Latest / The Innovation Show / EP 669 Alex Osterwalder: How to Use AI to Augment Value Proposition Design (Without Creating AI Slop)

Transcript

EP 669 Alex Osterwalder: How to Use AI to Augment Value Proposition Design (Without Creating AI Slop) · The Innovation Show

• 0:00
  Before we begin, thank you to our sponsor, Kyndryl.
• 0:03
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  You can find out more about Kyndryl at K-Y-N-D-R-Y-L, kyndryl.com.
• 0:22
  A few weeks ago , we were joined by today's guest, and he told us
• 0:26
  about how we do not use AI when it comes to value propositions.
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  Don't use it to replace the human work.
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  You need the human work.
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  You need to go through that struggle.
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  But now he has joined us again, but this time it's about how do we use
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  AI to augment value propositions.
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  This comes from the field.
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  I love having this guy on the show.
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  He never runs the same lap twice, so when he goes out there and he works
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  with clients, he brings it back, he puts it into his frameworks, and
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  boy, does he love a good framework.
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  He is the author of, for those watching us, all those books on the shelf behind
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  me, and today's focus is how do you use AI in value proposition design.
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  He is Alex Osterwalder.
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  Welcome back, sir.
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  Thanks for having me back.
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  It's always a huge pleasure to be on your show
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  Thank you for squeezing me in, man.
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  I, I know you're traveling all over the world, just off to Riyadh,
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  and I just got him, like, an hour before he's hitting the airport.
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  So let's not waste our time.
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  And I'm, I'm answering here, by the way, so many emails I've
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  got saying, "Give us more tools.
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  Give us more frameworks.
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  We've had enough with all your knowledge that you have on the show." So we're
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  gonna give people a little bit of something different today, a shorter
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  episode, which is also being called for, but from the horse's mouth, how
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  do we use AI in value proposition?
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  Maybe, Alex, you'll start with where this is coming from, what you're seeing in
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  the field, what are clients looking for?
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  So we've gotten a lot of demand around value proposition design, and we were
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  brought in by one of the clients to really, on a large scale, support
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  their product owners, product managers.
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  And in that process, we just learn a ton every time.
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  So we bring in the frameworks.
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  We actually design some new frameworks which
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  will be out on the Strategyzer platform soon, like
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  the buying decision map, value stack, different things.
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  But what struck me most is how these established companies are not fully
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  aware yet how to use AI to the max and how to augment the human work.
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  So they're still kind of just improving processes when actually
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  the work in particular of product owners and marketers are changing.
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  So I'll talk a little bit about that and, uh, zoom into it.
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  But it's, it's really from the practice.
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  So, so everything we do is informed by practice
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  So let's get stuck in.
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  I know you were working with a client here, and this, I love that you do this.
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  So you'd identify a need, 'cause the, it's like that Heraclitus quote that
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  nobody ever walks in the same river twice.
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  It's not the same river, and they're not the same person.
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  Things keep moving.
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  Technologies keep coming on.
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  That's why we're talking about AI a lot on the show.
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  We're not jumping on the bandwagon.
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  It is here to stay.
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  So clients are feeling this downstream.
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  So what, what has come from that work?
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  What have you talked, what have you felt from working with these latest clients?
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  I'll quickly visualize and I'll scribble it, and for those of you listening, the
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  voiceover should be clear enough, and if not, Aidan is gonna stop me, right?
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  So I'm gonna talk about how modern companies develop, test, and iterate
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  value propositions, and how AI actually plays into that, and what you need to do.
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  You need to do a lot of homework so that you can use AI well.
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  And from everything we've seen in practice, the human still has an
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  important role to play, even when at one point you are gonna use agents.
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  But so everything starts with an opportunity.
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  The opportunity could be in technology, in science, whatever, but also,
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  you know, the assets that you have.
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  Maybe you have a lot of a- customer access and you wanna come up with a new product.
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  So you go through this search phase, and then you start to
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  formulate your value propositions.
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  But here's the key.
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  this search phase, product development, you need to do quite a bit of homework
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  so that you can use AI afterwards to almost automatically generate webpages,
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  brochures, anything you want, and even sales documents, sales decks.
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  But if you don't do the homework first, gonna suck, right?
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  Because it's just going to be l- you know, high-level stuff in and
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  high-level stuff out, and if there's something that's terrible, it's this
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  fuzzy value proposition with AI slop.
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  So what do we need to do?
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  We actually need to start with the basics, and the basics are very simple.
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  We need a good customer understanding here, and we use a
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  customer profile, but then we need to support that with evidence.
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  So map out your customer.
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  Well, you select the customer segment you want to serve first, but then you test.
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  You go and speak to customers.
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  You can use AI to go faster with synthetic interviews or quick stuff you
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  can do, but you need to do that legwork.
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  You need to get the evidence.
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  Then only do you go to formulating your value proposition the benefits.
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  You don't build anything yet.
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  Again, you need to then go and test and find the evidence which benefits, which
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  value actually matters most to customers.
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  So again, it's not something you can shortcut.
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  You have to do that.
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  You'll be able to do it a lot faster with AI.
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  So typically, you can put stuff on, you know, the internet very quickly,
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  visualize it, and get feedback overnight.
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  you have to do that homework.
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  And then the third one is visualize your differentiation, not by looking
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  at just, you know, the competitors, by actually asking your customers,
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  does this differentiation matter?
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  Does this aspect-- You know, is this solved already by our competitors?
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  So again, the second piece here is the evidence, not just the differentiation.
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  now the fourth homework or artifact that we need to create is the buying
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  decision map, this new tool I talked to you about, and then the evidence.
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  So first you figure out how are customers buying, and then the evidence that they're
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  actually willing to buy, willing to spend.
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  They have that budget.
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  So in the product development process, you need to do this homework.
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  You need to go through these four things, create these four artifacts, test them.
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  And once you've done that, and again, you can do this much faster
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  with AI, but once you've done that, now you have you need, these value
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  proposition ingredients, use the magic of AI to create anything you want.
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  Sales deck, marketing video.
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  Uh, you know, the power of AI is crazy good when you have
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  the right data to feed in.
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  So then, and I'll just give you two silly examples, and the one that
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  I like a lot is a technique called value scene, a simple storyboard.
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  If you have the right ingredients going into your AI, you can
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  choose any LLM you want.
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  You will have beautiful storyboards coming out.
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  We use value scenes with three pictures.
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  Or you could The second one, create paragraphs like a hero, you
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  know, title and a hero paragraph and maybe an image for that.
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  So again, once you've done the homework on the left-hand side where you're
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  developing your product and you have those ingredients, it becomes
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  very easy to churn out anything you want, that's where AI is powerful.
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  But the human still needs to iterate, right?
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  So you get a first draft that's pretty powerful.
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  So when we've done this, not just for clients, but we started
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  obviously to use this for Strategyzer.
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  When we actually took
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  our customer profiles on our platform and we took the value stacks and we
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  said, "Hey, we need a hero line and a hero paragraph," it went so fast and
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  we were blown away ourselves by the, the quality of what came out of that.
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  So now what does that mean for marketers?
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  It changes the job of the marketer.
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  Your job is not to create a brochure anymore.
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  Your job is to use AI to iterate, so you're really more an overseer.
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  So it's very similar to what's happening in coding.
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  You know, software developers, they don't code anymore, but they need to
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  oversee the process because coding agents alone can't do everything humans can.
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  They can do a lot, but they still need the human oversight.
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  And when you don't have human oversight, you get some of the stuff
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  we're seeing in the press these days
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  We were just talking about that on LinkedIn, for example, the
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  amount of slop that, is out there.
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  I, I just don't understand, Alex, how people don't know it's slop.
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  I, I was do- I did a mock post recently about a kid that gets a gold star for
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  their writing from the teacher, and it was like, what happens when a kid
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  gets access to LinkedIn and an LLM?
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  And it was, like, going, "Absolutely honored to receive this gold star.
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  It's not about the teaching, it's about the process." And it was all
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  following all these patterns that you can, you can clearly spot now.
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  But it, it goes… The reason I share that is it goes to your point
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  about keeping the human in the loop, that you have to do the work.
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  If you're gonna write something, I, I always was told this, Confucius said
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  supposedly , speak because you have something to say, not because you have to
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  say something." And I think that that's valuable here in this point as well.
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  There's a couple of things I just wanted to show, and, and maybe you'll
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  show the, the slide behind you again.
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  There's a few definitions in there, because we are gonna double-click
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  on a couple of these and maybe do an episode on these - again in the future.
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  You mentioned your new tool there, value stack.
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  Maybe you'll describe what they are.
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  So customer profile, I think that's self-explanatory.
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  Value stack differentiation, and then willingness to pay.
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  Maybe you'll take us through what they mean.
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  Yeah, so the value stack is something we added because what we realized is
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  with the current value proposition canvas, which works really well,
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  um, is not as powerful when you wanna formulate value proposition.
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  So what happened with the value stack, it's actually a tool that
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  came out of working with AI when I was working on this process.
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  So right now, I'm pretty amazed how, uh, AI is helping us
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  develop better conceptual tools.
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  So the suggestion of adding some stuff came from AI, and then I realized,
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  "Hey, that's a tool." So what do we do?
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  We put all the benefits that we have and that we're creating with, with our
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  products and services, we put them into -- one of three boxes, so we kinda rank them.
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  The top box is key benefit, the key value driver.
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  It's the reason why customers will come to you.
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  So I'll give you a silly example from two thousand and four, I think,
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  or whenever, Google launched Gmail.
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  needed something to radically disrupt Hotmail at the time because
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  they were new into the market.
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  So what did they do?
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  Some people might remember.
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  They said, "We're gonna give you one gigabyte storage for email
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  for free." That's the feature.
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  And the benefit was never delete email again.
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  Because back in the day, you had to delete email to get emails,
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  as some of you might remember.
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  So that was the key reason that attracted millions and millions of users.
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  So you need to figure out what's that one benefit, that one value driver.
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  And once you have that, well, you start with, like, that's the
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  crown of our value proposition.
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  in the second layer, the second box, you put everything that supports the first
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  one that is valuable, maybe even a strong differentiator and new, but is not the
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  key reason why people will come to you.
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  So if I stick to well, it was the fact that you had threaded emails first
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  time ever, which is very powerful, but people didn't really go to Gmail
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  for the threaded emails, right?
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  Or Google level search in your email inbox.
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  That was also very Goog-google kinda, you know, thing.
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  But people didn't go there because they didn't even know what that meant.
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  But that was a key differentiator, but not the key benefit.
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  So those are the two, first two boxes.
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  And the third one is, is the one that came out of me working
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  with AI The table stakes.
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  So, you know, in product adoption, there's always been this thing of table stakes.
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  But when we talk about value propositions, in particular new ones, you still have
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  some benefits or features that are table stakes that customers will expect.
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  And there, you know, let's take, you know, reliability of your
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  email, that would go into Gmail.
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  Those are things that need to be there.
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  But it sounds trivial, but here's what's interesting.
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  When we were working with the client, using this the first time, we even
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  did it with their marketing team.
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  This is like a really, really big, European company.
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  But when we did this the first time, when they had their differentiators
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  and they needed to take the elements of the differentiators and put them
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  into the value stack, of a sudden, some of the things that they said
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  were differentiators, they pulled them down into the, into the table stakes.
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  So you can see how a simple tool can structure your thinking.
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  now when we show that and the teams have to really rank the benefits, not
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  just stack ranking, but put them into these three categories, of a sudden,
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  the value proposition becomes sharper.
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  So we just classify this, but then imagine now you have data structure, you have the
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  key, value driver, you have the supporting benefits, and then you have table stakes.
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  Now you put that into an LLM, it can much better handle,
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  okay, what actually matters.
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  That's the reason why these structured tools that we create have so much value.
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  So in the playbooks
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  that we run on the Strategyzer platform, one of the
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  things is while you're doing the work, you're already structuring
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  the data, and that's very rich because then you can use it in AI.
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  And it hit me, I didn't even realize we're actually doing the
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  groundwork for great AI work by
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  getting people into our Strategyzer playbooks.
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  I think that's what people miss, that context is king.
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  You need context for an LLM.
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  Also, something you said there, so I, see this increasingly happen where,
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  say, for example, you have a team in an organization and somebody's given a task.
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  Say it's a risk team, and you're Alex, you write us a policy on
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  AI use, u- respectful use of AI, ethical use of AI for the risk team.
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  You're, like, going, "Oh, I better say yes." You use AI, you
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  create it, send it to everyone.
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  You haven't written it.
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  Sends it to everybody.
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  Everybody then uses AI to summarize it and go, "Give me a few bullet points
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  here in case I'm asked a question at the meeting." They ratify it, then it
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  goes up a level to the board to ratify.
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  Nobody there reads it, and they sign off on it, and there's no-
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  literally no human in the loop.
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  And one of the concerns, I wondered what your thoughts are on this, were
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  you still have to go through the work.
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  You still have to go through the struggle.
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  It can augment you, and this is the point of how you- you're,
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  you're using it here now, but you don't replace the human entirely
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  No, you don't replace the human entirely.
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  I do think, like in coding, in, more strategic work, we're gonna see
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  something similar happen where some of the work will be done by AI agents.
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  So what I just showed you was, like, a bit more manual than we'd hope it would be.
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  So typically, let me say A- an AI agent could be a, you know, army of bots
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  going out and doing customer interviews.
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  Meaning, well, these aren't chatbots that, you know, contact
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  you and then you answer a chatbot.
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  This is very effective.
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  You could do 100 interviews within an hour.
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  Now, you know, is that the best type of interview?
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  Well, probably not, but it's still, you know, a very useful experiment.
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  And then you can augment that with, you know, face-to-face or remote interviews
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  where you're looking at people.
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  So you, you can do things with agents.
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  They can go and collect that, can synthesize it, can actually
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  create value, you know, uh, uh, can create customer profiles for you.
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  So some of that work will be done by AI agents, so they do the work, they
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  perform the work, but ultimately, they won't be able to make decisions.
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  I don't believe we're there by far if I'm, you know, talking to the experts.
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  Um, they-- they're, they're still saying that there's things humans
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  can't be… You know, you can't replace the human for that judgment, that
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  judgment call, because AI has no taste.
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  AI has no understanding of like a human.
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  They can do a lot of things.
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  They can do a lot of work really fast.
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  But when you're creating products, there's still a value judgment there,
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  and that is not replaceable yet.
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  Because let's remember, LLMs are statistics.
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  Like, it's quite amazing to see what stat-- what we can do with statistics.
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  It looks like intelligence, but as you said, if we go a little bit deeper,
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  you know, we can immediately see the limits of this artificial intelligence.
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  So the human will have this role of an overseer, like in coding, and is going
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  to orchestrate quite a lot of agents to do a lot of the work for them.
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  But ultimately, for a lot of these things, in particular in product development,
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  the human will still have a role.
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  But the profession is changing fundamentally.
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  Let's be clear about that, right?
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  So you're becoming an overseer of agents more and more.
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  That's happened in coding, it will also happen in more strategic work.
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  Okay, we're gonna, we're gonna wrap it there.
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  T- couple of things.
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  One is Alex is joining me here in Dublin in December.
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  December 1st and 2nd.
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  We are working with Enterprise Ireland and a group of companies
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  for Enterprise Ireland.
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  They're in for a real treat.
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  We're gonna do workshops, half-day workshops, morning,
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  afternoon, over two days.
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  But Alex is gonna be around as well, so if you're a company and looking for
• 18:03
  a keynote, he's in town, so never a better chance to grab the man, and we're
• 18:08
  gonna spend a bit of time together.
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  The other thing is I have a copy of Value Proposition Design
• 18:12
  up for grabs, a digital copy.
• 18:14
  Here's the question.
• 18:16
  On Alex's canvas behind him, there's a mountain.
• 18:20
  What is at the top of the mountain?
• 18:23
  Email me in admin@theinnovationshow.io.
• 18:27
  Let us know, and you will be in the hat to win a copy of that book.
• 18:31
  As always, Alex, always a pleasure.
• 18:33
  Author of all those b- I'm not gonna even say them anymore, all
• 18:35
  those books on the shelf behind me.
• 18:38
  We're gonna come back, and actually, we're gonna do an episode on the Value Stack,
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  Alex's new tool, and share that with the world f- world first, right, Alex?
• 18:47
  World first, absolutely
• 18:48
  World first.
• 18:49
  Alex Osterwalder, thank you for joining us
• 18:52
  Real pleasure.
• 18:53
  Thanks for having me
• 18:55
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• 19:00
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