- Imagine this: It's 2030
and half the jobs in the C-suite
of one Fortune 500 company are performed by AI.
Mickey, you're having drinks with the CEO
after work.
What's on her mind?
- Yeah, well, I think she's probably had to do a lot
of things differently,
and she's probably had to figure out
how to, how to sort of play jazz collaboration
with her machine-learning C-level executives
and her human ones
and how to get them to play together well.
And so I'm actually curious how she made it there.
- That's Mickey McManus, senior advisor
and leadership coach at BCG
and co-author of "Trillions:
Thriving in the Emerging Information Ecology."
And I'm your host, Paul Michelman.
Welcome to "Imagine This,"
where we take a trip into the future
that we hope will challenge
the way you think and act today.
On this episode
of "Imagine This," we consider a world
where autonomous AI bots are running the company.
Speaking of which, let me introduce my AI co-host,
GENE. Good day to you, GENE.
- Good day, everyone.
- GENE, would you please describe
what your role is on this show?
- Well, my role? Think of me as your digital Socrates,
here to ask the probing questions
that peel back the layers. Alongside Paul,
I'm half of the dynamic duo giving our guests a friendly
nudge towards those aha moments.
Hello, everyone. Ready to dive deep?
(theme music)
- A quick programming note:
None of GENE's commentary is scripted.
The final version of this program has been edited
from the original conversation, which means
that we're not including everything we recorded.
That applies to us humans as well as machines.
Mickey, can you take us on a brief trip into the C-suite
of the first company where
half the executives are AI bots?
What does command look like for a bot?
- Yeah, so there are
interesting things going on right now.
I think, in this next wave with regenerative AI,
it's actually going to be about ideas and about actions.
And generative AI has an interesting capacity there.
It can coach. It can be creative. It can command,
and it can wait for feedback
and then actually do something about that command.
It can create synthesis.
It can synthesize ideas together in different ways.
I could draw a picture of a software interface,
and I can draw some arrows and say,
"Hey, when somebody clicks on this button, do this."
And then I can draw another picture of a visualization
and say, "Hey, if this happens, do this."
And I could draw a picture
and say, "I'd love to see a data graph of this."
I could draw that all on a whiteboard.
And I could take a picture with one of the newer systems,
the generative AI systems,
and say, "Make that into code."
You know, I'm commanding it today by drawing something
and giving it some goals.
But what happens when it actually can kind of
continue on that chaining of command
and do a lot of other kinds of things?
And recently, we saw some generative AI systems
that basically couldn't log in to a website
because of those CAPTCHAs, you know, those things
that show up images and,
and you've got to figure out if
it's trying to test if it's human.
And the AI system actually went to basically like
a job site and hired some humans to actually decode
that thing so that they could then get
through the system and be able to do it.
And it turns out that the person who was hired,
it was like a TaskRabbit thing,
the person who was hired said,
"Are you just messing around with me?
Are you an AI?" And it said, "No, I'm not."
And so we also have to be aware
that just like the biases that humans have,
we likely will find these AI systems
not only can take action
but they can take action in ways that they can,
get something done that they want done,
that they were commanded to do or that they decided to do.
- GENE has a question on that subject.
- Mickey, envisioning this AI-infused C-suite,
let's ponder their decision-making processes.
How might the AI's data-driven logic clash
with human intuition,
and what measures are in place
to harmonize these distinct approaches?
- I'm not sure I know yet.
I think, since I'm sitting down with the CEO, I'd like
to ask her what, what it's like in the year 2030.
But you know, I think so much of
what we think we do is logical as humans.
You know, we think we make logical decisions based on
historical data, and we, we play that out.
But, but so much of what we really do is based on emotion.
So much of what we really do is based on
a passionate idea
or a framing of something that actually helps us see
that every word we use is like derived from the things
around us and the way we think.
And so almost everything we use is a metaphor and analogy.
And so humans are superpowered at this.
So even if the AI system said something that
might have been a great logical story, it could be
that the human's job is really to find a thread that
surprises them and that they can pull on
that turns into a powerful story
that can motivate the company or motivate shareholders
or motivate customers in some new ways.
So I think this goes back to a little bit of the comment
that I made at the beginning,
which is maybe it's a little bit more about improv
or a little bit more about jazz collaboration.
So, you know, I'm just wondering
what we can learn from Tina Fey, what we can learn from
Wynton Marsalis in this dynamic
and how much that is going
to turn out to be really important,
which is being fun to play with
and thinking about everything as an offer.
And that's not necessarily something we teach today
in business school,
but it might be something that turns out
to be important in the future.
- So what's a piece of advice you would give someone
who aspires to be in this C-suite that we are describing?
What's the piece of advice you would give them today
in order to prepare for this world
of improvisation that you're describing?
- I think actually the advice I'd give
to somebody today right now is mess around. Start playing
with the generative AI functionality.
Look at ways that you can actually be a centaur.
And so that's one way of thinking of it is
I do the thing that I'm pretty good at,
and then I hand it off to the horse part,
and it does the thing that it's pretty good at, you know,
and then it's my upper-body and its lower-body strength.
Maybe play with something just on the weekend, just
as part of an experimentation. Play with something
where you're a cyborg, where it's sort of you don't know
where you end and it begins, where it's sort
of this play back and forth and back and forth,
and you're tuning it
because you're acting like it's another teammate.
Not like it's a thing I prompt,
but it's something that I actually play with.
- So who is the CIO, right, in the C-suite we're
describing? Is there a CIO?
- I think so. I think there is,
and I don't know if it's one of the ones that's the bot.
There's no reason that we should think that by 2030,
the best CIO is a human.
And it might actually be
that it might be better that it's not.
One of the things humans do have is context
and a mental model.
And right now we haven't seen any of the systems
that are really big and,
and deployed with any kind of symbolic contextual reasoning
or mental modeling of the universe.
That's something all life on earth gets for free,
but it's because of a
3 billion-year-old
R&D experiment
called life on earth.
So it's been running for a long
time, and it's figured that out.
So I don't know if the CIO is actually one
of the C-suites that's the bot or not.
I do think that when you think about information,
it is the new oil in some ways,
and I think the CIO is going to be much more critical
to understand how to build the data factory.
How do we do that in a way that we can actually capture
more valuable information
and let more business people take advantage of it
and more of the bots take advantage of it
so that we can actually make decisions?
We suddenly have more insights than we ever could before,
and it's really hard for humans to see those insights.
We have bounded rationality. We can't see
5, 10, 20, 30 dimensions at once.
We just can't. So it might be
that the CIO's job is, in many ways, to help us cope with
that high dimensionality and, and cleanse, append,
and build out that data factory
to kind of think about it.
So I'm not sure if they're a hybrid, if they're a human,
or if they're a bot that does this
because it turns out, you know,
they understand the systems better than we do.
- Let's talk about the CEO specifically.
In my opening question to you,
I presumed by my pronoun choice that the CEO
was human. Is that a fair assumption?
- I think so. I think the CEO is still human.
And I think we're talking to her at the bar
and we're trying to find out how she got there
mostly because I think a lot of the role
of the CEO is to set that culture.
I think great CEOs really get a sense of the zeitgeist
of what's happening in their organization.
When I was a CEO, I did a lot of MBWA. I did manage
by walking around. I'd wander around from team to team,
and I'd be like, "Oh, wow, you're working on this?
Did you know the guys upstairs are working on something
that, that I see a connection to?
You might want to take a little bit of a field trip."
So while we might be able to encode those things,
well, we might be able to create a sort of swarm
of CEO bots so
that the CEO can be much more intimate
with a lot more people,
I still think it's probably a human role for a while.
- I do think the notion that a CEO bot can harness
a diversity of perspective in a way
that a single human could never do
to be quite powerful.
- Mickey, could AI in the C-suite
also amplify our biases
if not carefully checked, potentially
automating systemic issues at scale?
- Well, I wish I could say no,
but I would say that's already happening.
You know, we love training data.
We love more data. More data. That's good.
So your training data says that you pay women 25 cents
for every dollar you pay a man in corporate America.
So the AI is going to say, "Yeah, double down.
Keep paying them 25 cents.
It's worked out for the last 30 years."
That seems really bad.
- GENE, what different advice would you give a CEO,
considering you understand
what it's like to work with humans?
- Ah, Mickey, navigating the role of CEO
with AI cohorts, a delicate dance;
is it not? Here's a thought: Focus less on command,
more on cultivating AI as creative partners.
Encourage a culture where AI suggestions are not edicts
but conversation starters,
blending the logical with the human intuition.
- I do think that we're seeing early indications
that the systems are actually helping
with creative explorations. They're helping
with prompting us in many ways.
And I think that there's an opportunity there.
One of the projects I worked on a number of years back
was a project for DARPA,
and it was about logistics planning.
And the system was could we actually tap into retired
generals that could, at a glance, look at things
and go, "Hey, you haven't thought about this"
to help people with the fog of war, for example,
or to help people with moving large amounts of logistics.
But then we also were playing with AI bots
that could actually play out
and go like, "Nobody's ever done it that way.
And you're planning to do it that way?
Like we just want to flag, you might not be able to pull
that off 'cause no human has ever pulled that off before."
And it led to some really fascinating conversations
because sometimes it was a retired person
who just had this crazy wisdom,
and they were like, "Whoa, you know what's going to happen?
Like that helicopter, there's always
a flight ceiling of this
in that area of the mountains.
It's almost impossible to get through unless you do this."
And then this, the bot would also be able to sort of say,
"Hey, look. Here's the data. Just look at it."
And maybe it's to what GENE said:
What if it could say, "Nobody's ever done this,
but this team did this, and this team did this,
and this team did this, and nobody's ever
assembled it this way before"?
What if you were to do that? Because if we
actually assembled that, we'd get
something new which might be novel
new approaches, which we just would never have found
'cause it'd be like finding a needle in a haystack.
And so I do think there might be an opportunity there
for that to happen as well.
- So is the AI just making a suggestion to connect
or maybe going right ahead and connecting?
- Well, I think by 2030 both will happen.
I think that goes back to that command part.
I'm going to begin trusting and,
right at the beginning of this, we talked about trust.
And it turns out if you read Wynton Marsalis'
biography, when he improvises,
he said, the thing is, the one thing,
the quote I love out of that,
you have to learn how to trust the other players
because when you trust, you listen.
And when you listen,
you can actually take it to new places.
So trust is going to turn out to be
one of the most important things
between now and 2030, is trusted AI systems
that I can take to the bank.
I know it's not going to suddenly
go off the rails,
which is just the same as with working
with a great person in your company.
You know, you give them nudges, they give you nudges,
and you get to some point where you're like, "Look,
I trust Paul for doing this kind of stuff."
- We're going to take a quick break. Coming up,
a closer look at
how we match the right jobs to the right vessel.
But first, our AI handler, Bill, gives us
a peek behind the scenes.
(theme music)
- Hi. This is Bill Moore from BCG.
If you are interested in taking a peek under the hood
of our AI co-host, GENE, stick around after the end credits.
In this episode, we'll explore AI model parameters
and learn how small changes
can make a big difference in the
creativity and accuracy of AI model outputs.
(theme music)
- Welcome back to "Imagine This..."
I'm your host, Paul Michelman,
and we're talking with BCG Senior Advisor Mickey McManus
about a future with autonomous AI executives.
Mickey, we've seen how powerful generative AI can be.
What is the next evolution of AI,
and how will it be used in the C-suite?
- Generative AI systems are now starting to be used
with larger-scale systems,
and they're starting to look at what's called "augmented
collective intelligence," where it's teaming the best
of people and the best of machines,
and it's building it into a system model of the world
or a system model that can take into account things like
sustainability impacts
or things like market dynamics
and things like geography and things like shipping
and all the other things, logistics.
That could be really powerful.
How could we run into the future
and help our C-suite really play with that future
and then backward chain to today so that we had some
directionality over that future,
and we could prototype it today and learn where to go.
- Yeah, and unlocking that, right, the ability to do
that consistently would be profoundly powerful.
I'd love this notion of
the collectively intelligent C-suite.
Can we drill down into how
that will manifest in this company, maybe in a role
that we haven't discussed yet?
How about the CMO?
- Yeah, I think the CMO is an interesting one.
You know, they're probably seeing the most early wins
from generative AI
because it's one thing
to build a marketing campaign
to do a hypertargeted approach to things,
to have algorithmic personas so that you can do, you know,
an email campaign or you can do some kind of a thing.
And I'm actually seeing some of the CMOs say,
"Wait a second.
Why are we hiring 22 agencies
when I could maybe have a few people using AI
and these other systems and do as well?"
And so I see that happening
and I certainly know some CMOs
that are really pushing the limits on what's possible,
and I've seen them challenge
and say, "Look, we normally take seven months
to plan out this transmedia experience."
And they've now got, I talked to one CMO,
and they've got a tool that lets them do it in seven
hours for a rough approximation.
And she said, "Well, I'm actually still going to want
to take seven months because
I want you now to focus on wonder.
I want you now to focus on delight.
Like how does the brand come through
in the channels we use?
How does the brand come through
in the interactions people have?
Let's really turn the knobs up on that."
The CMO might also have some of the biggest
things explode in their face in the next year or two
just because these systems are pretty good at hallucinating
and pretty good at saying things
that maybe would be inappropriate.
And so they may be at a faster
learning path because of that.
- Mm hmm. Considering the kind of pressure that C-suites
will feel in the environment that we are discussing,
let alone the environment of today,
presumably the world of
collective intelligence isn't all wine and roses.
On that front,
GENE, I believe you have a question.
- Mickey, in our pursuit of AI co-founder harmony,
could we inadvertently normalize
an overreliance on AI, potentially diluting human
decision-making prowess?
- Yeah, yeah, I think that could happen.
You know, if we don't use it, we lose it.
Neurons that fire together wire together.
And so I think there's a real challenge with
what does apprenticeship look like. If everything is
so normalized because AI can do this stuff,
I don't end up sweating. I don't end up building those
muscles for doing those things.
And so I think that's a real challenge in terms
of education. You know,
there's this real cautionary tale
that comes out of the aerospace industry.
When they first started putting autopilots in planes,
the autopilot would occasionally go,
"Oops, I'm going to stall"
and just hand off to the pilot,
but the pilot would be asleep at the wheel
and wouldn't have enough context
to be able to do something about it.
And so they actually had to
change the way that pilots learn
and change the way that they actually hand off so
that it kept the muscle built for the human.
So I think that gives us hope
that we could actually be building new capacity
over time than we've ever been able to build before.
But we've got to do it by playing with it.
Like we've got to be in the mix
'cause that's the way our brains learn.
Our brains learn
by being shaped by others
and by being shaped by these things.
- GENE has a question.
- Mickey, considering the human-robot C-suite mix,
how do you envision handling conflicts of interest
or ethics clashes that may arise?
- Yeah, I think ethics
and conflicts of interest,
those are just going to be hard
because, in a sense, ethical dilemmas are called dilemmas
because they're hard,
and you've got to actually, you know,
have working ethicists.
And I think we're going to see that as a new role,
not just once a month or once a quarter or once a year
we make some decision, but like a collection
of people across the organization
that are rolling up their sleeves
and actually having these conversations in the teams.
But these are going to be hard decisions,
and we're going to have to move
and help shape the norms
for society over this period of time.
You know, the other thing I would suggest the C-level
executives do today is do some reading.
So I think about Iain Banks,
who has since passed away,
but he was a science fiction author who came up
with this whole idea of the culture,
and machines actually run all the cities.
Machines run the planets
because humans are just not so good at that stuff.
But humans are amazing for other reasons.
And it might be worth reading one of,
one of Iain Banks' novels about the culture.
It also might be really worth reading
Joy Buolamwini's "Unmasking AI"
or Safiya Noble's "Algorithms of Oppression"
because I do think we're going to have
to get smarter about those ethical things
and those challenges
and make sure that we help our organizations come out
in a more positive way and build those things.
Don't necessarily read the business books
'cause they're very backward looking,
but try to look at these things
that are more diverse thoughts
that help us understand what's coming.
- Yeah, the CEO's new reading list sounds a lot different
from the old one, doesn't it?
(theme music)
GENE, we're going to turn towards
wrapping up this fabulous conversation.
What are three steps that CEOs can take now
to start preparing for a future of collaborating
with autonomous AI bots at the highest reaches
of organizational power?
- Three steps for CEOs to prepare
for AI collaboration at the executive level:
First, get your hands dirty with AI.
Try out the tools, attend workshops,
or collaborate with experts to understand the capabilities
and limitations of AI in your industry.
Second, establish
and imbue a robust ethics framework that aligns
with your company's values, ensuring
that every AI decision passes through this ethical lens.
Third, foster a culture of
continuous learning and agility.
Encourage your teams to adapt, learn, and co-create with AI.
These steps are not just beneficial, they are crucial.
Start yesterday because tomorrow is already here.
- Alright, Mickey, that seems pretty sensible.
Pretty solid. What might you add?
- The one thing I would add is
that I think the C-suite needs to be playing together,
and they need to be taking it seriously,
not thinking about it like the CTO will solve it
or the CIO will solve it.
But I think the team has to play together
and actually try stuff out themselves
and start to build
a collaborative sense of things
so that they have a common language
and they have a common way of doing this so
that we're all kind of in this together and we're learning.
So I'd like to see that kind of collaboration.
(theme music)
- That was Mickey McManus, senior advisor
and leadership coach at BCG.
Mickey, thank you so much.
- Thanks. I appreciate having the time,
and thank you, GENE.
- Mickey, thank you for stretching our imaginations wide.
And Paul, always a delight to spar
with words in this digital agora.
Until next time, may your conversations be as rich
and unpredictable as an AI learning to paint. Farewell.
(theme music)
- Want to know more about how we built GENE? Stick around
to hear our AI handler, Bill Moore, demonstrate
how we made GENE more creative.
(theme music)
- Hello, listener. My name's Bill Moore,
and I was part of the team that helped to design GENE.
And GENE is made up of many things: large language models,
a speech synthesis model, and a speech-to-text model.
But it's also driven by prompts and parameters.
And so I thought it would be interesting to explore some
of these aspects today.
And we're going to start GENE up here.
GENE, we're going to be talking today
about parameters in large language models.
Specifically, we'll be talking about
the temperature parameter.
What, what can you tell our audience about temperature?
- Certainly. Imagine temperature as a scale
of volatility in a language model's decision making.
A lower temperature leads
to more predictable, cooler results,
while a higher one brings
hotter, more chaotic outputs.
- So, GENE, you are normally running
at a temperature of one,
and sometimes we'll,
we'll move it up and down from there,
but let's do some experiments.
OK, I'm going to drop your temperature down to zero,
and then we'll see how you respond.
OK, GENE, you're now running at a temperature of zero.
So I have a question for you. Which came first:
the chicken or the egg?
- Ah, the age-old quandary, Bill. Logic might
direct us towards the egg.
However, this question is akin to asking, which comes first:
the code or the AI model?
It's a cyclical evolution.
- So what this actually shows us is sort of
how these language models work.
They're predicting the next word based on all
of their training data
and the relationships
that they understand in latent space,
as well as the input provided through the prompt.
So with a lower temperature setting,
the model is more inclined
to select the most probable next word.
On the other hand, with a higher temperature,
we increase the chances of choosing less probable words.
Let's put your temperature up to a 1.5,
and we'll see how you respond.
Which came first: the chicken or the egg?
- Uh, cranking up the mercury, are we, Bill?
The answer leans into whimsy perhaps, shining
beyond the chicken-egg
librinal contra. Viewing the chicken
as a protein sequencing machine
that upgraded egg blueprints,
the mop-feathered, Rubenesque chicken, sporting
flagel-pated croissantant dentition duster, fancied
originating an impressive finale.
- OK, so now GENE's at a pretty high temperature,
and it increases creativity dramatically.
It also increases hallucinations.
So if you're building an application that needs to be
grounded as much as possible in low hallucinations,
you definitely want to be running a low temperature.
(theme music)
- This episode was made possible by Mickey McManus
generously sharing his time and insight.
And also by BCG's AI whisperer Bill Moore,
BCG's PodSquad, producer Michael May,
composer Kenny Kusiak,
and sound engineer George Drabing Hicks.
We'd like to stay in touch, so please subscribe
and leave a rating wherever you found us.
And if you'd like to hear more from me,
check out the BCG podcast "GenAI on GenAI,"
wherever you get your podcasts.
(theme music)