Latest / The Innovation Show / AI and the Octopus Organization: Autonomy, Distributed Intelligence, and Faster Decision-Making

Transcript

AI and the Octopus Organization: Autonomy, Distributed Intelligence, and Faster Decision-Making · The Innovation Show

0:00
we need to start treating our people with that autonomy because if we don't,
0:06
and if we don't put the governance mechanisms in place for them to do that
0:10
safely for the organization, we won't get the speed we need to move forward.
0:15
And what AI does is it gives you, for the first time that ability to superpower
0:20
the individual, to give them context, to give them tools, to improve executive
0:24
judgment, to think through scenarios at scale, and at the same time as a
0:29
leader, have that visibility into the organization about what's going on.
0:34
Before we start, I want to thank our sponsor Kyndryl, who runs
0:36
and reimagines the technology systems that drive advantage for
0:40
the world's leading businesses.
0:41
With a unique blend of AI powered consulting, built on unmatched managed
0:46
service capability, Kyndryl helps leaders harness the power of technology
0:50
for smarter decisions, faster innovation, and a lasting competitive edge.
0:54
You can find out more about Kyndryl at KYNDRYL Kydryl.com.
0:59
Bang and everything changed 66 million years ago, an asteroid the size of
1:04
Manhattan struck the Yucatan Peninsula.
1:07
With the energy of 10 billion Hiroshima bombs, massive clouds of toxic dust,
1:12
blotted out the sun, cooled the planet, and generated torrent of acid rain.
1:17
Within weeks, 75% of Earth's species were on the road to distinction.
1:24
One of those was the ammonite, and it was the exact opposite
1:29
to our friend the octopus.
1:31
A little teaser for today's episode today because why did the octopus survive and
1:37
thrive and continues to keep on thriving?
1:40
Our guests today are gonna tell us about that.
1:42
They are the authors of a brand new book, aI and the Octopus organization.
1:46
We're joined by both authors, Steven Wunker and Jonathan Brill.
1:50
Welcome to the show.
1:52
thanks
1:53
us.
1:53
Good to be here.
1:54
Great to have you both on the show.
1:56
I don't always get to cover books that are just out.
2:00
And the only reason I cover this was because I was supposed to cover one of
2:03
these guys older books, and then they pop out this brand new one as well.
2:07
So thank you for that.
2:09
Adding new books to my list as well.
2:11
I set us up there about the ammonite versus the octopus.
2:15
Maybe one of you'll tell us what the hell I was talking about.
2:18
300 million years ago, the world was obviously a different place
2:23
and that's when the octopus started to evolve into way it is today.
2:28
It doesn't have any strong natural defenses.
2:31
It doesn't have a shell, it doesn't have big teeth.
2:34
It doesn't have any of the things you, would kind of think
2:37
about, , as a way to defend itself.
2:39
And yet, for the last 300 million years after species, after species,
2:43
after species has gone extinct, it hasn't even had to evolve to thrive.
2:48
In fact, in our age of climate change, it is thriving more than ever.
2:53
And so the question we started to ask ourselves is, what can we learn
2:58
from the strategies of nature?
2:59
What can we learn from the strategies of the octopus to change the way we
3:04
think about resilience, innovation, performance, agility in our organizations?
3:10
one of the things that comes to mind then is.
3:12
When you're reading this intro, you're thinking to yourself, well,
3:16
digital was probably a big bang.
3:18
The internet was a big bang, but today we're seeing an exponential big
3:22
bang with ai, and that's the context into which you entered the book.
3:27
Absolutely.
3:28
We're seeing a, a nuclear chain reaction of innovation starting to occur.
3:34
And it's hard to even imagine what this looks like 10 years from now, which means
3:39
that it's hard to do long-term planning unless you plan in a really different way.
3:44
we see analogies in what people are doing today.
3:48
You would not have high frequency trading firms.
3:50
Without ai, you would not have, , digital advertising marketplaces
3:55
without ai you wouldn't have analyses of patients telematics, , as they're
4:02
outside of the hospital without ai.
4:04
So they might well need to be kept as an inpatient.
4:06
This is not futurism.
4:08
This is right now.
4:10
So we have to think not just about this very narrow vision of replacement
4:15
of humans, but what is now possible.
4:19
There are plenty of examples out there in the real world today where
4:23
companies have shown what they can do.
4:25
I, I think the difference, right?
4:26
The big thing to think about is that AI isn't about automating jobs.
4:32
It is about automating tasks so that we can automate workflows.
4:37
And it's about that shift, right?
4:40
How do we replace entire workflows with things that are better so that
4:44
our people are freed to do the work that creates value as opposed to
4:49
checking the boxes and conveying information and trying to remember
4:55
what the heck they did nine months ago.
4:58
I think that point is the biggest thing that we've all experienced, the digital
5:03
revolution when digital came along and people just basically put websites up.
5:07
They just digitized their existing processes.
5:10
Steven, I know you worked with the Great Clay Christensen.
5:12
He called it cramming.
5:14
, But AI is being used the same way.
5:16
We're, we're not changing the underlying architecture.
5:21
And this has to happen.
5:22
You talk about where most organizations sink before they even
5:26
start, which is organizational debt.
5:28
People are very concerned about technology debt, right?
5:31
We have 23-year-old.
5:34
Machines.
5:35
We're running our ERP on Fortran from the Vietnam War, you know,
5:41
whatever the issue is, right?
5:44
But what I think we miss more deeply is the organizational
5:49
debt that we face, right?
5:51
That, that to gain from artificial intelligence.
5:55
To get the real benefits.
5:58
What we need to do is profoundly change the way that we enable our people to work.
6:04
And it's in that culture shift.
6:08
It's in that process shift.
6:12
It's in the incentive shift that you will see the difference
6:15
between failure and success.
6:18
In two years, everybody will have made many of these transitions.
6:24
That's not gonna be the differentiator.
6:26
It's whether you have made the human transitions, not the technology
6:31
transitions that define the difference between being mediocre maybe worse
6:38
with all these new technologies and supercharging your potential.
6:43
You know, there's this disconnect.
6:45
That was already present between how we formally organized our companies
6:50
and how we actually behaved.
6:51
If you look at an org chart, that is a relic of the 1850s when those
6:57
things were created, and that is not actually how work gets done.
7:00
It occurs across these different pyramids in the organization.
7:05
Rarely is that charted out.
7:09
AI is a great opportunity to chart it, but to rethink it and to look at
7:15
the bottlenecks and the constraints where things are suboptimal.
7:19
It might be timing, it might be people's attention, it
7:22
could be suboptimal decisions.
7:24
Where can AI alleviate all of those pain points?
7:28
One of the.
7:30
Best capabilities of ai, but the one the least talked about is the
7:33
ability to get the right data to the right people at the right time.
7:39
Now, that can empower the front line.
7:40
It can revolutionize a middle management job, it can transform
7:45
senior management meetings.
7:47
we have to proactively think about that rather than starting with the technology.
7:52
Let's start with the problems we have and how we organize.
7:56
Then so much is possible as we rethink things.
7:59
in the book you chapters two through five, you showed the four pillars
8:04
of the octopus organization based on the biological traits of the octopus.
8:09
I love the eight arms part and how each of the arms actually has a brain and
8:14
then there's a central brain, and how it's just beautiful for decision making
8:19
and how decision making gets distributed in an age of ai because the people
8:24
at the edges are the people closest to the information, and up until now
8:28
there's been an asymmetry between power and knowledge inside an organization.
8:33
Let's share that because if there's nothing anybody else gets outta
8:36
today's show, it's that, particularly if you're a leader because you've
8:40
worked in Bain, for example.
8:41
Jonathan, you've worked as a consultant as well, that so much of
8:46
the knowledge is in the organization, but it's just stuck and it's doesn't
8:50
have a way of getting to the top.
8:52
Jonathan pioneered, the, idea of the Octopus Organization back in 2022
8:56
at South by Southwest, and he was talking about, mainly the RNA and
9:00
how you have radical adaptability.
9:03
And then we started looking as we started conceptualizing the book at
9:06
what else Makes an Octopus weird?
9:08
And there's actually a lot, , is how , you frame it in the different
9:12
elements of, those chapters.
9:14
So the fact that it has nine brains makes it a great analogy for
9:18
distributed intelligence, but it also is a secret to how an octopus
9:23
can thrive without any real natural defenses in this hyper competitive,
9:28
dangerous environment that it's in.
9:31
you have eight arms and they could be doing eight independent things.
9:34
They're all sensing and thinking and acting on their own.
9:38
And yet with complete contextual awareness about what's going on now,
9:43
this is what an organization needs to act like if it is going to be hyper
9:47
adaptable and hyperresponsive to the changes with the meteor strike of ai.
9:53
So.
9:55
What does it make possible when you have a brain, if you will, in each
9:58
of the arms of the organization, all the way down to the front line?
10:02
Now we can suspend all the past assumptions about what can and
10:07
can't be devolved to the front line.
10:09
I mean, look, Aidan, we've been talking for 40 years about the need to devolve
10:14
and Desilo organizations back since.
10:17
Tom Peters in the 1980s, and it hasn't really happened because you haven't had
10:23
the right information at the front line, nor the right amount of visibility by the
10:28
central brain senior leadership to what's going on in the arms of the organization.
10:33
Now, AI changes all of that.
10:35
It makes possible.
10:36
It changes the cost of doing right.
10:41
I spend more of my time looking at the psychology of management and less on , the
10:48
operational transformation, tactics.
10:52
And what strikes me the most about organizations is we've talked about
10:57
psychological safety, we've talked about becoming more innovative.
11:00
We've talked about being more agile.
11:03
and there are two reasons that hasn't happened.
11:04
One, has this been too energy intensive for managers to put two
11:10
hours a week into their 40 employees and then do their 40 hour a week job.
11:17
AI allows software to do 80% of that work for you.
11:24
But the second thing that we don't talk about maybe it wasn't a good
11:29
idea for you as a leader to convince your secretary that they could
11:33
be a corporate vice president.
11:36
And what we're seeing in this world of acceleration in this world where agents
11:41
superpower your people that it is not only in your interests to accelerate
11:48
their careers, it is the only thing that will enable you to accelerate yours.
11:53
I think that's why, out of all those things you talk about, the gates
11:57
that you have to go through, edge computing, all these kind of things.
12:00
AI software maturity, that that's where people put their
12:04
efforts, not on the human parts.
12:06
And there's the human parts that block the technological parts from actually
12:10
distributing it in the organization.
12:12
Absolutely.
12:13
I mean, I've worked with, a major holding company that basically said we're gonna.
12:19
We aren't gonna focus on the AI trans, the digital transformation side.
12:22
We're gonna focus on the human transformation side.
12:25
and they are successful across a whole range of industries.
12:30
You know, running multi-billion dollar portfolios with Excel, right?
12:35
The technology helps, but it's that human side.
12:40
It's the culture side that superpowers the technology and not the other way around.
12:45
The other beautiful biological trait that you talk about is the a neural
12:50
necklace, and you say insight radiates from the center, but discovery is
12:55
coordinated at the edges and the arms and heads are working as one.
13:00
Maybe we'll explain what this metaphor means for the organization.
13:04
So when you talk about, an octopus is a really interesting thing about
13:08
the intelligence and the octopus . if a shark bites off a tentacle from
13:14
the octopus, that, that tentacle will go and attack the shark.
13:18
It has enough intelligence in it to make decisions to go
13:22
after that shark on its own.
13:26
When we talk about distributing intelligence, when we talk about pushing
13:30
decision making down into the firm, when we talk about creating autonomy
13:34
in the firm, we're talking about is that how can the tentacles operate
13:40
on their own and yet coordinate to do something bigger than themselves?
13:44
I love that the idea of the arm encountering a shark and going, wait a
13:48
second, I just need to check with the boss if this is okay to escape and not get my
13:52
arm ripped off, because that's actually what happens inside organizations, Steven.
13:57
Yeah.
13:59
Jonathan and I have an article out this week in Top Sales Magazine, which is a
14:04
lean publication for, sales profession.
14:07
And we talk about AI makes possible.
14:12
Close to segments of one at least, you know, going from three
14:15
to five segments to 50 plus.
14:18
And in fact, it already has done this in industries like mobile gaming.
14:23
You can do that because you start to desi things like marketing and sales, and you
14:29
develop very detailed profiles on the customer, on their context, on the queues.
14:36
You can get a very clear picture about what is going to be important to them.
14:42
And then you can create marketing materials that are very bespoke to their
14:46
needs, is what mobile gaming companies do.
14:49
'cause they have all this data and they operate in this real time environment.
14:53
Now you can bring this to hyper complicated B2B sales environments even.
14:59
So you've desiloed these operations.
15:01
Now look, this opens up a lot of questions around who's gonna do what and where
15:06
are the decision rights lying how do you create the necessary competencies?
15:12
So it opens up a whole other series of questions, but it creates this remarkable
15:17
capability if you can start addressing those human obstacles to change.
15:22
super turbocharge your sales and marketing organizations.
15:25
That's just one example of that neural necklace.
15:28
Creating this fluidity of information that you just didn't have before.
15:33
AI could bring structure to unstructured data.
15:36
I love that you call out again, the human aspects that block the change.
15:42
So two gremlins that are liable to gum up The works are
15:45
groupthink and analysis paralysis.
15:48
When you do get the organization acting as one brain, not one voice.
15:53
You know, people can freak out when you have a lot of
15:58
information being presented to you.
16:00
And so either you gravitate to one easy, commonly understood framing
16:07
of the marketplace, is wrong.
16:09
In a VUCA world, that is a very dangerous thing to do, you freeze because you
16:15
have so much information, and that is incredibly wrong when you have this
16:20
hyper speed of transformation in an industry, in an in, in a broader economy.
16:26
So we have to name the dangers to be able to defeat them.
16:31
And I think there are certainly two.
16:33
It means that we have to change how we make decisions, who gets
16:36
brought into conversations.
16:39
We need to change our comfort level with uncertainty.
16:42
We need to insist as managers that we're presented with multiple options.
16:46
And we often take multiple options, probably different levels of investment.
16:50
but you have to think about your portfolio of options and not your one big bet,
16:56
is a hyper dangerous thing to do today
16:59
Jonathan, you worked as futurist in residence in a company I admire.
17:04
I know lately people are starting to not admire so much, but for different
17:09
reasons, for political reasons, but what a company Amazon is and what a company.
17:14
If you were lucky enough to have stock in that company in the early
17:17
days, how you've absolutely killed it.
17:20
But I love the principles regardless of people, what people think are not
17:24
of the company, of how they operate and how they're inventive of how when
17:30
they run into, oh, that doesn't exist.
17:32
Well, let's build it ourself like Amazon Web Services.
17:35
But one of the things I absolutely love is how.
17:38
The company used APIs and there's a common law called Conway's Law, which is where
17:44
the organizational technology mirrors how the organization actually works.
17:49
And Amazon were huge pioneers of API mandate and API using APIs,
17:54
Mm.
17:55
but equally they.
17:56
Mirrored that with small, close-knit teams and these service interfaces.
18:02
I'd love you to share firsthand your experience of that and how this ties
18:06
exactly into the octopus organization.
18:09
It was a deep insight for me.
18:12
I worked, on data centers, in the nineties and two thousands quite a bit.
18:17
And what was interesting was how Amazon kind of got this idea of virtualization
18:22
together and that you don't have one application on one server that starts
18:26
to spread these things across devices.
18:29
there was an insight in there.
18:32
I think that defined how Amazon operated, which was that you could have these
18:38
independent clusters, right, these neural clusters in the organization
18:43
that could operate very differently, operate on whatever software stack
18:46
operate within, with whatever way of working that the leader thought was
18:51
appropriate, as long as everybody else's code, as long as everybody else's
18:56
processes could jack into that team.
19:00
so I think there's a lot of wisdom in here about how the octopus works, right?
19:05
That the octopus has these neural nodes that operate independently.
19:10
And just to clarify what it was saying before, it's not that the octopus
19:15
asks if it can get its arm bit off.
19:17
If the octopus gets its arm bit off that tentacle, the tentacle on
19:21
its own no longer attached to the octopus will attack that shark.
19:27
And so what I'm suggesting is the level of autonomy, the level of independence
19:32
that you give your people, them the inability to innovate when you look
19:37
at special forces operators in the military versus, versus infantry, right?
19:43
Especially in, in, in the uk mil military, the British military, what you'll find is
19:47
that the enlisted men in the general, they don't have a sense of seniority and the
19:52
way that you might a CEO and an intern.
19:55
They operate at the same place.
19:57
They eat at the same mess.
19:59
And the reason behind that, and I had a special forces planter who, rent
20:04
used to run my strategy capability.
20:07
And, you know, he'd never quite say what he did, what he
20:11
would do was say where he was.
20:13
Now imagine if you're operating 250 people, 2 24 hours a day, seven days
20:18
a week the border of North Korea.
20:20
Just imagine what you're doing every night.
20:23
Now imagine for a second what happens if you get caught or anything goes wrong.
20:29
the general said you should do is irrelevant.
20:33
To the international disaster that's about to occur.
20:37
My point is we need to start treating our people with that autonomy because if we
20:44
don't, and if we don't put the governance mechanisms in place for them to do that
20:49
safely for the organization, we won't get the speed we need to move forward.
20:54
And what AI does is it gives you, for the first time that ability to superpower
21:00
the individual, to give them context, to give them tools, to improve executive
21:03
judgment, to think through scenarios at scale, and at the same time as a
21:08
leader, have that visibility into the organization about what's going on.
21:13
In the Navy, it's the shift from what we call kill chains, where you
21:17
know, the president says something, therefore the admiral says something,
21:20
blah, blah, blah, blah, blah.
21:21
Down to the swabby.
21:23
Right?
21:23
To the swabby, sees something, takes an action and It impacts strategy.
21:32
Right.
21:32
In the world that we're moving into, we're moving from what the Navy calls
21:36
kill chains into what the Navy calls kill webs, where the person closest
21:40
to the action who has the information and the capability to make a decision.
21:45
Or in the case of the Navy, increasingly I believe the software
21:51
they need to have the decision right.
21:53
take the action.
21:54
How are you gonna do that in your organization?
21:57
That's the big question of the next five years.
22:00
There's great counter example that comes from the Navy too.
22:03
, Around 1900 Navy captains had a tremendous amount of authority.
22:08
To go out there and basically enact the admiral's orders, go out on the high
22:13
seas and execute commander's intent.
22:16
And by 1930 that autonomy had almost entirely vanished, and it was due to
22:22
one form of technological progress, which was the radio, because
22:28
the admirals could give orders.
22:30
They did.
22:32
And that's human nature, right?
22:34
We all think that we know best.
22:36
And we're gonna give the orders.
22:39
You could use AI for that to get tremendous visibility into what's
22:44
happening at the front line, and then be barking orders all day long.
22:48
And that is not the spirit of the octopus organization that is gonna lead
22:53
you to be an ammonite, to be this, very slow moving fossilized relic, fighting
22:59
yesterday's battles and not being responsive to what's happening today.
23:03
let's just be aware of our human natures and realize we
23:07
have to act in a contrary way.
23:11
There's a meditation I do, and it's called Heart Brain Coherence.
23:14
The whole idea is that you sync your heart and brain, and I thought of it
23:18
when I was reading the book because.
23:20
First you talk about the importance of the nine brains, each limb having
23:24
its own brain and the central brain.
23:26
But then you talk about the beautiful concept that I didn't know about the
23:29
octopus, that it has three hearts, and I thought about how the brain
23:35
and heart working together creates this ultimate adaptable organization,
23:39
Or
23:39
an organism that if that's adapted by the organization,
23:43
it can at least thrive for a while in these rogue waves, in
23:48
these treacherous waters that are in the business world Today.
23:52
Let's share this beautiful concept of the three hearts and what each of them
23:55
do and how it reflects the organization.
23:58
so an octopus has different hearts for different purposes, it has a
24:02
heart to pump its blood, it has hearts to power its skills, and it
24:08
can direct energy to the different hearts, depending upon the needs.
24:12
It can even give itself a heart attack, which is just astonishing.
24:17
so that it can just have maximal energy to do what is necessary in the moment.
24:23
Organization's, hearts are their processes.
24:27
Sometimes they're articulated, sometimes they're invisible, and
24:30
therefore we call culture, but they operate or should operate at different
24:36
speeds and for different purposes.
24:38
So we define three of them.
24:40
There's the analytic heart.
24:42
Most organizations, particularly the larger ones, are pretty good
24:45
at being the analytic heart.
24:47
There's the agile heart of being entrepreneurial and flexible, and
24:54
usually the bigger the organization, the worse they are at that.
24:59
And then critically, particularly for ai, there's the aligned
25:02
heart to make sure that people.
25:05
Have a common purpose.
25:06
They have a clear vision about where things are going.
25:09
They feel that the organization is not out there to cross them, but they're sort
25:13
of, both, functionally and emotionally aligned on what needs to get done.
25:19
And with all that dislocation, that AI plus everything else is
25:23
creating in organizations, you need to have that aligned heart.
25:28
So think about what are the different hearts you need for different purposes,
25:33
and then really take stock about how good you are at each, and whether people know
25:39
the rules of engagement for the different purposes in a particular meeting.
25:43
Is this more about analysis?
25:44
Is it about agility?
25:45
Is it about alignment?
25:47
Sometimes it's all three, but you need to have a sense about what the waiting
25:51
is in order to hit the right notes to make sure that people interact in the
25:55
right way, but also that they listen to the messages in the right way, too.
26:00
One of the examples, because you called it out there.
26:02
the difficulty for a large organization to have an agile heart, but an example
26:07
you give in the book is L'Oreal, which is a massive organization.
26:10
Maybe we'll share how they engage the agile heart.
26:15
I could not believe this when I interviewed a, a friend of mine who works
26:20
as a executive at L'Oreal, so this is the world's largest cosmetics company.
26:25
It's enormous, tens of billions of dollars.
26:28
They can go from concept in a conference room to product on shelf in six
26:34
weeks, which is just breathtaking.
26:39
The reason they can do that is because they have to.
26:44
In the cosmetics world, trends go very, very quickly, and so you have
26:50
to be responsive at that rate in order to thrive, and so then they've
26:54
developed all the muscles around that in order to be able to do so.
27:00
The world is picking up for all of us in terms of its rate of change.
27:04
Maybe we don't need to go from concept to product in six weeks, but it's
27:07
definitely gonna be a faster timeframe than it has been historically.
27:12
So we need to think a little bit like L'Oreal.
27:14
How do we optimize our systems to operate at that rate rather than hope the world
27:20
optimizes to our internal clock speed.
27:24
I thought this is important.
27:25
I'd love you to give some examples of companies either you've worked
27:29
with or you've seen that start small.
27:32
Like the example you give is Walmart's syntilla platform.
27:35
This, AI platform that, that can just really sense what's
27:40
happening in the marketplace.
27:42
But maybe some other examples of what you're seeing out there would
27:44
be great, just so people can see.
27:47
You don't have to be massive.
27:48
You don't have to have huge pockets to get started.
27:50
You can get started in a smaller way.
27:53
You know, the, the realities here are these right companies like
27:58
Lovable, which does vibe coding.
28:01
You can explain in English what you want a website to do, what you want a piece
28:05
of software to do, and then AI will write that code for you, to give a sense of
28:10
speed of scale, You can explain, I want this website to do so on and so forth.
28:16
Maybe in 15 minutes and it will write the code.
28:20
It will give you a webpage and so on and so forth.
28:22
It'll take another couple of hours to jack in all of the APIs, get
28:26
everything tested, so on and so forth.
28:29
But when you think about what had to happen before, right?
28:32
You had to explain what you wanted Client manager would listen to what you said,
28:40
write it down wrong, give it to an artist, a designer who would make the wrong thing,
28:46
give it to a coder who would then make the wrong thing, and then you would get to
28:50
pay to do it over again, and six or eight weeks later or six months later, you might
28:56
have a product that kind of did what you needed and the market would've changed.
29:01
Now for, call it a hundred bucks a month as a product manager,
29:05
you can do that yourself.
29:07
Now, will it get there a hundred percent?
29:10
No.
29:12
Will it give everybody on the team everything they need to do it
29:15
right the first time in three days?
29:18
Yes.
29:20
What we're talking about is that shift in mindset, that shift in workflow.
29:26
Like we said before, I don't think that makes all the jobs go away.
29:30
I think everybody's gonna do this approximately at the same
29:33
time, and therefore everybody needs to do 10 times as much work
29:38
with the same number of hours.
29:40
So the question that we need to ask is how are we going to
29:43
do higher quality work faster?
29:45
How are we going to innovate harder?
29:47
How are we going to push that capability, those rights, to make
29:52
decisions down into the firm?
29:53
Because unless we allow our tentacles to explore and take action faster
29:59
than we can tell them what to do, like the Navies of the early
30:06
second World War, we will be sunk.
30:09
Lemme give you an example
30:10
from, uh,
30:11
from,
30:11
uh,
30:11
my work, uh, new markets advisors, consulting firm I
30:14
run, we're about 15 people.
30:16
So we have not changed our headcount.
30:18
We do a lot more now and we do it a lot faster.
30:23
We have a consumer healthcare client.
30:25
Previously we might have created concepts with them based on
30:29
some in-depth market research.
30:31
Now we can.
30:32
Sketch out what these concepts will physically look like if they're, devices.
30:37
For instance, we can get an engineering schematic for different
30:41
ways that this might work, whether it's mechanical or electronic.
30:44
We can get a rough bill of materials on the costing.
30:48
We can get a rough regulatory analysis around where it might
30:51
be easier to bring to market, at least in a test market sense.
30:55
Now, is that gonna replace an industrial designer or a lawyer in the end?
31:00
No, but it's gonna allow us to winnow down concepts a lot
31:05
faster than we could before.
31:06
So there's gonna be a lot fewer, dead ends that we run into.
31:10
And when we finally do the turnover, then they're gonna be able to devote their
31:15
energy and their very high cost labor to the things that are the most promising.
31:21
we can do this all so much faster than we did before.
31:24
So, you know, you typically think of cheaper, faster, and better, and so
31:27
much of the world is focused on cheaper.
31:30
I mean, in some instances, sure.
31:31
Cheaper, but there's a whole lot of faster and better that people are just
31:37
starting to broach the possibilities of.
31:40
To me, I think that the interesting.
31:44
about so much of AI and workflows is you're suddenly getting all
31:48
three, faster, better, and cheaper.
31:52
that's what people are kind of missing, is that it's no longer
31:57
an A plus B equals C equation.
32:00
You can put all three on the same side.
32:03
Let's share Jonathan as well, what you're working on, because you're working on
32:06
a product that actually can do what Steve is doing, but for the individual.
32:10
So if you have an idea, you can really move fast to market and actually see,
32:15
do you have a patent on your hands?
32:17
I am the head of invention at a company called Deep Invent, and
32:21
one of the things we're doing is reinventing the patent workflow.
32:25
And it's stunning when you think about how patents are created
32:29
and why they're created today.
32:31
So we create patents to create a monopoly around an area of invention.
32:39
The, when we do it today, right, we do it typically after we have an
32:44
idea that we think has market value.
32:47
So you're six or nine months often into an invention process
32:50
before you file a patent.
32:52
And the reason is that it often costs about half a million dollars.
32:55
You've gotta get, an applied inventor or engineer, maybe some scientists involved.
33:00
You have a product manager who's figuring out what the product opportunity is.
33:04
You have, patent lawyers.
33:06
And, you know, so, so you're six or nine months into this thing and you haven't
33:10
protected your, your area of invention.
33:14
What would happen if your mom could have an idea on Tuesday?
33:19
Say, I have a problem in my quilting circle, then.
33:24
AI could say, okay, well that's interesting.
33:26
Here are all of the companies that kind of deal with stuff around this space.
33:30
Here is everything that's ever been written about this.
33:33
Here's all of the scientific literature that's ever been developed.
33:37
Here are some areas of invention that might be interesting to you.
33:41
Which of these.
33:42
Are interesting.
33:43
And then let's come up with 10 inventions.
33:45
It'll come up with 10 ideas for how you might solve this problem, and you
33:48
spend maybe 20, 30, 45 minutes with it.
33:51
Say, Hey, yeah, all right, I got it.
33:53
This is really, I think, the right thing.
33:55
And you press a button and it files the patent for you.
33:59
Now what's interesting about what we've done at Deep Invent is our
34:03
last rev of the software, we actually had the AI tools write the software.
34:11
Figure out what was useful.
34:14
the patent themselves in this case through our attorney because it had
34:17
to, you had to do that because the government's not caught up to this
34:21
idea yet and file its own patents on inventions it had created, created.
34:31
for products we are selling.
34:33
That is the shift that we're talking about.
34:38
We're creating tools that are faster.
34:40
That are better and that are cheaper all at the same time.
34:46
It is incredible opportunity.
34:48
I, I want to share one thing.
34:49
I can't let you guys go without sharing because I want to show you have the
34:53
scar tissue of what it's like to have an organization block your ideas.
34:58
That's why I wanted to tee you up with the idea of Deep Invent, because back in
35:03
2009, you both have experiences of this, and it's probably why you do the work
35:06
you do today to try and prevent people experiencing the frustration you had.
35:12
So.
35:13
Another great company you worked in was HP Jonathan, and then I'm gonna come
35:17
to you, Steven, and talk about Scion.
35:19
But back in 2009, you saw an opportunity just like our friend Chuck House by
35:25
the way, a predecessor of yours in HP saw with the screen for the computer
35:30
and he was blocked, and he wrote a book called Permission Denied, but
35:34
you saw this when you were like, how the hell are we not pursuing a
35:38
marketplace for touchscreen devices?
35:41
And you were told to be a fast follower and it was terrible advice, but I wanted
35:46
to share this because it's what the ai, octopus Organization can solve.
35:51
It can solve bottom up information, getting blocked, get to the top of
35:55
the people, making decisions from the people who see it on the field.
35:59
Maybe you'll share your story and then it'll come and share Stevens.
36:02
well, I think there are two challenges here, right?
36:05
The thing that I learned, at HP the way tops down organizations work
36:11
and most organizations today are tops down , is shareholders have
36:15
a set of shareholder objectives.
36:16
They hire a board.
36:17
The board enforces those shareholder objectives.
36:20
They hire the executive teams.
36:21
They incentivize all the way down.
36:24
If you are not aligned with the shareholder objectives.
36:28
You are not gonna have a thing happen.
36:31
what happened at HP was it was really oriented toward reliable performance,
36:36
especially after Mark heard the CEO in.
36:41
There was a moment.
36:44
Where there was an opportunity to get a couple of years ahead, potentially on
36:48
the capacitive touchscreen technology.
36:50
I was a consultant at that point that came in.
36:52
They said, Hey, here's the opportunity.
36:54
Here's what it looks like.
36:55
Here's what other competitors are doing.
36:57
Here's where I think the market is.
36:59
I got brought in, the president of the PC group came in, CTO came in.
37:03
We had a big.
37:04
Chat about and the said, Well, here's the thing is that our strategy is
37:08
we are going to fast follow Apple.
37:10
We're just gonna let them build the market and then take it.
37:15
That didn't.
37:17
Work.
37:18
What happened was they got caught with their pants down.
37:21
They tried to buy something called Web os, which was palm computing, if
37:26
you remember that, their operating system, which was not set up for
37:30
the next generation of devices.
37:33
So they went there, they fast followed, they tried to buy the
37:35
assets they needed, they didn't work, and then they got their pants.
37:40
Taken off of them.
37:42
They got their shirt eaten or whatever mixed metaphors we can come
37:45
up with here, and it was entirely knowable that was going to happen.
37:51
The issue was that the people at the bottom knew this.
37:56
in the middle weren't listening to them or asking them to actually vet
37:59
the technology, and the leadership was disincentivized to think about the
38:04
world beyond their revenue windows, their personal revenue windows.
38:08
And so you ended up with an almost inevitability here of the investor
38:13
objectives, which are growth, but conservative, reliable growth were in
38:21
conflict with the reality that there was a sea change in the market that
38:26
the people on the ground could see, but that investors could not yet.
38:30
it's always the case, and I get stuck in the middle, a decade previous, Steven,
38:36
you had a similar experience with another product that Apple went on and
38:40
not so successfully developed, which was A PDA, but this was in 99 at Psion.
38:46
Maybe you'll share your scar tissue as well.
38:49
So I left Bain and Company in 99, because I wanted to do something exciting.
38:55
I was working on a private equity project in plastics, and I thought
38:59
the whole world was passing me by.
39:01
So I joined up with a, another, ex baney.
39:04
he was the CEO of Scion, which is the British company that had actually
39:07
invented the PDA back in the eighties.
39:09
And they were competing against Palm Pilot, a different sort
39:12
of,, machine, different form factor, and doing pretty well.
39:16
The CEO had this idea of going into, what do you called a smartphone?
39:20
This was very novel term at the time.
39:22
So basically, connecting a, you know, A-A-P-D-A into being a phone.
39:27
And that was about all that we had, , as a concept.
39:29
And we were gonna do this on a white label basis for Motorola and a
39:34
participant for Eric Erickson as well.
39:36
So we were the guts behind their initial entries into the smartphone, business.
39:41
Now, I quickly got intoxicated by what a smartphone could do.
39:46
You could play games and you could do maps, and you can even have short videos.
39:51
It was really beguiling and so we started developing a pretty high spec device
39:58
that could do all these things based on components from elsewhere, but integrating
40:02
them together for the very first time.
40:04
All the while there was this lone industrial designer on the team who
40:08
said, Hey, how about we just have a basic phone, but with a keyboard
40:13
rather than just a numeric keypad?
40:17
And I said, oh, you, you, you simpleton you.
40:20
Uh, no, no.
40:21
We can do far more glamorous things here.
40:25
And so he broadly was ignored because we were, entranced by this
40:30
high-end device and the demands of our big demanding customers.
40:36
and we wanted the prestige.
40:38
So it was the culture, it was the classic innovators dilemma track of
40:42
being, uh, trapped by, by what our big, demanding customers wanted.
40:47
And, sure enough, we developed all sorts of dependencies on components.
40:51
There was a critical software supplier that led us down, in terms of the data
40:55
connectivity with early mobile networks.
40:58
And that was a big, big problem.
41:00
And it turned out that the industrial designer was describing
41:04
what became the blackberry.
41:06
Because those guys were working on the same problem at the same time,
41:09
and they came out with the blackberry and they cleaned up and they dominated
41:13
the smartphone business for seven years until the iPhone came out.
41:18
So I took away some lessons from that, for one, listen.
41:23
Secondly, for heaven's sake, read Clay Christensen.
41:27
I didn't read his stuff until the weekend before I interviewed.
41:30
You know, with the great man, and then I just, I smacked my forehead and I
41:34
thought, oh, I was such an idiot for not reading this stuff beforehand, because
41:38
that's exactly the trap that I fell into.
41:42
And then, yes, have a vision about where the market is gonna go.
41:45
And this definitely applies for ai, right?
41:47
Have that futuristic vision, have your priorities and your
41:52
step-by-step route to get there.
41:55
Way too many companies are at either end of the spectrum.
41:58
They've got this.
41:59
This g whiz Jetsons version of the future.
42:02
And then they have their very boring here and now let's put a chatbot in for
42:07
something and try to replace a human.
42:09
And they're not thinking about all the steps in between.
42:13
Had we done that, we should have been Blackberry.
42:15
We had every right to be Blackberry, but largely 'cause I just, I was 30 years old,
42:21
I didn't know the trap I had fallen into.
42:25
And I learned from that experience.
42:27
I think that's why we do the work we do, because that knowledge that you can learn
42:32
from just reading an article or a book can change the lens through which you see
42:37
and you see something totally different.
42:39
You see opportunity where others see threat or see nothing at all.
42:42
It's why I love sharing brilliant work like this.
42:44
We haven't even gotten near.
42:46
The amount that's in the book, there's a chapter on how do you
42:49
create strategic serendipity.
42:52
There's a chapter at the end, chapter eight, where the guys bring you
42:54
as an organization, as a leader.
42:56
Through a load of questions to ask yourself and exercises to
42:59
implant into the organization.
43:01
Doesn't cost you money, cost you a bit of time, cost you a bit of
43:04
reorganization of the organization as well, to become an octopus organization.
43:10
Guys, let's share each of you where people can find you.
43:12
I know you do keynotes.
43:14
I know you do consulting.
43:15
I know Steven, you do a lot of jobs to be done work.
43:18
And of course, the patent work that you do now, deep invent jonathan as well.
43:23
Really valuable stuff.
43:24
Let's share maybe Jonathan first
43:26
My name's Jonathan Brill.
43:28
I'm a business futurist.
43:29
You can find me at jonathanbrill.com.
43:32
I do keynotes and workshops around the world.
43:35
I'm in about 80 cities a year, so I might be near you today.
43:40
and Steven.
43:41
Best way to find me is on LinkedIn.
43:43
Please write, I do respond to all my emails, so I would love
43:47
to, just correspond and hear what you're thinking about all of this.
43:51
The company is new markets advisors and you can find us in many places online.
43:57
Authors of AI and the Octopus Organization, Jonathan
44:00
Brill and Steven Wunker.
44:02
Thank you for joining us.
44:03
Thank you.
44:04
Thank you so much.
44:05
Thanks again to our sponsor, Kyndryl and the Kyndryl Institute.
44:09
With a unique blend of AI powered consulting, built on unmatched
44:13
managed service capability.
44:14
Kyndryl helps leaders harness the power of technology for smarter
44:18
decisions, faster innovation, and a lasting competitive edge.
44:21
You can find out more about Kyndryl and the Kyndryl Institute
44:25
at K-Y-N-D-R-Y-L Kyndryl.com.