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The AI Revolution & Business Model Transformation | Osterwalder, Yu & Choudary · The Innovation Show

• 0:00
  Before we start into a very special episode of the Innovation Show X, I
• 0:03
  want to thank our sponsor Kyndryl, who runs and reimagines the technology
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  systems that drive advantage for the world's leading businesses.
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  With a unique blend of AI powered consulting, built on unmatched managed
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  service capability, Kyndryl helps leaders harness the power of technology for
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  smarter decisions, faster innovation, and a lasting competitive edge.
• 0:23
  You can find Kyndryl.
• 0:24
  Kyndryl.com.
• 0:25
  I also want to thank the team at Thinkers 50 for allowing me the time
• 0:30
  to record at this year's event, the Gala dinner in London at the Guild
• 0:36
  Hall with this magnificent lineup.
• 0:38
  I won't give you any more over to that episode now.
• 0:41
  AI is shaping business, but most companies are chasing tools
• 0:45
  rather than restructuring the businesses and the business models
• 0:49
  underlying those businesses.
• 0:51
  The picture is confusing 95% according to MIT of Gen AI projects fail or
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  under deliver on their projected value.
• 1:02
  If they add any value at all.
• 1:04
  Tech giants and consulting arms are letting go of suede of people only
• 1:09
  to rehire them weeks or months later.
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  The picture is confusing, the puzzle is incomplete.
• 1:16
  So we have to look at what is underlying these businesses.
• 1:20
  It reminds me of Amaras Law.
• 1:22
  Amaras Law is a technological law where we
• 1:27
  the value of a technology in the short run, but undervalue that extent
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  of that change in the long run.
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  But Amara's law applies not only to the technology, but the businessing
• 1:38
  impact of that technology.
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  It is a puzzle, it's a paradox.
• 1:43
  And here at Thinkers 50 in London 2025, in an magnificent episode in of
• 1:51
  Innovation X, I'm joined by three of the world's best thinkers on this to unpack
• 1:56
  different lenses to look at this puzzle.
• 1:59
  We're joined over here by Alex Osterwalder.
• 2:02
  Thanks for having me.
• 2:03
  Great to have you in person on the show.
• 2:05
  Howard, Yu welcome back, sir.
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  What a great treat.
• 2:09
  Great to have you and Sangeet Paul Choudary as well.
• 2:12
  Two guests previously on the innovation show, X joined by Alex.
• 2:15
  Now we promised you extra value.
• 2:18
  We've given you the best possible value you can have.
• 2:21
  So let's get stuck in Sangeet.
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  I thought I'd come to you first.
• 2:24
  Sure.
• 2:25
  One of the things you talk about in reshuffle is this
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  idea of containerization.
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  I love it as an analogy of what we're seeing with the reshuffle
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  that AI does to business landscape.
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  Let's share with the audience a little bit about that.
• 2:40
  Yeah, I think the idea of containerization is interesting.
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  I start the book reshuffle with the four words, why unintelligent AI matters.
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  And the reason I say that is because today our dominant metaphor for
• 2:53
  looking at AI is intelligence.
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  We're constantly debating do we have a fifth grader or a PhD in our pocket?
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  And , which kinds of exams and benchmarks can it work towards?
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  And the other metaphor we have is of automation, making
• 3:06
  things faster, cheaper, better.
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  But all of these assume that the impact of AI will play out within fixed
• 3:14
  structures, whether it's organizational structures, industry boundaries,
• 3:18
  business models, nothing's gonna change.
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  It's just gonna be automation.
• 3:21
  The reason I chose containerization to make my point is that the shipping
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  container is the polar extreme of ai.
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  It is not an intelligent technology.
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  It's a dumb simple box.
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  And if you had thought of the shipping container as a technology and its effects,
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  your first order thinking would have led you to believe that the effects of the
• 3:41
  shipping container would be automation.
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  And it did automate the ports because before the shipping container
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  cargo used to be break bulk.
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  And so you needed manual labor to move cargo on and off ships.
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  With the shipping container, you standardized that interface and
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  hence you could now have cranes.
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  But if you had just stopped at the impact the shipping container and read it as
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  automation, you would've missed out on everything that unfolded beyond that.
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  Because what really dramatically, changed things with the shipping
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  container was when trucks, trains, and ships agreed on a common
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  standard for the shipping container.
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  The reason that was important was now you could move from source to destination
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  independent of whether you were landlocked or at a port with the same Reliability.
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  And reliability has incredible value in a world where business
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  boundaries are determined by what we call transaction costs, and
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  because shipping became reliable.
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  The whole logic of manufacturing change because before that, you wanted to keep
• 4:44
  things in-house as a manufacturer, or you wanted to keep your suppliers close
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  by because shipping was unreliable.
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  As a retailer, you wanted to rely on distributors because they would keep
• 4:54
  the stocks to manage unreliability.
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  The moment shipping became reliable, manufacturing got unbundled.
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  We started seeing the rise of global supply chains.
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  We started, most importantly seeing component level competition because now
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  you did not need to build everything in-house so companies could specialize,
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  plug into a global supply chain and compete at the level of components.
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  And once components improved, the larger product innovation
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  happened with improving components.
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  And that's what gave us the computer industry and many other things.
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  So my key point with all of this is the impact of AI will play out
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  not through making things faster, better, cheaper, automating, agentic.
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  All of that is a distraction.
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  The real point is think of the second and third order effects and
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  see the reshuffling of the system.
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  One of the things I took from your analogy was the people would've invested
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  in cranes or they would've , invested in the technology to move the shipping
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  containers, much like we see with ai.
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  That's right.
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  And also, it's a nice segue for Alex, because I thought of the moving of these
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  containers, much like your business model canvas and actually how that helps
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  for many of us, me included with the shift from, say, media, where I worked
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  the shift from analog to digital meant.
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  That it wasn't just a layering of digital on top, although that's how it started.
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  Most companies, for example, a newspaper just had a brochure Yeah.
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  That sat on a website and it didn't move, it didn't have any interaction.
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  But to your point about un the underlining model and unbundling it,
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  it meant a total restructure of the entire organization about the skill sets
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  and what'll come to you, Howard, about this, the capabilities needed for that
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  organization, but most importantly, through the business model underlying it,
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  and that doesn't always spell new value.
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  It can actually unbundle value in a negative sense as well.
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  I'd love you to give your view.
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  You did a webinar recently about this, about the effect of AI on business models.
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  Yeah.
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  And I thought bringing you gents together was a superpower to give
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  people a true lens of what's going on.
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  Yeah.
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  And I love, I love the analogy.
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  Looking at how the environment is changing and everything that needs to happen.
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  I'll kind of zoom into one company, one business model,
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  but I'll use an analogy as well.
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  And Steve Blank actually came up with this, the inventor of the mm-hmm.
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  Customer development lean startup methodology.
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  And we were talking about this this summer.
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  He was saying, Alex, do you know in the 19 hundreds there were 4,000 wagon
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  manufacturers and yeah, well, do you know how many actually became car companies?
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  And the answer is like, a lot of people say, oh zero.
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  Well, there was one Studebaker is a brand that made it from, you know, wagon
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  manufacturer to become a car manufacturer.
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  Ah, and then you can stretch it a little bit and say, Billy Durant,
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  he sold all of his wagon, you know, manufacturing assets and then started
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  a car company and then later on GM.
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  But the point is, if you look at what happened there is
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  there's a technology component.
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  But the big one, the big lesson to take away is there was a lack of vision and
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  leadership to actually use this in new ways and make things happen, right?
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  So one is the environment has to change.
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  You need leaders there who see, okay, well you know, AI is now here, what
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  could I do to support that ecosystem?
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  But the big one is leadership within companies.
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  Yeah.
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  So entrepreneurs are pretty good at that.
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  And then, you know, one out of out of a hundred will maybe succeed big,
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  but when we look at large established companies, many of them are gonna
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  disappear because they do not have that visionary entrepreneurial leadership.
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  You always have to run the company running at the same time you need to transform.
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  So all that to say AI, at the end of the day is just a technology.
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  It's a very powerful one, enables a lot of things, but without the visionary
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  leadership and then the drive to implement that and the right systems in place to
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  allow people in a company with drive
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  and we want to try to let them thrive is not gonna happen.
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  Right.
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  So we're gonna see a lot of change, the macro aspect of how the economy's
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  gonna work, but also a lot of companies are gonna disappear.
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  And I'm not one to often say that, but it's just inevitable.
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  Yeah.
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  Because the leadership we see in, in many companies today, the establishment
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  is not an entrepreneurial leadership.
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  It's a very managerial leadership.
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  And that managerial leadership is not equipped for massive change to me.
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  the capabilities of those leaders often aren't trained.
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  So we'll talk later on , you.
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  Expand on the work of James March, Tushman and O'Reilly, the idea of exploit explore
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  and how many leaders are exploiters of the business model they've uncovered.
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  And they can't, they don't have the capability to explore, nor do they take
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  the time to, build up those capabilities.
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  And Howard, I was very fortunate.
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  I'm always struck by the serendipity of doing the innovation show.
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  I'm currently reading a book by Chuck House.
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  Charles House.
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  Yeah.
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  He's the gentleman who led to computer screens, et cetera,
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  earlier prototype in HP.
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  You've studied in depth hp.
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  And one of the things he did was he talks about how.
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  When there was a shift to transistors from vacuum tubes many of the people he studied
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  with, engineers were gone overnight.
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  And it wasn't a case of doing a night course to try and build out capability.
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  It was simply gone.
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  Because it meant more than a new skill.
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  It was a shift in mindset of how you actually dealt with that new skill.
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  And it made me think exactly of your work, because you're looking
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  always at the capabilities needed for seismic shifts like AI will bring.
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  Mm-hmm.
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  And what Alex, as well as Sangeet just described there, I never made that
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  connection before, but essentially it calls for almost like an architectural
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  innovation, but at the industry level.
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  Yeah.
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  So Rebecca Henderson and Kim Clark, we study architectural
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  innovation inside firm.
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  But what you both just described is the architectural innovation happening at
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  the ecosystem or at the industry level.
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  So less, let's assume a company already have the vision.
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  Let's imagine we are wealthy enough to bring a group of CEO to Silicon Valley,
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  to Sheen and look across the world.
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  They have the time and they immerse themselves and come back with a vision.
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  Let's assume vision is there.
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  I suspect companies can get it right.
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  They need one more thing.
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  They need to know exactly how things work today in order to map out how
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  things should work in the future.
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  So who knows what, how things getting worked done, because so
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  many company we work with, they don't even know how it works.
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  Take a pharmaceutical company, anybody, right?
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  Even generic drugs or you know, biopharma, if you ask for five level
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  down, no one know how things work.
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  Mm-hmm.
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  And without knowing who knows what exactly how things work at a master plan level
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  first is you cannot reimagine a future.
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  Because if you change one thing, you worry the whole things would have a
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  meltdown, catastrophic consequence, or.
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  You cannot even leverage AI to give you some new way of working to eliminate
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  what should not exist from day one.
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  So I could really see there are two sides of the companies.
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  One is having a vision and also the prerequisite knowledge about
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  how we are working right now so that they could reimagine the
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  future for this architectural innovation at the industry level.
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  Hard.
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  This is why I think only a handful can make through that way, but hey look,
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  it's always the winners takes, all right?
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  When you face a seismic shift.
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  One of the things that came up, I think is 50 was say for example an
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  organization doesn't know where to bet.
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  You talk about, for example, an organization should only have three
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  priorities any more than that.
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  They don't have any priorities.
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  Yeah.
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  But where to play as well is very difficult.
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  And things like the Metaverse didn't help us, where organizations
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  were putting bets in there, maybe investing in it, trying to build
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  capabilities there, and it didn't turn.
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  Mm-hmm.
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  It didn't play out.
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  Yeah.
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  . So that's been known as fomo.
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  Fear of missing out.
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  Mm-hmm.
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  Right.
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  But one of the things that it brought to mind was how do you know where to
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  build the capabilities for the future?
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  Yeah.
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  If you don't know where the future's gonna be.
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  Yeah.
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  So I would just give a very short answer then, you know, Alex
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  and Sangeet, please build on.
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  But I think there is almost like conservative company having
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  this like simplistic view.
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  Transformation is after deep analysis, one big gamble and it plays out.
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  Yeah.
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  That wrong.
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  Doesn't work in that wrong, dammit.
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  Because.
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  Steve Jobs almost shut down iPhone three times before he launched it.
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  The famous Alexa project by Amazon was born out from the
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  collapse of the Fire phone.
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  So capability is different than product offering, but of
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  course, once you learn, you fail.
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  Now this is hard.
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  You need to keep the engineers not firing them.
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  Do real postmortem, capture the knowledge, show it, use it, and recycle it.
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  Most company just don't have that emotional resilience to have those type
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  of conversation, let alone show it across.
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  So yeah, I think capability is very different than a product offering itself.
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  You are looking at Meta, right?
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  Yeah.
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  Metaverse is like crazy.
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  But there are capabilities they built around hardware, which
• 14:13
  you see expressed in RayBan.
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  We don't know how things would unfold, but if you don't keep on stacking
• 14:20
  up, where would you have the future?
• 14:22
  If we keep on polishing our historic competence, we all end up like
• 14:26
  Kodak, Polaroid, and Blockbuster.
• 14:28
  so fomo, fear of missing Out I was thinking about this recently.
• 14:31
  I was like on, there's also another FOMO type phenomenon.
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  I call it Photo, FOTO, fear of throwing out.
• 14:39
  And by that I mean you've built a tech stack.
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  The tech stack has been a massive investment by an organization then.
• 14:49
  The CEO or the C-suite to Alex's point, don't really understand what that does.
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  They're relying on somebody in the organization who may not actually
• 14:56
  know what the next move is.
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  And certainly not to your point, to reshuffle that entire stack,
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  which totally takes an unbundling of the architecture you have.
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  And there's a law called Conway's Law, which is that the architecture, the
• 15:09
  organizational structure will actually be matched in architecture of technology.
• 15:13
  Yeah.
• 15:13
  There's a lot in there, but this is a huge problem when you
• 15:16
  come to a new technology like
• 15:18
  ai.
• 15:18
  Yeah, absolutely.
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  Let me just say one thing.
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  This is such an interesting conversation because Howard's view is inside out.
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  What's the capability stack?
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  My view is outside and what's the playing field?
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  And , I feel, and I don't wanna simplify, oversimplify, but I feel
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  Alex bridges the two things really well, and, and he does, he really does.
• 15:37
  Right?
• 15:38
  So I, I'll, I'll just compliment what Howard just said to, to answer
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  that question, one way is to really think of how is your capability
• 15:46
  stack matching what's required?
• 15:49
  But the other question is where to play what is required?
• 15:52
  There are two factors that determine what is required and that's deter those
• 15:57
  two factors basically answer a simple question, where is value moving and how
• 16:01
  can we play where that value is moving?
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  So there are two, I mean there are many different factors that come
• 16:05
  together, but two that really come to mind as being very important on
• 16:09
  determining where value is moving and how the playing field is changing.
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  The first is this idea of complementarity.
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  So we say industry boundaries are dissolving and are becoming porous,
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  but really what all of that means is that previously siloed, unconnected
• 16:24
  industries are now complementary.
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  One of the primary drivers for complementarity was data.
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  So you could capture data from a car and move it into the insurance
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  industry and connect the two together.
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  Now we have ai.
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  You can now take non standardizable information, messy knowledge.
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  Which used to be very industry specific and somehow feed it into models and
• 16:47
  make the two industries complementary and allow capabilities to be packaged
• 16:51
  in one and moved into the other.
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  And I think that's super powerful in terms of changing your playing field.
• 16:56
  The second factor that determines how value moves is commoditization because
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  technology constantly commoditized capabilities that held value in the past.
• 17:05
  So take AI as an example.
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  A lot of companies in the knowledge economy were built on a simple assumption
• 17:11
  that knowledge is scarce and you need to go to Harvard, you need to be trained.
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  And even once we hire you, we had to keep investing in training you.
• 17:19
  Now, if some forms of knowledge will become commoditized and become
• 17:23
  available with a well-structured prompt, value is not going to sit there.
• 17:27
  It's gonna move somewhere else.
• 17:28
  And so really the way I see this whole thing is.
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  You need to look at matching your architecture to the technology because
• 17:39
  the architecture of your playing field is getting matched to the technology anyway.
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  And so in order to play successfully over there, you have to follow what's
• 17:46
  the architecture of the new technology.
• 17:48
  And the way to do that is to keep an eye on what are the new complementarities,
• 17:53
  what's getting commoditized, where is value moving, and how can I
• 17:56
  then move my capability stack?
• 17:57
  Yeah.
• 17:57
  In response to that.
• 17:58
  One of the big fears though on that is actually what Alex talks about is
• 18:01
  that if you, if you have a skillset and you've built a business based on
• 18:07
  the stack as it was for that time in history, that that was needed for,
• 18:12
  so you were, you had a competitive advantage then, then you were exploiting.
• 18:16
  Yeah.
• 18:16
  But the, the fear of exploring.
• 18:19
  And then harmed in some way by history where like the metaverse,
• 18:23
  those type of things that I tried something and I, and they kept
• 18:26
  receipts on me in the organization, so they're not letting me away with it.
• 18:29
  And I have that blemish on my record.
• 18:32
  Yeah.
• 18:32
  There's a huge fear about that.
• 18:33
  Alex and you, I know you work with some magnificent companies
• 18:37
  at leadership level Yeah.
• 18:38
  To redirect the ship.
• 18:40
  Yeah.
• 18:40
  In entirely new directions.
• 18:42
  I'm actually thinking, 'cause you're talking about the different laws and
• 18:44
  since you know everything that goes on in innovation, you read every single
• 18:47
  book that ever existed on innovation.
• 18:49
  Actually, not yet.
• 18:50
  I'll, I'll ask you if that law exists, otherwise we're gonna
• 18:52
  have to invent it with this group.
• 18:54
  It's actually the law of the existing business model spewing out corporate
• 19:01
  antibodies that kills anything that looks differently because once you've
• 19:05
  found a business model that works, actually what happens is the company
• 19:09
  is really good at doing just that.
• 19:11
  And there are very few companies.
• 19:13
  That are able to either reinvent themselves or create a portfolio
• 19:17
  of business models, extremely rare.
• 19:19
  Mm-hmm.
• 19:19
  And the leaders I work with, you know, they don't understand the force
• 19:26
  of their existing business model.
• 19:28
  It will kill anything that looks differently from the radically different
• 19:32
  business model to maybe just the new value proposition that has slightly
• 19:35
  different margins because the sales team is not gonna wanna sell that because
• 19:38
  they're not gonna get their bonus.
• 19:39
  So the existing business model, there's a law of the existing business model
• 19:43
  killing anything that looks differently.
• 19:46
  And the best companies, and there are few of them, they
• 19:49
  are paranoid of disappearing.
• 19:52
  So they create a fertile ground where people can explore and there'll be
• 19:58
  tons of failures, tons of failures, but the winners will emerge.
• 20:01
  And I think with the technology as powerful as ai, you just
• 20:06
  don't know what's gonna work.
• 20:08
  So you have to explore.
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  But when your existing business model continues to kill everything that you
• 20:13
  know, you're trying, it's not gonna work.
• 20:15
  Right?
• 20:15
  So a lot of companies, you'll have, you have innovators.
• 20:18
  They find something that works because you don't lack ideas, you don't lack,
• 20:22
  you know, the people who could build it.
• 20:24
  But then the system, the business model, like how you run your company, will
• 20:30
  kill anything unless the leadership has a very clear vision where we're
• 20:35
  going and is gonna help support some of those that look like winners invest
• 20:40
  more or scale them, because most ideas, they die in the scaling phase.
• 20:44
  Yeah.
• 20:45
  Companies have amazing ideas.
• 20:47
  Now, the wrong part, just quickly back to the exploit, what companies do often
• 20:53
  very wrong is they want to win big.
• 20:55
  So they invest in one big idea.
• 20:57
  Yeah.
• 20:57
  So the corporate antibodies won't kill it because it's already so big.
• 21:02
  Mm-hmm.
• 21:02
  That's the problem.
• 21:03
  They couldn't shut it down.
• 21:04
  They couldn't shut it down.
• 21:05
  Right.
• 21:05
  So I remember one person telling me, oh, Alex, in our company, it's actually very
• 21:09
  hard to get money for project, but once we got it, it's hard to turn off the town.
• 21:14
  Right?
• 21:14
  So they continue doing things that won't work instead of creating more like a,
• 21:18
  an ecosystem where they can explore.
• 21:20
  So that's why these giants have a hard time too.
• 21:22
  Is there a law in that?
• 21:23
  Because that was the original question.
• 21:24
  Ostweralder's Law.
• 21:25
  Yeah.
• 21:27
  Of the killing business model.
• 21:28
  There is a solution around, but I just haven't seen enough example.
• 21:32
  But let's pull our heads together, right, because there is a idea about modularity.
• 21:38
  Yeah.
• 21:38
  And you apply into Inside Company.
• 21:41
  That could be quite powerful because what you described is that I need
• 21:44
  to be able to stitch together a business model and dismantle it.
• 21:49
  At speed with scale.
• 21:51
  And I could only think about one theoretically is the modularity.
• 21:55
  Sure.
• 21:55
  That any capabilities or any way of working a small
• 21:59
  team is like microservices.
• 22:00
  And here we are thinkers 50.
• 22:02
  We all heard about a higher story.
• 22:04
  I see a little bit about MasterCard, but I just haven't seen enough company who have
• 22:10
  gone down the long train of hard work.
• 22:14
  Mm-hmm.
• 22:14
  Break up the monolith from tech stack to organizational design to, you
• 22:21
  know, institutionalize a new culture.
• 22:24
  Yeah.
• 22:24
  But once you actually have that theoretically conceptually you
• 22:29
  could actually adapt quite quickly because the sunk cost is lower.
• 22:33
  Yeah.
• 22:34
  And also you could build something relatively quick and when it doesn't work,
• 22:38
  you could dismantle re-channel elsewhere.
• 22:40
  Yeah.
• 22:41
  Any other example?
• 22:42
  No, let
• 22:42
  me, let me build on that because that's actually a really good point.
• 22:45
  Modularity makes you structurally agile, so you're building
• 22:48
  capabilities, you can recombine them.
• 22:51
  There's a challenge with modularity as well, and that is that traditionally
• 22:56
  companies were defensible because they had high fixed costs, right?
• 23:01
  But if you just build out all these building blocks and
• 23:03
  everybody else's building blocks, the values to entry go away.
• 23:06
  Oh my God.
• 23:06
  Where's the new value to entry?
• 23:07
  So the way I see this happening, and I'll take the example that we
• 23:11
  both love so much Shein, right?
• 23:13
  The barrier to entry is you need to be modular, but you need to
• 23:16
  integrate across the modules where you can compound your learning.
• 23:20
  Mm.
• 23:21
  Traditional vertical integration was based on the idea of minimized transaction
• 23:24
  costs and maximize your margin that you can capture from the customer.
• 23:28
  Yes.
• 23:28
  Today that does not matter.
• 23:29
  You know, value chains are going modular, but still a new vertical integration
• 23:33
  that we see with companies like Shein and intelligent manufacturing companies like
• 23:36
  Tesla is make your capabilities modular.
• 23:40
  Yes.
• 23:40
  But identify which capabilities will help you create a learning engine through
• 23:45
  which you are able to create a proprietary learning engine across those capabilities.
• 23:48
  And over time, once you've captured the learning, then you
• 23:51
  can modularize and commoditize those capabilities if you want to.
• 23:54
  But if you don't create that learning engine, that learning loop, you
• 23:57
  become modular and commoditized.
• 23:59
  That's the challenge.
• 24:00
  And Shein is a very good example because you know, just to wrap this traditional
• 24:03
  manufacturing is to work on the logic of.
• 24:06
  Market research, let's get ideas from the market.
• 24:09
  Let's convert that into new design, new production.
• 24:12
  So H&M
• 24:13
  right?
• 24:13
  For instance, h
• 24:14
  and m. But again, it, it's, it's, the logic applies not just to fashion.
• 24:18
  Correct.
• 24:19
  Across the board.
• 24:20
  Yeah.
• 24:20
  You know, it applies to material science, it applies to Moderna and
• 24:23
  how it came out with the vaccine.
• 24:25
  All of that is possible when you reimagine, going back to your
• 24:30
  point, your architecture as take a small piece of evidence, whether
• 24:35
  from the market or from the lab.
• 24:37
  Convert it into a small batch production, take it to the market and do a small test.
• 24:41
  And you can do that only if you're modular, but then own the whole chain
• 24:44
  so that you're learning and you're capturing the intelligence for yourself.
• 24:47
  That's really good.
• 24:49
  This is why I love this conversation, right?
• 24:50
  It's like there are so much discovery for me right now because what you
• 24:54
  suggest there is the traditional competitive advantage is over.
• 24:57
  Yeah.
• 24:58
  We are not talking about transient advantage here neither.
• 25:00
  No.
• 25:01
  But when you build a feedback loop, then you could always run
• 25:04
  faster than any competition.
• 25:06
  Absolutely.
• 25:07
  And and that's interesting because I could see fashion, you describe in
• 25:11
  your book John Deere, that they don't just make tractors, but each of these
• 25:16
  tractors becomes a little information gathering out there in the field.
• 25:19
  Yeah.
• 25:19
  Across worldwide.
• 25:21
  Figma against Adobe in your book again.
• 25:24
  But is this idea you building so many feedback loops.
• 25:28
  So that you know much better at a global basis, what are the
• 25:33
  little tweak I could do to win?
• 25:35
  I call one inch ahead, right?
• 25:36
  In the end, you don't need to be perfect.
• 25:38
  Wow.
• 25:39
  It's to what?
• 25:40
  Powerful idea.
• 25:42
  I'll add something to the equation.
• 25:43
  Yeah.
• 25:43
  So a hundred percent with you, the continuous feedback loop to go faster,
• 25:49
  but within an existing business model.
• 25:52
  So what you need to add is, right, investing in many
• 25:56
  ideas of which many will fail.
• 25:58
  Yes.
• 25:59
  Right?
• 25:59
  So you can't iterate yourself to success, right?
• 26:02
  So what, what we have in venture capital, which works really well, is the fact that
• 26:05
  we invest small amounts of money in a lot of ideas and you know, 90% will die
• 26:10
  and then we double down and invest in the 10%, the whole portfolio approach, right?
• 26:13
  So what's missing in addition to those quick feedback loops is also the idea.
• 26:19
  Failure, if we do it the right way, is actually gonna let the best ideas emerge.
• 26:23
  So we start small.
• 26:24
  That's beautiful.
• 26:24
  We invest small and then we double down.
• 26:26
  Absolutely.
• 26:26
  And it comes back to, so the, this is actually an interesting
• 26:28
  phenomenon and that's why probably 95% of projects in AI don't work.
• 26:33
  Mm-hmm.
• 26:33
  Is that in, established companies, what gives you prestige, ah, how
• 26:37
  many people you manage and how much money you, you, you, you manage.
• 26:42
  Right?
• 26:42
  So we think, oh, big company, we need big return, which is true, but the problem
• 26:47
  is now we make big investments upfront.
• 26:49
  Now what happens if you give a bunch of engineers and a bunch of scientists a
• 26:53
  lot of money, they're gonna spend it.
• 26:55
  Yeah, yeah.
• 26:56
  So there's no, there's no feedback.
• 26:58
  There's no learning loop.
• 26:59
  Ah, so you,
• 27:00
  invest the same amount of money in a hundred teams or 200 teams, and you
• 27:06
  after three months kill the projects if they can't show you evidence
• 27:09
  that the customers actually care.
• 27:10
  So you create a very similar system to venture capital, but within your company.
• 27:14
  Yeah.
• 27:14
  Yeah.
• 27:14
  And again, the small thing is the challenge because.
• 27:17
  Every large company started small Nestlé, you know, 150 years ago mm-hmm.
• 27:21
  Was a startup.
• 27:21
  Mm-hmm.
• 27:22
  Mm-hmm.
• 27:22
  It started small before it became big.
• 27:24
  And in large companies, we want to cut corners, make a big
• 27:26
  investment, which works in exploit, but doesn't work, in explore.
• 27:30
  Right.
• 27:30
  So that's the big challenge, is that small things become big, but in, in
• 27:36
  large companies you don't like small, but every, every big idea starts small.
• 27:39
  Yeah.
• 27:39
  So the best company may not be armed with the best idea, but they
• 27:43
  certainly let the worst idea fail fast.
• 27:46
  Mm-hmm.
• 27:46
  Exactly.
• 27:46
  Exactly.
• 27:47
  There's one thing, , it's , again, serendipity.
• 27:48
  I'm reading this book, permission Denied, it's called 'cause Chuck House,
• 27:52
  this guy, he was told , by Packard, don't be working on this project.
• 27:57
  I don't wanna see this project in a lab in a year.
• 27:59
  Mm-hmm.
• 28:00
  So he interpreted it purposely, he selectively heard what he heard.
• 28:04
  And he said, okay, so you want it in the field in a year.
• 28:09
  But what he was told specifically not to do it, and he did it behind closed doors.
• 28:13
  And he himself, he's an engineer, went out with his family on a tour,
• 28:18
  drove around to different types of customers, took out his front seat of
• 28:21
  his Volkswagen Beetle and put in this machine and showed it to customers.
• 28:25
  And the only way that project took off was because one of his colleagues had
• 28:29
  had a heart attack and wanted to see his heart monitor on a heart monitor.
• 28:33
  And his wife was a nurse and his wife said, this would
• 28:36
  be a very valuable project.
• 28:37
  Now I say all that to say a, he was told by the leader of the company, the owner of
• 28:43
  the company, the founder of the company.
• 28:45
  Who, and this is where I did the re do the research.
• 28:48
  I looked at his book, and in his book he said, give engineers the space to explore.
• 28:54
  Yes.
• 28:54
  Mm-hmm.
• 28:55
  And that is a contra a, a contradiction or a paradox that
• 28:59
  you see time and time again.
• 29:01
  And I really wanted you guys to share your view of that because you
• 29:04
  work with CEOs and I think sometimes they believe they're giving people
• 29:09
  permission, but if those permissions, you mentioned the fire phone, if they fail.
• 29:13
  Yeah.
• 29:13
  And I use that term.
• 29:14
  You keep a receipt.
• 29:15
  Yeah.
• 29:16
  You're, you have a blemish in your career.
• 29:17
  Chuck how said this about himself, he said he did a second project
• 29:21
  after having that hero project.
• 29:23
  He himself said, we need to kill the project.
• 29:25
  Mm-hmm.
• 29:26
  And he lost all his management.
• 29:28
  All his budget, and all of a sudden he was starting, he was, he went from hero
• 29:31
  to zero and he had to build that again.
• 29:33
  Yeah.
• 29:33
  That is a problem.
• 29:35
  And that makes people like him give up.
• 29:38
  Now he didn't, but they're very, very rare.
• 29:40
  And the corporate immune system doesn't let them flourish.
• 29:42
  And that's because we still have that hero syndrome.
• 29:45
  It's that one person who needs to fight the system.
• 29:47
  Like, yeah, well then you'll get one innovation every, you know, 50 years.
• 29:51
  So why didn't ne Nestle come up with one Nespresso?
• 29:54
  Right?
• 29:54
  Because that's the hero system, right?
• 29:56
  Mm-hmm.
• 29:57
  So what we need is actually a real system that leadership supports.
• 30:01
  Mm-hmm.
• 30:01
  So that many can explore.
• 30:03
  And if you fail, it's not gonna blemish you.
• 30:05
  And, and you know, you can love Amazon or hate Amazon, but when Jeb Bezos was CEO,
• 30:10
  he did say like, you know, invention.
• 30:12
  He called it invention.
• 30:13
  Success and invention, like the, flip side.
• 30:16
  You cannot invent, do new things without failure.
• 30:19
  And then he would say, which is.
• 30:20
  Kind of crazy, right?
• 30:21
  , The bigger we get with Amazon, the bigger our failures need to get, otherwise
• 30:25
  we're not gonna move the needle.
• 30:26
  Yeah.
• 30:26
  But what he didn't say is people will get that one wrong.
• 30:29
  Everything starts with small failures until they get to a big failure.
• 30:33
  There's often very many iterations, right?
• 30:35
  Yeah.
• 30:35
  But you mentioned that, right?
• 30:36
  The Kindle fire phone team was not fired.
• 30:39
  They, they moved.
• 30:41
  They moved.
• 30:42
  And be, those engineers came up with Alexa.
• 30:45
  Alexa afterwards.
• 30:46
  But what is interesting is you could actually audit a company.
• 30:51
  To what extent they could embrace that resilience or that thick skin.
• 30:56
  You just ask how do you conduct your after action review?
• 31:00
  Do you write it down to what extent you will share?
• 31:04
  So in a microcosm, and people wrote about it, and you're
• 31:09
  looking at booking.com, right?
• 31:11
  They do tons of experiment and engineers need to log their
• 31:15
  experiment in a centralized system.
• 31:18
  And then no permission needs to ask.
• 31:20
  So a junior guy in Sydney can look up What's going on with a VP
• 31:25
  in Amsterdam is, you know, right to know, not need to know basis.
• 31:29
  And so there are actually day-to-day habit, we as external can help
• 31:35
  audit and to what extent we are embracing radical transparency.
• 31:39
  Without transparency, there's no learning because knowledge doesn't flow around.
• 31:44
  Yeah, and I'll just add one point to because these are very interesting
• 31:47
  perspectives going back to your point about architecture as well.
• 31:51
  And I, I think it's absolutely, you know, you are absolutely spot on about the
• 31:56
  fact that there is no way to really get to the next breakthrough without that
• 32:02
  permission to fail and without letting so-called a thousand flowers bloom.
• 32:07
  And what helps kind of, as a company, what's important for you
• 32:11
  to then succeed with it, because you want things to succeed, but
• 32:15
  then how do they fit together?
• 32:16
  Mm. What's that picture?
• 32:17
  So you need to be very clear about what do we want to become, you
• 32:20
  know, as the world is changing, what do we want to become in, in, in a
• 32:23
  year from now, two years from now?
• 32:25
  You know, it's not a one year, two year plan.
• 32:26
  You can't plan when, when the structure is uncertain.
• 32:29
  But you need to have a vision of here's what we wanna be, here's the logic of
• 32:34
  how we'll operate 1, 2, 3 years from now.
• 32:35
  And constantly update that based on how things are changing.
• 32:38
  But then as these experiments succeed, see how they align towards that.
• 32:42
  Because you're not just sharing fin financial returns, and this is the only
• 32:45
  mild deviation from venture capital where the portfolio model itself is sufficient.
• 32:50
  Here, you don't want this a portfolio.
• 32:51
  You want architecturally cohesive returns.
• 32:55
  And so you need to have that vision, that architecture of capabilities, and
• 32:58
  then say, okay, that maps back here.
• 33:00
  Mm-hmm.
• 33:01
  But here's, can I just build on what you said at the end?
• 33:04
  Because I think that vision piece.
• 33:07
  Is missing a lot in established companies.
• 33:09
  Right.
• 33:10
  So just ask yourself, how many CEOs can you now, you know, count on your one
• 33:16
  hand probably that, that you can remember having a very clear vision of the future.
• 33:21
  Right.
• 33:21
  So you mentioned MasterCard was just very beautiful example of having a very, very
• 33:24
  clear articulation of what that vision is.
• 33:27
  And then they don't know exactly how to get there.
• 33:29
  Right.
• 33:29
  That's the experimentation part.
• 33:30
  Right.
• 33:30
  But the vision will not change.
• 33:31
  And the vision is not a budget.
• 33:33
  I think
• 33:33
  a lot of time we've seen is that it's a
• 33:35
  vision of the future.
• 33:36
  We have a vision.
• 33:37
  Yeah.
• 33:37
  How much time do I need to tell you that we want to grow by X amount?
• 33:41
  That is not a, that is not
• 33:43
  inspiring.
• 33:44
  Inspiring.
• 33:44
  And
• 33:45
  MasterCard said the former CEO kill cash.
• 33:49
  That's it.
• 33:49
  And that's the vision, right?
• 33:50
  That's the, we operate within that,
• 33:52
  the vision and everything just fall through throughout the organization.
• 33:55
  Now I'm in Africa, a small team working in Kenya.
• 34:00
  Oh.
• 34:01
  I need to kill cash.
• 34:03
  So I'm gonna work with local telco company so that there will
• 34:06
  be mobile transaction happening.
• 34:08
  I mean, Italy, Sicily, again, kill cash.
• 34:11
  What does it mean?
• 34:12
  And, and I thought this is so interesting, right?
• 34:14
  Because it's hard work
• 34:16
  and, and try things because not everything is gonna work, right?
• 34:19
  A hundred percent.
• 34:19
  So some ideas will grow.
• 34:21
  Into that, how are we gonna make that vision?
• 34:24
  But from all those experiences, you have some that will succeed.
• 34:26
  No, I think this is
• 34:27
  really a, a really compelling example.
• 34:29
  The, the idea of kill cash within it has the assumptions
• 34:33
  of where we play and how we win.
• 34:34
  Exactly.
• 34:35
  It assumes that we are not playing in a cash economy and the way to win
• 34:38
  is not to continue investing that.
• 34:40
  So cool.
• 34:40
  And I, I think, you know, just a one line really quick, interesting
• 34:43
  example happening right now.
• 34:45
  Mm. Is shutter stock is having a Kodak moment today, right?
• 34:49
  Because just like you know, photos, you can generate photos hundred percent,
• 34:53
  but Shutterstock has reimagined itself to say, we are going to be
• 34:57
  the place to guarantee good photo usage in a world of abundant photos.
• 35:01
  Mm. Love it.
• 35:02
  So artists will be compensated.
• 35:04
  We are going to do a partnership with OpenAI so that they can use our catalog,
• 35:07
  but then the artists will be compensated.
• 35:09
  And if you're an organization.
• 35:11
  You don't need photos, but you need consistent photo
• 35:14
  usage across the organization.
• 35:15
  We'll do that governance for you.
• 35:17
  We'll move it away from execution to governance.
• 35:18
  Mm-hmm.
• 35:19
  So it's again, you know, vision and then aligning everything in that direction.
• 35:22
  And FYI, I
• 35:23
  love Shutterstock is like, you know, you, you going into different platform
• 35:28
  and this is like all these are junk.
• 35:29
  And now I understand why.
• 35:31
  Yeah.
• 35:31
  We were talking
• 35:32
  about competitive advantage before.
• 35:34
  And I think there's one thing that is very interesting to look at today is that
• 35:38
  you still get some competitive advantage from a very strong business model.
• 35:42
  So why is, you know, you have, we have Apple with iOS and
• 35:45
  we have Google with Android.
• 35:46
  They're just two.
• 35:47
  Yeah.
• 35:47
  Why?
• 35:48
  Because their business models are literally invincible.
• 35:51
  But now when you look at the most successful companies, you can actually
• 35:55
  see that they have a culture Yeah.
• 35:57
  Of experimentation.
• 35:58
  So just back to those who made it explicit, take Amazon, take Netflix.
• 36:03
  They made very clear.
• 36:05
  How they invest in innovation.
• 36:08
  So they don't just use the words they clearly show, like, take Amazon, we
• 36:13
  invest long term and we invest short term.
• 36:15
  Yeah, we're gonna be world class at managing warehouses, but we're also gonna
• 36:19
  be , world class at exploring new things.
• 36:21
  So you could actually read, now I'm not a great investor, but I
• 36:25
  always look back at those things.
• 36:27
  The writing was on the wall, Netflix, same thing.
• 36:29
  Mm-hmm.
• 36:29
  So you can see which companies actually invest not in a technology.
• 36:34
  So not Oh, metaverse ai.
• 36:35
  That's not the point.
• 36:36
  Mm-hmm.
• 36:36
  What they invest in is explicitly in the key aspects of an innovation
• 36:42
  culture within the company.
• 36:43
  And we can map those.
• 36:44
  You can see those, yes.
• 36:46
  So then it doesn't matter if it's ai, if it's whatever, any technology.
• 36:51
  So the technology is never the challenge.
• 36:54
  That's the opportunity.
• 36:55
  The challenge is that the company hasn't built the ability to reinvent itself.
• 37:00
  Yeah.
• 37:00
  So you can look at that and we could codify that.
• 37:02
  It would be actually great academic research.
• 37:04
  Can we codify an innovation culture?
• 37:06
  I believe so, because you know, you read Deb Bezos's investment letters.
• 37:11
  Mm-hmm.
• 37:11
  He's been, he's been saying exactly what he is doing.
• 37:14
  Yeah.
• 37:14
  You could copy that.
• 37:15
  Yeah.
• 37:16
  This is, that.
• 37:17
  Very few companies have actually copied that innovation.
• 37:20
  DNA
• 37:20
  and what Alex mentioned there is so important because the
• 37:25
  exploration bit, they actually codify as explicit mechanism.
• 37:30
  Yes.
• 37:30
  As like organizational stuff.
• 37:32
  You could see Yes.
• 37:33
  The six pager before even ChatGPT coming along, people need to write
• 37:38
  a press release, work backwards.
• 37:40
  Yeah.
• 37:40
  The one way door.
• 37:40
  Two-way door.
• 37:41
  Correct.
• 37:41
  Correct.
• 37:42
  So these are the artifact you could say is auditable.
• 37:47
  You are walking in, you ask.
• 37:49
  And what strikes me is whenever we have conversation with AWS and middle manager.
• 37:53
  They just roll it out from their tongue, so it's not some C-suite corporate talk.
• 37:59
  They live in day to day.
• 38:00
  That mechanism for explore, mechanism for exploit and the Handover between
• 38:06
  the two to scale and shut things down every steps along the way.
• 38:11
  That's mechanism codified processes getting us there.
• 38:20
  There's a very important point in here that I really don't want our audience to
• 38:23
  miss, and especially if you're a leader.
• 38:26
  You mentioned about Bezos letters and.
• 38:29
  Through his letters, he's constantly communicating with shareholders.
• 38:32
  Yes.
• 38:32
  He's constantly communicating with his own teams.
• 38:34
  Yes.
• 38:34
  He's putting it out there for a very important reason.
• 38:37
  Yes.
• 38:37
  And to your point about vision, there's a concept in innovation called hunting
• 38:42
  zones, where it's, where do we play?
• 38:44
  Where do we explore?
• 38:45
  So an example, Nvidia gaming or autonomous vehicles.
• 38:49
  So you're giving people a kind of a place to go, oh, I can explore here.
• 38:53
  Much like the war on cash type of idea.
• 38:56
  Yeah.
• 38:56
  That is, that doesn't matter about ai, that doesn't matter about digital, doesn't
• 39:01
  matter about whatever your business does.
• 39:04
  It's actually something that's so controllable by leaders.
• 39:07
  And I'd love you to share a bit about this because it's so
• 39:10
  absent in so many organizations.
• 39:12
  Like your point about in Amazon, they can roll it off the tongue.
• 39:15
  Yeah.
• 39:16
  But most organizations, as you go down the elevator That's right.
• 39:18
  In the organization, the, the message is diluted as you go down.
• 39:22
  Mm-hmm.
• 39:22
  We work with one company that doesn't have hunting zones, but they actually
• 39:28
  did invest in some really great ideas and they got them to a certain stage.
• 39:33
  They, they're experimenting, these are real businesses, but
• 39:37
  they're not breaking through.
• 39:38
  Why is because they do not have a clear vision of the future.
• 39:42
  So not the hunting ground per se, because they did the, that work.
• 39:45
  So the businesses could actually scale, but now there's no explicit
• 39:50
  vision of where the company's going.
• 39:52
  Like how the, how's the world gonna look like?
• 39:54
  How are we gonna go there?
• 39:55
  And then there's no explicit protection of these ideas that
• 39:59
  made it to a certain level.
• 40:01
  So here's what's gonna happen.
• 40:02
  If there is no protection of those ideas, yeah, they're gonna die because the
• 40:06
  corporate antibodies are really strong.
• 40:09
  So in that case, because I do think we have different cases in that case,
• 40:13
  is really, you have the idea, you have the evidence, everything is proven.
• 40:17
  But from here, to turn that into a meaningful part of the multi-billion
• 40:21
  dollar company is actually the willingness to now scale these ideas.
• 40:26
  Mm-hmm.
• 40:26
  To take that risk as leadership.
• 40:28
  And the challenge you have is that the CEO and the C-suite
• 40:31
  doesn't actually call the shots.
• 40:33
  Mm.
• 40:33
  It's the board and the investors.
• 40:36
  So there's a huge personal risk to leaders taking that last step.
• 40:41
  It is easy to do the portfolio part and the small bets and the failure, but then
• 40:46
  actually investing is the last piece.
• 40:49
  So, you know, it's not possible everywhere.
• 40:51
  Amazon could invest in Amazon web services.
• 40:53
  Why?
• 40:53
  Because the ownership structure, because the stock market said you guys are crazy.
• 40:58
  Why are you doing I.T. infrastructure when you're an e-commerce company?
• 41:01
  Mm-hmm.
• 41:02
  So there is a thing there.
• 41:03
  And take Paul Pullman, you know, with the whole sustainability,
• 41:06
  sustainability vision, he took a huge personal risk and he did something,
• 41:11
  you could almost argue it's maybe illegal.
• 41:13
  He told shareholders, if you want it.
• 41:15
  You know, invest with us, think long term.
• 41:17
  Mm-hmm.
• 41:18
  What he forgot to mention is they're the owners.
• 41:20
  You can't tell the owners what to do.
• 41:21
  But, so there's something there that I think in the conversation is that
• 41:26
  we talk about the C-suite as if they made the decisions, but when you do not
• 41:30
  have board members in the stock market that understands how a company has to
• 41:34
  transform, it's gonna be very hard.
• 41:36
  Mm-hmm.
• 41:36
  And they nearly have to face an existential crisis like Shutterstock.
• 41:40
  Yeah.
• 41:41
  Yeah.
• 41:41
  In order to, to get that architectural shift inside an organization.
• 41:45
  You've got great examples of this.
• 41:46
  No, I, I think, you know, we are, we are sort of, kind of dancing around
• 41:50
  a really nice theory over here.
• 41:51
  There's corporate antibodies, which I really love.
• 41:54
  There's a second piece, which is architectural alignment.
• 41:57
  Mm-hmm and vision.
• 41:58
  And you need to balance that together.
• 41:59
  And I'll add a third thing over here, which I think ans you know, adds some
• 42:04
  flavor to what you're talking about.
• 42:05
  And, and, and that is that.
• 42:07
  We have, you know, we have been brought up in a culture of getting to answers.
• 42:12
  We want to be, we wanna be certain of, here's the answer, I've figured it out.
• 42:16
  We want to, you know, give confident presentations.
• 42:19
  And the way to get to answers is you build really good sensing mechanisms.
• 42:24
  You know, get all the information, get all the data, let's create some
• 42:27
  nice reports, which give the answers.
• 42:29
  Well, I believe we are moving to, you know, to a world where answers are cheap.
• 42:35
  You know, I mean, a, a simple manifestation is, you know, answers
• 42:38
  used to be $400,000 to McKinsey, and now it's one prompt to ChatGPT.
• 42:42
  And I'm not saying that they're perfect substitutes, but they're
• 42:44
  directionally very close.
• 42:46
  Awesome.
• 42:48
  Actually, maybe better, right?
• 42:49
  So like
• 42:50
  research pay
• 42:50
  for
• 42:51
  the premium 300 a month.
• 42:52
  Do not want to pick up pipes with McKinsey.
• 42:55
  But, but the, the point is very clear that directionally we're heading in a
• 42:59
  direction where answers are cheaper.
• 43:00
  So what do you do when answers are cheaper?
• 43:02
  The value moves away from getting to answers to asking the right questions.
• 43:06
  Because asking the wrong questions have opportunity cost of getting
• 43:10
  you down the wrong rabbit hole.
• 43:11
  The right questions have compounding benefits.
• 43:13
  Yeah.
• 43:13
  One inch ahead compounded is really powerful.
• 43:16
  Miles.
• 43:16
  It's not just five inches anymore.
• 43:18
  And the way to get to that is to move from sensing to sense making.
• 43:23
  Love it.
• 43:23
  And that is, don't just look at data, create maps.
• 43:27
  Mm-hmm.
• 43:27
  You know, it's the age of exploration.
• 43:29
  When Portuguese exploration started, they had not figured out Cape
• 43:33
  of Good Hope, and you know, how we get to Asia and all of that.
• 43:35
  They constantly created better maps and kept on betting with better maps.
• 43:39
  Mm-hmm.
• 43:39
  That's what we need today.
• 43:40
  So we need to move from sensing to sensemaking at every level of
• 43:44
  the organization because without that, we're just gonna be caught
• 43:48
  in, give me better answers.
• 43:50
  Where's the data for this?
• 43:51
  Yes, we need data, but for sensemaking.
• 43:52
  Mm-hmm.
• 43:53
  Not for getting two answers.
• 43:54
  So I think that's, you know, that's the final part, and I'll
• 43:57
  throw into this theory that we
• 43:58
  are, I mean, what Sangeet you just mentioned is, you know.
• 44:01
  The most effective executive must have the ability to zoom in and zoom out.
• 44:06
  Mm-hmm.
• 44:07
  And range of breadth.
• 44:08
  And, and, and they cannot just take some reckless bet.
• 44:12
  And we have seen company take reckless bets.
• 44:15
  What happened is like in the name of courageous move
• 44:18
  Yeah.
• 44:18
  The C-suite actually do not know how to zoom in.
• 44:21
  Mm-hmm.
• 44:21
  The best leader is you, you looking at all these iconic leaders from Procter
• 44:25
  and Gamble to Unilever, from Nvidia to the, at the height of power of Intel.
• 44:31
  Right.
• 44:31
  These leaders are not just curious, they don't just hang out at Davos or go to
• 44:37
  so by Southwest, but they actually are curious enough to zoom in and skip level.
• 44:42
  Right.
• 44:42
  They're not gonna put up BS from the direct report.
• 44:45
  They're gonna go three levels down, but not to just the existing operation,
• 44:50
  but dive into new, new venture as well.
• 44:53
  Yeah.
• 44:53
  And I think if C-Suite outsource their thinking, why should they
• 44:57
  command such a nice package?
• 45:00
  And the the thing, what I like about what you're saying is that going back
• 45:04
  to managerial leadership and, and, and entrepreneurial leadership, the
• 45:09
  what does an entrepreneur actually need to do on the path to success?
• 45:13
  Zoom in, zoom out all the time.
• 45:15
  Yeah, a hundred percent.
• 45:15
  And so you're from, oh, I'm gonna sign a contract with my first client to how
• 45:19
  is the world gonna change which business model, you know, is gonna make money so
• 45:23
  you are forced to zoom in and zoom out.
• 45:26
  Now in most companies, I shouldn't generalize, but what I see very often
• 45:31
  with the clients we work with is you'll have world class exceptional
• 45:35
  leaders in a specific domain.
• 45:37
  Mm.
• 45:37
  Marketing, operations, finance, but they're not used to zooming in and out
• 45:42
  of all the topics and how they connect.
• 45:44
  Because in an established business, in the exploit part, you don't have
• 45:49
  to always understand everything.
• 45:51
  These are well oiled, oiled machine.
• 45:53
  Yeah.
• 45:53
  But as soon as you do something new, it's not gonna be enough
• 45:56
  to just understand operations.
• 45:58
  Oh, clients don't care.
• 45:59
  Okay.
• 46:00
  We might want to change that.
• 46:01
  Oh.
• 46:02
  You have to have to sign this contract with the government
• 46:04
  to be allowed to do that.
• 46:06
  So you have to navigate and that entrepreneurial thinking and zooming in,
• 46:11
  zooming out, and then adapting constantly.
• 46:14
  That's not a skill that, you know, it does exist in, in established companies,
• 46:19
  but it's not one we value very much.
• 46:21
  You say, oh, that's an annoying person now.
• 46:23
  Yeah.
• 46:23
  She's bringing up this and that and you know, like that is the
• 46:26
  skill that allows you to explore and that exists in companies.
• 46:31
  They don't have a place to explore it.
• 46:32
  They
• 46:32
  don't, and they often get thrown out like an antibody would crush Yes.
• 46:37
  Crush a a, a pathogen.
• 46:39
  One of the things that came to mind, and it was based on something that you
• 46:42
  said, Sangeet, about the idea of data access to data from diverse sources.
• 46:47
  I thought about this and excuse the, the analogy I think
• 46:50
  in analogies and metaphors.
• 46:52
  I was reading recently about what the effects of psychotropic
• 46:57
  drugs do to the brain.
• 46:58
  So psilocybin, for example, is that the brain has, these, has been
• 47:01
  compartmentalized or balkanized, you mentioned that term earlier on.
• 47:04
  It's been, it's got these compartments for specialties and what these drugs
• 47:09
  do is that they turn them off so the brain starts to connect across Right.
• 47:13
  And make, literally make connections that were broken in the past.
• 47:17
  Mm-hmm.
• 47:17
  And I thought about the data thing, but then I thought more importantly, and you
• 47:21
  were saying about how you've had some insights today, and this is the goal
• 47:25
  of this innovation show X is to bring people who have expertise together to
• 47:29
  have discussions across those boundaries.
• 47:32
  But inside organizations, people exist within these silos for very good reason.
• 47:38
  Yeah.
• 47:39
  But now because the architecture of the organization to the point
• 47:42
  of Conway's law needs to change.
• 47:44
  They need to be flowing into each other, sharing information and be,
• 47:50
  be, not, feel threatened by that.
• 47:52
  Mm-hmm.
• 47:52
  And that's a huge, huge issue.
• 47:54
  Again, to the point of.
• 47:55
  This is a reshuffle, not of technology, but of structure.
• 47:59
  Yeah, no, I think so I love the analogy, you know, irrespective of
• 48:03
  what people have issues with the, with the exact topic, but we need
• 48:07
  psychotropic drugs for organization.
• 48:09
  It's in your drinks, everybody's finished.
• 48:12
  Yeah.
• 48:12
  That's why, that's why we are making connection, right.
• 48:14
  Another refill everybody.
• 48:16
  So we need that.
• 48:18
  And we need that because I, I believe that you know, a lot of the theories on which
• 48:23
  we are basing our ideas today, were built in an era which was relatively stable.
• 48:28
  So modularity, explore, exploit.
• 48:31
  When you were, when, when these theories were built out, that
• 48:34
  was an era of stable structure.
• 48:36
  You still thought of industries you thought of, markets you did not
• 48:39
  thought of, to think of continuous customer journeys and industry
• 48:42
  boundaries melting together.
• 48:44
  So the point is that all of that is still important, but you can't compartmentalize.
• 48:48
  That's the team that does exploring and here's a team that does
• 48:51
  exploiting because you're constantly, you're constantly going to have to
• 48:56
  manage both things all the time.
• 48:58
  It's not, yes, you want to explore and then you want to exploit,
• 49:02
  but you don't move to phases.
• 49:04
  You have to balance them constantly.
• 49:06
  You might be exploiting something while something else shows up on the horizon.
• 49:08
  So it's, we are going to be living in cycles and and, and not in stages.
• 49:14
  And if we are going to live in cycles, we have to.
• 49:17
  You know, really inject organizations with those psychotropic drugs.
• 49:19
  That's the only way to do that.
• 49:21
  Otherwise, you, you're, you're otherwise, you're just going to, you
• 49:24
  know, look at balance sheets, you're gonna look at teams, you're gonna
• 49:26
  look at budgets, and you're gonna say, okay, that's the explore part.
• 49:29
  And let them do that till, till we kill them.
• 49:31
  And here's some exploiting that.
• 49:33
  That's very good.
• 49:33
  Let's throw money over there.
• 49:35
  That's, it's just a wrong way to think about uncertainty and change.
• 49:39
  Yeah.
• 49:39
  A hundred percent.
• 49:39
  But you, you activate, this is back to the vision, back to the hunting
• 49:42
  zones, back to the shareholder letters.
• 49:45
  You activate the certain part of the brain, the reticular activating system.
• 49:49
  Mm-hmm.
• 49:49
  To actually go, these are important.
• 49:52
  These are places to look.
• 49:54
  These are places for hints for the future.
• 49:56
  And the exploiters of the organization are usually on the edges.
• 49:59
  And Andy Grove famously said, snow melts from the edges.
• 50:01
  Mm-hmm.
• 50:02
  That's where the changes first start to happen.
• 50:04
  And the exploration teams are usually in a lab somewhere or an
• 50:08
  R and d department where they have no access to the outside world.
• 50:11
  Yeah.
• 50:11
  And it's that thing, and I, I know some certain people are more exploration
• 50:16
  minded, maybe more curious, but when people are activated to go, this is
• 50:20
  actually important to the organization, then they can start to notice things.
• 50:24
  Yeah.
• 50:24
  But the way it actually works is you cannot have black and white.
• 50:28
  So what people sometimes confuse is they think explore lifts somewhere,
• 50:32
  exploit lifts somewhere else.
• 50:33
  So what I like to talk about is the dual mindset is that I'm a CEO.
• 50:38
  So guess what?
• 50:39
  You know, I have a meeting with the sales team for a known product while
• 50:43
  I'm in exploit mode, and then I have a meeting with my product team looking
• 50:46
  at something new, the same person.
• 50:48
  I need to ask completely different questions.
• 50:51
  So I'm in explore mode.
• 50:52
  So it's not that it lives there, it lives here.
• 50:54
  No, you flip between these all the time.
• 50:57
  And then you'll have people that, you know, maybe in sales, they
• 51:00
  see some needs there and they become part of an explore team.
• 51:03
  Yeah.
• 51:03
  So you have that fluidity that you're kind of referring to.
• 51:07
  That's true.
• 51:08
  Explore, exploit.
• 51:09
  It's not left, right, black, white.
• 51:11
  It's like, it's that fluidity where we constantly explore and we'll have people
• 51:16
  who are more comfortable with exploit.
• 51:18
  They'll rarely be part of an explore team.
• 51:20
  Mm-hmm.
• 51:20
  But they need to maybe enable an explore
• 51:22
  team.
• 51:22
  Yeah.
• 51:23
  So that fluidity is gonna be incredibly important.
• 51:27
  And I do think some companies are better at it and some companies are not.
• 51:30
  And I think the worst ones are when they, when you have
• 51:33
  Oh, the explorer's over there.
• 51:35
  Yeah.
• 51:35
  And then you have cynicism.
• 51:36
  Oh, you're gonna go put up some sticky notes or play ping pong and you know, I
• 51:40
  don't like the cynicism because there's something there that is actually true.
• 51:44
  Yeah.
• 51:45
  But it's because we don't fully understand yet.
• 51:47
  I'd say that.
• 51:48
  Often the leaders haven't fully embraced this, but once you tell
• 51:51
  them how this works, I see a lot of leaders who say, wow, I wanna do that.
• 51:56
  Now, do they make the jump?
• 51:57
  There's a risk to it that that's another question.
• 51:59
  And just in case some of the audience may feel like, oh, but I work in a
• 52:02
  conservative industry or in a conservative region, GDPR wouldn't allow this.
• 52:08
  And, and I'm gonna translate Alex description into workable solution.
• 52:12
  Yes.
• 52:12
  And
• 52:12
  everyone needs to play a part.
• 52:14
  Yes.
• 52:14
  Now, the CEO must,
• 52:16
  yes.
• 52:16
  Call out a vision.
• 52:18
  Now, if you know the C-suite have no idea where we're gonna moving in and just a
• 52:21
  budget, they're not doing the homework.
• 52:24
  But then at the team level, divisional level, the team lead must
• 52:29
  be the one who opens up sandboxes.
• 52:31
  Okay?
• 52:32
  You're in healthcare, life science, a lot of constraint.
• 52:34
  Fine.
• 52:35
  Create sandboxes so that others, a junior guy can combine your internal
• 52:41
  data, external market data, run an AI experiment within a safe sandbox.
• 52:45
  Yes.
• 52:46
  Report back in two weeks.
• 52:47
  Yes.
• 52:48
  If that junior guy requires five approval, seven approval form, fix it.
• 52:53
  That's the team responsibility in pdf, right?
• 52:56
  In PDF as well.
• 52:57
  And then if you happen to be junior or individual contributor, when you
• 53:02
  see the sandbox open, volunteer.
• 53:05
  So what is beautiful about what Alex said is you could apply to life science in
• 53:11
  Europe in the most conservative country.
• 53:14
  And that's fine because what you're aiming for, again, it's not perfection
• 53:18
  like Nvidia, but one in your head, right?
• 53:19
  Yes.
• 53:20
  And then you win.
• 53:20
  Yes.
• 53:21
  Yes.
• 53:21
  There's an important thing I wanted to bring up.
• 53:23
  Alex mentioned about you.
• 53:25
  It's very difficult to reorient the organization on unless it's board level
• 53:30
  approved or, or it comes from the top.
• 53:33
  And I, I, I thought about Joe Bower's work.
• 53:36
  Yeah.
• 53:36
  So you studied under Joe Bauer and location resource allocation process.
• 53:40
  Yes.
• 53:40
  And, and I always think of resource allocation process as
• 53:44
  there's, it's like oil and water.
• 53:46
  There's a layer that doesn't mix.
• 53:47
  And that layer is often controlled by management down the organization.
• 53:51
  And for the point of Andy Grove.
• 53:54
  Yeah.
• 53:54
  Andy Grove didn't decide the organization was beginning.
• 53:57
  Mm-hmm.
• 53:57
  Become a mm-hmm.
• 53:58
  Semiconductor.
• 53:59
  The rules in the organization did.
• 54:01
  That's right.
• 54:01
  The, and the middle managed decide where to put those resources.
• 54:05
  And I wondered where that played a role because it can hijack the
• 54:09
  strategy of the organization that leadership think they're doing this.
• 54:12
  Mm-hmm.
• 54:12
  But management or people who actually control the resources, they're doing this.
• 54:16
  Correct.
• 54:16
  And that can be positive sometimes, like we saw with Intel, but
• 54:20
  sometimes it can actually hijack the organization in the wrong way.
• 54:23
  A hundred
• 54:23
  percent.
• 54:23
  So I think this is why that Burgelman extension at that autonomous
• 54:27
  activity is really important.
• 54:29
  Now, back.
• 54:30
  You just need a few autonomous activity.
• 54:32
  That's fine.
• 54:34
  Because the world was slow.
• 54:35
  Yes.
• 54:35
  And what we are discussing here is like you need to turbocharge.
• 54:38
  Yeah.
• 54:38
  And so you need to have mechanisms going back earlier so that whether sandboxes,
• 54:44
  whether it is documentation, so that that autonomous activity could go much faster.
• 54:50
  And when something works, you can then scale through intermodularity
• 54:55
  within the organization or shut things down as quickly as possible.
• 54:59
  And that is, the contour is still right for sure.
• 55:03
  But the speed of change does demand an additional accelerant.
• 55:08
  And, and that add to that, that innovation is always bottom up, but you need
• 55:12
  the top down systems building so that the innovation can actually happen.
• 55:16
  Yeah.
• 55:17
  So then I add, I like to add to this mix when I'm working with some companies.
• 55:21
  I say, why is it dangerous when the CEO has an idea?
• 55:25
  What's the problem with the CEO's idea?
• 55:27
  We've all seen this, right?
• 55:29
  Nobody tells the CEO their baby's ugly.
• 55:32
  So actually innovation should not come.
• 55:34
  Interesting.
• 55:35
  Come from the top in terms and when we're truly talking innovation Yeah.
• 55:38
  Exploit is different.
• 55:39
  Yeah.
• 55:39
  But new breakthrough ideas don't come from the top.
• 55:42
  Now, what comes from the top is the vision.
• 55:44
  This is the direction where we're going, how we're gonna do it.
• 55:48
  Like what do customers care about?
• 55:49
  What are they willing to pay for?
• 55:51
  Leaders don't know if we're talking about the new world.
• 55:53
  Yeah.
• 55:53
  Because what they know is the past world.
• 55:54
  Yeah.
• 55:55
  So they'll make decisions based on the past world.
• 55:57
  So what we actually need to start to do is.
• 55:59
  Have this evidence-based investments mm-hmm.
• 56:02
  That are bottom up.
• 56:03
  Mm-hmm.
• 56:04
  Yeah.
• 56:04
  But within a strategic direction, within a vision.
• 56:06
  So kind of, you know, left, right, bottom up and zooming and zooming out.
• 56:10
  That's right.
• 56:11
  That's right.
• 56:11
  That's what we need to create these organizations that are
• 56:13
  extremely, I I would say fluid better than more than Agile.
• 56:17
  'cause Agile has been hijack.
• 56:18
  That's right.
• 56:19
  I, I fully agree.
• 56:20
  I mean, I'm, I'm really glad you bring that point up because I think Agile comes
• 56:23
  from a world of operational uncertainty, which means that the structure is fixed.
• 56:27
  We know what game we are playing.
• 56:29
  Yes.
• 56:29
  We just have to figure out how to play it better.
• 56:30
  Mm-hmm.
• 56:31
  Fluid is that we need to morph, and that's because the game has to constantly change.
• 56:36
  So you need to keep morphing, but the challenge with morphing is you need
• 56:40
  to ask yourself morph towards what.
• 56:42
  Right.
• 56:43
  And so increasingly.
• 56:46
  The biggest driver of shareholder returns, I believe will be the ability
• 56:51
  to place a bet, take the risk against it and say, this is what we morph towards.
• 56:55
  And that's not one bet.
• 56:56
  It, it's constantly changes.
• 56:57
  But the, the leaders, and, and I'm not just saying CEOs, the CEOs
• 57:01
  CEO pay is exponentially higher than the rest of the organization.
• 57:05
  But in increasingly, I, I believe that we'll have to move towards
• 57:08
  new ways of managing organizations.
• 57:10
  Where we, reward people who are able to place bets, stand by it, show results, and
• 57:16
  do that continuously, because continuous bets one inch ahead, I keep coming back to
• 57:21
  this one inch ahead has to be compounded.
• 57:24
  Mm-hmm.
• 57:24
  And the way to do that is to have vision and structure and, you
• 57:28
  know, take the risk against it.
• 57:29
  I,
• 57:29
  I'll give you a challenge because an example, 'cause we're talking about
• 57:32
  AI at the end of the day, right?
• 57:33
  Mm-hmm.
• 57:33
  So I think a lot of these established companies are not aware how AI
• 57:39
  actually might disrupt them concretely.
• 57:41
  So if you're a skincare company today.
• 57:44
  You say, oh, you have a lot of data.
• 57:46
  Well, probably you have the data on consumer behavior and buying.
• 57:50
  Right.
• 57:50
  But like if we're talking ai, it's not gonna be consumer
• 57:53
  behavior that we care about.
• 57:54
  We we're gonna care about hugely personalized skincare.
• 57:58
  That is just for us.
• 58:00
  And what does that mean?
• 58:00
  We need access to health data, not consumer buying data because people
• 58:04
  today they don't know how to buy.
• 58:07
  That's right.
• 58:07
  Skincare products that are good for them.
• 58:08
  Yeah.
• 58:09
  The only way to decide is actually knowing, you know, either through
• 58:12
  the mouth or further down.
• 58:14
  Yeah.
• 58:14
  Like what is the, what is the data
• 58:16
  microbiome?
• 58:17
  Yeah.
• 58:17
  And I do not see.
• 58:18
  Any of the skincare companies today actually asking that question, where
• 58:23
  are we gonna get the data from?
• 58:25
  Right.
• 58:25
  So whoever has that data, whoever in skincare, you know, Proctor
• 58:30
  and Gamble, Unilever, whatever, whoever has that data is gonna win.
• 58:35
  And companies do not realize sometimes how this whole, you know, containerization
• 58:41
  is gonna change the landscape.
• 58:42
  Mm-hmm.
• 58:43
  Radically.
• 58:43
  So their business model is already dead today, but they don't realize.
• 58:47
  Right.
• 58:47
  So they're not Absolutely.
• 58:50
  So, you know, I, I, I, I so love this example because skincare is such an
• 58:56
  interesting example of, you know, you can't just paste it on preferences,
• 58:59
  it's also health, et cetera.
• 59:01
  On the other side, AI is having another interesting effect, which is that
• 59:06
  skincare is based on compounding.
• 59:08
  You have to figure out new chemicals.
• 59:10
  Mm-hmm.
• 59:10
  And
• 59:11
  AI can figure out chemicals better than human scan.
• 59:13
  Yeah.
• 59:14
  But.
• 59:15
  AI cannot figure out which chemical map to which process map to which end user.
• 59:19
  Yes.
• 59:19
  Need of personalization and health will work best.
• 59:23
  And that go
• 59:23
  to market to pop up on the feed exactly at the right time
• 59:27
  with the button right there.
• 59:28
  Right.
• 59:28
  And so the way to do that is to have that learning engine that very interesting.
• 59:31
  All the way from compounding to the, there we
• 59:33
  go.
• 59:34
  There we go.
• 59:35
  And to my knowledge, I don't see, I don't think so.
• 59:38
  I don't think so.
• 59:39
  Great opportunity for startup.
• 59:41
  Exactly.
• 59:42
  there's an important thing that I didn't wanna leave unsaid, which
• 59:46
  was, you kind of got at this a little bit, Alex, earlier on we mentioned
• 59:49
  Alexa and the fire phone, that, that really involves killing a project.
• 59:54
  Mm-hmm.
• 59:54
  And why I brought up Chuck House, this gentleman again was, he had a
• 59:58
  hit product with this screen, then he had a second product and he said,
• 1:00:03
  and he talked about the visceral feeling, the emotional decision.
• 1:00:06
  It was irrationally.
• 1:00:07
  Made total sense to kill the project.
• 1:00:09
  Right.
• 1:00:09
  He, he couldn't see a future for it.
• 1:00:11
  He couldn't see it making any pain, any dividends within two years.
• 1:00:14
  But emotionally, he said, my career depended on it.
• 1:00:17
  It meant that my dream of being a manager was gone.
• 1:00:20
  It meant letting go of this resources it meant actually a blemish on my career.
• 1:00:24
  And that killing the project is not, even though he had the bravery
• 1:00:30
  to say it to the organization, he still got punished for that.
• 1:00:32
  Mm-hmm.
• 1:00:32
  This is a huge thing, and I know it's something you've talked about.
• 1:00:35
  It's,
• 1:00:35
  it's big, right?
• 1:00:35
  Because, so when I give a talk at a conference, I always ask,
• 1:00:40
  how many of you are working on a project, you know, should be killed?
• 1:00:44
  And at least a third of the room hands go up.
• 1:00:47
  Then another third probably are just, they don't, they're doing it.
• 1:00:50
  The boss is there, and then one third might actually be working on some success.
• 1:00:54
  So then I ask, well, why are you guys still working on that?
• 1:00:56
  Are you stupid?
• 1:00:57
  And of course, just, you know, mm-hmm.
• 1:00:58
  No, the, and everybody knows it's the process and the system.
• 1:01:03
  That doesn't make it possible to kill or doesn't make it easy to kill projects.
• 1:01:07
  Yeah.
• 1:01:08
  Yeah.
• 1:01:08
  So the kill rate should increase.
• 1:01:10
  And what happens often is that the decision was made to do
• 1:01:15
  this and then the team learns.
• 1:01:17
  They actually have the learning engine.
• 1:01:19
  It's not even that the learning engine is missing.
• 1:01:21
  They know we should kill this thing.
• 1:01:23
  It's doomed.
• 1:01:24
  But there's a disconnect between the team that just learned and
• 1:01:27
  who's allowed to make the decision.
• 1:01:29
  Mm.
• 1:01:29
  And then the whole cultural aspect that you're bringing in the Shein of.
• 1:01:33
  So that's why we continue to invest in projects that should be killed.
• 1:01:37
  People know it's not that they don't know.
• 1:01:38
  That's the crazy part.
• 1:01:40
  Right.
• 1:01:40
  So, so we need to build a better system where it's okay to kill projects.
• 1:01:44
  So I talk about this and we were mentioning the resource allocation thing.
• 1:01:48
  Yeah.
• 1:01:48
  We need better resource allocation today to be able to kill the
• 1:01:53
  project that should be killed.
• 1:01:54
  Mm-hmm.
• 1:01:55
  Because we know it's not rocket science.
• 1:01:57
  Right.
• 1:01:57
  We often know can be better at the metrics and then we invest
• 1:02:00
  in those that have the potential.
• 1:02:02
  So the resource allocation today in companies is still pretty broken, right?
• 1:02:07
  We all know those budgeting processes, they do not work.
• 1:02:10
  So there's a huge thing there that, you know, we need to address as well
• 1:02:13
  if we want these fluid organizations.
• 1:02:15
  Yeah.
• 1:02:15
  Because at the end of the day, what do people, you know, work on?
• 1:02:19
  It's where the money goes, right?
• 1:02:20
  The budget's for the people, et cetera.
• 1:02:22
  So, and we, we need to change that.
• 1:02:24
  Very clear
• 1:02:25
  closing thoughts, gen closing thoughts.
• 1:02:27
  So bring it back to where we started, which was ai, the impact on
• 1:02:31
  business model reshuffling the entire architecture of the organization.
• 1:02:35
  Closing thoughts May
• 1:02:36
  Yeah, maybe I'll just bring together my summary of, you
• 1:02:40
  know, what we have discussed.
• 1:02:41
  Right?
• 1:02:41
  Sounds great.
• 1:02:43
  I, I end my book with this line.
• 1:02:45
  You don't need an AI strategy.
• 1:02:46
  You need a strategy for the world that AI creates.
• 1:02:49
  And I think that's what we are fundamentally talking about over here.
• 1:02:52
  And that is, you know, going back to your point.
• 1:02:54
  Where to play, how to win.
• 1:02:56
  Both those things are constantly changing in order to work towards that.
• 1:02:59
  What we are also saying over here is it's not enough to be agile.
• 1:03:03
  In fact, many of the practices that we learned in, in what we call agile, will
• 1:03:07
  actually hold us back and prevent us from thinking about how to be liquid,
• 1:03:11
  how to be fluid, how to morph to what we need to become next, and in order to
• 1:03:16
  really create a good home for innovation.
• 1:03:18
  I think also what we are saying is you need to let a thousand flowers
• 1:03:21
  bloom, but that's not enough.
• 1:03:23
  A culture of failure management is not enough.
• 1:03:25
  You also need vision, structure, architecture and ensure that there's a
• 1:03:31
  way to map that back to, you know, all your experiments, back to, to that and,
• 1:03:36
  and to create that culture that enables and when I say culture, I also mean
• 1:03:40
  incentives that enable that to happen.
• 1:03:42
  So I think, you know, that, that to me is, I mean what I capture
• 1:03:46
  from what we've just talked about,
• 1:03:47
  beautiful Howard, and what we described here, of course are the key principle.
• 1:03:53
  That organization will prosper in that future AI world.
• 1:03:57
  Now the listener may be like, oh my God, my company's so backwards.
• 1:04:00
  It's like, oh my God, I get stuck.
• 1:04:03
  I don't have the decision power, but in the end, we don't
• 1:04:06
  need to shoot for perfection.
• 1:04:08
  It's like you don't need to figure out 2030, the 10 capability critical.
• 1:04:12
  Mm-hmm.
• 1:04:12
  It really boils down to, in your local environment, maybe it's in brussel
• 1:04:17
  meeting this B2B client tomorrow, what kind of one thing you could do to start
• 1:04:24
  doing it differently that would allows you to build up that new skills so that
• 1:04:28
  you can perform and transform today?
• 1:04:30
  Again, nobody needs to become like Nvidia.
• 1:04:33
  You just need to, again, one inch ahead of your local competition,
• 1:04:37
  beating the same client so that your spiel would, you know, cutting across.
• 1:04:42
  And that little win allows you to compound the success.
• 1:04:46
  You would feel the joy of success, and that was snowball.
• 1:04:51
  And Alex.
• 1:04:52
  For me, it's all about back to business basics.
• 1:04:55
  So we get blinded by technology, you know, metaverse, ai, whatever.
• 1:04:59
  At the end of the day, it's a means to an end, and the business rules
• 1:05:04
  haven't actually changed ever.
• 1:05:06
  It's like, we need to create value for customers and we need to create value
• 1:05:09
  for our business, otherwise we're done.
• 1:05:11
  And then ideally, we start to add, like, how are we creating value for society?
• 1:05:15
  But just get the business basics right?
• 1:05:17
  And, and, and understand how your organization should be set up to explore
• 1:05:22
  how we create value for customers, for our business, for society.
• 1:05:27
  Then you can deal with any technology, right?
• 1:05:29
  Mm-hmm.
• 1:05:29
  So now AI is indeed a very powerful technology, but if you don't
• 1:05:33
  get those business basics right, you're not gonna get it right.
• 1:05:36
  So don't invest in the technology, invest in your organizational
• 1:05:40
  structure and capability first.
• 1:05:43
  To explore things that can work with
• 1:05:45
  you.
• 1:05:45
  Absolutely.
• 1:05:46
  So where can people find you?
• 1:05:48
  Gents, alex Strategyzer.com.
• 1:05:50
  Strategyzer with a z, right?
• 1:05:52
  Strategyzer.com.
• 1:05:53
  Howard one inch ahead.
• 1:05:55
  Substack
• 1:05:56
  and Sangeet platforms.
• 1:05:59
  The plural word platforms.substack.com.
• 1:06:02
  The singular brings you to Substack's platform, right, Substack.
• 1:06:05
  Yeah.
• 1:06:07
  And it's been an absolute pleasure having these gens.
• 1:06:09
  We've to get off to a black tie dinner.
• 1:06:11
  I need to shave my legs and the gents here, I, I have to
• 1:06:15
  get and get their tuxedos on.
• 1:06:17
  It's been an absolute pleasure.
• 1:06:18
  Thanks for joining us on the Innovation Show, Sangeet Paul Choudary, Howard Yu
• 1:06:22
  Alex Osterwalder, thanks for joining us.
• 1:06:24
  Thank you so much.
• 1:06:24
  Thank you so much.
• 1:06:25
  Great pleasure.
• 1:06:25
  This possible.
• 1:06:26
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• 1:06:27
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• 1:06:31
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• 1:06:34
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• 1:06:37
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• 1:06:40
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• 1:06:43
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• 1:06:47
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