December 3, 2025 ⢠27 mins
Tim and Tom sit down with Steve Wunker â Managing Director of New Markets Advisors, author, and early pioneer of the smartphone â to explore the big ideas behind his latest book, AI and the Octopus Organization. Steve breaks down why AI shouldnât just âbolt ontoâ old processes, how distributed intelligence reshapes the firm, and what leaders can learn from one of natureâs most adaptable creatures. From organizational plasticity to the changing role of middle managers, Steve offers a pragmatic roadmap for thriving amid rapid AI-driven transformation.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Welcome it, change makers to the deck show with Tim
Flower and Tom McGrath. Let's get into it.
Speaker 2 (00:09):
Hallo, change makers.
Speaker 3 (00:11):
It's Tom McGrath back for another sort of year's end
deck show. We're winding down. We've got an eye on
the holidays everybody, but no change to the schedule programming.
We've got another fantastic guest and I'm joined as ever,
bites Tim Flower. How are you doing to me? You
feel you feeling festive?
Speaker 4 (00:26):
Staid.
Speaker 3 (00:26):
There's a bit of snowfall out there at the moment.
Speaker 2 (00:29):
Yeah, first measurable snow of the year. Be a nice
little view out the window. And fortunately, like we were
talking to Tom, I got a guy to come take
care of it. So it's it's nice when you're on
the inside.
Speaker 3 (00:40):
He's a staring out of his window as this poor
gentleman is blowing around with his shovel. Picture of a
picture of decadence, Tim, And we've got we've got a wonderful,
wonderful guest today. He's a Steve Wonker. He's a managing
director of New Markets Advisors. That's a consulting firm that
works with twenty nine of a fortune five one hundred
(01:01):
and among his many distinctions in his career. Steve has
developed one of the world's first smartphones, which I'm going
to be asking him about momentarily, and worked across a
lot of different AI initiatives across the last twelve years.
He's an author. He's written hundreds of articles for places
like Forbes and Harvard Business Review, as well as for
best selling books. So we're very fortunate to have him.
(01:23):
They were excited to talk to him. How are you doing, Steve,
I'm doing great. Thanks for having me our pleasure. So
going out of interest, we're going to get into your
new book momentarily or your recent book, I should say,
which is AI and the Octopus Organization building the super
Intelligent Firm splendid title. But before we do, I have
to pick up on this development of an early smartphone.
(01:46):
Tell us about the role you played in and tell
us tell us about that that fascinating sounding chapter in
and You Buy Over.
Speaker 4 (01:54):
Oh You Bet tom So.
Speaker 5 (01:55):
Back in two thousand, I was recruited by Cion, which
is a British consumer electronics company that had invented the
personal digital system back in the eighties. They had software
that became Symbian, which was the operating system for some
of the early smartphones, and their CEO wanted to go
(02:16):
from pure play digital systems into what we were calling
a smartphone.
Speaker 4 (02:21):
It was a very novel term back then. We were
also going up the connected PDA.
Speaker 5 (02:26):
That was not a term that's stuck. The smartphone's stuck.
So we on a white label basis were developing smartphones
for Motorola and ericson to bring out to the market.
And I led that that program area. So you know,
those lessons stick with you. Sometimes you don't know the
(02:49):
full potential what you have. We knew this was good.
We knew this had a lot of potential. Not that
it would addict several billion people, we were not really
aware of that. But we did have a view about
how it could be used for maps and gaming, not
social media, nothing like that. But I learned a few
(03:09):
things from that. I learned how big innovations can sometimes
come from just amalgamating a lot of small innovations. The
smartphone itself, at least to us, was a fairly obvious
extension of the different parts that were coming together, and
now we could integrate them in a pretty novel way,
not trivialized it. It was difficult, but it wasn't an
(03:33):
outlandish notion. But then you also need to have sort
of the on ramp to new behaviors. My landlady in
London had seen me on British TeV talking about smartphones
and she was a little baffled by it and she
asked what it was good for and I sort of
explained really enthusiastically, and she paused and she said, oh, well,
(03:57):
does it send a text? And not really, it was
very good attending to get text. I mean, you could
tap stuff out with a stylist, but if that's what
you want to do, you could just use your thumbs
and tap something out much more quickly. So you know,
it showed me that, yes, you need to add that
future in mind, but we also need to give people
(04:18):
a smooth adoption curve.
Speaker 4 (04:20):
They're not going to just leap the future overnight.
Speaker 3 (04:24):
Going yes, so we're going to get into this momentary.
But I was just saying it, and it arguably right.
I mean, let's start with this, do we We're obviously
living through the most significant period of technical, technological innovation
and consumer innovation since the launch of a smartphone. I
think that's a fair comment, right, gentlemen, although do we
neglect social media? Oh my goodness, I don't know.
Speaker 4 (04:47):
Where are we? Where does it rape? But yes, Tom,
certainly hand the last twenty five years.
Speaker 3 (04:53):
In the last twenty five years, this is this is
the key moment, and it does it? Does it outweigh?
And what where could can you give concrete examples of
those lessons you took from that last period of a
popularization and growth that you just described.
Speaker 5 (05:09):
So look, if we look at AI, a lot of
people are talking about this destination of artificial general intelligence
or artificial superintelligence, and that is really the wrong thing
to be looking for. We're not looking for Skynet, this
sort of all knowing machine that might destroy the human race.
(05:31):
All we need is AI to be good enough to
be better than what humans are doing now in certain areas,
or able to do things that we either can't do
or won't do because they're just so time consuming, like
taking careful notes of every meeting and creating a summary
for all participants, just not something that would usually happen
(05:53):
because we couldn't be bothered. So by those metrics, AI
is actually plenty good enough today to have a transformative effect.
So we need that sort of on ramp of behavior,
which we're already looking at. But let me let me
use another example. It goes a little bit further back.
So back in the eighteen eighties, electrification started really penetrating
(06:14):
into factories, and the first thing that factories did was
swap out their steam powered machines for electric machines, which
were better on many accounts, they were more reliable, you
could put more of them on a factory floor, et cetera.
But it wasn't really the big unlock of productivity. That
didn't happen for another thirty five years until the assembly
(06:37):
line was created. Now, you couldn't have the assembly line
without electricity, but it was only once you had that
that you had these massive increases, in many fold increases
in individual productivity. So you can't just think about this
one for one swapping out of what we used to
(06:57):
do for an AI and line. You need to rethink
the whole system, as people did with the assembly line
to really create the massive games.
Speaker 3 (07:08):
Let's delve steve a bit into your your your key,
the key metaphor at the center of your of your
new book, if your octopus organization and you.
Speaker 5 (07:17):
Mentioned on the cover to usual atibate books have an
octopus on the cover.
Speaker 4 (07:22):
I asked you.
Speaker 3 (07:24):
Were quite all right, quite right, and but we do
know that the octopus is octopi.
Speaker 5 (07:30):
It's all right, it's a Greek term, not a Latin.
Speaker 3 (07:33):
Term, an octopus. Let's go the name of the James.
But one side. And we do know very extra extraordinarily
intelligent creatures. Maybe maybe that's part of the picture. But
one thing you discussed in the book, I believe is
the extinction of the ammonite versus the survival of the octopus.
(07:55):
Unpack this whole metaphorical conception for us.
Speaker 5 (07:58):
Sure thing, So let's go back sixty six million years.
These creatures called ammonites were everywhere in the ocean. There
were over ten thousand species of ammonites.
Speaker 4 (08:10):
These were these.
Speaker 5 (08:13):
Thickly armored, tightly coiled shellfish basically that were where everywhere.
They could range from a few inches in diameter to
ten feet. It could be enormous. And the octopuses were
the sort of marginal creature in the seas. And the
ammonites had developed this thick armor over eons to defend
(08:35):
themselves against well known threats like the gigantic dinosaur fish.
For instance, and then a meteor struck the earth and
it killed all the dinosaurs. It wiped out seventy five
percent of species on Earth. One thing it did was
create acid rain, and the acid rain changed the chemistry
(08:58):
of the seas, and the seas dissolved the shells of
the young ammonites, so what used to be their fortress
became a prison and they were trapped there, and almost
immediately the ammonites went extinct. The octopus is actually three
hundred million years old. That's seventy million years older than
(09:20):
the dinosaurs. It's been around a really long time, and
it has almost no natural defenses. What it can do
is adapt itself super rapidly because it can actually recode
its RNA within hours. So you can take an octopus
from the Antarctic and PLoP it into the Caribbean and
within hours it will be just fun. So the octopus
(09:44):
could very rapidly adapt to the justessification of the seas.
And with all these competitors wiped out, it thried it
was having a breaton. So we need to be like octopuses,
super adaptable. But most corporations are like ammonites. They've got
these very the coats some armor against the very well
(10:06):
known threats that are out there, and yet those defenses
can actually become a prison. They can impede change, whereas
the octopuses of the world thrive on it.
Speaker 2 (10:20):
And that's fascinating. And Steve, you talk about the fact
that AI isn't just a tool, that it's a force
that reshapes how organizations think. Is it helping us figure
out how to think or is it actually thinking for us?
Expand a bit on that, if you could.
Speaker 4 (10:39):
That's a great question.
Speaker 5 (10:40):
I think it does a bit of both. In very
rudimentary functions like say, I know call center, it can
do the thinking for you. It can lift up relatively
mediocre call center people to be decent, and its limited
effects on people who are really good. Back in high school,
(11:04):
I had one of the worst jobs in my life.
I was a telemarketer selling meat over the phone. Nobody
wanted to buy from me, and I sucked. But you know,
if I had had an AI script sort of indicating
what to say in response to what people might be raising,
I probably would have done better. My colleague was also
in high school. He was really naturally charming. I don't
(11:26):
think it was going to help him very much. So
in this case, AI can do the thinking for us.
But if you're at a higher level of capability, then
AI is really a thought partner. So look in my
firm here, a consulting firm. I don't want the AI
thinking for us.
Speaker 4 (11:43):
I want it as a thought partner.
Speaker 5 (11:46):
Poke holes in what I've done. Find me an example,
by the way, how I found the ammonite as an example,
we asked AI for a particular example. So you sort
of engage in a dance with it. It's not like
you're doing your dance and the AI is doing it. Stance,
get together, and jointly you create something that is much
better than what either of you could have created on
(12:08):
your own.
Speaker 2 (12:09):
And Gardner makes that point a lot as well, that
AI has different value to different levels of thinking humans
and different places in their career. Right, a seasoned, experienced
lawyer is going to use AI as that thought partner
and is going to understand the responses that they get
back and validate. Is this legit?
Speaker 5 (12:29):
Is this real?
Speaker 2 (12:30):
Did it just make something up?
Speaker 4 (12:31):
Right?
Speaker 2 (12:32):
We've seen cases of AI just making up cases out
of thin air, so it definitely can, and I use
it as that thought partner and creative partner all the time.
You've argued that in doing so, that companies shouldn't just
simply and we're going to coin a term here, a
coin a word. Maybe we'll get this in Webster's dictionary.
(12:52):
A shout out to Tom. Companies shouldn't simply AI afi
existing processes, right. You see that a lot in this industry,
traditional tech companies playing catch up, They bolt on an
AI capability and they call it AI and move forward.
What's the risk of retrofitting AI onto old ways of
not only old ways of working, but old ways of thinking.
Speaker 5 (13:16):
Look, it's like swapping in an electric machine for a
steam machine. That's nice, but it's not going to revolutionize
how you do things, and it may actually ossify the
way you've done stuff. Right, if you just brought in
all this I infrastructure to automate each step of your
(13:39):
seventeen step process, you're probably not going to rethink and say, well,
maybe it should be four steps, and it should be
four very different steps. So, as we work with enterprises
on how they really scale up AI adoption, what we're
finding is that you're not just fixing the technolgical ways
(14:00):
of doing stuff you're thinking about who has authority to
do different things, how do different functions interact, how quickly
can decisions get made? Where are the decision rights? Those
are all the sorts of questions that should get answered
as you're thinking about a workflow, and as you do that,
you might fundamentally rethink how stuff is being done.
Speaker 2 (14:24):
So let's go back to the book. Love the image
on the cover, but help us more clearly define what
that means to build an octopus organization and maybe share
a few real world examples of one that's actually in practice.
Speaker 5 (14:39):
Sure thing, So look, an octopus is a lesson for us,
not only in its hyperadaptability, but in its biology. An
octopus actually has nine brains. It has one central brain
that its head, and then a brain in each of
its arms, and these arms brains can coordinate amongst themselves
(15:01):
without even involving the central head. So it is a
model of distributed intelligence and action. Two thirds of its
neural tissue is actually not in its head, it's throughout
the body. So we need to change from our conception
of organizations, which is like how us humans are, with
the central head commanding the arms and of whatnot, to
(15:22):
something that's quite alien, and that's more octopus like, and
that enables rapid action. If you look at a high
frequency trading firm, for instance, that's how they are organized.
They have these different pods, and the pods of traders
can act fairly autonomously. They work very tightly with AI algorithms.
(15:46):
They're almost never placing the trades themselves. They're governing the
algorithms that do, and then they can coordinate amongst themselves
as necessary. The central brain can look at what it
really needs to like collectively as an enterprise, are we
taking these sorts of appropriate risks? Are we complying with regulations?
(16:06):
Hello Fresh is another example in a totally different industry.
They're the world's largest meal kit delivery company. They have
used AI to create radical customization in the menu that.
Speaker 4 (16:19):
It offers to people.
Speaker 5 (16:20):
So rather than giving people a handful a dozen or
so choices, now there might be two thousand different things
that people might have, and they're going to know based
upon your history. Tom prefers chicken to pork, and I
like things a little bit spicy.
Speaker 4 (16:37):
Now.
Speaker 5 (16:37):
What that's meant, though, is that in the operations of
the company, you can no longer have this central menu construction.
It's radically decentralized, and then the kitchens also can have
some master plan on how they're going to be producing
meals for the week. That would be impossible to create
a master plan for two thousand meals. AI has to
(16:59):
do that, and that means the production managers critically are
not telling people do this, do that, and they're not
creating the plan. They are stewards of the model. They're
seeing what might the model not be seeing that they're
seeing because perhaps they have fuller context, and they're engaging
in the sort of dialogue with the models to make
sure that the production plans are appropriate and then explaining
(17:23):
this change to the production workers that they have. So
the job of middle managers and an octopus organization fundamentally transforms, and.
Speaker 2 (17:34):
It that's fascinating because it people really get enamored and
connected with the technology and the tooling and the new
shiny tool that they can bring into their environment. What
guidance would you give for folks that are reading the
book or really getting their head around this really distributed
(17:55):
computing on steroids? What's your guidance for it teams that
really want to lead and enable true AI transformation and
not just bringing in and bolting on that AI tool.
Speaker 5 (18:10):
So we shouldn't be thinking about this as an IT implementation.
You should be thinking about it as a transformation of
how an organization operates. So the IT team really should
be a transformation team. Now, sure they have to have
tentical strength, and these systems have to work, they have
to have certain guardrails, they have to by certain cybersecurity protocols, etc.
(18:36):
Of course, but we need to be thinking about that
whole system of use, and importantly, not just how do
you aiifi the current system of use, but how do
to change the system of use to one that is
much more appropriate to where the organization is going. That's
why Hello Fresh is a great example. Rethought what its
(18:59):
customer value opposition should be, and then it worked backwards
to what does that mean in terms of the kitchens,
What does that mean in terms of ingredients sourcing.
Speaker 4 (19:07):
It had to think about.
Speaker 5 (19:08):
The whole knock on effects that were created, and the
IT systems then had to accommodate it.
Speaker 4 (19:14):
This is what IT system IT people need to do.
Speaker 5 (19:17):
They need to think much more broadly and look, I
used an example the organization the insurance company Travelers I
interviewed their head of it. But critically, I think her
title is very telling. She is the chief Technology and
Operations officer, So it's both and whether or not that's
(19:41):
your title, you need to think that way.
Speaker 3 (19:46):
I had a question about a phrase using your work
around organizational plasticity. I think because I like p phrase
so much, Steve but stood out to me. But I
feel like we've you know, but it's it's it's quite
kind of in parent and a lot of what you've
been talking about. Is there any nuance to it we
haven't covered already, or any any way of unpacking that
(20:06):
concept further and more concretely when we've already done.
Speaker 5 (20:12):
Yeah, organizations tend to be very fossilized. They tend to
stick in a shape, even when that shape doesn't make
a whole lot of sense anymore. I have never been
a big fan of reorganizations. I think they can create
a tremendous amount of dislocation and confusion. And so I'm
(20:34):
not necessarily saying redraw your org chart. Maybe, but what
I'm really saying is redraw how you behave as an organization.
Where do the decision rights lie? Do we really devolve authority?
How do we communicate cross functionally and octopus has devolved
authority to its arms. It has the cross silo coordination.
(20:58):
One arm knows one another one is doing. And look,
this is not a new thought. We have been talking
about devolution and desiloing since the nineteen eighties, and yet
it hasn't really happened because the information did not flow
as it could. Now with AI, AI creates transparency and
(21:20):
flow of information, so the right data gets to the
right people at the right time. And then it also
creates transparency into what's going on in the organization. People
have been afraid to devolve authority because they don't know
what kind of shenanigans people are going to do in
the arms of the organization. Now with AI, there's a
lot more visibility into that. Critically, you can't use that
(21:44):
as an excuse for centralization. Now that I can see
everything that all my people are doing, I can dictate
even more and more what's going on.
Speaker 4 (21:53):
People are going to be tempted to do that.
Speaker 5 (21:54):
That is a human tendency, But instead we need to
use that visibility to let things sort of play out.
Intervene were necessary, but by and large, intervening on an
exception basis not as a rule.
Speaker 3 (22:11):
It's been so interesting to have you on. So first,
you've I think you've really complemented a series of really
exceptional conversations we've had over the last month or two.
Would point people to the episode agentic AI and the
end of Traditional It with Rob Wilson. We were kind
of considering with him a little bit more about what
(22:32):
it meant for employees, for individuals in this very new
world and the way we approach our lives and quote
unquote careers in twenty twenty five and beyond. You've kind
of focused a bit more on organizational structure, of course,
but with a very similar kind of ethos and message.
But I wonder how you would translate that into let's say,
(22:54):
advice to organizational leaders may be feeling overwhelmed by the
pace and ambiguity of AI change. What would you tell them?
What would be your orientation advice as it were, for
approaching the future in the strongest spirit.
Speaker 5 (23:13):
So a couple people pieces of advice. First of all,
we have a chapter in the book called three Hearts,
and this derives from the octopus actually having three different
hearts that it uses for different purposes. Another weird aspect
of octopus biology. We translate that through into different leadership styles.
(23:35):
There is an analytics style that you need and typically
organizations already have. You certainly need to retain that where
we're not saying analysis goes way by any means. AI
may help with that analysis, but doesn't replace it. Second, though,
you need the agile part. You need to be able
to deal on a very flexible basis where flexibility is required,
(24:00):
and that is going to happen increasingly with the pace
of change. AI is just one element in accelerating that
pace of change. So companies need to be able to
get more agile where appropriate and to shift between analytic
and angel modes. And then finally you need that aligned part.
You need to think about the emotional toll that this
(24:20):
is going to take on a whole lot of people
and really talk to the change in culture, the change
and maybe safety that people feel, and be sensitive to
all of that. So I think that is the number
one thing that people need to do is their leadership styles.
The second thing is a little bit more cerebral. They
(24:43):
need to think about what does the organization really need
to be good at strategically, So for Hello, Fresh They
determined that what IT really needed to be good at
was understanding user preferences and delivering on those. All this
data from fourteen years of history about what you substituted
(25:04):
your meals for, what you rated your your meals, and
yet it wasn't really using that. It decided to change
that into its competitive mode. So AI can fundamentally change
who you are as an organization, what your competitive strategy
and differentiation is. What should that mean for your enterprise?
They have to do that too.
Speaker 3 (25:27):
That's it's all absolutely amazing. And I think if if
if anyone's listening to this at at the very least,
they're going to be less likely to enjoy Octopus when
they're next on on a Spanish holiday. All this people
who get into into octopuses, as I now know it is,
they can't they can't bear the fact that we even
when it's the nice culumary, all these multiple all these
(25:50):
multiple hearts and brains, they have magnificent complexity, and we
just we just munch them when we're in Barcelona like
at eight, No thing but absolute pleasure. Steve to have
you on. I think your your your your thoughts on
organizational change in this era are genuinely unique and valuable,
(26:11):
and thanks so much for coming on and sharing with
any anyway you want to point the listeners to to
find out more about yourself or your work.
Speaker 5 (26:19):
Sure so. The website for the book is AI Mdoctopus
dot com. You can look me up on LinkedIn. Stephen
Luonker and our company is New Markets Advisors. We engage
with organizations that are trying to undertake these sorts of transformations.
Speaker 1 (26:37):
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(27:00):
Until next time,
Welcome it, change makers to the deck show with Tim
Flower and Tom McGrath. Let's get into it.
Speaker 2 (00:09):
Hallo, change makers.
Speaker 3 (00:11):
It's Tom McGrath back for another sort of year's end
deck show. We're winding down. We've got an eye on
the holidays everybody, but no change to the schedule programming.
We've got another fantastic guest and I'm joined as ever,
bites Tim Flower. How are you doing to me? You
feel you feeling festive?
Speaker 4 (00:26):
Staid.
Speaker 3 (00:26):
There's a bit of snowfall out there at the moment.
Speaker 2 (00:29):
Yeah, first measurable snow of the year. Be a nice
little view out the window. And fortunately, like we were
talking to Tom, I got a guy to come take
care of it. So it's it's nice when you're on
the inside.
Speaker 3 (00:40):
He's a staring out of his window as this poor
gentleman is blowing around with his shovel. Picture of a
picture of decadence, Tim, And we've got we've got a wonderful,
wonderful guest today. He's a Steve Wonker. He's a managing
director of New Markets Advisors. That's a consulting firm that
works with twenty nine of a fortune five one hundred
(01:01):
and among his many distinctions in his career. Steve has
developed one of the world's first smartphones, which I'm going
to be asking him about momentarily, and worked across a
lot of different AI initiatives across the last twelve years.
He's an author. He's written hundreds of articles for places
like Forbes and Harvard Business Review, as well as for
best selling books. So we're very fortunate to have him.
(01:23):
They were excited to talk to him. How are you doing, Steve,
I'm doing great. Thanks for having me our pleasure. So
going out of interest, we're going to get into your
new book momentarily or your recent book, I should say,
which is AI and the Octopus Organization building the super
Intelligent Firm splendid title. But before we do, I have
to pick up on this development of an early smartphone.
(01:46):
Tell us about the role you played in and tell
us tell us about that that fascinating sounding chapter in
and You Buy Over.
Speaker 4 (01:54):
Oh You Bet tom So.
Speaker 5 (01:55):
Back in two thousand, I was recruited by Cion, which
is a British consumer electronics company that had invented the
personal digital system back in the eighties. They had software
that became Symbian, which was the operating system for some
of the early smartphones, and their CEO wanted to go
(02:16):
from pure play digital systems into what we were calling
a smartphone.
Speaker 4 (02:21):
It was a very novel term back then. We were
also going up the connected PDA.
Speaker 5 (02:26):
That was not a term that's stuck. The smartphone's stuck.
So we on a white label basis were developing smartphones
for Motorola and ericson to bring out to the market.
And I led that that program area. So you know,
those lessons stick with you. Sometimes you don't know the
(02:49):
full potential what you have. We knew this was good.
We knew this had a lot of potential. Not that
it would addict several billion people, we were not really
aware of that. But we did have a view about
how it could be used for maps and gaming, not
social media, nothing like that. But I learned a few
(03:09):
things from that. I learned how big innovations can sometimes
come from just amalgamating a lot of small innovations. The
smartphone itself, at least to us, was a fairly obvious
extension of the different parts that were coming together, and
now we could integrate them in a pretty novel way,
not trivialized it. It was difficult, but it wasn't an
(03:33):
outlandish notion. But then you also need to have sort
of the on ramp to new behaviors. My landlady in
London had seen me on British TeV talking about smartphones
and she was a little baffled by it and she
asked what it was good for and I sort of
explained really enthusiastically, and she paused and she said, oh, well,
(03:57):
does it send a text? And not really, it was
very good attending to get text. I mean, you could
tap stuff out with a stylist, but if that's what
you want to do, you could just use your thumbs
and tap something out much more quickly. So you know,
it showed me that, yes, you need to add that
future in mind, but we also need to give people
(04:18):
a smooth adoption curve.
Speaker 4 (04:20):
They're not going to just leap the future overnight.
Speaker 3 (04:24):
Going yes, so we're going to get into this momentary.
But I was just saying it, and it arguably right.
I mean, let's start with this, do we We're obviously
living through the most significant period of technical, technological innovation
and consumer innovation since the launch of a smartphone. I
think that's a fair comment, right, gentlemen, although do we
neglect social media? Oh my goodness, I don't know.
Speaker 4 (04:47):
Where are we? Where does it rape? But yes, Tom,
certainly hand the last twenty five years.
Speaker 3 (04:53):
In the last twenty five years, this is this is
the key moment, and it does it? Does it outweigh?
And what where could can you give concrete examples of
those lessons you took from that last period of a
popularization and growth that you just described.
Speaker 5 (05:09):
So look, if we look at AI, a lot of
people are talking about this destination of artificial general intelligence
or artificial superintelligence, and that is really the wrong thing
to be looking for. We're not looking for Skynet, this
sort of all knowing machine that might destroy the human race.
(05:31):
All we need is AI to be good enough to
be better than what humans are doing now in certain areas,
or able to do things that we either can't do
or won't do because they're just so time consuming, like
taking careful notes of every meeting and creating a summary
for all participants, just not something that would usually happen
(05:53):
because we couldn't be bothered. So by those metrics, AI
is actually plenty good enough today to have a transformative effect.
So we need that sort of on ramp of behavior,
which we're already looking at. But let me let me
use another example. It goes a little bit further back.
So back in the eighteen eighties, electrification started really penetrating
(06:14):
into factories, and the first thing that factories did was
swap out their steam powered machines for electric machines, which
were better on many accounts, they were more reliable, you
could put more of them on a factory floor, et cetera.
But it wasn't really the big unlock of productivity. That
didn't happen for another thirty five years until the assembly
(06:37):
line was created. Now, you couldn't have the assembly line
without electricity, but it was only once you had that
that you had these massive increases, in many fold increases
in individual productivity. So you can't just think about this
one for one swapping out of what we used to
(06:57):
do for an AI and line. You need to rethink
the whole system, as people did with the assembly line
to really create the massive games.
Speaker 3 (07:08):
Let's delve steve a bit into your your your key,
the key metaphor at the center of your of your
new book, if your octopus organization and you.
Speaker 5 (07:17):
Mentioned on the cover to usual atibate books have an
octopus on the cover.
Speaker 4 (07:22):
I asked you.
Speaker 3 (07:24):
Were quite all right, quite right, and but we do
know that the octopus is octopi.
Speaker 5 (07:30):
It's all right, it's a Greek term, not a Latin.
Speaker 3 (07:33):
Term, an octopus. Let's go the name of the James.
But one side. And we do know very extra extraordinarily
intelligent creatures. Maybe maybe that's part of the picture. But
one thing you discussed in the book, I believe is
the extinction of the ammonite versus the survival of the octopus.
(07:55):
Unpack this whole metaphorical conception for us.
Speaker 5 (07:58):
Sure thing, So let's go back sixty six million years.
These creatures called ammonites were everywhere in the ocean. There
were over ten thousand species of ammonites.
Speaker 4 (08:10):
These were these.
Speaker 5 (08:13):
Thickly armored, tightly coiled shellfish basically that were where everywhere.
They could range from a few inches in diameter to
ten feet. It could be enormous. And the octopuses were
the sort of marginal creature in the seas. And the
ammonites had developed this thick armor over eons to defend
(08:35):
themselves against well known threats like the gigantic dinosaur fish.
For instance, and then a meteor struck the earth and
it killed all the dinosaurs. It wiped out seventy five
percent of species on Earth. One thing it did was
create acid rain, and the acid rain changed the chemistry
(08:58):
of the seas, and the seas dissolved the shells of
the young ammonites, so what used to be their fortress
became a prison and they were trapped there, and almost
immediately the ammonites went extinct. The octopus is actually three
hundred million years old. That's seventy million years older than
(09:20):
the dinosaurs. It's been around a really long time, and
it has almost no natural defenses. What it can do
is adapt itself super rapidly because it can actually recode
its RNA within hours. So you can take an octopus
from the Antarctic and PLoP it into the Caribbean and
within hours it will be just fun. So the octopus
(09:44):
could very rapidly adapt to the justessification of the seas.
And with all these competitors wiped out, it thried it
was having a breaton. So we need to be like octopuses,
super adaptable. But most corporations are like ammonites. They've got
these very the coats some armor against the very well
(10:06):
known threats that are out there, and yet those defenses
can actually become a prison. They can impede change, whereas
the octopuses of the world thrive on it.
Speaker 2 (10:20):
And that's fascinating. And Steve, you talk about the fact
that AI isn't just a tool, that it's a force
that reshapes how organizations think. Is it helping us figure
out how to think or is it actually thinking for us?
Expand a bit on that, if you could.
Speaker 4 (10:39):
That's a great question.
Speaker 5 (10:40):
I think it does a bit of both. In very
rudimentary functions like say, I know call center, it can
do the thinking for you. It can lift up relatively
mediocre call center people to be decent, and its limited
effects on people who are really good. Back in high school,
(11:04):
I had one of the worst jobs in my life.
I was a telemarketer selling meat over the phone. Nobody
wanted to buy from me, and I sucked. But you know,
if I had had an AI script sort of indicating
what to say in response to what people might be raising,
I probably would have done better. My colleague was also
in high school. He was really naturally charming. I don't
(11:26):
think it was going to help him very much. So
in this case, AI can do the thinking for us.
But if you're at a higher level of capability, then
AI is really a thought partner. So look in my
firm here, a consulting firm. I don't want the AI
thinking for us.
Speaker 4 (11:43):
I want it as a thought partner.
Speaker 5 (11:46):
Poke holes in what I've done. Find me an example,
by the way, how I found the ammonite as an example,
we asked AI for a particular example. So you sort
of engage in a dance with it. It's not like
you're doing your dance and the AI is doing it. Stance,
get together, and jointly you create something that is much
better than what either of you could have created on
(12:08):
your own.
Speaker 2 (12:09):
And Gardner makes that point a lot as well, that
AI has different value to different levels of thinking humans
and different places in their career. Right, a seasoned, experienced
lawyer is going to use AI as that thought partner
and is going to understand the responses that they get
back and validate. Is this legit?
Speaker 5 (12:29):
Is this real?
Speaker 2 (12:30):
Did it just make something up?
Speaker 4 (12:31):
Right?
Speaker 2 (12:32):
We've seen cases of AI just making up cases out
of thin air, so it definitely can, and I use
it as that thought partner and creative partner all the time.
You've argued that in doing so, that companies shouldn't just
simply and we're going to coin a term here, a
coin a word. Maybe we'll get this in Webster's dictionary.
(12:52):
A shout out to Tom. Companies shouldn't simply AI afi
existing processes, right. You see that a lot in this industry,
traditional tech companies playing catch up, They bolt on an
AI capability and they call it AI and move forward.
What's the risk of retrofitting AI onto old ways of
not only old ways of working, but old ways of thinking.
Speaker 5 (13:16):
Look, it's like swapping in an electric machine for a
steam machine. That's nice, but it's not going to revolutionize
how you do things, and it may actually ossify the
way you've done stuff. Right, if you just brought in
all this I infrastructure to automate each step of your
(13:39):
seventeen step process, you're probably not going to rethink and say, well,
maybe it should be four steps, and it should be
four very different steps. So, as we work with enterprises
on how they really scale up AI adoption, what we're
finding is that you're not just fixing the technolgical ways
(14:00):
of doing stuff you're thinking about who has authority to
do different things, how do different functions interact, how quickly
can decisions get made? Where are the decision rights? Those
are all the sorts of questions that should get answered
as you're thinking about a workflow, and as you do that,
you might fundamentally rethink how stuff is being done.
Speaker 2 (14:24):
So let's go back to the book. Love the image
on the cover, but help us more clearly define what
that means to build an octopus organization and maybe share
a few real world examples of one that's actually in practice.
Speaker 5 (14:39):
Sure thing, So look, an octopus is a lesson for us,
not only in its hyperadaptability, but in its biology. An
octopus actually has nine brains. It has one central brain
that its head, and then a brain in each of
its arms, and these arms brains can coordinate amongst themselves
(15:01):
without even involving the central head. So it is a
model of distributed intelligence and action. Two thirds of its
neural tissue is actually not in its head, it's throughout
the body. So we need to change from our conception
of organizations, which is like how us humans are, with
the central head commanding the arms and of whatnot, to
(15:22):
something that's quite alien, and that's more octopus like, and
that enables rapid action. If you look at a high
frequency trading firm, for instance, that's how they are organized.
They have these different pods, and the pods of traders
can act fairly autonomously. They work very tightly with AI algorithms.
(15:46):
They're almost never placing the trades themselves. They're governing the
algorithms that do, and then they can coordinate amongst themselves
as necessary. The central brain can look at what it
really needs to like collectively as an enterprise, are we
taking these sorts of appropriate risks? Are we complying with regulations?
(16:06):
Hello Fresh is another example in a totally different industry.
They're the world's largest meal kit delivery company. They have
used AI to create radical customization in the menu that.
Speaker 4 (16:19):
It offers to people.
Speaker 5 (16:20):
So rather than giving people a handful a dozen or
so choices, now there might be two thousand different things
that people might have, and they're going to know based
upon your history. Tom prefers chicken to pork, and I
like things a little bit spicy.
Speaker 4 (16:37):
Now.
Speaker 5 (16:37):
What that's meant, though, is that in the operations of
the company, you can no longer have this central menu construction.
It's radically decentralized, and then the kitchens also can have
some master plan on how they're going to be producing
meals for the week. That would be impossible to create
a master plan for two thousand meals. AI has to
(16:59):
do that, and that means the production managers critically are
not telling people do this, do that, and they're not
creating the plan. They are stewards of the model. They're
seeing what might the model not be seeing that they're
seeing because perhaps they have fuller context, and they're engaging
in the sort of dialogue with the models to make
sure that the production plans are appropriate and then explaining
(17:23):
this change to the production workers that they have. So
the job of middle managers and an octopus organization fundamentally transforms, and.
Speaker 2 (17:34):
It that's fascinating because it people really get enamored and
connected with the technology and the tooling and the new
shiny tool that they can bring into their environment. What
guidance would you give for folks that are reading the
book or really getting their head around this really distributed
(17:55):
computing on steroids? What's your guidance for it teams that
really want to lead and enable true AI transformation and
not just bringing in and bolting on that AI tool.
Speaker 5 (18:10):
So we shouldn't be thinking about this as an IT implementation.
You should be thinking about it as a transformation of
how an organization operates. So the IT team really should
be a transformation team. Now, sure they have to have
tentical strength, and these systems have to work, they have
to have certain guardrails, they have to by certain cybersecurity protocols, etc.
(18:36):
Of course, but we need to be thinking about that
whole system of use, and importantly, not just how do
you aiifi the current system of use, but how do
to change the system of use to one that is
much more appropriate to where the organization is going. That's
why Hello Fresh is a great example. Rethought what its
(18:59):
customer value opposition should be, and then it worked backwards
to what does that mean in terms of the kitchens,
What does that mean in terms of ingredients sourcing.
Speaker 4 (19:07):
It had to think about.
Speaker 5 (19:08):
The whole knock on effects that were created, and the
IT systems then had to accommodate it.
Speaker 4 (19:14):
This is what IT system IT people need to do.
Speaker 5 (19:17):
They need to think much more broadly and look, I
used an example the organization the insurance company Travelers I
interviewed their head of it. But critically, I think her
title is very telling. She is the chief Technology and
Operations officer, So it's both and whether or not that's
(19:41):
your title, you need to think that way.
Speaker 3 (19:46):
I had a question about a phrase using your work
around organizational plasticity. I think because I like p phrase
so much, Steve but stood out to me. But I
feel like we've you know, but it's it's it's quite
kind of in parent and a lot of what you've
been talking about. Is there any nuance to it we
haven't covered already, or any any way of unpacking that
(20:06):
concept further and more concretely when we've already done.
Speaker 5 (20:12):
Yeah, organizations tend to be very fossilized. They tend to
stick in a shape, even when that shape doesn't make
a whole lot of sense anymore. I have never been
a big fan of reorganizations. I think they can create
a tremendous amount of dislocation and confusion. And so I'm
(20:34):
not necessarily saying redraw your org chart. Maybe, but what
I'm really saying is redraw how you behave as an organization.
Where do the decision rights lie? Do we really devolve authority?
How do we communicate cross functionally and octopus has devolved
authority to its arms. It has the cross silo coordination.
(20:58):
One arm knows one another one is doing. And look,
this is not a new thought. We have been talking
about devolution and desiloing since the nineteen eighties, and yet
it hasn't really happened because the information did not flow
as it could. Now with AI, AI creates transparency and
(21:20):
flow of information, so the right data gets to the
right people at the right time. And then it also
creates transparency into what's going on in the organization. People
have been afraid to devolve authority because they don't know
what kind of shenanigans people are going to do in
the arms of the organization. Now with AI, there's a
lot more visibility into that. Critically, you can't use that
(21:44):
as an excuse for centralization. Now that I can see
everything that all my people are doing, I can dictate
even more and more what's going on.
Speaker 4 (21:53):
People are going to be tempted to do that.
Speaker 5 (21:54):
That is a human tendency, But instead we need to
use that visibility to let things sort of play out.
Intervene were necessary, but by and large, intervening on an
exception basis not as a rule.
Speaker 3 (22:11):
It's been so interesting to have you on. So first,
you've I think you've really complemented a series of really
exceptional conversations we've had over the last month or two.
Would point people to the episode agentic AI and the
end of Traditional It with Rob Wilson. We were kind
of considering with him a little bit more about what
(22:32):
it meant for employees, for individuals in this very new
world and the way we approach our lives and quote
unquote careers in twenty twenty five and beyond. You've kind
of focused a bit more on organizational structure, of course,
but with a very similar kind of ethos and message.
But I wonder how you would translate that into let's say,
(22:54):
advice to organizational leaders may be feeling overwhelmed by the
pace and ambiguity of AI change. What would you tell them?
What would be your orientation advice as it were, for
approaching the future in the strongest spirit.
Speaker 5 (23:13):
So a couple people pieces of advice. First of all,
we have a chapter in the book called three Hearts,
and this derives from the octopus actually having three different
hearts that it uses for different purposes. Another weird aspect
of octopus biology. We translate that through into different leadership styles.
(23:35):
There is an analytics style that you need and typically
organizations already have. You certainly need to retain that where
we're not saying analysis goes way by any means. AI
may help with that analysis, but doesn't replace it. Second, though,
you need the agile part. You need to be able
to deal on a very flexible basis where flexibility is required,
(24:00):
and that is going to happen increasingly with the pace
of change. AI is just one element in accelerating that
pace of change. So companies need to be able to
get more agile where appropriate and to shift between analytic
and angel modes. And then finally you need that aligned part.
You need to think about the emotional toll that this
(24:20):
is going to take on a whole lot of people
and really talk to the change in culture, the change
and maybe safety that people feel, and be sensitive to
all of that. So I think that is the number
one thing that people need to do is their leadership styles.
The second thing is a little bit more cerebral. They
(24:43):
need to think about what does the organization really need
to be good at strategically, So for Hello, Fresh They
determined that what IT really needed to be good at
was understanding user preferences and delivering on those. All this
data from fourteen years of history about what you substituted
(25:04):
your meals for, what you rated your your meals, and
yet it wasn't really using that. It decided to change
that into its competitive mode. So AI can fundamentally change
who you are as an organization, what your competitive strategy
and differentiation is. What should that mean for your enterprise?
They have to do that too.
Speaker 3 (25:27):
That's it's all absolutely amazing. And I think if if
if anyone's listening to this at at the very least,
they're going to be less likely to enjoy Octopus when
they're next on on a Spanish holiday. All this people
who get into into octopuses, as I now know it is,
they can't they can't bear the fact that we even
when it's the nice culumary, all these multiple all these
(25:50):
multiple hearts and brains, they have magnificent complexity, and we
just we just munch them when we're in Barcelona like
at eight, No thing but absolute pleasure. Steve to have
you on. I think your your your your thoughts on
organizational change in this era are genuinely unique and valuable,
(26:11):
and thanks so much for coming on and sharing with
any anyway you want to point the listeners to to
find out more about yourself or your work.
Speaker 5 (26:19):
Sure so. The website for the book is AI Mdoctopus
dot com. You can look me up on LinkedIn. Stephen
Luonker and our company is New Markets Advisors. We engage
with organizations that are trying to undertake these sorts of transformations.
Speaker 1 (26:37):
To make sure that you never miss an episode, subscribe
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help you improve your digital employee experience, head over to
next Think dot com. Thank you so much for listening.
(27:00):
Until next time,
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