselaar, the Council Director for our
0:18
Innovation Leadership Councils throughout Asia and Europe.
0:23
John, welcome to the program.
0:25
Thank you so much, Steve.
0:26
Pleasure to be with you and I'm honored to be invited.
0:29
Yeah.
0:29
John, everybody knows what AI is.
0:31
They're two letters.
0:32
They're A, and they're I, right?
0:33
But they really stand for artificial intelligence.
0:35
Lots of stuff has come out in the last couple years.
0:38
Things are changing but how do you define artificial intelligence?
0:42
I like the fact that we started fundamentals, actually.
0:44
When I teach I get back to definition before we jump into things
0:47
that we don't fully understand.
0:49
The essence of AI really is about representing encoding
0:53
pattern recognition at scale.
0:56
And as a consequence, it allows to process vast data sets that identify
1:01
correlations that we might never perceive.
1:03
This contrasts with human intelligence that excels at more nuance judgment
1:08
within specific boundaries and contexts.
1:11
At its core, John, it's software, right?
1:13
It's still ones and zeros.
1:15
It's the next phase of the digital evolution.
1:17
And that's something you've been talking about for nearly
1:20
40 years, the digital evolution.
1:22
This is just the next frontier, isn't it?
1:24
Yes and no.
1:24
What sets AI apart today is the ability to learn from experience
1:29
rather than follow predetermined rules.
1:31
We're observing a shift here that software does not only follow instructions but
1:37
can start to infer things and look at constant improvement, constant learning
1:42
and, therefore, unleashing huge potential.
1:45
You're right.
1:45
You hear all these terms, John, with AI.
1:48
Anytime you're launching into a new frontier people use different terms.
1:52
But in the business world today, you hear mostly about generative AI,
1:57
agentic AI, machine learning, large language models, robotics, and so forth.
2:03
Can you just talk through these forms of AI being used by business today.
2:07
Let's just start with generative and agentic.
2:12
Let's start indeed at generative AI.
2:14
It's mostly, at this point, about generating things.
2:16
Today it is mostly used as a sophisticated drafting tool.
2:20
Marketing teams generate content variations.
2:23
Legal departments use it for contract templates, software
2:27
developers get help in coproduction.
2:29
But it only scratches the surface of its potential.
2:32
The more transformative developments that involve using generative AI, you have to
2:36
start thinking about rapidly prototyping solutions across domains, number one.
2:40
Number two, testing product concepts through synthetic customer interaction.
2:45
Don't run through very long real customer experience anymore but
2:48
get real quick data and results through synthetic interactions.
2:52
And then maybe the biggest one is exploring strategic scenarios by
2:55
generating different market simulations.
2:58
There's also things like developing training materials
3:00
that are tailored to individual learning patterns, for instance.
3:03
The key here for implementation and for success is to connect that strength of
3:08
AI with that human refinement, rather than going for the finished product.
3:13
So generative AI is using the tools to generate something.
3:17
Content, you said, whether it's written or audio or video
3:22
or the creation of something.
3:23
And the next term that's used a lot is agentic AI, which has the word agent.
3:28
So AI is acting as an agent or as an automated tool.
3:33
Absolutely.
3:33
The key word here is autonomy.
3:35
This is the frontier that we are now.
3:37
This is very hot in today's environment because we're really starting to touch
3:41
on that frontier and play around with it.
3:44
And this is where systems operate with increasing autonomy towards very
3:48
clearly defined objectives rather than executing specific instructions.
3:52
So, for instance, right now early developments on this, just to bring
3:55
this to life a bit, procurement agents.
3:57
They're able to negotiate contracts with predefined parameters-- that
4:01
remains an absolute critical limitation.
4:04
Research agents that synthesize information across databases and then
4:07
they present those synthesized findings to the humans to be integrated with
4:12
that judgment that I mentioned earlier.
4:14
And then things like operating agents.
4:16
They monitor systems and implement corrective actions.
4:18
They can monitor an incredible number of parameters that humans can never do.
4:23
But again, within established guardrails.
4:25
So the critical discipline for business leaders here becomes to define
4:29
the scope of the autonomous action precisely, clear boundaries, and then
4:34
build robust monitoring in there.
4:36
So this is a relatively new frontier, as we both mentioned.
4:40
And I would say, try and move forward with caution in general.
4:43
Get experience with the new AI systems.
4:45
Learn then constantly refine.
4:47
It's absolutely critical that you built that experience within an organization
4:51
because, I think I heard you say that as well at CNBC, others are doing it as well.
4:56
There is this competitive pressure that is definitely at play.
4:59
People don't want to get left behind.
5:00
And when you're starting off into a brave new world, you don't
5:03
know what it looks like, so you have to rush ahead and try it.
5:05
A couple other terms, as I said, that are being bandied about are machine
5:09
learning and large language models.
5:11
Where are they?
5:12
They are the more basic elements of AI.
5:15
We've gone beyond that with generative AI and with agents.
5:18
Machine learning is you learn as a machine and that can be turning into language.
5:24
And, by the way, it's interesting.
5:26
We are almost three years from the launch of ChatGPT.
5:29
And, of course, that was the first time where the connection with
5:32
machines becomes intuitive and not through any difficult coding anymore.
5:39
So large language models really have sparked that hype that we're seeing today.
5:44
And development is moving really quickly.
5:46
It's interesting how much ChatGPT is being used as a more
5:51
sophisticated search engine.
5:52
Google was very sophisticated at one point.
5:54
It gives you a list of websites but then you have to plow
5:57
through all these websites.
5:59
Using ChatGPT, it comes up with an explanation, the
6:02
answer that you're looking for.
6:03
May not be precise, it's got issues, sometimes it hallucinates, meaning
6:08
it's got wrong stuff in there.
6:09
But that's not different than any website.
6:13
You have to check your sources and so forth.
6:14
But it is interesting how these tools, as they're introduced over time, get
6:19
used in a way and they settle in.
6:21
We've seen that in the digital evolution over the decades.
6:25
Yes, we have seen that in the digital evolution.
6:28
You mentioned that, of course, we've seen this in the nineties, right?
6:31
And so there is some similarity in terms of moving things forward.
6:34
This is the next wave, you can argue.
6:36
Having said that, though, I do believe that AI's economic potential can exceed
6:41
that of the digital transformation while facing different constraints.
6:45
Because think about it.
6:46
Digital transformation primarily eliminated friction in specific areas.
6:50
Communication delays, information silos, breaking through those
6:54
manual processes that got automated.
6:57
Things got easier.
6:58
AI promises something more profound.
7:00
It promises enhanced decision quality across an entire organization.
7:05
That's the potential but that's also the challenge for organizations.
7:08
Leaders need to start to think very boldly about where they want to take
7:13
their organization, their company, fundamentally re-imagining operations
7:18
when human processing capacity now no longer is limiting organizational
7:22
courage and structural flexibility.
7:25
It's not so much the technology that is holding us back right now;
7:27
that's advancing quite nicely.
7:29
We're advancing really quickly.
7:31
But it is more what I call the institutional inertia and the
7:35
political complexity of genuine operational transformation.
7:39
Almost like you want to get to new business models but your operating
7:43
system that you developed over so many years is holding you back.
7:46
Chand, one of our senior fellows from Cambridge, is calling this business model
7:50
incoherence, where the business model and the operating system are now out of whack.
7:55
How do you go and reinvent yourself?
7:57
Absolutely critical years breaking through these silos.
7:59
That is the promise and the challenge to fully tap into the potential of AI.
8:04
Yeah, it's all true.
8:05
If you go back to thinking about AI as a software, all the things that you just
8:08
described were using software tools.
8:10
But it also enables new technologies on the hardware side.
8:13
As an example, robotics.
8:15
In manufacturing facilities, AI is being used as the backbone and
8:19
the brain, if you will, behind the whole robotics introduction.
8:24
So there's a complimentary hardware, software evolution going on as well.
8:30
I think that's another part of the potential of AI, indeed, that you
8:33
connected with hardware and leverage robotics in so many different places
8:38
for so many different purposes.
8:39
Of course, everybody has seen the Amazon robots going around in that
8:43
very complex matrix without colliding.
8:45
That's so impressive.
8:46
But again, we're still at the cusp really of the full potential here.
8:50
Where are we on the development cycle with AI?
8:53
Is this just stage one?
8:55
And then, how many stages are there?
8:58
We need to recognize and we need to deal with and we need to tap
9:00
into, frankly, the fact that we're conditioned to think linearly.
9:04
But that can be very dangerous when you look at new technologies
9:07
because they typically follow, I should say, an exponential dynamic.
9:11
The patterns show really slow initial progress followed by surprisingly
9:16
rapidly mainstream integration.
9:18
We've seen that in the past, like the point that you made earlier
9:20
around digital transformation.
9:22
With AI, I believe this dynamic can be particularly dramatic because of
9:27
that capability for self-learning.
9:29
Each successful implementation creates new learning that
9:32
accelerates subsequent deployments.
9:34
When you go for technical challenge in the past, you would fix it and then
9:37
move on to the next technical challenge.
9:38
Now, when it gets solved, you can apply it at scale across different use cases.
9:43
The organizational resistance that is still in place, sometimes at
9:46
all levels of the organization, depending on the company and the
9:49
individuals, that will diminish, right?
9:51
Familiarity builds, value gets demonstrated.
9:54
Think about all the stakeholders that you deal with, your
9:56
suppliers, your customer base.
9:58
They will start to use the system as well.
9:59
So integration with them is going to be facilitated and smoothened and,
10:03
therefore, allow a much faster progress.
10:07
These are real factors.
10:08
And then again, we mentioned already the competitive dynamics.
10:12
Once peers demonstrate significant AI-driven advantages,
10:15
lagging organizations will face existential pressure.
10:19
I always say that organizations that plan AI strategies based on
10:24
conservative slash linear timelines, they are preparing for yesterday's
10:27
space rather than tomorrow's reality.
10:30
Yeah, and it's interesting.
10:31
In The Conference Board's latest CEO confidence survey, we asked CEOs
10:35
their views on AI and they told us that their biggest worry is being
10:39
left behind, as we mentioned before.
10:41
Their primary objective with AI investment is to make sure that doesn't happen.
10:45
And then when you ask them how they intend to deploy it, it's mostly around cost
10:49
reduction and operational efficiency.
10:51
Therefore you hear this thing about, oh, all these jobs are
10:53
going to get taken away by AI.
10:56
What's your view on that?
10:57
If you think back to the eighties and nineties and the deployment of the
11:01
personal computer and Microsoft Office and all the tools that came with it,
11:06
yeah, there were some jobs that were destroyed, but a lot more created.
11:11
And a lot of people got more efficient.
11:12
I don't know, Steve.
11:13
My speculation is that I don't think we're going to see so
11:16
many jobs being replaced by AI.
11:19
As so many people say, it's not AI that's going to take your job, it's
11:22
the person who is able to deal with AI that's going to take your job.
11:27
And I think that's a better way to look at it.
11:28
I think AI will have a huge impact, as I said earlier, I think already
11:32
about the middle management.
11:33
How does middle management, the task that it does.
11:36
Many of the tasks can be delegated now already and in the future,
11:39
even more time-consuming, tedious tests can be delegated.
11:41
Middle management, for instance, can be freed up much more towards
11:45
higher-value opportunities.
11:47
As a consequence, they'll play a key role in identifying solution
11:51
spaces that used to be way too vast for traditional methods.
11:55
And this shifts advantage.
11:57
This is interesting.
11:57
I think this shifts the competitive advantage towards organizations that
12:01
can formulate the right questions and evaluate AI-generated possibilities
12:06
rather than those that keep doing what they've been doing over the last years.
12:10
I was actually just listening to a YouTube video yesterday.
12:13
One individual that I follow was saying, he was very wise.
12:17
He said, become unbelievably curious at all levels of the organization,
12:21
unlearn, become unbelievably detailed, learn how to prompt, give the right
12:26
guidance, and become unbelievably human.
12:28
Because that's where the compliment with AI lies, right?
12:31
And the interface is now so natural.
12:34
So he was concluding those three points by saying, make AI your thinking partner.
12:39
And I think that will be hopefully, not only serving
12:43
businesses, but also humanity.
12:46
We're talking about the state of AI in the world.
12:47
We're going to take a short break and be right back.
12:51
Welcome back to C-Suite Perspectives.
12:53
I'm your host Steve Odland from The Conference Board, and I'm joined today
12:56
by John Metselaar, Council Director for the Innovation Leadership Councils at
13:01
The Conference Board in Asia and Europe.
13:03
So John, we were talking about what CEOs are saying about AI.
13:09
Another objective for CEOs is to help them drive innovation
13:13
in new products and services.
13:15
Give us some examples of where you see this happening.
13:25
AI is indeed being used in developing new products and services.
13:28
That can range from AI-powered products in their own right.
13:32
Also, maybe even more importantly, at least more broadly, I believe, is
13:36
AI can help and maybe even redefine how we innovate, as we used to call
13:40
it the P and G many decades ago.
13:42
Just a few illustrations for this.
13:44
Pharma, they employ AI to go and identify promising molecular structures
13:48
on a timeline that is now days versus years of testing in the lab.
13:53
Financial services firms develop hyperpersonalized offerings.
13:57
They synthesize individual behavior patterns with market dynamics at scale.
14:02
Manufacturing organizations optimize product designs because they take
14:05
those millions of parameters and they create combinations in ways that human
14:10
engineers in the past could never practice and could never evaluate.
14:14
These are a few examples.
14:16
It really is about AI enabling exploration of solution spaces
14:20
too vast for traditional methods.
14:22
That's the way to think about this.
14:24
It comes back to the point I made earlier, which is it's all about learning.
14:28
And so curiosity, become detailed, be human and then
14:32
create this learning organization.
14:34
That has to be the norm for the future.
14:37
Some people are worried about AI sentience, meaning AI actually
14:42
becomes more human and takes over and basically overtakes humanity, even.
14:48
It sounds like science fiction but there are groups that are urging
14:52
governments to put restraints and controls on all of this.
14:56
What's your view on sentience?
14:58
How close are we to that being reality and what are the risks?
15:02
This is a hard one.
15:03
I think we all want a crystal ball on that one, to be honest.
15:05
What is clear today is that there's no meaningful sentience
15:09
by any reasonable definition.
15:10
For now, AI is, as you mentioned, software, a very sophisticated software,
15:15
very capable software but software.
15:16
And it process patterns without understanding and generate
15:19
responses without comprehension.
15:21
That's what it does to you and that's where you need the human judgment to
15:23
come in and that's where the power lies if you make that connection proper.
15:27
I'm a bit worried that whole discussion around sentience takes attention away
15:32
from more immediate governance challenges.
15:35
I see the relevant concerns here center on three things: concentrated
15:38
power, who benefits from AI; systemic bias; and the erosion of human
15:44
judgment, as it overrelies on algorithm and what the software spits out.
15:49
These risks, they're very present already now and they will only
15:52
get worse and, in my view, they require institutional responses.
15:55
So yes, I think you do need to go and develop regulatory frameworks
15:59
for high-stakes applications.
16:01
I think you need absolute transparency for consequential decisions that
16:04
affect mankind, in particular.
16:07
And then you need to preserve human agency.
16:09
I hope we're not getting lazier through AI.
16:12
I hope we use it in the way that I described earlier, as a
16:14
plus, to augment our capability.
16:17
And I think that will help humanity be better than rather than worse.
16:22
I don't hear any fear in your commentary.
16:24
Is that mainly because we're so far away from it or because you have faith
16:29
that these systems will be guided by proper judgment and rules as we go?
16:36
As I said, the excitement here is about genuine enhancement of human capability.
16:40
It will extend our analytical reach, accelerate scientific discovery,
16:44
hopefully for the better of humanity.
16:45
Also, again, that use complexity that AI is now able to grapple with and
16:49
deal with and generate insights from.
16:52
The potential is there and I think it can be very powerful.
16:55
The worry I see is honestly is what I said earlier.
16:58
It's not so much the technology itself but it's more how we
17:02
are able to work it ourselves.
17:03
As leaders-- political leaders, business leaders, policy leaders--
17:07
how we organize around it.
17:08
Will we be able to have AI augment human judgment or replicate it?
17:13
Will we able to distribute these benefits broadly?
17:16
Or will they be concentrated narrowly and a few gain and a few start to rule what
17:20
we all, what everybody else needs to do?
17:23
And then the governance framework.
17:24
We do need to establish governance frameworks that ensure accountability,
17:28
transparency, and fairness, while still allowing innovation to happen.
17:33
These questions, in my view, they demand immediate attention from
17:36
institutional leaders . That's urgent because technology evolves rapidly
17:39
and our capacity for thoughtful governance doesn't always follow.
17:42
These are really important points.
17:44
Let's go back to the company because, most of our listeners are
17:47
in companies or in leadership roles.
17:49
As we've polled executives, we find that AI has a head start in human resources
17:55
(HR) functions and marketing functions.
17:56
HR has been using machine learning, large language models-- the
18:00
fundamentals, as you said, the building blocks-- for a long time.
18:03
Marketing is now using generative AI more.
18:07
Give some examples of these areas and then where are the next deployments?
18:12
Where's the most fertile ground?
18:14
HR and marketing, these functions, they're particularly amenable to AI precisely
18:19
because they involve processing large volumes of semi-structured information
18:24
toward probabilistic outcomes.
18:26
HR applications, take resume screening.
18:30
They identified qualified candidates much more consistently with much less
18:34
manpower than a manual review or than multiple manual reviews, obviously.
18:38
And they can analyze employee sentiment for early signals of retention
18:41
risk that can tap into different communication channels and sources.
18:45
Take marketing.
18:46
They deploy customer segmentation at granular scale.
18:50
They can optimize campaigns across channels in near
18:53
real time, huge advantage.
18:55
Or content personalization, that we're seeing already happening now in our
18:58
daily lives, has become a bit of a norm.
19:00
But you can adapt the messaging to individual engagement patterns.
19:04
These are obvious areas.
19:06
We have to be really careful that we, again, constructively bring together
19:11
those data sources with human judgment.
19:14
I've seen several instances where that led to mistakes, so don't
19:18
delegate decisions to the machine.
19:21
That is true for HR, marketing, but even beyond as well.
19:24
Again, the strength, the potential lies in connecting
19:27
the machine with human judgment.
19:30
Really words of wisdom there.
19:32
Some people have said that whatever you think the rate of
19:35
AI is going to be implemented, really just double, triple it.
19:39
And this goes to your point about, it's not linear is going to be exponential.
19:43
Why is that?
19:44
Is it just that the technology's going to get better or is it just
19:48
that as we get into this thing, it's the natural learning curve and people
19:51
just start adopting at a higher rate?
19:53
I already touched on that.
19:55
It happens with every technology, right?
19:56
When it comes in, they follow the S-curve, as they call it,
19:59
in the innovation practice.
20:00
So slow, initially slow, and then you start to get to the exponential
20:04
part of the S-curve and suddenly, things happen really quickly until
20:07
finally, after many years, technology wears out and the S-curve flattens.
20:12
Where we have been over the last several years is still on the
20:14
relatively shallow side of the S-curve.
20:17
Through experience, technological advancement, but also as I said earlier,
20:20
through organizations learning how better to use it and make some of
20:24
these bold organizational changes.
20:26
Modify your operating system to be more consistent with the new business
20:30
model that you want to get into.
20:32
With AI this can be particularly steep.
20:34
And that's why again, folks need to step into it urgently if in business because
20:39
of the self-learning capability of AI.
20:42
That allows that acceleration to go faster, in my view, than
20:45
what we've seen in the past.
20:46
Last question and just to wrap up.
20:49
When you look at the potential of AI, and you've been looking at digital
20:52
transformation for a long time, I do view this as the next phase.
20:55
Just it's more digital transformation in a sense.
20:58
It's different, of course, each phase is.
21:00
But one of the things that excites you and what worries you?
21:04
What excites me.
21:05
It offers the potential of better decision-making through more
21:09
comprehensive information synthesis.
21:11
As I said earlier, analyzing the incredible complexity of data and
21:16
then combine that with human judgment.
21:18
That's one.
21:18
There's better decisions, particularly if you're able to go beyond the operational
21:23
application in specific departments, are able to go and really deploy and employ
21:29
and leverage AI at an organization level by breaking through the silos, and are
21:33
able to reinvent your organization.
21:36
Second is faster innovation because you can much more quickly
21:40
explore solution spaces and then, hopefully, more equitable outcomes.
21:45
Because we inevitably have biases.
21:47
And if we get to transparent AI systems and that is in my view,
21:51
a must, then we can actually get to better outcomes and less bias.
21:56
But that obviously requires the input to be unbiased and transparent.
22:00
Alright, we're going to leave it there.
22:02
John Metselaar, thanks for being with us today.
22:05
It was real fun.
22:06
I hope you found this helpful.
22:07
Thank you.
22:09
And thanks to all of you for listening to C-Suite Perspectives.
22:12
I'm Steve Odland and this series has been brought to you by The Conference Board.