Latest / C-Suite Perspectives / AI Is Transforming Organizations. How Will Yours Change?

Transcript

AI Is Transforming Organizations. How Will Yours Change? · 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 how organizations
0:12
are transforming for AI, what strong AI strategy is necessary going
0:18
into 2026, and how organizations can successfully redesign their
0:23
strategies, structures, and processes.
0:27
Joining me today are Matt Rosenbaum and Erka Amursi, both principal
0:31
researchers with The Conference Board's Human Capital Center.
0:35
Matt, Erka, welcome to the show.
0:37
Thanks for having us.
0:39
You all did a great study that you've just published.
0:43
And for our listeners, that paper is available at tcb—our initials, The
0:50
Conference Board—tcb.org, and then click on the Human Capital Center under North
0:55
America, and you can find their great study called "Transforming Organizations
1:01
for AI." But we're going to spend the rest of this podcast and we're going to
1:03
talk about some of the highlights of this.
1:06
Erka, let's start with you.
1:08
Why did you write this?
1:12
Interesting question.
1:13
I was very interested in how AI would impact organization design because it's
1:19
not just a technological update, upgrade, but it has a transformative power.
1:26
So, moving forward with the same structures, with the same workflows and
1:32
processes, with all this transformation coming in, seems very challenging.
1:37
And a lot of organizations need guidance in terms of how to adapt
1:43
with the design in order to be able to use AI and its benefits at its best.
1:50
And Matt, this is different than just taking on yet another software tool.
1:56
So it's not like, we're going to roll out Teams or Zoom, because
2:02
AI just has so many tentacles into the business, doesn't it?
2:06
Yeah.
2:06
I think one of the main differences is it's not just a tech implementation.
2:11
That was one of the points that came up in our interviews with various enterprise
2:15
leaders, was the need to treat this as a bit more of a wholesale change than
2:19
just "we're swapping out some software."
2:22
And I think part of that is because one, AI use cases often cut across
2:26
the organizational boundaries.
2:28
So you have a lot of folks who are maybe collaborating more closely with
2:31
partners than they may have in the past or in different ways, whether it
2:34
be between HR and IT or HR and legal, or your risk management functions,
2:40
lines of business leaders, depending on your organizational structure.
2:43
So there is just a lot of partnership required.
2:47
And then also, AI changes what differentiates you as an organization.
2:52
One of the folks I was talking to is a leader at a large education
2:56
company, and she was saying because of AI capabilities, they had to put
3:01
personalization much more at the forefront of some of their offerings.
3:04
So it just changed the products that they were offering to
3:06
customers in the first place.
3:08
So it really does affect every facet of the enterprise.
3:11
So Erka, your point was that structures needed to change, and that's not
3:15
something that usually happens when you adopt different tools.
3:19
And so this is more than a tool, as Matt was saying.
3:22
It's really a transformation.
3:24
It's a strategic adoption, if you will, which then does require
3:29
organizations to be aligned about that.
3:31
And that's really what you're saying in the paper.
3:35
Yeah.
3:35
As long as we have a transformative power, you better have a strategy.
3:40
Otherwise it can be chaos, right?
3:43
So, that's why having a strong strategy is especially important.
3:48
You want to make sense of what is happening, and you want
3:51
to be able to adapt better.
3:54
And not only adapt, but also be more productive, be more efficient,
3:58
innovate better through this new, more-than-technology thing.
4:03
And that's why strategy is helpful.
4:06
And what do you see as the key components, the key strategy
4:09
components for AI in a company?
4:12
So what we discussed in our report is this integration of top-down and bottom-up
4:19
elements and finding the best balance between the two because you need the
4:24
vision, you need to have some perspective on how you're going to implement AI.
4:29
Are you going to build your internal tools, or are you
4:32
going to purchase something?
4:34
Or how you are going to design your workflows and processes based on it?
4:39
But you also need to experiment and have your own lab through bottom-up innovation.
4:45
You have AI implemented in different teams in the organization, and
4:48
you have those people coming up with ideas on how to better use
4:52
AI, on how to improve efficiencies on specific teams or processes.
4:57
So the integration of these two directions, top-down vision and
5:02
bottom-up experimentation and ideas, is probably the ideal path moving forward
5:10
into designing these organizations.
5:14
So Matt, if you're in a company, and you're trying to
5:18
put together an AI strategy.
5:21
As Erka was saying, what are the key components that you
5:23
advise people to consider?
5:26
Yeah.
5:26
I would start with the "why" of, what are we trying to accomplish here?
5:30
What are our goals as an organization?
5:32
Do AI capabilities change that in any way?
5:35
What differentiates you and your business.
5:37
From all your competitors?
5:38
And then looking at the "what," specifically the task level of, what
5:44
are we trying to do as an organization?
5:46
What is the work that we need to keep doing?
5:47
What are some of the things we need to change?
5:49
And then you can look more closely at the "how" of, what are the actual workflows
5:54
by which we accomplish that work?
5:55
Is it a person doing this?
5:57
Is it a person in partnership with machine?
5:59
Are we automating it entirely?
6:01
But ultimately, the goal with the strategy is not just to be able to
6:05
determine what you're going to say yes to, but what you're going to say no to.
6:09
I think a lot of organizations sometimes forget or misunderstand just some of the
6:14
trade-offs that need to be made when it comes to resources for AI initiatives.
6:19
So having a clear vision of "this is what we're trying to accomplish" and "this
6:22
is what we're going to prioritize," is going to help you make a lot of those
6:26
decisions and avoid wasted effort.
6:28
But you don't do AI for the sake of doing AI, right?
6:31
I mean, you do it for a constituency, and in today's business world,
6:36
there are multiple constituencies, customers, employees, owners,
6:40
community, and so forth.
6:42
So you have to start with that: What is it that we're trying
6:44
to accomplish in our strategy?
6:46
It's not an AI strategy, it's a business.
6:48
It's how does AI compliment and facilitate the business strategy?
6:53
And so that business strategy is targeted to this constituency group.
6:57
And so what do we do to be more innovative around our customer, to facilitate growth?
7:02
What do we do around our employees to ease their—you know, yada, yada, yada.
7:06
My point is that you start with, it's a business strategy that is
7:10
really focused on those constituents.
7:12
Isn't that right, Erka?
7:14
Yes.
7:14
And of course, you need to align your AI strategy with your business strategy.
7:19
So you need to see how AI will help you with your business strategy, but
7:23
also AI has the potential to actually help you innovate in different areas,
7:29
and maybe AI may help you develop new business strategies or focus on areas
7:35
that you haven't considered before.
7:38
Yeah.
7:39
Matt, what do you say to those people who say, AI is just an evolution of software.
7:44
It's not that big of a deal.
7:45
People are really overreacting."
7:49
I think there's some element of truth to that skepticism.
7:52
Like there are certainly people who are overreacting.
7:54
There is a lot of hype, there is a lot of noise.
7:57
But fundamentally, even if the technology does not continue to progress and improve,
8:05
just integrating what we currently have available is going to take quite a
8:08
lot of time and quite a lot of effort, particularly as people figure out new
8:12
ways to use existing tools and systems, let alone if there is continued progress.
8:20
The world as we know it is changing in terms of who does work, what can be done
8:26
by a machine that previously couldn't.
8:28
And we've had pathways of innovation.
8:31
We've had Excel, replace, or not replace, but get rid of some of the back office
8:36
functions and the way spreadsheets used to work back in the '70s, for instance.
8:41
That world has gone away, but we haven't necessarily seen the
8:45
large-scale effects that some people are predicting from AI specifically.
8:49
And I think that comes down to the question of, we've had
8:54
automation come for manual work and seeing the effects of that.
8:59
And I think now the question is, are we getting automation to affect
9:02
knowledge work in a similar capacity?
9:06
That is yet to be determined.
9:07
But I think that's the big question that people are asking is, if we have
9:11
the same effect on labor from knowledge work perspectives that we've had on
9:15
some of the manual work that's been done in the past, what does that change
9:19
for how we operate as an organization?
9:21
Erka, when you were saying before that you are excited by the need
9:25
for a transformation of structure and processes for AI, it's not
9:31
for the sake of AI, as we said.
9:32
It's really in order to make sure that AI is deployed effectively.
9:36
But what is it about traditional structures, legacy structures and
9:40
processes, that may hinder AI adoption?
9:44
Yeah.
9:44
If we think about legacy structures, they have all been built around humans,
9:50
and they have been built to deal with humans' potential and limitations.
9:56
If we go back, for example, it comes to mind, all the work that has been done at
10:00
the early last century with industrial psychologists trying to understand human
10:07
behavior at the workplace, how efficient they can be in a certain amount of time,
10:12
how much tasks they can do in a factory, et cetera, or scientific management.
10:17
All of that work has been designed to understand humans, their capabilities
10:22
and organize those capabilities into a system so that organizations
10:27
could produce more complex products or delivers more complex services.
10:33
But humans are limited, like each of us has a profession.
10:37
It's one person, one profession.
10:40
And work has been designed to integrate all of that with AI.
10:46
We may have AI systems or tools or agents that can have multi domains.
10:55
They can have the legal knowledge, the economic law knowledge.
10:59
They can be physicians, engineers, researchers.
11:02
So we have all of these domains in one AI system, right?
11:07
And all of this is a new capability that is added to the organization.
11:12
And it will impact the way you organize your work around these new capabilities.
11:19
It'll require new processes and new workflows because these AI systems
11:24
can be the new glue that concentrate all these knowledges and points
11:28
of view from different domains.
11:30
And if you put this complicated, this multi-domain integrated system into
11:37
a siloed process, it'll not add much.
11:41
It may help with automating some things, but the system will remain broken.
11:46
While the potential of these systems are to integrate the processes,
11:51
to integrate the workflows, to change them from beginning to end.
11:55
So by using them into siloed, hierarchical, old patient legacy systems,
12:04
you are not gaining their full potential.
12:05
You are just increasing some efficiencies, but the system
12:10
will remain slow and broken.
12:13
Yeah.
12:13
And Matt, the reason that makes a lot of sense is cause AI is like really
12:17
like nothing else that we've had before.
12:18
It is on one hand, a tool, another hand, it's software, on another hand.
12:23
It's the closest we've had to machines being able to quote-unquote think.
12:28
They're not sentient.
12:29
But this gives us power, great power analytically, creatively, innovatively.
12:35
Agentic AI allows some level of replication of human interaction.
12:41
The reason is that it is so different.
12:44
And it is so applicable to so many different areas that you
12:48
can't think about just overlaying it on a current organization.
12:54
Yeah.
12:54
I think the things that stand out to me about what makes AI different,
12:58
as it were, is one, the capacity for some sort of autonomous action.
13:03
It may not be much yet, but certainly, agents are developing that in the terms
13:09
of the task length that you can expect an agent to accomplish without human input.
13:13
That increases day by day.
13:14
So that's one element that's very different.
13:17
And then as Erka was mentioning, the multi-dimensional capacity to have
13:21
all of that bound up in a single system versus having to have a team
13:27
of different people with knowledge domains that they can bring together.
13:31
That's another element, as well.
13:33
As one of the people we talked with said, in a way, the human has become
13:37
the integration layer for the AI, in so far as you have humans in the loop
13:42
kind of overseeing AI systems and nudging them in different directions
13:45
if they're going off the rails.
13:47
But ultimately, a lot of the work now is being done, or looks like it can
13:52
be done by AI systems, and then humans will plug in and fill those gaps.
13:58
We're talking about the transformation of organizations for AI.
14:02
We're going to take a short break and be right back.
14:06
Welcome back to C-Suite Perspectives.
14:08
I'm your host, Steve Odland, from The Conference Board, and I'm joined today
14:11
by Matt Rosenbaum and Erka Amursi, both principal researchers with The
14:16
Conference Board's Human Capital Center.
14:18
And they're co-authors of our latest report, "Transforming Organizations for
14:22
AI," which is available on our website, tcb—that's The Conference Board—tcb.org.
14:30
And you just click on the Human Capital Center, and you'll find their latest work.
14:35
So, Erka, let's come back to you.
14:37
What leadership models are organizations using to guide these AI redesigns?
14:43
During our study, we conducted a survey.
14:45
We sent the survey out to leaders in the organizations, and we asked them about
14:51
the leadership model that they are using.
14:53
From their answers, we understood that there is a variety of
14:56
types of governance here.
14:58
Starting with cross-functional project management teams that are assigned
15:03
the role of leading these projects.
15:05
Added AI oversight on the existing executive roles is
15:09
another option, another variant.
15:11
Task line managers with driving AI redesigns and adoption is another
15:17
option that organizations have used.
15:19
Delegating AI leadership to functional and business division heads is
15:24
also another way of governing AI.
15:28
So there is a variety out there, with different kinds of settings.
15:34
And some organizations, 32% of them, for example, said that they have
15:38
created a new AI-focused executive position in the organization.
15:42
So yeah, there is a variety.
15:46
OK, so Matt, when you think about transforming organizational
15:48
design, all that, it's like, that's human resources' job, right?
15:52
But what I hear you saying is, this is so different, because it requires
15:57
strategic transformation first, and then organizations follow that.
16:01
So you start with what's the business strategy?
16:03
How does AI integrate with the business strategy?
16:06
And then what models of organization come out of that, right?
16:11
So it seems like this really goes beyond traditional HR, doesn't it?
16:16
Absolutely.
16:17
I think this cuts across numerous different remits.
16:21
HR obviously plays a key role, but certainly a lot of organizations
16:26
are focusing this either in their IT organization, or maybe operations
16:29
is leading the charge on this.
16:31
Or, as Erka mentioned, sometimes they're appointing some sort of
16:34
AI executive officer to oversee everything across the enterprise.
16:38
But ultimately, I think, regardless of whether you have it be the remit of
16:43
one specific person who's overseeing all of AI across the enterprise, or if
16:47
it's a team of people, there is a need for collaboration across the C-Suite
16:51
and across the enterprise because of just how far-reaching the change is.
16:56
And it should flow from that central question of, OK, what
16:59
is our business strategy?
17:00
How does AI enable that?
17:02
And then everything else will kind of trickle down from there.
17:05
So the entire C-Suite needs to be involved here.
17:07
And really, truly all business leadership needs to be involved to make sure
17:11
that each of their areas is organized in a way to effectively deploy this.
17:17
Yeah.
17:18
Yeah.
17:19
Erka, you can't just drop this stuff on an organization and
17:23
just say, OK, have fun, right?
17:24
There is training and change management that needs to go with that.
17:28
So talk about how you do that in an organization.
17:32
We talked about strategy.
17:34
AI is a tool of strategy.
17:36
You follow it with organizational design, but then you have to deploy it.
17:40
You have to implement it.
17:41
And that's where the change management comes in.
17:43
How do you suggest people go about that?
17:46
One of the things that we are hearing over and over from the leaders
17:49
is the engagement of people as early as possible in the process.
17:54
Being transparent, having your employees involved in designing
17:59
the change that will affect them.
18:01
Having them listening to their concerns and fears.
18:05
Being as transparent as possible with your vision and the way
18:09
you are going to implement AI.
18:12
A lot of studies are showing that trust is really important in adoption
18:15
from employees, so engaging there.
18:18
Engaging them early, being as transparent as you can.
18:22
Providing as much data and information that will back up
18:25
your decisions is essential.
18:28
But also having them come up with new ideas on how they are using AI.
18:33
Having them come up with suggestions is important.
18:36
Listening to them constantly about fears that are emerging.
18:40
Because fears can emerge during any change implementation process at different times
18:46
because of different communications.
18:48
It can come from internal implementations or communications, but it can come
18:52
from also external things that are happening to other organizations.
18:55
So being able to manage that, attitudes and concerns and
18:59
emotionality, is important.
19:01
And as well as providing guidance on how the roles are going to be transformed.
19:07
What is going to be taken away from your current role, but
19:10
what is going to be gained?
19:12
What skills do you need in order to do this new role better?
19:16
And what is the exact support that the organization is giving you in that regard?
19:21
Is it a formal training?
19:23
Are we providing you tools that you are going to learn on your own?
19:27
Are you going to get a coach or a mentor or an AI coach to help you with that.
19:31
So being clear on all the resources that you're going to offer in order
19:36
for them to build the required skills.
19:39
Matt, your thoughts on change management.
19:43
Like Erka was saying, there's a lot of fear and concern, so I would just
19:46
highlight the need to address that and assuage those concerns as best you can.
19:50
But also what we were talking about earlier, the clarity of vision.
19:55
What are you trying to accomplish with AI?
19:57
and having that be clear to people across the organization and consistent in the
20:02
messaging is, I think, an incredibly important part of the change management.
20:07
But also one of the things that stands out to me from our conversations
20:10
with executives is, yes, you want to focus on adoption, but
20:14
adoption is just the beginning.
20:15
Ultimately, what you're trying to accomplish here is the outcomes
20:18
that come from using AI effectively.
20:20
And the challenge there is a lot of organizations don't really have a good
20:24
baseline to measure, is this AI tool or system that we're introducing actually
20:30
helping us in this regard or not?
20:32
So if anything, I would say the change management component that also needs
20:37
to be addressed is understanding the baseline performance of the organization
20:40
as best you can and buttressing that where needed, so you can determine
20:45
whether your AI initiatives are producing the results you want to see or not.
20:49
So, Erka, both you and Matt have indicated this isn't something you can
20:52
just simply drop on an organization.
20:55
You can't just drop AI on them.
20:56
You have to train AI.
20:58
You can't just drop AI in the absence of strategy.
21:00
You have to really link it all for people.
21:02
You have to build a case so that people understand.
21:05
You got to really train people so that they understand, and you
21:09
wipe out the fear and that people live in the state of possibility.
21:13
All of these great things.
21:14
But that suggests that it's not something that leaders just
21:17
throw against the organization.
21:19
This needs to be an interactive process.
21:22
Leaders and all the people in the organization working together to
21:27
figure out how to implement, adopt, and make AI most effective, doesn't it?
21:33
Yes, exactly.
21:35
And this collaboration needs to be long term, because AI will continue to
21:39
evolve, and new tools and capabilities will continue to come up, which
21:43
will need continuous collaboration.
21:46
Which on one side will be positive for the organization, because that
21:51
closer communication will help build trust across levels and will help
21:58
with other change adoptions, will help organizations move forward faster because
22:04
they will be forced to have easier ways of communication between them.
22:10
There is no other way.
22:12
Yeah.
22:12
So Matt, what's the best way to engage employees in this process?
22:17
So one of the important things there is to start early.
22:20
I think one of the most surprising findings from our survey was, we asked
22:24
leaders and workers how involved workers should be in AI organizational redesigns.
22:30
And to my surprise, 56% of leaders and 48% of workers said that workers should be
22:36
fully involved, which means they're part of the kind of co-creation of what we're
22:40
trying to accomplish as an organization with AI from the ideation phase, rather
22:44
than the leaders coming up with what we're trying to accomplish, and then the workers
22:48
are involved in implementing it only.
22:51
And I don't know how realistic that is for some elements of AI redesigns.
22:57
Like I think the strategy is probably going to be set by
22:59
leaders, whether we like it or not.
23:01
But ideally, there will be some input from workers on that component.
23:05
But certainly when you get into some of the structural and procedural elements—how
23:09
should we redesign this workflow?
23:11
That is especially crucial to have workers be working hand in hand with
23:15
leaders, rather than leaders come up with whatever they think is best and then
23:19
telling workers to go and implement it.
23:22
OK.
23:22
And finally, Erka, talk about the incentives that have to be
23:26
in place to make all this work.
23:28
The incentives.
23:29
First of all, before going to incentives, organizations are doing a lot of work
23:35
in terms of understanding what are the skills that they actually need,
23:39
skills and capabilities, mentalities, behaviors, attitudes that they will need
23:45
in the future when workers will work more closely with AI agents or tools.
23:51
So identifying those critical skills and behaviors will be essential in
23:57
developing the performance evaluation systems and the rewards that will
24:03
be attached to those evaluations.
24:05
So whole performance evaluation systems will need to change based on
24:11
new priorities of skills and behaviors that you'll need to see in the future.
24:19
Yeah.
24:19
And tie that to the reward system.
24:21
So it may be the same rewards that we have, but tied to different
24:26
behaviors, to different outcomes, to different capabilities and skills.
24:31
Matt, last word.
24:32
Any remaining thoughts that we want to share regarding the paper?
24:37
I think the overarching point that I would end on is just
24:40
there's a lot of change happening.
24:42
There's a lot of change coming.
24:44
But it's important to start where you are.
24:46
You have to walk before you can run.
24:48
And I think there might be some organizations that are saying, we'll just
24:51
wait for this to all settle down, and then Accenture, we'll pay them a couple
24:54
million or whatever it might be, and have them come in and tell us what to do.
24:58
But there really is a need to experiment and learn as the technology develops,
25:04
and learn what your organization can do well and effectively, and where some
25:09
of the areas internally might need to be buttressed or developed in order to
25:12
use these systems effectively, versus waiting to see how this all shakes out.
25:17
I think the important thing is to get in there, start that experimentation,
25:21
and start developing that culture now.
25:24
All right.
25:24
Words of wisdom.
25:25
And again, you can find this new paper from Matt and Erka on our website.
25:32
It's called "Transforming Organizations for AI." It's at tcb.org under the
25:37
Human Capital Center, the North American Human Capital Center.
25:40
Just click on it, and you'll find this paper.
25:43
It's really worth the read.
25:44
Matt, Erka, thanks for being with us today.
25:47
Thanks for having us.
25:50
And thanks to all of you for listening to C-Suite Perspectives.
25:53
I'm Steve Odland, and this series has been brought to you by The Conference Board.