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Expected Value xV - Revolutionizing Innovation Metrics with Simon Hill · The Innovation Show

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  Let's get stuck into today's episode a little Intro boards Demand proof,
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  CFOs ask the hard questions, and too often innovation leaders are left
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  with activity reports and no impact.
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  Today's book introduces a system that changes the game.
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  It's called Expected Value or xV.
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  It's a new language, it's a new logic and a new way to prove that
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  innovation doesn't just look good.
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  It can deliver value.
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  It's a pleasure to welcome the author of that book.
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  Great support of the show, author of Expected Value, the System to
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  Measure, prove, and Scale Value.
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  Simon Hill, welcome to the show.
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  Thanks, Aiden, and well done getting through that mouthful
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  I wanna also say on the shelf there behind me, you'll see one Smart Crowd, which is
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  Simon's former book that he wrote with Alpheus Bingham, who was the founder
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  of Innocentive, which is part of the Wazoku, clan . Let's get stuck in Simon.
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  The first thing I wanna say I saw so much of.
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  myself in this book.
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  I saw so many people I know who work in innovation in this book, and I also
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  saw the difficulty for innovators to accept this new system, and by that
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  I mean it's hard work to actually go and put manners on innovation
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  and to put metrics on innovation.
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  And it's easier sometimes to just blame the dinosaurs that they don't
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  get it, but they have to get it, and it's our job to help them get it.
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  Thank you for that.
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  And look one of the big reasons I wanted to come on this show, to launch the book,
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  which just launched yesterday, right?
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  So we're in the first 24 hours of pushing this thing out, was because I
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  know that you live and breathe this, you read the books, you care about them,
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  you'll give me real honest feedback.
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  And I've enjoyed our little chats offline as you've been
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  skipping through this as well.
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  And I really value that feedback, right?
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  That was all part of the research and the work that went into trying to get
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  this right, because I think fundamentally that innovation isn't broken, but the
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  way we measure it is, and if we do not measure the value that we are producing
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  as innovation teams and innovators, then.
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  We risk slipping into the world of innovation theatre.
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  And that may be some of the reasons why the Chief Innovation Officer role
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  is one of the shortest tenured roles in organizations globally, because we
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  simply do not have a good measure that finance can sign off on and that the
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  business really buys into and believes.
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  This is, a long, hard labor of love this book, but I'm super proud of
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  what we've produced and put out.
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  I don't know who it was recently said, CIO.
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  Chief Innovation officer, CIO career is over.
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  And I was like going, oh, I told my wife, I was like going, look,
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  it wasn't me, it's everybody.
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  And, this is part of the problem.
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  The problem is many of us get put in the role because we're creative or
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  we're innovative, or the organization thinks maybe being digitally literate
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  or being interested in AI or reading books and innovation means you'd
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  be a good head of of innovation.
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  But for things to really work.
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  have to have metrics that the organization and in particular the CFO can understand
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  or whoever is funding these ideas.
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  And I wanted to share your forward 'cause The Forward was written in
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  pure frustration, I felt, and it was a frustration, not just yours, but so
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  many people you worked with and who trusted you and who are customers of
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  Wazoku, couldn't get it over the line.
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  And that's where this, the essence of this book comes from.
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  and I say in it, I wrote this book out of frustration.
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  It's line one of the book.
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  Right.
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  And I say, not frustration with innovation teams.
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  , 'Cause as I observed quickly, they, they are some of the most passionate,
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  hardworking people that there are.
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  But the frustration is with the system or the lack of one as you've alluded
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  to, I think you said, finance teams, all the people funding innovation.
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  I think it has to be finance teams.
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  We have to find a way to help innovation speak the language of finance, not
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  most notably the language of the p and l and the balance sheets, right?
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  That might sound like we're suddenly trying to strip away creativity.
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  We're not that, we're trying to strip away all the things that make
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  innovation inherently innovative.
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  We're not, but we have to find a way to translate all of that
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  creativity and all that good activity.
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  Which ultimately is cost and investments right into
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  something future value creative.
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  Into a framework and a system that can talk the language of the people that
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  can draw , big fat red lines through that innovation budget, 1, 2, 3 years
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  down the road and kill all of the hard work that's been done and help
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  to justify the values coming arguments or whatever else might look like that.
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  So it is difficult, right?
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  But we don't have good forward looking metrics, good forward looking ways that
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  the business really buys off on buys into, and the mechanisms of ROI and discounted
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  cash flows and net present values just don't work in the fuzzy front end of
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  what's the value of an idea, right?
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  And so that's really, that's really what we're trying to tackle here.
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  You know, that Mark Twain quote, history doesn't repeat, but it rhymes.
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  One of the rhyming verses of every case that we see in innovation is the
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  moment that many innovators and less experienced innovators will blame the
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  CFO as the enemy and kind of go, oh.
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  John or Mary totally killed it.
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  They just, and you're, but we flipped the lens with this book, and it's one of
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  the things you learn as you go through it, is it's actually the onus is on us.
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  It's not a fault, but the onus is on us to actually, as you
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  say, speak their language.
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  And I'd love you to share this rhyme that we see all the time.
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  I'll set you up with a little quote.
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  You said that "one moment.
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  Crystallized it.
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  This is the frustration.
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  This is the impetus to write the book.
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  A global financial services company had invested millions, thousands
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  of ideas, dozens of pilots, plenty of buzz, then headwinds hit.
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  And the CFO asked a simple question, what has this delivered to our bottom line?
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  the team were full of stories, full of activity, metrics, engagements, of all
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  the things that we say, but they had no system for answering the real question.
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  And they blamed the CEO or the CFO when actually that's on us.
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  totally.
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  And that does walk us into the, to the story you said in your introduction,
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  you felt like you could really associate with this, and I told you.
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  Running into this.
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  That is part of some of the mentorship work that I do.
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  I gave a, some of the early chapters to some of the mentees that I work
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  with who just wanted more of the book.
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  'cause they could also really associate with that.
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  Right.
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  It's told in parts through a fictional first person narrative of
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  an innovation manager called Freya and a small team around her who starts
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  to, who, and she has to walk into that familiar setting that we have.
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  Right.
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  Which is a, it's a fictional story, but built around that example you gave
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  of this financial services business and thousands of others that I've
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  experienced over the years where yeah, we walk into that room, that budget
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  meeting, that annual strap meeting, the innovation portfolio review full of.
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  Nice charts and things that are measuring momentum and velocity and activity,
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  but are they really measuring value?
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  And so as Freyer's walking through her presentation and feeling like she's
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  really making, making inroads with this senior leadership team who seems to
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  be nodding and smiling, and then the CFO just, casually says, that's great,
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  Freya, but what's the value of all this?
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  And as she scrambles for an answer, she realizes , how empty all of the metrics
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  really are back to that point around speaking the language of finance, right?
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  And it's not that they hadn't done great work, right?
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  They'd run incredible projects, they had initiatives that were creating value,
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  but there wasn't an easy mechanism to translate that back to the organization.
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  And I know , that there are thousands and thousands of examples, and
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  that's the deep tragedy of this.
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  As I said, it's not a frustration with innovation teams.
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  It's a frustration with a lack of the system to help to articulate that
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  language into something that translates.
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  And just, just to really emphasize this point, there's still today on a
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  monthly basis, over 60,000 searches in, , Google or AI these days, I
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  guess, for what is innovation, right?
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  Like there are lots and lots and lots of people out there looking for it.
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  We're professionalizing innovation management and professionalizing
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  innovation, full stop.
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  But people are still wondering what it is, right?
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  And the book details some of that, right?
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  Again, I play that through in one of the chapters where Freya goes and
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  does this Google search and gets all these contradictory answers back and
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  is trying to find something that's simple that she can align around
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  and bring to life in a way that we don't have to keep Googling it.
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  It can't be so complicated.
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  It can't be so abstract in the things that we're trying to do.
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  And at the end of the day, if we go back to this concept of
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  expected value, innovation is about the creation of new value.
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  Right?
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  Creation means that you actually have to realize it as well.
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  Not just conceptualize it.
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  And if it's about the creation of new value, then we need ways to formally
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  recognize the value that we might be creating, the expected value of things
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  that can translate into the realized value of things later, which should then appear
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  in the p and l, which should be able to translate to assets on the balance sheet.
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  Which should, so there's a lot of quite basic fundamental work
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  that needs to go into this.
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  And whilst you said it's a new system, I think it rehashes a lot of the old
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  things that we've been taught and are in all of those books behind you.
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  Right.
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  I was very clear that I was standing on the shoulders of giants in trying to
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  create this thing, but trying to fill a gap in a key part of the upstream ways of
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  thinking and working that help to move all that great practice or that great research
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  or that great thinking into something that ties it together into a financial metric.
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  funny you said standing on the shoulder of giants.
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  When I write my weekly article, it's based on the episode and I usually
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  start writing out go down rabbit holes as I'm reading the book.
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  So , I'll pull out a piece and I'll go, oh, that, and then I'll think
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  about a nice metaphor, et cetera.
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  But what I actually thought about here was the opposite.
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  That what happens in organizations is you can be the second CIO, the second head of
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  innovation, or the second transformation officer somebody who's badly failed.
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  And by that I don't mean they necessarily have failed, but they may have brought
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  in bad habits or they may have given a bad impression to the organization.
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  And the articles, roughly, I haven't finished it yet, but some idea about
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  standing on the shoulders of dwarves.
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  And what I mean by that is that they've damaged credibility for
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  innovation in the organization.
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  And what it made me think of is, well, this is what happened in marketing.
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  In many many organizations, marketing was very easy pre-digital.
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  So had your press, you had your outdoor.
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  had your tv, you had radio, and it was kind of easy and it 'cause it, there
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  wasn't very much science behind it or certainly not many marketers had that
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  type of science that is available today in data marketing and digital marketing
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  because the metrics are very, very clear.
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  And I felt that that's the shift that's needed in innovation to actually put
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  manners on it, to be able to bring it to an organization and show metrics and
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  metrics that aren't just based on users or as you talked about, things like NPS score
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  or engagement inside an organization.
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  So with that shift, we'll introduce this dynamic model, the xV model.
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  Let's share at a high level and then we'll double click on
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  the different elements of it.
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  But let's explain what is a high level and then each of the
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  metrics that fall underneath it.
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  Yeah, and maybe, maybe I'll start actually by talking about
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  where xV as a concept came from.
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  Right.
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  So in, in doing research for this book, and I mentioned up front, this was a long
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  time project and a real labor of love.
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  Right?
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  I think there are, God knows how many versions of this book that were more like
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  the maniacal scribblings you might see in a Hollywood movie on a blackboard or
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  a chalkboard or something of just complex
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  . Exactly, exactly.
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  Complex formula that I'm like, this just can't be this.
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  I need to find a way to make it simple, right?
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  Because otherwise you need a PhD and God knows what to make this
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  thing work, it has to be simple.
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  And I read all the things I could find.
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  I've got every, I've got a compendium somewhere of every single book
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  and piece of content that's been written on innovation metrics,
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  innovation, accounting, innovation measurements, et cetera, et cetera.
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  And I just didn't feel like they, I didn't think they were wrong, but I
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  felt they were very hard to apply and I thought I was walking the same tightrope.
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  Again, in my thinking around it and just reinventing a wheel that is, it
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  feels like a , square wheel, right?
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  It feels like it wasn't really working in the way that it should and building.
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  And so I went outside of innovation land and outside of even business world in
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  some senses and started to look in other areas to think about where have we found
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  a way in to measure things that are inherently quite hard to measure, right?
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  That are, 'cause innovation is an idea, is it's fuzzy and
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  uncertain and everything else.
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  And ended up in an area that both of us, I know, love quite a lot,
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  which is, which is the science and metrics of sports in set instead.
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  And I love the business of sports.
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  I actually really like the science of, you know, that sits behind trying to predict.
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  Games and which players will do well and the sports science that sits behind
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  it, and even the betting markets.
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  I think they're super fascinating, interesting things, right?
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  And so unpicking that and for folks that, that follow the world
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  of soccer or football, there's a metric called expected goals, right?
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  And expected value, whilst it's very different, is borrowed very, very
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  closely from the idea of expected goals.
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  And, and so I spend a long time really digging into the numbers, the
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  processes, the thinking, and actually the history behind all of that as well.
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  Read all of the books, all of the research papers.
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  And spent months just in that world thinking, is there an application of it?
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  The book again, sort of tells my journey of that, , but disassociated
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  from me through the main characters of it that brings this to life.
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  And I think that's quite a nice journey to sort of set this up, right?
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  Because expected goals isn't perfect in the world of sport, but it's
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  an indicator based off of a set of inputs that always existed, right?
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  The data collection may have got more sophisticated as we've realized the
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  empirical evidence actually does work, and that can help you to make better decisions
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  around all different aspects of culture and strategy and leadership and all the
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  things actually as you start to use those words that give you business meaning.
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  And we can apply in the world of innovation inside
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  a company or something else.
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  And so that really was the underpinning of taking a concept from sports
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  and trying to apply it to the.
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  The abstract and confusing and unclear world of innovation from
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  expected goals to expected value.
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  Just to start that, that introduction.
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  tip the hat as well to Freya.
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  So Freya is actually named a bit of nepotism here.
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  She's named after Simon's daughter, and Axel is Freyer's brother in real
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  life, but also in the book and Axel's the data scientist that has this idea,
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  that has this spark of genius that says, actually you should look at this expected
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  goals and let's take it from there.
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  Because I love that setup.
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  And I, and I did make that connection where I was like, on this is nice, what he
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  is doing here he is using this other, this other field literally and metaphorically
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  to actually go, well this actually applies very well to organizations,
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  but, but it goes way more than.
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  Trust me here.
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  The expected value of this is something it, as you said, it goes into all these
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  different elements and all these different lenses, and what it reminded me of,
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  Simon, was the whole idea of that blind man and the elephant that they all, what
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  we have in most books on measurement and innovation is somebody seeing it from one
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  perspective, but you actually do give it the holistic view to actually give you a
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  much, much better measure or much better predictor of what ex expected value is.
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  So with that, let's double flick on these.
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  Let's talk about it and we can come back to that blind man in the elephant,
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  because I do think there's a whole black box analogy that fits quite nicely
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  with that, that we can dig into later.
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  But yeah, so you, you introduced it upfront, expected value, simple formula
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  equals confidence times, predicted value times, time sensitivity, times fit.
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  Great.
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  Like what does that all mean, Simon, basically and I would just add to this
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  is not something that's been developed in theory and not put into practice, right?
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  We've run and road tested this with organizations now globally as well as
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  living and breathing it internally from a Wazoku, perspective as well, and.
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  You will find yourself as you adopt this, not just applying it to innovation, but to
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  applying it to all things in, in, in life.
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  It works.
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  I've now started to use expected value in our sales process,
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  replacing our historical one because I think it works better than
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  than that, but maybe I'm biased.
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  Let's see.
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  So let's dig into each part of the of the equation and I explain
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  how they build and how they work.
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  'cause they're all doing something that is interconnected, part of a system.
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  They don't stand alone.
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  They can borrow from historical learnings and also feed future learnings as we
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  know innovation is prone to want to do.
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  But also they fundamentally try and answer different questions , that
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  add up to the, add up to the whole.
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  So let's forget the expected value piece for now.
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  'cause we'll come back to that.
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  That's the number that we are trying to derive.
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  The idea is it's be marketable as well.
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  So you can apply this number to all kinds of different types of.
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  Ideas, whether that's different classifications from a horizon perspective
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  or from an innovation categorization perspective, whether it's about products
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  and features or services or something different on top of that, right?
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  However, not everything should be X feeded.
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  'cause some things are a part of the work that we do to derive expected value.
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  So let's dig into each of them.
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  The first one, and these are all inherently structured to try
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  and derive a specific answer.
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  Not just xV, but two key, two key components, right?
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  The first part of the equation confidence, predictive value, and time
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  sensitivity are trying to help answer the question of, is this a good idea?
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  Is it something that we should do?
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  Just quickly back to expected goals, land expected goals, calculations kick in
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  at around minute five of a game, right?
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  So we're starting to predict from five minutes in what the score might
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  be based off of what's happening.
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  If you take that analogy to an idea, it's kind of at the point you have it, right?
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  So an idea spurs, is this a good idea?
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  Well, what data do we have at this point in time?
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  The confidence lens, and this is an interesting one, right?
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  All of these are indexed in slightly different ways.
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  So we can in the world be anywhere between 0% cnce and a hundred percent confident
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  in something if we're brutally honest.
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  As human beings, there are very few things in the world.
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  We're a hundred percent confident of.
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  Right.
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  I can't be a hundred percent confident.
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  We'll make it to the end of this podcast.
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  I can probably be quite highly confident that something awful won't happen.
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  But I can't be a hundred percent confident if you think about that, I
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  sit in a lot of startup pitches and see founders, I've been one of those
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  many times pitching for funding or for access to an accelerator or something.
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  And we're deeply passionate in those moments, right?
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  Like we believe with absolute confidence that our idea is going to change the
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  world and our revenue line's gonna look like, lovable or open ais and,
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  we're gonna be the next deck of corn driving X, y, or z. If you really
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  dig into that though and think, well, what are the empirical things?
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  What are the quantifiable things that we need to do to substantiate
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  real confidence in this?
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  We need to be able to show real willingness to pay for, against a
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  real problem in the marketplace.
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  Not just a, not just something else that a lot of that's
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  based off of gut feel, right?
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  Even if we've done some of the good early learned, start lean startup,
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  thinking back to standing on the shoulders of giants and all of the
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  things that we wanna do, we don't.
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  Have much evidence to support a lot of this right now.
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  So in a standardized way, it's very hard for an early idea to have
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  very high confidence against it.
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  Maybe 20, 30, 40, 50%.
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  Some of that will be dictated by things that we've used in our previous
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  examples of innovation world where we say what we failed fast and we
• 21:20
  learned, okay, well can we use that learning now to enhance our confidence
• 21:23
  in this specific area right now?
• 21:25
  Because if we can, that's great.
• 21:27
  And that's that balance sheet idea I was giving you earlier, right, of applying
• 21:30
  learning xV credits to the balance sheet that can be traded for accelerating.
• 21:35
  Certain aspects of the system as we go forward so we can increase our confidence
• 21:39
  'cause we failed somewhere else.
• 21:40
  That's helping us to learn now.
• 21:42
  Fantastic.
• 21:43
  But confidence is always somewhere between 0% and a hundred percent
• 21:46
  and that can move around, right?
• 21:48
  This idea of xV being dynamic back to the soccer example , that can shift quickly.
• 21:52
  If something happens in a game, a red card happens, an injury happens, a substitution
• 21:57
  takes place, the weather changes significantly, whatever it might be.
• 22:00
  The strategy and tactics of the other team shift.
• 22:03
  In business land, that's like a budget changes.
• 22:06
  Regulation kicks in.
• 22:08
  Competition does something very differently.
• 22:10
  A new AI release changes the momentum of your thing positively or negatively.
• 22:15
  Your confidence can do this or do this on a regular basis.
• 22:18
  And so anything between zero and a hundred or in my case, yeah.
• 22:23
  Zero and a hundred, right?
• 22:24
  So you're trying to, you're trying to score that as an
• 22:26
  index ac across all of that.
• 22:29
  The predicted value is where we then derive a value from this, right?
• 22:34
  What we are looking for here, and in the early stages of an idea, this can
• 22:37
  be quite tricky to do, is what is it gonna, what do we have to invest in this
• 22:42
  to get there and what do we think as a minimum value they should be worth, let's
• 22:47
  say over a three year period, right?
• 22:49
  My benchmark says three years is a good metric for this.
• 22:51
  It kind of ties into things like the Vitality Met Index and other things
• 22:55
  as well, but you can play around with that from a business perspective
• 22:58
  to tailor for your timeframe.
• 23:00
  But the idea is, you know, we have to invest a million, we think it might derive
• 23:04
  3 million, whatever it might be, right?
• 23:06
  A hundred million, a billion, right?
• 23:07
  We have to, we have to play around that.
• 23:09
  The idea is you can standardize that from an organizational perspective, and
• 23:12
  if you don't know, you can use t-shirt sizes of small, medium, and large for
• 23:16
  things that apply to your organization.
• 23:19
  Based off of the best evidence you have at that point in time.
• 23:22
  Right?
• 23:22
  And there's lots of things, market research and, and other things that
• 23:26
  we do as organizations that are quite black boxy at the moment, but
• 23:30
  actually should be tying through to the thing that we're trying to do.
• 23:33
  If we don't know predictive value, you can reverse engineering.
• 23:36
  I'll come to that probably later in the discussion.
• 23:38
  And then time sensitivity, not every idea feels like it's a super
• 23:42
  urgent idea, probably, right?
• 23:44
  You know, we must run an AI tomorrow, otherwise we're gonna be miles behind.
• 23:48
  Some may argue that the best businesses have run a little bit slower at AI than
• 23:52
  the, than the ones that have burned a lot of money, really to probably
• 23:56
  not generate much expected value and certainly not much realized value
• 24:00
  from an AI initiatives at that point.
• 24:02
  At this point in time, that doesn't mean it won't come, it means that
• 24:05
  perhaps the speed and the urgency we're applying to it should have
• 24:08
  been slowed rather than accelerated.
• 24:11
  So time sensitivity is an area we can over index on, on, on,
• 24:14
  on the expected value algorithm.
• 24:16
  But also under index on.
• 24:17
  So anything from 0.5 up to 1.5 right of a, of, of, of a, of on a ratio.
• 24:23
  Remember, this is a formula that we're calculating through and so,
• 24:28
  you know, it's gonna feel like it's super urgent, but why is it?
• 24:30
  And again, there's a bunch of tests and a bunch of things that we brought
• 24:33
  into the book to help you figure out whether actually going slower
• 24:37
  might be a strategic advantage.
• 24:38
  Whether just doing a business as usual, cadence is the thing you should do, or
• 24:42
  you really should reprioritize things in your pipeline and do this quickly.
• 24:45
  Otherwise you risk.
• 24:48
  Losing the risk and opportunity cost, or you risk you know, , a
• 24:51
  real cost, a reputational or regulatory charge or something.
• 24:56
  But we need to try and substantiate all of those things by creating
• 24:59
  that first part of it, right?
• 25:00
  And that, you know, we play that through and go, this is
• 25:02
  a, I've got 50% confidence.
• 25:04
  I've got some evidence of willingness to pay.
• 25:06
  I've got some evidence of market attractiveness.
• 25:08
  I've got a predicted value of, let's call it a hundred million pounds.
• 25:12
  So 0.5 times a hundred million gives you 50 million.
• 25:15
  My time sensitivity is just business as usual.
• 25:18
  So let's just say one.
• 25:18
  So right now my expected value is 50 million on this potential a
• 25:22
  hundred million pound project, right?
• 25:24
  Quite simple and crude.
• 25:25
  Obviously there's a lot more sophistication behind it.
• 25:27
  We will be able to work out from that.
• 25:30
  On a portfolio based approach and on the amount of money that we're
• 25:34
  spending in that predicted value piece.
• 25:36
  Two things.
• 25:36
  One, is it a good idea against all the others that we've got in there, right?
• 25:40
  Can we, how much do we have to invest to create this much expected
• 25:43
  value at this point in time, right?
• 25:46
  Is part of the efficiency piece.
• 25:48
  And we've benchmarked that.
• 25:49
  I can tell you where you should be from an innovation perspective.
• 25:52
  Back to the idea that innovation is the creation of new value.
• 25:56
  Innovation teams should be the most efficient creators
• 25:59
  of value in an organization.
• 26:01
  If they're not, they're probably not an innovation team, right?
• 26:04
  Like, it's our job to create new value efficiently.
• 26:07
  R and d get a different rope to create value in different ways with a different
• 26:11
  cost lens and a different risk lens.
• 26:12
  Innovation is not there to do that.
• 26:14
  We have to be the most efficient value creators in the organization,
• 26:18
  so we can start to benchmark and judge that and also define where
• 26:21
  the organization's risk appetite is.
• 26:23
  On those things as well, right?
• 26:25
  That first part.
• 26:25
  Now we'll know if this, is this a good idea?
• 26:28
  The last part, strategic fit is then trying to ask the question for us, right?
• 26:33
  So is this a good idea for our organization?
• 26:35
  There are many good ideas that aren't good ideas for this organization.
• 26:40
  The financial services firm you mentioned up front, we're guilty of that.
• 26:43
  They built something that was probably a good idea, but not for them and
• 26:47
  not in the way that they did it.
• 26:48
  Which meant that it persisted.
• 26:50
  A lot of money was put into it, but ultimately it was killed.
• 26:53
  And a lot of people lost their jobs as a consequence of that.
• 26:56
  And probably it should never have been done in the first place because it
• 26:59
  didn't meet the strategic fit piece.
• 27:01
  It was a good idea, but not the strategic fit component.
• 27:04
  And so there were a number of checks in there, probably we
• 27:07
  can dig into them separately.
• 27:08
  So I'm not talking for the next hour on this, but the idea is this a
• 27:12
  good idea for us as an organization?
• 27:14
  And again, that has a rating to it.
• 27:16
  That will give you a number and you multiply those all together
• 27:19
  and it gives you an expected value.
• 27:21
  But what it also shows you is where you are weakest to allow you to then
• 27:24
  go say, look, in order to improve R xV, we're gonna go and spend some time
• 27:27
  digging into this area here, whether it's confidence related or strategic fit
• 27:32
  related, or time sensitivity related or predicted value related, because those
• 27:36
  can move expected value significantly.
• 27:39
  And so in two weekly sprints, we're gonna go and do that work and see
• 27:42
  how it shifts xV over that period.
• 27:45
  And so it's a dynamic living metric that feeds into the work that
• 27:48
  we're doing, but focuses the mind on the things that we're doing.
• 27:52
  And just lastly, and then I promise I'll stop talking and let you get in,
• 27:55
  is the idea of this isn't just you.
• 27:57
  Prioritize isn't just that you then can justify the projects you want to justify.
• 28:01
  But actually should stop , the emergence of zombie projects, of the things that you
• 28:05
  are forced to do because we're creating real transparency of decision making.
• 28:10
  Confidence has a person's name tied to it who signed off on this confidence, right?
• 28:14
  Aiden, why did you put your name a 0.8 80% confident in this idea?
• 28:20
  Where was your evidence?
• 28:21
  I didn't have it.
• 28:22
  Fine.
• 28:22
  Well, you really shouldn't be like, we can't over index on these things because
• 28:25
  it's skewing these things and it forces through projects that should never have
• 28:30
  been delivered, but now they're done Empirically, this ties into a system
• 28:33
  that's trying to drive the right things.
• 28:35
  It doesn't kill risk, it doesn't kill an appetite of experimentation.
• 28:39
  It does it in a very conscious and knowing way so we can tra trace things
• 28:43
  through and back the right projects at the right time in the right ways.
• 28:46
  so much money in it, isn't there?
• 28:48
  I was I was thinking about , how much wastage we have.
• 28:52
  When you mentioned r and d or you mentioned even innovation funding.
• 28:56
  so much wastage and in the mind of many innovators we'll say.
• 29:01
  , I need to fail my way towards success or would use terms like that, which there is
• 29:06
  a lot of strength in, when you have some type of formula or some type of metric to
• 29:13
  work towards, it just helps you, like, one of my biggest annoyances is when you're
• 29:19
  doing work, say for example training, you're going to the gym, working out and
• 29:23
  you're doing the wrong program and you end up getting injured and the, the worst
• 29:27
  thing is you were working hard, you were actually, you made the effort of doing
• 29:30
  the work and then , you get it wrong.
• 29:32
  That, that for me is why even you said about I put the effort
• 29:36
  into the show of reading books.
• 29:38
  I will have no regrets at the end there.
• 29:40
  I'll know I, I did everything possible to make that the best it
• 29:43
  could be so I can walk away and go.
• 29:45
  The outcome is the outcome.
• 29:47
  I worked on the input and, and I say that I remember a quote, it, it's attributed
• 29:52
  to a guy called John Wanamaker who pa I think he died in the early 19 hundreds.
• 29:56
  He was a retail and advertising pioneer, and he said, he's the guy
• 30:00
  who was the origin of this quote.
• 30:03
  Half my advertising is wasted.
• 30:05
  The problem is, I don't know which half, And I I always think of that with, with
• 30:10
  this innovation funding because we, we, we confuse activity for progress
• 30:16
  all the time in every aspect of life, but particularly in, in innovation.
• 30:20
  And the cost of this is so huge inside organizations when we need it to work.
• 30:26
  And one of the goals from, from my work, Simon, I know you're the same, is to
• 30:30
  actually help organizations survive, let alone thrive, to actually survive.
• 30:36
  Because the cycles of change are getting faster and faster.
• 30:40
  And the collective cost you, called us out for innovation is.
• 30:44
  Budget vulnerability.
• 30:45
  So innovation investments are the first to be cut when resources tighten.
• 30:49
  We all know that strategic disconnect, innovation activities drift far from the
• 30:54
  core needs, like the chocolate teapot, which looks great, but has no relevance
• 30:58
  to the organization or we don't even have capability to do anything with it.
• 31:02
  Then there's talent disillusionment.
• 31:04
  This is a huge one.
• 31:05
  hire good people, they have no formula or way to work, and they get tired of
• 31:11
  fighting for legitimate legitimacy or even just respect in the organization.
• 31:17
  It's a battle.
• 31:18
  Then there, then there's portfolio bloat, which you, say is where projects linger
• 31:23
  in zombie states, neither progressing or properly being terminated, and they're
• 31:27
  usually pet projects or they're the ones you got over the line, or they're the
• 31:31
  CEO's favorite loads of reasons for them.
• 31:34
  finally this one is the biggest one.
• 31:37
  And this is where I was standing saying You stand on the shoulder
• 31:39
  of dwarves is credibility erosion.
• 31:42
  So ahead of innovation comes in the organization have invested in that.
• 31:47
  They do their best with the tools they have, but they don't have
• 31:50
  this tool and they don't have a way to speak to the organization.
• 31:54
  And it becomes innovation theatre.
• 31:57
  And then the next head of innovation comes in often not
• 32:00
  knowing what happened beforehand.
• 32:02
  And they're coming into an absolute mess and they have no credibility.
• 32:07
  So it's just all innovation theatre and therefore they totally get disrespected.
• 32:12
  They get the solution and they get disillusioned.
• 32:13
  And that cycle complete goes over and over again.
• 32:17
  And I'd love you to share your inside view here.
• 32:20
  So you anonymized, as you do in the book, or in a way maybe
• 32:24
  summarized that this problem that xV.
• 32:29
  Solves and some of the instances where you saw some business executives go, oh, okay,
• 32:37
  now I'm interested because ultimately that's what we're trying to do here.
• 32:41
  And I think you, you, before I jump into that, you know, you, you use the word
• 32:45
  innovation theatre and I'm quite explicit in the book that if we're not measuring
• 32:50
  innovation and we can't really get anyone to sign off on the value, then what are
• 32:54
  we doing other than innovation theatre?
• 32:56
  And I think that I've tested that message and whilst it can be quite a
• 33:00
  hard message to give to people, I think it's hard one to push back on, right?
• 33:04
  If you've got no one in the finance, if your CFO or whoever it is that
• 33:09
  owns that budget, but it should be a CFO that cares about this really.
• 33:12
  Like again, if you are the real vehicle.
• 33:14
  Of future value creation and the CFO doesn't care about it.
• 33:18
  Back to my theatre point again, right?
• 33:20
  For that reason.
• 33:21
  But it's also tongue in cheek why I wrote the book in four acts as
• 33:24
  if it's like a theatre performance.
• 33:26
  Because innovation is right, like there's four different acts, it can be read and,
• 33:30
  , you could do act one and leave and come back to it as if it's, the sequel and the
• 33:34
  chapter three, chapter four or whatever, or like the four parts of a series.
• 33:38
  It's not necessarily designed to be consumed and digested in one big
• 33:42
  sitting, although I do appreciate you firing through it for this.
• 33:45
  But it's quite heavy going because it is quite heavy going.
• 33:48
  But the act thing was very much a play on words and a res, a recognition that a lot
• 33:53
  of this just is unfortunately theatre.
• 33:56
  And that's sad, but that's really where this kicks in, right?
• 33:59
  So in going to companies, I. Probably haven't had a xV
• 34:05
  product market fit like for that.
• 34:06
  If I use xV in that analogy for anything, like anything else that
• 34:11
  I've done other than xV before.
• 34:12
  Which is a great sign for it because going to speak to innovation teams and
• 34:16
  we're in one of those innovation cycles at the moment where innovation teams
• 34:20
  are fighting for their lives again.
• 34:21
  So it's, in some ways it's a good time for xV in other ways.
• 34:25
  There's not a lot I can do if there's not a lot of value capture been done already.
• 34:29
  But it starts to bring some, it resonated heavily with lots
• 34:32
  of different companies, right?
• 34:34
  Businesses that are desperately trying to help to showcase that we've
• 34:39
  been doing all this incredible work.
• 34:40
  It does feed through to value.
• 34:42
  There is a pipeline of value coming through.
• 34:45
  Help me to visualize this and help me to understand it.
• 34:47
  And I, I talk in act one of the problem with innovation performance.
• 34:53
  Around this idea of black box innovation.
• 34:56
  And if you think about the algorithm or the formula that I just spoke
• 34:59
  through, again, confidence isn't this standalone thing, right?
• 35:03
  You will do work in the areas of time sensitivity, predicted value, strategic
• 35:08
  fit, that will inform your confidence.
• 35:10
  You will spend money in other projects before this one.
• 35:13
  That will inform your confidence, that will inform the things that you are doing.
• 35:17
  They're very interconnected and systemic in the things that you are doing, right?
• 35:22
  So get greater strategic fits the business.
• 35:25
  LAU launches a strategy that's very much aligned to this idea.
• 35:28
  Your confidence will start to increase.
• 35:30
  Your predicted value will probably start to increase because you think the business
• 35:34
  has got more appetite to invest in it.
• 35:36
  Strategy shifts the other way.
• 35:38
  What I mean by that is that they're interconnected right now.
• 35:41
  A lot of the activity that happens in our organizations,
• 35:44
  however, is hyper disconnected.
• 35:45
  Think about how the innovation value chain, I'm astounded when I find
• 35:50
  innovation and strategy teams that barely talk to each other, right?
• 35:53
  Forget innovation and finance teams, that's, those are two worlds that
• 35:56
  just can't, as we've discussed, but innovation and strategy teams, the
• 36:00
  handover between research teams that might be doing scouting or other work
• 36:04
  into latter stages of the innovation pipeline is often very analog.
• 36:10
  Quite informal in a lot of cases, and Redu results in a huge
• 36:13
  amounts of lost value as we go.
• 36:15
  As well.
• 36:15
  These are all black boxes.
• 36:16
  They often live in very different systems using different metrics and measurements.
• 36:21
  Each of them trying to justify their own existence and their own
• 36:24
  budgets rather than tying it into a universal value creation budget
• 36:29
  or expected value budget, right?
• 36:31
  And so the need to try and see this as not a set of functions and
• 36:35
  jobs to be done in an organization.
• 36:38
  But to tie it into an overarching system that can then go,
• 36:43
  yeah, look what you do next.
• 36:44
  But this doesn't happen very often.
• 36:47
  I'm astounded when I go and go, but what happens next?
• 36:49
  And people like, I'm not a hundred percent sure.
• 36:52
  Or the, that's someone else's problem , or that value metric is different.
• 36:56
  And I'm like, it isn't right.
• 36:57
  It's all part of the same system to drive through to the same thing.
• 37:01
  And I appreciate that.
• 37:02
  You know, sometimes in sales and marketing, it's hard to attribute what
• 37:05
  PR might do to the ultimate sale, right?
• 37:07
  Or something along those lines.
• 37:09
  But it's not really actually, if you think back again to there are me mechanisms for
• 37:13
  attribution, but you can xV those things.
• 37:15
  And that's why I said I start to see it used in lots of other areas
• 37:18
  and perhaps there are things you shouldn't spend money on as well.
• 37:20
  That's also the other piece of it.
• 37:22
  Not just hiding away behind abstraction and ambiguity, but really trying
• 37:26
  to tie these things back around.
• 37:28
  But if you don't know how the system works, and in
• 37:30
  many organizations we do not.
• 37:32
  Then forget the culture argument.
• 37:34
  Forget the metrics argument.
• 37:35
  Forget the expected value argument.
• 37:37
  You are just, you're just gonna keep running.
• 37:39
  You're just one hamster running around on a wheel, right?
• 37:41
  And so we've got to get better at tying these things together.
• 37:44
  And a lot of this discussion forces that realization, that forces that
• 37:48
  internal conversation that starts to look this as not just little isolated
• 37:52
  budgets, but a real value chain.
• 37:55
  And a real way of doing things that can be tracked and can be measured
• 37:59
  and can be metricized in a way that the business can really understand.
• 38:03
  And , there are loads and loads of these examples, right?
• 38:05
  And I'm as proud of the projects that don't get started as the ones that do.
• 38:09
  I'm as proud of the ones that get funded more quickly because we can start to
• 38:12
  see these things like right now and as part of the ones that get killed fast
• 38:15
  as well, because something shifted, but it was now done in a truly empirical way
• 38:20
  that the organization could get behind.
• 38:22
  And no sales team has a once a year sales forecasting meeting, right?
• 38:26
  The best ones are doing it.
• 38:27
  We forecast nearly daily.
• 38:29
  Some are forecast weekly, biweekly, monthly innovation.
• 38:32
  And xV has to have the same cadence, the same discipline, the same
• 38:35
  understanding behind it, because where sales is driving revenue,
• 38:39
  we're driving future value creation.
• 38:40
  you reminded me of I'm re researching Nvidia a lot for my new book and they, one
• 38:46
  of the big realizations for Jensen Huang was that, most companies, most chips.
• 38:53
  Get disrupted every two years.
• 38:55
  So he's like, well, I need somebody working on the new lifecycle while
• 38:59
  we're managing the current lifecycle.
• 39:01
  And we'll look through the lens of an S-curve.
• 39:03
  You know me man, I love a good S-curve, but one of the things I wanted to
• 39:07
  share was actually xV in action.
• 39:10
  So I'm gonna share on the screen one of what this actually looks
• 39:13
  like when it's filled out.
• 39:14
  So we'll have a little bit of empathy, Simon, for those who are just listening
• 39:18
  to us, which is most of the audience.
• 39:20
  But one of the things that just popped into my mind was a guy we had on the show,
• 39:23
  brilliant guy called Rasmus Ankerson.
• 39:26
  He wrote a book called The Goldmine Effect.
• 39:28
  We had him on the show to talk about that, and it was about why you get this pockets
• 39:32
  of excellence in different sports all over the world, but like you, his ultimate.
• 39:37
  Reason, his frustration, why he wrote the book was he, he was a football
• 39:42
  coach and he actually, he went on to be football management for Brentford.
• 39:46
  He now runs a fund that bought Southampton, for example, in the u uk.
• 39:52
  one of the biggest frustrations he had was there was a kid took on to
• 39:56
  their team in Denmark at the time.
• 39:59
  And the only reason they took this kid onto the team was because his dad
• 40:03
  was the janitor or the groundsman.
• 40:06
  The groundskeeper.
• 40:08
  they wanted to keep him the dad.
• 40:10
  So of like, here, look, we'll, we'll put your kid into the academy.
• 40:14
  with no expectation of this kid, he goes on to become I'm gonna
• 40:18
  say Simon Kjaer, who end ended up being the captain of Denmark.
• 40:23
  Amazing captain, amazing player.
• 40:25
  And it bugged Rasmusson for the rest of his life.
• 40:28
  He's like, how did I not see the potential in this guy?
• 40:32
  for me, that's a metaphor for, that's what.
• 40:36
  does.
• 40:37
  It's like data science will actually show you like Moneyball, how somebody
• 40:42
  might be at the A Sunset player, or maybe they don't have the skills that
• 40:47
  we will need in the new technological cycle or the way the organization's
• 40:52
  going, or the way the industry's going.
• 40:54
  And it's one of the things that xV does is actually gives you not
• 40:58
  only a measurement of the present.
• 41:02
  Also when we need to kill something.
• 41:04
  And one of the things you talk about is kill credits.
• 41:07
  I love that term, but also how not to back a pet project.
• 41:12
  And before I actually share this thing, let's share that because one of the
• 41:16
  things you talk about was where they had this project, Freya team, of the
• 41:21
  team had this thing, it was really well established, it looked really promising,
• 41:26
  through the lens of xV, they're like going, oh, this does not look good at all.
• 41:31
  We need to actually kill it.
• 41:33
  And it takes the person away from the decision, which when you think about
• 41:38
  it, and that's why I mentioned football.
• 41:41
  many people get backed because of a preference of a manager, or they might
• 41:46
  like this player, they might like their personality, versus this is raw data
• 41:51
  about how they're performing without any, you like them or you don't.
• 41:55
  And I.
• 41:55
  think this is a really important aspect and positive aspect of why
• 41:59
  xV is so, so important in projects.
• 42:02
  Yeah.
• 42:02
  And you have to remove the emotion from it, right?
• 42:05
  Like, I try and do that in the narrative of the book, and I think
• 42:08
  it comes , across quite well.
• 42:09
  And everyone's a bit nervous about how it might be perceived.
• 42:11
  But once you trust a system like a sales forecast, again, I, use examples in
• 42:17
  the book to try and pull us back into something that we can associate with
• 42:20
  in, in other walks of business life.
• 42:23
  If the process works and it's something that the business uses, , I have one of
• 42:28
  my investors , in Wazoku, is senior person , in one of the global recruitment firms.
• 42:34
  And he took me in the early days of the business around the different sales floors
• 42:39
  of of recruitment of the recruitment firm.
• 42:42
  And on the walls, he is you see all these charts.
• 42:43
  He's you go into any office in the world, you see exactly the same charts
• 42:46
  and everyone understands them to you.
• 42:48
  They look like gibberish, right?
• 42:50
  But every single person understands 'em.
• 42:51
  There's a system here.
• 42:53
  And that system drives a competitive nature every single day.
• 42:56
  People understand it, right?
• 42:58
  And anywhere in the world it's the same.
• 43:00
  And there's an analogy to that, but innovation totally lacks it.
• 43:03
  And so we've gotta find a way of creating that standardization so we understand it.
• 43:07
  When you do, you remove , the emotion from all of it.
• 43:10
  'cause of course, if you've worked really hard and someone comes in
• 43:12
  and says, we're killing this thing.
• 43:14
  It feels awful, but if you understand why and you communicate why, and
• 43:18
  it's not necessarily dead either.
• 43:20
  It's not just, it's not just disappeared because we're embarrassed about it.
• 43:23
  No, we've made an informed empirical decision to prioritize something else
• 43:28
  over this because it has an higher xV, it has a higher xV efficiency.
• 43:32
  We can make more dollar for spending a dollar here, right?
• 43:35
  And that's a really important piece.
• 43:36
  We're creating value.
• 43:38
  If I show you two, two projects and one can create a hundred million
• 43:41
  dollars, but it costs a hundred million dollars and that's lots of
• 43:44
  innovation projects, if not worse.
• 43:46
  AI might be negative xV efficiency by for some years to come.
• 43:51
  Doesn't make it bad, it just means it's a different investment criteria and
• 43:55
  a different thing to move into versus one that create a hundred million
• 43:59
  and only cost you a thousand dollars.
• 44:01
  That's a very different lens, right?
• 44:02
  And obviously those two extremes feel ridiculous.
• 44:05
  But once you start to get into narrow comparisons.
• 44:07
  Look at the different levers that are actually driving those,
• 44:10
  then it becomes quite easy to disassociate yourself from that.
• 44:13
  Not always, but does.
• 44:15
  And so the book tries to deal with that in quite a nice way.
• 44:18
  There's two other terms.
• 44:19
  You mentioned kill credits, and I, I said up front, I think we've
• 44:21
  gotta learn to speak the language of the p and l and the balance sheet.
• 44:25
  One of the things that I look at is, how can I take the idea of failing
• 44:28
  forward and of the credits that, I've heard so many times, , what do I get?
• 44:34
  I have to run these innovation bootcamps or innovation learning courses,
• 44:37
  or I'm encouraged to fail fast.
• 44:39
  And so there's value in that, right?
• 44:41
  I'm like, maybe.
• 44:42
  And if there is, then we take the expected value at that point in time, right?
• 44:46
  Whether that's a dollar or $10 million or more, and we lock it into a kill credits
• 44:52
  balance sheet for the organization.
• 44:54
  And because it needs to be applied learning, it has a shelf life of
• 44:59
  no more than 12 months, right?
• 45:00
  It should depreciate over a 12 month period.
• 45:03
  In whatever makes sense, I argue in a straight line, right?
• 45:06
  So in month one you've got, let's call it a hundred million month two, you've
• 45:10
  got 12 12 less and et cetera, et cetera.
• 45:14
  However, if you use it, and as I said earlier, if that learning helps me
• 45:18
  to go, my confidence has just gone up by 10% because of this thing.
• 45:22
  That 10% gain you get on that specific idea is a, is taken from
• 45:27
  the kill credits list, right?
• 45:28
  You can manage that and say, we just gained a. 10 million of expected
• 45:33
  value on this project over here or on these five projects over
• 45:36
  here because of that learning.
• 45:37
  We can attribute them back.
• 45:39
  And of course that's complicated, right?
• 45:40
  But it's no more complicated than many of the other system-based
• 45:44
  things that we do in other parts of the organization right now.
• 45:48
  And so there are many ways of managing assets on the balance sheet.
• 45:52
  And learning knowledge has to be one of those skills Correct.
• 45:56
  Capabilities and others, right?
• 45:57
  And so there are ways that Wazoku, has that we can do those things
• 46:01
  and that we'll continue to work on as well that show that, yeah.
• 46:05
  You did get some credit for that separate term, just 'cause I It
• 46:08
  will, it'll slip out my mind.
• 46:09
  And it's not related here, but you mentioned Brentford, you mentioned
• 46:12
  Denmark, and we've talked about the football thing as well, is
• 46:16
  there's quite a nice concept and I think it's in a Danish newspaper.
• 46:19
  They publish this again, I mentioned it in the book, that takes xg and it applies.
• 46:24
  How the leagues would've turned out if the data played out purely right.
• 46:28
  So in many games you might see the XG was one to this team and three to this
• 46:33
  team, but actually the result was two, one to the other way round, right?
• 46:36
  And the team won.
• 46:37
  So there's a League of Justice that's published every single week, right?
• 46:41
  And that shows how if the data purely played out, how it would've happened.
• 46:46
  That's a fun thing to do.
• 46:47
  But also it shows that this isn't a perfect system, right?
• 46:50
  And my xg that x that xg, my xV, is not a perfect system.
• 46:55
  It's better than nothing.
• 46:56
  And I think there's something really important in that, better than
• 46:59
  nothing, because there's a whole bunch of other systemic factors,
• 47:02
  including luck and bad luck that can get in the way of these things.
• 47:06
  But an empirical framework versus a non empirical, invisible framework should win
• 47:12
  out every single time over the long run.
• 47:14
  And in general, you may get seasons of underperformance or over performance, but
• 47:18
  in general it reverts to the mean, right?
• 47:21
  And that's the way that we have to start viewing these things.
• 47:23
  But there is no mean right now.
• 47:24
  My son plays soccer.
• 47:25
  And the team asked me to speak to the players.
• 47:29
  And I, the thing I said to them was like, I didn't say I didn't do a table
• 47:33
  of justice or no X year that I thought, I just said that there's a big difference
• 47:37
  between losing a game and being beaten because losing means you had it.
• 47:43
  You, you were in charge and you let it slip between your
• 47:46
  hands versus you lost because.
• 47:49
  The referee made a call that you couldn't have done anything about it.
• 47:52
  Somebody gave away penalty, stuff like that, that just was beyond your control.
• 47:56
  And that's being beaten.
• 47:58
  And you can always accept being beaten way better than losing a game.
• 48:03
  this is what the whole idea is.
• 48:05
  If you're going to do it, at least have a system.
• 48:08
  And the other thing that I always encounter, Simon, even , in my consulting
• 48:12
  work, is it doesn't actually matter what framework you take as long as
• 48:17
  you actually implement that framework and all align around that framework.
• 48:22
  Framework.
• 48:23
  And what I felt with xVxV is it's a big lift.
• 48:27
  Initially to get this work, to implement this work, but you
• 48:30
  don't have to do it all at once.
• 48:32
  You can go frame by frame, act by act, scene by scene, implement this, and just
• 48:39
  go, okay, to start, what we're gonna do this, we're gonna implement this
• 48:42
  part of it, then we're gonna build on it, and then we're gonna build on it.
• 48:45
  And I wanted to build that up to actually show people on our screen
• 48:49
  here what it can actually look like when you have it in place, because it
• 48:53
  is the language of finance eventually.
• 48:56
  So I'd love you to talk about here, I took two projects, project Alpha, project
• 48:59
  Beta here, I'd love you to talk through, bearing in mind that most of our audience
• 49:04
  are audio, , what we're seeing here and what it actually sounds like when
• 49:09
  you're explaining it in the board meeting versus an image or a predictive image
• 49:15
  of what it will look like in the future.
• 49:17
  Or give me another six months, I promise we'll have something to show.
• 49:20
  This actually gives you
• 49:22
  Yeah,
• 49:22
  of finance.
• 49:23
  in the book, these are two examples of projects that have been running
• 49:26
  for a while, but again, in before the system is invented, had no real way of
• 49:32
  thinking and structuring the expected value of what they're trying to create.
• 49:36
  So there's Project Alpha and AI powered customer service assistant,
• 49:41
  very thematically topical at the moment, I suspect for many.
• 49:44
  And Project Beta, an internal process automation idea.
• 49:48
  Concept that's, that's being worked on.
• 49:50
  And we talk through.
• 49:53
  The, the, the, the concepts we described them and other things, and you need
• 49:57
  all that qualitative work to go in.
• 50:00
  But in expected value lands, we are looking at, okay,
• 50:03
  let's start with confidence.
• 50:04
  Right?
• 50:05
  What is the current confidence in this idea?
• 50:07
  Well, the AI powered customer service assistant assistance is
• 50:11
  still an early stage concept.
• 50:14
  It has a bunch of assumptions, but they're really gut feel at this point in time.
• 50:18
  There isn't a lot more that we've had the chance to test in it.
• 50:22
  And so whilst the business was extremely excited by this, right, it
• 50:26
  was an AI customer service assistant, they could see how it could create
• 50:30
  lots of efficiency and, and customer improved customer satisfaction scores.
• 50:35
  Actually, once you start to empirically look at confidence,
• 50:38
  there's no reason to score it any more than a 0.3 30% confident in it.
• 50:42
  Right?
• 50:43
  There's just not enough weight behind the evidence yet to support it.
• 50:47
  And as we play that forward, that really hurts this idea, but it should, right?
• 50:50
  And so this was something that was getting very senior backing
• 50:53
  and getting a lot of push in it.
• 50:54
  The predicted value of successful was 2 million in cost savings including
• 50:59
  the revenue that had to be invested or the, the, the money that has to be
• 51:02
  invested in it at this point in time.
• 51:04
  So, not huge, right?
• 51:05
  It's a, it's, it's quite small on that basis, but that's also
• 51:09
  because it's an early stage idea.
• 51:11
  It's most likely to have a much higher PV predicted value in the future.
• 51:16
  But it's being penalized because we don't know.
• 51:18
  Right.
• 51:18
  It would be pure guesswork.
• 51:19
  And we're not trying to guess around billion dollar ideas here.
• 51:23
  We're trying to use substantive evidence to support it.
• 51:26
  There's no urgent competitive market pressures around it, so
• 51:29
  it has a time sensitivity of one.
• 51:31
  IE we're not, and we're not slowing it down.
• 51:33
  We're not speeding it up.
• 51:34
  It'll just work to the normal cadence of how this business does things.
• 51:38
  And it has strategic fit of of 0.8.
• 51:42
  And we didn't really talk about strategic fit much earlier on,
• 51:47
  but strategic fit seats to.
• 51:50
  Address four different checks.
• 51:52
  Right?
• 51:53
  So just, just to dig into those a little bit, right?
• 51:54
  We talked about all the other, the, the first part of the equation,
• 51:57
  but not necessarily strategic fit.
• 51:59
  So there's four different component areas to this.
• 52:01
  One is, is it a high value problem?
• 52:03
  Like does it really matter now and is there enough reason to do that?
• 52:08
  And there's some alignment for that, for this in, in the business, but
• 52:11
  actually they don't know enough about it to really, really make
• 52:13
  that work competitive advantage.
• 52:15
  Do we, do we as a company have a right to win and to do this?
• 52:18
  There's a little bit of buy versus build in some of this as well, right?
• 52:21
  That, that many of us will be f familiar with.
• 52:23
  Is, is this something we should really do ourselves or should we,
• 52:26
  we find someone else to do it for us or should we not do it at all?
• 52:30
  Is it an attractive market?
• 52:32
  So is there something big growing, accessible for us to do on this
• 52:36
  product that we're looking to take out to market and drive?
• 52:39
  And does it fit with an emerging trend?
• 52:41
  Well, AI riding wave, yes, yes, yes.
• 52:44
  So it scores quite highly on, on the trends part, but less so.
• 52:47
  On market attractiveness, company advantage.
• 52:50
  And so it ends up slightly under a hundred percent waiting on
• 52:53
  strategic fit at at 80% or 0.8.
• 52:56
  And so you multiply all those things together, including the cost that
• 53:00
  has to go into it and the resources that need to do, and that will
• 53:04
  derive you and expected value and an expected value efficiency for this.
• 53:10
  This specific project.
• 53:12
  So North Point three times 2 million times, one times 0.8 gives you a number.
• 53:18
  We compare that to Project Beta, which is the process automation idea, which has
• 53:23
  a much higher confident confidence score at, at 80% because the pilot has been run
• 53:29
  already strong evidence to support it.
• 53:31
  Not subject subjective, but but really quantitative evidence to support it,
• 53:37
  which is obviously boosting this idea.
• 53:40
  It has a predicted value of only half a million dollars, so quite a bit less than
• 53:45
  the, so yeah, half a million dollars.
• 53:46
  So.
• 53:47
  Quite a bit less than the 2 million of the other idea, a quarter of the value,
• 53:50
  but is obviously over-indexing versus the other one because one's 30% confident.
• 53:55
  The other's 80% confident.
• 53:57
  Equally, there's a big system migration happening, and so this needs to get
• 54:01
  done quickly, and so time sensitivity is also over-indexed at 1.3.
• 54:05
  We need to accelerate this, not decelerate it, or business as usual,
• 54:09
  and there's a strong strategic fit for this specific reason.
• 54:13
  It fits closely with the areas that we have just discussed earlier.
• 54:16
  It's not hugely expensive to deliver only $75,000.
• 54:20
  And so you multiply all those together and you actually end up with an xV of
• 54:23
  more than $500,000 on this idea, right?
• 54:26
  Because there's a reason to do it quickly.
• 54:28
  There's a cost of not doing it quickly, but also that, that that
• 54:32
  xV of over 500,000, I can't do the math in my head here, but whatever
• 54:36
  it is, o is only costing us 75,000.
• 54:39
  So the efficiency to create that is also much higher as well.
• 54:42
  We invest a dollar in this case to create multiple more dollars than we would in the
• 54:47
  AI evidence base, AI system at the moment.
• 54:50
  Just two simple examples, right?
• 54:52
  There's lots to unpick in all of it.
• 54:53
  There's lots of reasons why that doesn't do justice to any of it.
• 54:56
  Just in a, in a quick example, but you can see therefore what you would need to go
• 55:00
  and do to, to add greater xV to all of the different areas of the AI assistant and
• 55:05
  why you might want to run a bit slower.
• 55:07
  Before you've really got the evidence behind all of that because you're gonna
• 55:10
  have to sink a lot more money into it.
• 55:12
  And part of the reason we end up running these zombie projects is we sink so much
• 55:15
  in, we don't wanna give up on them, right?
• 55:17
  And we don't want to be wrong when we've sunk half a million in, but we
• 55:20
  can test faster, get a willingness to pay evidence faster or not.
• 55:24
  We can do lots of things in the world now faster than ever before, and
• 55:28
  therefore we should be bold enough to want to, to test those things
• 55:30
  quickly, slow down when necessary.
• 55:33
  And this gives you that really good, clear thing that the
• 55:35
  AI project was the sexy one.
• 55:37
  It's the one the business wanted to do 'cause and the
• 55:39
  innovation team wanted to work on.
• 55:41
  But the other one , had a higher xV, right?
• 55:43
  And therefore it should be prioritized.
• 55:46
  It had a higher urgency to it, greater confidence behind it.
• 55:49
  And it doesn't mean we don't get to do the other things.
• 55:51
  It means that in a world of finite resources, we, it's clear which ones
• 55:56
  we should back and there are reasons why we may still back the other one.
• 55:59
  There are reasons why we may try and do all of those things and burn
• 56:02
  ourselves out or whatever it might be.
• 56:04
  But we now have a data point that we can work off the back of.
• 56:07
  It's so important, man.
• 56:08
  I was thinking like the, I'm doing that series as you know, on resource
• 56:12
  allocation process, the Joe Bower series and where his work back dating
• 56:18
  right back to the 1960s, 1970s.
• 56:20
  Was that most projects get stuck in the middle based on the opinion of.
• 56:25
  a middle manager who is responsible for the budget based on you, the
• 56:30
  person who's asking for that revenue, based on did you have credibility
• 56:35
  or did you do a good job last time?
• 56:36
  Because if you tried something that didn't work, your name is dirt around
• 56:40
  here 'cause you're making me look bad.
• 56:42
  And that's one of the things this was, it removes that personal opinion.
• 56:46
  That's what I was saying about Rasmus Ankersen that this idea
• 56:49
  that it's not the coach's opinion, it's the data that decides.
• 56:53
  And that's what I love about it.
• 56:54
  One last thing I thought we'd add though, which is important and not just
• 56:57
  'cause I love our Good S-curve, the importance of the S-curve as a lens
• 57:02
  through which to look at all this.
• 57:04
  And there's a brilliant quote that I love by Steve Jobs and he said that,
• 57:08
  and this was at the time, just to put it into context, he said, apple.
• 57:12
  At Apple at the time was a company with not much resources.
• 57:16
  So they have to be very, very, very strategic about
• 57:20
  what initiatives they back.
• 57:22
  And he said they back technologies in their springs.
• 57:26
  And I, I thought that was a nice setup for the idea of the S-curve because it
• 57:30
  helps you map your portfolio and also helps you decide is it a Sunset project,
• 57:35
  is it a trend that's going to grow?
• 57:39
  But also where does it, all these things fit across the S-curve.
• 57:43
  So maybe you'll start just so we're all on the same page as people listen
• 57:47
  to us, what an S-curve is, and then talk to us through how it maps to xV.
• 57:53
  Yeah, as I wrote this chapter, obviously Aidan, you were front of mind for
• 57:57
  me the entire way through knowing that you are Mr. s-Curve as well.
• 58:00
  So hat, hat tips are you on on the, on, on this chapter?
• 58:05
  And I quite enjoyed writing it.
• 58:06
  , Paul Nunez, by the,
• 58:07
  way, who wrote the book, jump With the S-Curve before I even knew him, and
• 58:11
  which is a, a magnificent book, also an episode, a link to in the show notes.
• 58:15
  Great guy.
• 58:17
  And I understand why as well.
• 58:18
  Right.
• 58:18
  And I think off the back of many of our chats, I have dug deeper into it
• 58:23
  and really brought it into the core of where xV can be quite valuable, because
• 58:29
  it's the hardest leap to take right.
• 58:31
  To recognize or not that , the thing that's fed you until now, the product,
• 58:35
  the service, whatever it might be.
• 58:37
  Is reaching the end of its natural life, right?
• 58:40
  It's in, its, it's in its maturity, late maturity, decline phase.
• 58:44
  And we need to start to invest in the thing that's going to leap us to the next
• 58:48
  place, whatever that might look like.
• 58:50
  That is pure innovation land in many ways.
• 58:52
  But we lack a way of quantifying and capturing that as well, right?
• 58:56
  The s curve's beautiful, but how do you quantify it and how do you
• 58:59
  turn it into the empirical metric that shows that it's better to sell
• 59:04
  this car now and go get a new one?
• 59:05
  Because the cost of maintaining it is actually more, even if it's, even if
• 59:09
  you're gonna run it into the ground.
• 59:10
  And maybe that's a bad example, but you see what I'm trying to say with it, is
• 59:13
  it's quite hard to go and spend that loads of money to get to the next thing
• 59:16
  that's super ambiguous, and you're not quite sure if it's gonna do the thing
• 59:19
  and you can't quite see the exponential thing that's about to hit you on the
• 59:25
  other side of the S-curve, because the exponential change at the end of
• 59:28
  that decline phase come pretty fast.
• 59:30
  , It's pretty well documented that.
• 59:32
  The best years of many organizations were within 18 to 24 months of the year,
• 59:37
  two years after they were dead, right?
• 59:39
  Like very many organizations had peak year followed by very rapid
• 59:43
  decline on the other side of it.
• 59:46
  So xV, I think, gives us a metric to start to track and manage that.
• 59:53
  That piece of it right across each of the different phases and innovation
• 59:57
  struggles with many of these languages of horizons and everything else, right?
• 1:00:01
  Like there's a lot of different models.
• 1:00:03
  I chose to not really take that on in the book actually, but the S-curve kind of is
• 1:00:07
  my way of at least tackling that, right?
• 1:00:09
  We have an emergence phase and acceleration phase, a maturity
• 1:00:12
  phase, and a decline phase.
• 1:00:13
  Quite simply put for most things that we will do, right?
• 1:00:17
  As human beings, as products, as services, whatever it might be.
• 1:00:20
  And those curves are probably accelerating,
• 1:00:24
  probably in, in most sectors.
• 1:00:28
  I guess xV can probably tell me more about what those look like,
• 1:00:31
  and maybe that's a project for me to, for, to think about doing.
• 1:00:34
  But if you think about the confidence assessment, the value estimation, the time
• 1:00:39
  sensitivity, all of these urgency pieces , and value metrics and everything else
• 1:00:43
  are very aligned to where we might be.
• 1:00:47
  On an scur and when we might need to start to think about over investing in the
• 1:00:52
  thing that's going to leap us to that next piece, I think it will shorten the gap and
• 1:00:56
  make it more easy more, more frictionless.
• 1:01:00
  Maybe that's a good way of talking about it, to take some of those leaps
• 1:01:03
  as well, because we can start to tie it back to the language of the p
• 1:01:07
  and l and the balance sheet, right?
• 1:01:08
  The assets that we are creating, , the old assets that are coming to the end
• 1:01:12
  of a life, the different value levers that exist in our organization, and
• 1:01:16
  actually start to see ways to speak to those things in a slightly different way.
• 1:01:20
  I think there's more work for me to do on this, right?
• 1:01:22
  This book will be followed up sometime in the future by a much more practical
• 1:01:27
  handbook version of this, right?
• 1:01:29
  This is very much a storybook with useful concepts and out and frameworks
• 1:01:35
  and other things in it, but there'll be a much more hands-on version of
• 1:01:37
  it, and I'll probably dig deeper.
• 1:01:39
  Into this topic because I think it's critical, right?
• 1:01:42
  You know, part of what I want to get to now is helping people to
• 1:01:47
  back more of the right projects.
• 1:01:48
  The world needs it.
• 1:01:50
  We are not short of innovation budget, we are short of innovation
• 1:01:53
  outcomes and value creations.
• 1:01:55
  And I think that my hypothesis is again, it's not because
• 1:01:59
  , we're bad at doing the work.
• 1:02:00
  Not because we're bad people are trying to not do good things.
• 1:02:03
  It's because we don't have the right ways of justifying the things we should
• 1:02:06
  be doubling down on and are spending too much money on things we should have killed
• 1:02:10
  a long time ago that are wasting time and resources in for huge cost, right?
• 1:02:16
  Not just monetary cost, but environmental cost and other costs as well.
• 1:02:20
  So that's my immediate goal was just to try and help get more of the
• 1:02:22
  right projects funded with more of, with the money that already exists.
• 1:02:25
  I'm not think we need more, we just need to spend it better.
• 1:02:29
  , And more effectively.
• 1:02:30
  It brings together, as you say, all the different elements.
• 1:02:33
  And one last one to ask, and then I'll let you go back to your own, Matilda
• 1:02:38
  and Freya and Alex, all the people you dedicate the book to, the AI
• 1:02:44
  element, you do mention that because AI actually has a role to play in xV
• 1:02:49
  as well, and you do call that out.
• 1:02:51
  So it, it is like this extension that we can add to the whole pie,
• 1:02:57
  but not to replace us, but to actually augment us in some way.
• 1:03:01
  I stripped back a lot of the AI thinking from the book, actually, there was a
• 1:03:05
  whole chapter on it, and we decided to take it out and sort of blend more
• 1:03:08
  of the AI language in partly because there's so much happening that I felt
• 1:03:12
  like it could be outta date very quickly.
• 1:03:15
  A lot of the stuff that we'd written on it.
• 1:03:18
  But what I absolutely believe to be true is that in, in a world where we can break
• 1:03:23
  down black boxes, and the reason I'm quite pro, a lot of the business change
• 1:03:29
  and transformation that's being driven by the rush to try and make AI the value
• 1:03:36
  creator, which I don't think it will be, I think it'll be a toolkit to help
• 1:03:39
  unlock some of the things going forward in most cases, is that it's forcing us
• 1:03:45
  to bring together data in a much greater horizontal way than we have before.
• 1:03:50
  If we can break the black boxes of innovation and
• 1:03:52
  then apply the lenses of xV.
• 1:03:55
  And other frameworks to that connected data sets.
• 1:04:00
  We will start to see patterns emerge.
• 1:04:02
  We will start to be able to understand the full innovation value chain, the skills,
• 1:04:07
  the resources the empirical weighted value of different approaches, right?
• 1:04:13
  I have so much value, so much evidence to show that, open
• 1:04:16
  approaches are the most efficient way of creating new value, right?
• 1:04:20
  The xV and open innovation efficiency significantly outweighs
• 1:04:25
  any other approach, right?
• 1:04:26
  Quantifiably, benchmarked evidence-based, and yet it's a
• 1:04:30
  fraction of the adopted ways of getting stuff done in organizations.
• 1:04:34
  IE we prefer wasting money, right?
• 1:04:37
  Like that's, , and not creating value.
• 1:04:39
  And so the more we can start to embrace.
• 1:04:42
  Smart machine capabilities to bring the right data to bear in
• 1:04:46
  standardized structures that are bought into and understood will
• 1:04:51
  help us to make better decisions.
• 1:04:53
  Not gut field-based decisions, not hierarchy based decisions,
• 1:04:57
  not, I don't care what the data says I want you to do anyway.
• 1:05:00
  Well, that's fine Mr. C-E-O-C-F-O, whoever but your name's going against
• 1:05:05
  this in the system that we're working against, and you will be judged on
• 1:05:08
  judgment day against it, not me.
• 1:05:10
  And so there were just clear patterns that we need to start to bring
• 1:05:13
  forward, and that's where I'm excited for what AI can do on both sides.
• 1:05:18
  I think you, you introduced me to the Icarus concept as well in some of one
• 1:05:22
  of our discussions or one of your great talks or whatever, and I talk about it.
• 1:05:26
  yeah.
• 1:05:27
  I know, I know.
• 1:05:27
  It's Seth's idea.
• 1:05:28
  So, and I know you're very good at passing the real credit to other people, but you,
• 1:05:32
  you introduced me to Seth's concepts.
• 1:05:35
  But the piece that, that, that you added in that I, I sort of knew
• 1:05:38
  but hadn't really brought to the idea of, it's not just about flying
• 1:05:41
  too high, but also flying too low.
• 1:05:44
  And I think that many innovation teams are more guilty of flying
• 1:05:47
  too low than flying too high.
• 1:05:49
  Right.
• 1:05:49
  And that there are ways of trying to measure that through
• 1:05:52
  poor xV efficiency, right.
• 1:05:54
  Through a there is no point spending a dollar to make a dollar, right.
• 1:05:58
  Or, or spending a dollar to make a dollar that you can't even empirically
• 1:06:01
  evidence is, and, and that's probably where most teams are today, if not worse.
• 1:06:06
  And that there's just a lot to do to unpick those aspects of, of this.
• 1:06:11
  Right.
• 1:06:11
  And so, yeah, I think there's, there's s-curve jumps and all these big,
• 1:06:15
  bold visions, but there's a lot of just fundamentals we gotta fix first.
• 1:06:18
  man, all this applies to life as well.
• 1:06:20
  Don't waste your time.
• 1:06:21
  It time is such a precious resource.
• 1:06:24
  Simon, for people who want to find out more about the book, I know.
• 1:06:27
  There's a website dedicated to it.
• 1:06:28
  You're very generous with concepts you're giving away.
• 1:06:31
  Also, Simon's kindly gonna agreed to come back on the innovation show
• 1:06:35
  for the innovation show audience.
• 1:06:37
  So we're gonna have a webinar of some sort.
• 1:06:39
  Email me and that's how you'll get in 'cause we have limited
• 1:06:42
  amount of places available.
• 1:06:44
  admin@theinnovationshow.io.
• 1:06:47
  Email us, let us know if you're interested in attending.
• 1:06:50
  We'll put you in there.
• 1:06:51
  Very limited places available for that one.
• 1:06:54
  And Simon, for people who wanna find out more about Wazoku, and
• 1:06:57
  xV, where's the best places?
• 1:07:00
  Yeah, so a few bits on that.
• 1:07:02
  Thanks Aiden as well, and I would be delighted to.
• 1:07:05
  To run that session with folk if they wanna really get under the skin of, of xV.
• 1:07:09
  We're also working on more tools and more supports.
• 1:07:12
  You can find those at xVbook.com.
• 1:07:15
  And that website, I'll keep getting built out with more tooling.
• 1:07:19
  Wazoku, you can visit our website, Wazoku, dot com.
• 1:07:22
  I also have a podcast as you know that, that we're going to serialize the audio
• 1:07:29
  version of the book one chapter at a time over the course of the next few weeks.
• 1:07:34
  So.
• 1:07:35
  Check out the Total Innovation Podcast as well arrival, but friendly podcast.
• 1:07:41
  Aiden's been on it as well many times over the next few weeks, and
• 1:07:44
  there'll be a specific xV version, and then later in the year we're
• 1:07:47
  gonna release the full audio version.
• 1:07:49
  We want as many people as possible to read it.
• 1:07:51
  So there's that.
• 1:07:51
  We're not making any money on these books.
• 1:07:53
  We've dropped them to the lowest price we can on the distribution
• 1:07:57
  channels that we've got 'em through.
• 1:07:59
  So all usual places, podcasty Amazon, and others as well.
• 1:08:03
  So yes, that's where folk can find more about it and hopefully, we hear
• 1:08:07
  the language of xV being adopted across more boardrooms and more
• 1:08:10
  innovation teams around the world.
• 1:08:13
  no rivals, man.
• 1:08:14
  Innovation needs friends.
• 1:08:15
  We are complementary author of expected value, the system to
• 1:08:20
  measure, prove, and scale value.
• 1:08:21
  Simon Hill, Thank you.
• 1:08:23
  for joining us.
• 1:08:25
  Thank you
• 1:08:25
  I finally just want to thank our sponsor, Kyndryl, who run and reimagine the
• 1:08:29
  technology systems that drive advantage for the world's leading businesses.
• 1:08:33
  With a unique blend of AI powered consulting, built on unmatched managed
• 1:08:37
  service capability, Kyndryl helps leaders harness the power of technology for
• 1:08:41
  smarter decisions, faster innovation, and a lasting competitive edge.
• 1:08:45
  You can find out more about Kyndryl at K Y N D R Y l.com.