August 11, 2025 • 48 mins
In this episode of the SuperCreativity Podcast, host James Taylor interviews tech humanist Kate O’Neill, founder and CEO of KO Insights and author of the new book What Matters Next: A Leader’s Guide to Making Human-Friendly Tech Decisions in a World That’s Moving Too Fast.
Kate has advised global organizations like Google, Adobe, Microsoft, and the United Nations on how to design technology and digital transformation strategies that are ethical, human-centered, and built to last. In this conversation, she explains why we must move beyond shallow futurism to embrace strategic foresight, how to distinguish transformation from innovation, and why meaning is the most important compass for the future of leadership.
Whether you’re a CEO, innovator, strategist, or simply curious about the future of humanity and technology, this episode will equip you with frameworks for clearer decision-making and sustainable success.
Key Takeaways:
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Transformation ≠ Innovation: Transformation is about catching up; innovation is about moving ahead.
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Strategic foresight is not futurism: Leaders must develop insights and foresight simultaneously to navigate fast-changing environments.
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Meaning drives decision-making: Whether semantic, emotional, or strategic—understanding “what matters” is the key to human-centered leadership.
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Synthetic data and digital twins offer powerful tools to test future-facing decisions without risking real-world failures.
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Cross-pollination of ideas across disciplines is where creativity and insight thrive.
Notable Quotes:
“Transformation is catching up. Innovation is moving ahead.” – Kate O’Neill
“Leaders need clearer thinking, not shinier tools.”
“Foresight is not about predicting the future—it’s about preparing for meaningful outcomes.”
“We don’t need timid incrementalism—we need right-sized steps into what matters next.”
“AI lets us build serendipity into our thinking—if we use it thoughtfully.”
Timestamps:
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00:09 – Welcome and Kate O’Neill intro: Tech Humanist and KO Insights founder
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01:14 – Her early career at Netflix and evolution into strategic foresight
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03:59 – Why Kate rejects futurism in favor of actionable foresight
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06:37 – Lessons from visionary leaders and bad leadership
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08:40 – Speaking truth to power and confronting with compassion
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10:57 – Innovation vs. transformation: what's the difference?
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14:55 – Helping leade
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:09):
Welcome to the Super Creativity Podcast where we explore the intersection of technology,humanity and innovation.
Today we're honored to have with us Kate O'Neill, renowned as the tech humanist.
Kate is a leading expert in aligning business success with human-centric technology.
As the founder and CEO of KO Insights, she has guided organizations like Adobe, Google,Microsoft and the United Nations towards more meaningful and effective digital
(00:37):
transformation.
Her latest book, What Matters Next?
A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving TooFast, offers a roadmap for leaders to navigate the rapid pace of technological change
while keeping humanity at the forefront.
So let's delve and dive into Kate's insights on making technology work better for businessand importantly for people.
(01:01):
Kate, welcome to the Super Creativity Podcast.
Thank you, James.
Great to see you.
Well nice that we've met a few times before now and it's just wonderful because I knowthis is your, is this your fourth book I'm guessing?
Third, fourth.
book in the business and tech space.
I have six overall and I've contributed to a few others, but so it's always kind of afunny question.
(01:21):
Like how many are there?
Well, it depends on how you count them.
But yes, four in the business and tech space.
Now, what were you doing before you became known as a tech humanist?
Because I've always known you as a tech humanist, and that's the title that you often get,you is used.
What were you doing before you were out there giving speeches, writing books, consulting?
(01:41):
You know, so my career has been in technology for...
30 years now, but it's been in different fields of technology.
in different fields that didn't have names at the time.
before we were calling it information architecture, I was doing information architecture.
Before user experience and before customer experience, was doing that content management,content strategy.
(02:03):
So all these different fields that are, in some ways, the interesting thing about them isthat what they all have in common is that they are this interesting synthesis of
understanding language.
and the way humans organize in our brains and the way technology best organizes.
And so it's kind of always bringing those things together.
(02:23):
And then over time, realizing that I was always, in every organization, the person who wasthe sort of fiercest advocate for the customer or the user or the people on the other side
of the equation.
And that became sort of my go-to role.
I just became the person who
who realized like, need to make sure the business is successful, that the businessobjectives succeed, but we also need to make sure that in doing that, we're providing for
(02:51):
human success as well.
And so that became that morphed into this field over the last 15 years of speaking andresearching and writing around tech humanism.
Well, it's interesting, you use that, the tech humanist, it's an interesting phrasebecause I don't think of you as a futurist in that way.
(03:15):
I think I've seen you talk about futurist adjacent, which I thought is quite nice becausewhen I often think of traditional futurists, it's like intellectual Red Bull.
It's just like a high, a sugar high, and there's nothing really much there.
But you kind of go deeper.
this, tell me about this, the tech, the humanist start, was there a particular point thatyou decided, because you could have gone and just done the typical futurist thing and gone
(03:46):
down that route and break shany thing syndrome, but you chose a slightly different path toface it a little bit more around ethics, around humanity, was there a particular point
that you went that this is the direction I want to go with this?
Yeah, I think like you, I see the whole futurist space.
as one that's filled with a lot of conjecturing and a lot of posturing that doesn't reallyfeel like it pays off in many respects.
(04:10):
It doesn't necessarily feel like it benefits business leaders either.
You know, the people who most need to consume that content are the people who are tryingto make the high stakes decisions and want the most guidance.
And what it feels like is much more relevant is what I would think of as more likeforesight strategy.
And so that's one of the things that I really lean into in this book is the differencebetween futures
(04:33):
them in foresight, really trying to use a model where we're using insights thinking thatyields foresight along the way so that we can triangulate our decisions for what we need
to do now with what we need to do next.
But that came about because I did work around the future space for a good bit of mycareer.
(04:53):
I worked around fields that were very much trying to be on the cutting edge of things.
And sometimes it worked and sometimes it didn't.
And the times when it worked, like
You know, I know that in my bio you probably have for For viewers to have seen by now thatI was one of the first hundred employees at Netflix It was a really incredible experience
But what was for me the most incredible part of it was getting to see the visionaryleadership at that company So for example, this is in 99 2000 2001 Netflix is still very
(05:27):
much the very tiny company for compared to blockbuster, which is the 800 pound gorilla
in the video rental space in the US and largely internationally.
And even at that time, Reed Hastings and the executive leadership team made decisions toinvest research and development, money and resources into what we were then calling
(05:50):
set-top boxes, which was the predecessor to streaming as we now know it.
And so you, just, it's so mind blowing to me, the confidence and the vision to be able tosay, here we are struggling to actually gain any kind of market share whatsoever.
But we're also going to be doing this research and putting money into what we're going tobe doing if and when we succeed in sort of dethroning the, the king and becoming the
(06:18):
dominant player in the space.
We know that there's probably going to be new technologies, new platforms, and we have tobe ready for that.
And to me, that's far more useful than sort of an intellectual exercise in futurism.
It's really trying to think ahead and trying to put your resources where they most willmake an impact.
Now there's a lot of, I guess, people kind doing what you do, but internally within anorganization.
(06:42):
You mentioned your work at Netflix, and there's a documentary I remember seeing years agocalled 16 Feet from Stardom, I think it was called.
And it was about the backing singers in Famous, and there was a line, I think it was maybeBruce Springsteen said, that 16 feet from the back of the stage where you're the backing
singer or the drummer or the bass player to the front of the stage where you are the mainperson.
(07:06):
where the spotlight is on you, that's quite a distance to go.
What gave you the confidence to go from that backstage hero within organizations likeNetflix to going onto the front of the stage and to really being that thought leader we
know you today?
think you just called me Darlene Love and I like that.
(07:26):
I'll take it.
I'll take it.
No one gets those vocals like she does.
But I think for me it was just the exposure to good leadership and good decision makinglike what I saw with Reed Hastings.
I saw a number of other instances of good leadership.
I also saw some bad leadership and I think you learn even more from a bad example thanfrom a good example.
(07:48):
So getting to see some choices being made that I would like to have seen made different.
had a kind of a...
life-altering interaction with a CEO I was working with one time who who was frustratedwith my Consists constantly trying to guide the decisions on what I how I saw the the
market and the field playing out And he sat me down once and he said Kate I know you thinkyou could lead this company better than I do I said no I think you could lead this company
(08:16):
better than you're doing and that's really where this comes from.
It's not it's not arrogance I don't think it's it's really the sense that
In my last company, Before KO Insights, which I've now run for 11 years, I had an agencybefore that was an analytics and strategy agency.
And we evolved our values through actual work with clients.
(08:38):
And one of those values that we came to was speak truth to power, but confront withcompassion.
And I really feel like that second piece of that,
that idea is an important one because I think people are just doing their best sometimes.
They're really trying to figure out how to solve the problems in front of them and they'reintractable problems and especially today and that's really what comes into fruition with
(09:01):
What Matters Next.
I think a lot of leaders are really struggling with how much complexity there is, how muchacceleration there is, how much they feel like the field is changing all the time, how
much it seems like technology is just constantly in flux.
And there's a lot of anxiety about, what if I make this investment in this technology andtry to, you know, update my customer service function and three to six months from now,
(09:24):
there's an entirely new technology space and we've made the wrong investment.
And that's a, that's a valid concern.
It's one that I have an answer to.
And that answer usually is you're going to be so much better off because you're not stuckin inertia.
Just having some momentum around change actually makes you more agile and you can begin toswap out for whatever you
(09:45):
find that the more important latest technology needs to be much more readily than if youwere still stuck in your previous implementation in most cases.
So I think the compassion though for that situation is what I learned along the way is tobe able to really empathize with the fact that these are complex decisions and leaders are
(10:06):
in a really difficult role.
And I like to find myself in a role where I can actually help.
affect that change in ways that scale, in ways that make pretty significant impactsbecause it's downstream from those decisions that most people experience what the company
has to offer and what the impacts of those decisions are going to be.
(10:27):
Now, one of the things that you do in this book, I know you have a love of language, theuse of language.
We were speaking earlier, you speak ridiculous number of languages as well.
You are definitely beating me on the Duolingo stakes at the moment.
One of the things you talk about in this book, which is a useful thing, and I think it'sgood someone with an understanding of language really calls us out, is the difference
(10:52):
between innovation is that term that we hear all the time gets used.
but you kind of make a definition between transformation versus innovation and how theseare not necessarily the same things.
We think of digital transformation being used all the time, innovation, and they're usedin the same phrase, same thing often, but you make a distinction about that.
(11:14):
can you just talk to about when did that idea come to you?
Where did you see that as a problem in a lack of clarity in what people were trying to do?
Yeah, I think that I was seeing that in the questions and answers and interactions I washaving with with executives and business leaders after keynotes, you know, having these
sessions where in many of it, and this probably happens for you too, where in many of theconferences I speak at, one of those sort of perks that an organizer might set up is you
(11:42):
get a 45 minutes alone with with the VIP attendees and you get to sort of field theirquestions in a much smaller group.
And those kinds of things are just their goal.
They're so
useful.
And I think the attendees really enjoy that too.
So note to the event planners, that is a really nice feature.
But in those kinds of sessions, I would often hear much more candid kinds of questionsthan I would hear, you know, in a 3000 room audience, 300,000 person audience, because you
(12:11):
don't want to speak up with those those personal questions.
But the kind of thing that I was hearing, eventually made me realize that what washappening was that leaders who came up to the CEO role or to executive
leadership roles in a field other than technology, which is most of them.
We have only recently begun to see technology executives make their way into the topsenior roles in organizations.
(12:34):
So most CEOs come from a field other than technology.
And the way that they're thinking about the technology imperatives was conflating whatneeded to happen in terms of bringing the organization up to current standards with what
they needed to do to look ahead and
be ready for the future.
(12:55):
And I just found it very useful to make this distinction to say, look, what you're talkingabout most of the time when you're talking about digital transformation, you're talking
about catching up.
You're talking about looking at what the market already expects you to do.
What you're talking about, what your competitors already do.
You're talking about what there is an sort of existent set of models and technologies thatyou can use to get to where you need to be.
(13:22):
Look at COVID, for example, digital transformation was incredible during COVID, but it wasa lot of things that existing companies could already do, like, you know, the mobile
ordering and things like that.
so retail and fast food and these kinds of services had to quickly implement those kindsof things where lead
in the space already had them, like Starbucks or Dunkin' Donuts or those kinds ofcompanies were already ahead of the game.
(13:46):
So that's a catch up.
But innovation is different.
Innovation is looking ahead.
Innovation is standing firmly in this moment and looking ahead and saying, again, like theNetflix example, what might the next series of changes be?
If we ask the meaningful questions about this moment, what people need, what looks likeit's changing, what kinds of trends are actually sort of changing and evolving the
(14:09):
landscape?
around us, where do we probably need to be or where might we likely need to be in 10 yearsor so?
And how do we think about the kind of meaningful and significant changes that we need tomake over the next few moves that position us there?
And I actually have a few models that help people conceive of that better, and some ofthem are in the book and some of them are more conceptual consultative models, but...
(14:35):
I think that distinction is one of the clearer, more useful ones in the book and that fora lot of, especially for non-technology CEOs, I hope that's going to be a really
disambiguating, very helpful clarifying type of model that will help them give betterheadspace to the kind of decisions that they need to make.
(14:55):
I guess that's one of the benefits of bringing someone like you in, whether it's inconsulting or speaking, is there's that phrase, you can't, you don't know what's inside,
you can't see the label from the inside.
There's something like that, that phrase, that type of phrase where I think withtransformation, it seems to be a lot of the time that people within the organization, they
(15:15):
kind of know the questions that they should be asking.
They might not have a process to think about that, about how you use those.
but they kind of know because they're quite close to it and they're quite close to thecustomer versus the innovation part where they don't know what they don't know, they don't
know the questions that they don't know.
guess bringing someone like you in allows them to ask, help them ask better questions.
(15:37):
Or you, right?
I mean, you find that as well, I would imagine.
Don't you find that transformation and innovation to be conflated ideas that happen sooften when the discussions you encounter?
Yeah, it's funny, it often comes back down, I think it comes back down to the curiosity,having curiosity about asking questions.
I think it's really hard to innovate within an industry.
(16:02):
If you're embedded, if you look at Nobel Prize winners, many Nobel Prize winners areboundary crosses.
They have expertise in chemistry, but then they kind of, I've got this interesting biologything just now.
And I think, know,
my brother-in-law owns a taxi company.
And taxi companies were changed dramatically with Uber because the average age of a taxidriver was 58.
(16:26):
And they're not sitting around making apps really.
So I think it's quite hard to innovate within an aside.
And I've seen different companies try to attempt different ways of doing this almost likehaving a kind of a, almost a separate part of the business that's focused on
growth, going 100 % for growth while they have another team that's focused on that kind ofjust transformation, getting the business to work more efficiently, more effectively,
(16:52):
because it's slightly different head spaces.
And I think it's very difficult sometimes to be boundary cross in that way.
Right, right.
But the value is there.
think it's so right.
The one thing that you and I have in common is that we bring these different fields ofinterest to the work that we do, know, music and language and everything else.
And you and I both have an interest in animal rights.
(17:15):
And, you know, I think just having those those radar, those antenna up for differentfields and what's meaningful and relevant in those fields, you know, this ties into the
title of this book, What Matters Next is that I think one of the things that we miss isthat
what is fundamentally human is our sense of meaning.
(17:36):
What is meaningful is what matters.
Meaning is always about what matters at every level.
So whether we're talking about semantic meaning, how we communicate through language orpatterns or purpose or significance or truth or relevance, or all the way out to the
biggest, most macro, big picture, what's it all about?
Why are we here cosmic, existential kinds of meaning?
(17:57):
It's always at every level we're talking about what matters.
And I think that distillation is really handy as a shortcut for asking the right kinds ofquestions in an environment, whether you're crossing boundaries in an organization or not,
inside of a silo in an organization or crossing across many layers of the growth people,the retention people, the people who are thinking about the two moves ahead, what matters
(18:23):
and what's going to matter.
And what I advocate for in this book is really about
not timid incrementalism, where you're too timid to take bigger steps, but the right sizedincrementalism where you're moving yourself a step at a time into the next thing and the
next thing, because you're making very sensible choices about how to navigate between whatyou know matters now and you're balancing that perspective with what you can see about the
(18:49):
future, what you can see about what is likely to be the priority, what's likely to matterwell ahead of you.
And I think that's
That's what helps make those blended perspectives really helpful too.
When you're bringing together people from across the organization who have differentmetrics for success, different OKRs, different priorities and ways that they're gauged for
(19:12):
success.
But if they're able to bring it together at a higher level and say, what matters acrosseverything that we're talking about is this fundamental concept, then you start having
really meaningful conversation.
that word idea of meaning, I always find it interesting talking with clients sometimes andthey'll say we're thinking about this, this is what we want.
(19:33):
And then just asking the follow up question, what do you mean by that?
Because me being the outsider, I can ask dumb questions and I have no problem about askingdumb questions.
And what it does is it makes them just have to pause and to think about what do they meanby that?
And I guess this is the great thing that you and I get to do, we're not academics.
(19:55):
So we are getting to have real deep relationships often with clients and have thatiterative process on our work and finding what's working, how do we need to refine that,
how do we need to define that in a slightly better way.
As you've been working with clients, I'm interested to get an understanding of, in thebook you have these set of tools to really help leaders make decisions specifically about
(20:22):
the future.
and you mentioned the kind of insight foresight.
Was there a particular occasion that when you were working with a client, it really helpedyou distill any of these particular tools or it made you kind of rethink actually what
they think I'm thinking, I'm saying is not that thing, but actually that's interesting orjust that kind of little quick feedback loop that you meant, actually this is, I'm gonna
(20:42):
change this tool because it's gonna be more useful for them.
Yeah, yeah, it's something that's evolved over the years and it-
It's my own insights, my own process that I talk in the book about keeping an insightsinventory, you something where you're, you're keeping a log of the most meaningful
insights and revelations that you've had about your business, about your life, about yourpriorities, you know, whatever they may be.
(21:08):
But it was something that over the years of working in different industries, differentcountries with different types of leaders, um, I would just hear the, these kinds of
patterns.
of feedback.
For example, one of the observations is that we think that we learn by asking questionsand getting answers and that's how we make decisions, but that's actually only the very
(21:30):
first step and that usually results in when you ask a question, I'll ask this of yourlisteners and viewers right now, when you ask a question, how often do you get a different
answer from every single person in your team or in the room, right?
It's probably every time.
And the reason for that is not that everybody's wrong, but it's that proverbial elephantthat we're all on different sides of, right?
(21:56):
We're all seeing the trunk or the nose or the tail or whatever.
And it's really important that what we do is synthesize that, that we take all thosepartial answers, which are part of a truth, and look for what the whole truth is.
At the best we can.
We're still human.
We're only going to be able to see what we can see.
But what we do is we try to make some kind of whole list
(22:19):
sense out of that and then use that kind of clarity that we get from that insight.
That is an insight, know, the sort of compressed truth of those partial vantage pointsthat then becomes this prism, this lens that lets us see more clearly into the decisions
we're making.
So we may be able to then take it and say, look, this decision we have to make todayabout, you know, implementing this call center software, we have to decide what our
(22:49):
taxonomy is going to be.
Like, are we deciding between this breakdown and that breakdown?
It's going to help us to be able
to say, well, one of the insights we have about our company is that we truly value designand experience.
Branded design is a really important facet to us.
So we're going to skew toward this decision over here.
It helps us make decisions more readily.
What happens along the way, though, is so interesting is that we sort of have this exhaustof
(23:14):
foresight I call them bankable foresight because what you don't have to do anything withthem right now you can set them aside but it starts to give you a clue about things that
are going to matter like even in that decision that taxonomy decision it's totally made upand not
not very vividly depicted, but you can even see how maybe saying, well, we're going tomake this decision toward this organization of our content because we're a design and
(23:41):
experience oriented company starts to say, well, but also we need to reconcile at somepoint, what do we do about this other organization?
If people have a more natural inclination to that, we need to address that at some pointand figure out, does this mean a partnership?
Does this mean, you know, a referral type of situation?
Those are just the
types of opportunities that become much clearer and more cogent by working through thisprocess diligently.
(24:07):
And I've just observed that through road work, right?
Like you said, it's just that the routine of being in the room and being in boardroomsand...
having the privilege of being on a whiteboard with customers again and again, and being ina room, being asked the questions and being able to turn the questions back around to
(24:28):
people and ask for clarification, ask for what that means to you, et cetera.
And you just start to see these patterns.
And so hopefully the distillation of these patterns to be able to share them with othersthrough the book is going to help there be a lot more clarity as people are making these
important decisions about.
technology deployment that affect us all.
(24:51):
sounds almost a little bit like Charlie Munger has his mental models, you're creatingthese kind of mental models that you can see.
it's, I'm reading a great book just now about Vienna during the, I guess, the end of theWeimar Republic before and then before kind of early 1940s, it went through this, it was
called Red Vienna and then Black Vienna.
(25:12):
And it's one of the things that was pushed back on because it was known as beingempirical, you know,
Can we test this?
Because that, and that was probably the greatest thing that Vienna gave to other parts ofthe world.
It's a sense of, is this testable?
Is it repeatable?
And if it's not, then it's just, that's a nice idea, but it's not real.
(25:32):
It's not like a mental model.
So what you're saying is you're able to kind of create those insights from that, and thenyou're able to test them with different clients and say, is this true at the end of it?
Is the response we're getting, is that correct?
Right, right.
Or does it lead to clarity?
think, you know, some things are testable in a quantitative way and some things aretestable in more of a qualitative way.
(25:58):
And the qualitative sense of, you know, does a leader sort of sit up more in his or herchair and go like, that makes so much sense.
I suddenly have a new insight.
I was doing a guest lecture for my friend at Harvard who teaches a class there withexecutive leaders.
And so, you know, the really wonderful thing about
(26:19):
doing guest lectures in schools where executives are going to get further education isthey're coming already with experience and business projects and some examples that they
start to think about as you talk through these scenarios.
And I was sharing this process.
so at each step of the process, I was asking these executive learners, do you haveexamples in your organization that you can see how this might apply?
(26:44):
And every time somebody would go like, my gosh, yes.
share an example and how it was immediately helping the clarity of their thinking to beable to put these these steps in place.
And I just I remember having this incredible sense of being able to affect change likethrough that moment and witnessing people coming back with very vivid examples, in some
(27:10):
cases, you know, way too long a story, but very helpful to get to hear, like here's the
Here's the way that we're gonna be able to take the questions to partial answers anddistill it into insights.
Immediately I can see how the many respective peoples on my team, that their perspectiveis going to be at odds with one another unless they have.
(27:35):
This was something we did at Netflix.
each had our own area of measurement for what we needed to affect as a team, sort of atour team level.
But those needed to roll up holistically into a metric that we all cared about as adepartment or as a company and so on.
It's almost like a taxonomy of metrics.
And I really found that process to be so helpful.
(27:58):
It's one that I share a lot with clients, you know, to be able to have that one unifyingmetric, but then everybody has their own version of it or something they feel contributes
to it.
So you're not only looking at your one metric for your own team, you have to have some waythat that resolves to something more significant than your own work, something that has
(28:19):
more wholism than that.
But it is, so wonderful to hear the real life
lived experiences of executive learners in those kinds of situations who are in the momentapplying these insights into their world and figuring out how they're going to solve
problems and take them back to their organization.
I guess one of the other interesting things we're starting to see now, I you have a, Iknow you've written a lot and you've spoken a lot about the use of data for good and for
(28:47):
bad.
And a few years ago, I was introduced to this idea of synthetic data.
And I thought, oh, this is fascinating.
Where, let's say San Francisco, where I I used to live and I used to be based as well.
If we want to change something around, let's say policing in this area, then there's acertain amount of
data that we have, we could think about how could that affect the crime rate in thatplace.
(29:12):
Or what we can also do is we can add synthetic data in order to get a wider data pool inorder to test it.
And then we get some different results.
So I guess what's interesting, what you're talking about now is, some of these things, youjust have that conversation with that CEO or that senior leader and trying different
things.
But I guess what we can also, going a few steps forward,
(29:35):
is you can take some of these ideas and you say, listen, rather than actually test it outon the real business or this real department, let's create the digital twin of that with
some synthetic data and let's see what actually happens.
Let's test our assumptions.
Yeah, that's interesting.
I had seen that kind of thing in practice in a couple of different areas.
(29:55):
So when I read my previous company, the analytics and strategy company, we were doing alot of experimental kind of modeling and helping companies figure out how to model their
company in data.
sort of say, are the meaningful things we need to capture here?
We need to know about inputs and we need to model what's happening kind of internally andthen we need to know about outputs and what the overall system looks like.
(30:20):
And along the way, in order to synthesize or to be able to simulate that, to know if achange over here would affect the outcome over here, you would often have to create
synthetic data.
You would have to create kind of bogus use cases and inject synthetic data into differentplaces
of the model.
yeah, as it relates to the insights and foresight, I hadn't thought about that parallel.
(30:44):
It's pretty interesting.
I also think, you know, synthetic data is something that comes up a lot when you'redealing with AI ethics and, you know, counterbalancing some of the bias in data sets and
so on.
So that, again, circles back to many of the underlying
sort of dilemmas that we're trying to deal with when it comes to using these models in thefirst place, using the insights and foresights and the now next continuum and the harms of
(31:11):
action and harms of inaction.
All the other models that I've introduced in this book are very much intended to deal withthe reality of the challenges that a data-based and algorithmically optimized and
technologically advanced.
a society is leading us to.
(31:31):
You know, we're leading, we're being led into or leading ourselves into a world where manyof these, decisions that we make have scale and capacity and consequence well beyond what
many leaders were ever taught to deal with in business school.
Or if they didn't go to business school, what they learned through years and years,decades of being in the trenches.
(31:56):
But the consequences are just so much bigger.
So they need new models.
They need clearer thinking and they need the ability to have some sense that what comes inover here is going to have this kind of an echoing impact over there.
And the only way we can really do that is by trying to demonstrate it with different usecases, different examples as we've done throughout
(32:19):
the book, showing with some clarity how some of these past to present to futureconsequences play out, trying to give some clarity around some of these ideas around like
transformation and innovation like we've already talked about.
You know, all of it just comes down to clearer thinking.
And at the end of the day, if someone is approaching these technology decisions withclearer thinking, I'm really hoping that they're going to be making better decisions
(32:46):
overall.
So you're brought in by these companies to help them with that clearer thinking, thatdecision making.
Where do your ideas come from?
Where do you go to get inspired?
How do you go about developing your ideas?
there things that have tried and tested for you that you kind of caught you in your kindof box of tricks that you tend to go to?
(33:08):
Yeah, you know, I, like you, I am drawn to many fields and I agree with the notion thatinnovation or, you know, new ideation comes often from the juxtaposition of ideas from
different spaces.
Like you were talking about Charlie Munger's sort of mental models and I also am a fan ofthe Zettelkasten, you know, the idea of kind of a thought process, like a thought
(33:33):
cataloging process.
So I read a lot of across a
lot of different fields, economics, history.
physics and so I'm constantly recognizing these meaningful sentences or something thatsomeone says that I go like, that's really interesting.
I wonder if that's true in another space.
I wonder if that would be true if you applied it over here.
(33:55):
And just keeping a set of interesting observations that maybe sometimes you encounterrandomly as you're looking for something else.
A lot of that is cataloged in my notion.
I use notion as many people probably do who are listening here.
And so you go to search for something if I'm looking for if I just search for the wordinsights in Notion, I might come back with a whole lot of different documents that I
(34:21):
wasn't looking for, but it might go like, yeah, the Amsterdam engagement that I had, thatwas an interesting one.
And thinking about smart cities and AI ethics and, you know, the responsible deployment oftech as it relates to multiple stakeholders, that could really apply in this health care
company example that I'm thinking about right now.
And so I think you do.
(34:41):
you just kind of, if you design your workflow well, you sort of build a certain amount ofserendipity into it.
And you get some of these cross-pollinization opportunities sort of just by the way ithappens.
And so that's what I look for.
And sounds like that's probably somewhat similar to your approach too, is it not?
(35:02):
Yeah, I mean, I have a lot of fun.
I used to have, I'm listening to a book just now, The History of the Notebook, going wayback into the Egyptians and then the Venetians and the Tuscans.
And so this idea of like just keeping a note, things are obviously important you want toreflect on.
(35:23):
So I've always done this.
I've always an idea, a thought, or I read something or I see something, I carry mynotebook everywhere with me.
But then I decided to go with AI, because I'm fascinated about how we can use AI to becomemore super creative.
I then said, okay, so this is the concept, this is the idea.
(35:43):
So I then I use an AI to then say, take this idea, this concept, this phrase, and turn itinto a visualization, a visual representation of this idea.
That could be a graph, it could be something else.
Okay, now give me something in terms of a metaphor that I could use.
that relate to that story.
Then give me a phrase that pays.
(36:05):
Then give me the data that supports that.
So what I'm trying to do is because often we're speaking to different types of learnersand different types of audiences, I'm trying to figure out at what stage am I better, like
just with a real sharp phrase that really just kind of lands.
And then what point am I better actually with something visual that represents that or ametaphor or...
(36:29):
a key data, a key graph or something as well.
So that's why I have a lot of fun using AI just now to help take that idea, that notion,that thing, and then kind of go across.
And then that kind of builds that intellectual property, like kind of database that I cango.
And sometimes then that feeds on something else and I can say, actually that image, I cando something else.
(36:52):
I know that a lot of, you know, especially like you and I, speak to lot of globalaudiences and
One of the downsides, we have a mutual friend called Sylvie Di Giusto, and she gets bookedall the time.
And one of the benefits being a German speaker, a native German speaker speaking English,is often she's much more direct in her language.
(37:13):
Whereas as us, as native English speakers, we can get a little bit overly flowery, a bitfancy, I guess, which is a detriment sometimes when you're speaking to people whose
English isn't their first language.
So if I'm speaking in parts of the world with English, like Vietnam,
I knew it was much easier to get my message across with a very strong visual rather thanlike two paragraphs on a particular idea.
(37:37):
So I do kind of what you do, like taking these notions, finding a way to capture them andthen thinking how can I expand that so it's multimodal in some way.
Yeah, that's great.
I love it.
That's so fun.
And I love that it ties back to this history of the notebook thing, because that is verymuch my kind of nerdery.
we're very aligned on that.
(38:00):
fascinating reading all the what the Venetians used to do.
They used to have like five different notebooks and how they would keep these notebooks.
And if you've ever read any of Leonardo da Vinci's notebooks, we used to write right toleft.
So you didn't think anyone would copy his work.
So a lot of things are particularly new.
But what I find so amazing just now is how, especially with generative AI, we're able tohelp that.
(38:25):
even richer, even fuller as a way to be able to do it.
And for me, that's the exciting thing.
And I hope that we go a little bit more towards that because my biggest concern, talkingabout the ethics, is we end up going the same way that we went with social media, where it
had so much optimism, know, when it initially started many of these social medias, and wekind of went to a slightly different place with it.
(38:51):
There probably wasn't, and a lot of people that were starting those businesses didn'treally
envisage.
And that's my thing with AI is like having people like you speaking events, talking aboutthe human side and talking about what are the questions that we're not maybe asking that
we should be asking.
think that I think is a really powerful thing to do.
So you mentioned Notion, are there any other tools or apps that you just findindispensable for the creative work that you do?
(39:18):
Yeah, yeah, mean, the writing obviously takes place across its own set of tools, Scrivenerand Notion and Evernote and so on.
But I'm a big mind mapper.
I love MindNode and I love the ability to sort of sketch out in MindNode, whether it's tojust get at a crude level, you know, sort of like organize, what are the component parts
(39:42):
of this thought?
Let me break them up and see them, you know, visually.
Or in some cases, it's for really trying to catalog something that I'm not as familiarwith.
Like for example, if there's an opposing viewpoint to my own, like if I see someone putout an idea that they're advancing that I think ideologically, philosophically, like we're
(40:06):
not on the same page, but I need to understand what their perspective is.
in order to refute it even, you know, or if I want to have some standing, if we're on adebate panel together or something like that, I want to be able to understand their
perspective.
And so I may just have to take it apart in my note, I find as a really useful tool for meto do that and just plot different aspects of it.
(40:30):
But I use AI a lot as well.
Sorry, go ahead.
that's great.
Someone told me the other day when they write a book, they do the first draft and thesecond draft and then the third draft that they actually show to people and then they also
do another draft.
Well, what they do is they use AI to basically strong man, I guess, against the ideas inthe book and read it from the perspective of someone who is the absolute opposite who is
(40:55):
going to destroy the book and the ideas in the book.
Yeah.
with What Matters Next.
Strawman, I think, is what you're looking for, right?
The idea like here, walking through this, there were a few places where there are people Iname who have different ideas.
Mark and Andresen, for example, holds different philosophies to mine, and they're opposingphilosophies in some respects.
(41:19):
And so, yeah, I would use generative AI sometimes to say,
pretend you're Mark Andreessen and you're reading what I've just written, how would youpush back on this?
And it's so helpful.
It's so helpful.
Because you can think what that might be, what the objections might be.
But when the tools that have actually been trained on lots and lots of documents thatunderstand Mark Andreessen's point of view, know, like that come from a...
(41:46):
come from myriad perspectives of Marc Andreessen's writings over the years, it's justgonna be better.
It's just gonna be that much more able to distill what the pushback is likely to be.
And so it helped me to say either I need to shore those things up or I just need to notcare.
And one way or the other, it doesn't matter which way you choose to go.
(42:07):
It's just very helpful to understand what those objections are likely to be.
So yeah, that's a really helpful tool in the process.
But also just using generative AI, like you described a really wonderful rich process formodeling different types of learning.
I also use it to distill many times across the sort of digital Zettelkasten where I mighthave the...
(42:28):
the physics clips and the economics clips and politics clips and all sorts of things allin one space and have AI kind of summarize.
If you go through and look at all these notes that I've saved, what seemed to be some ofthe common themes that maybe I'm missing, that I seem to be drawn to?
And it might say like, you seem to be drawn to the concept of entropy across all thesedifferent systems.
(42:50):
Like, yeah, that's true.
I am really drawn to that.
Good observation, AI.
So maybe now I'm actively going out and reading about entropy.
more directly so that I understand what it is my mind is instinctively drawn to.
I just think that there's such opportunities, as you say, for us to use these tools inways that just make us better at what we're already doing and take us leaps and bounds,
(43:14):
you know, fields down the road so that we can just do what we're doing and do it much moreeffectively.
Now we're going to have a link to your new book, but I'd also love to know, is there abook you think our listeners should be also checking out, maybe some that's been
influencing you over the course of the past few months, you just kind of been returningto, it's kind of, maybe you reassess an area or got really interested in a particular
(43:40):
area.
There's so many.
I think that in this book, the ones that, these are classics, but the ones that I taped upthe tables of contents of Good to Great and Blue Ocean Strategy as two of the really most
effective business strategy books of the last few decades.
(44:03):
And I wanted to make sure that,
Conceptually, there was a flow that was happening in the book that would bring readersthrough that transformative process of, you know, sort of starting at a more abstract
argument and working your way through to how do you implement, how do you operationalizeand how are we going to take step by step through that?
(44:25):
So for me, those were books I returned to many times.
I just flipped good to great or bluish and strategy open to any page and sort of read aparagraph over it.
and thought, all right, I'm not hitting that level yet.
I need to level up what I'm doing.
And it's funny, because it actually sort of, for me, I know you and I have music in commonand songwriting in common, and it was similar to a process I learned from Pat Patterson,
(44:51):
the really wonderful songwriting instructor at Berkeley University in New England.
He has this idea that if you've written a verse and a chorus,
And then another verse in the chorus that you might look at one of those verses, maybeyour second verse, because the second verse usually is weaker than the first, and you'll
(45:11):
kind of go like, oh shoot.
He usually says something different than oh shoot, but oh shoot.
Like now I've written a better verse and I need to go and write a better second verse.
So it's always whatever is your level that you've hit with one section, your othersections need to level up too.
And that's what I find across my writing.
(45:32):
It's a trick I've learned from my songwriting that if you really hit it out of the park,it's like, shoot, now I just made my job harder across the entire rest of the book.
I have to level the rest of it up.
that's great.
I mean, I think it's, you know, because there was an, I remember there was an Ezra Kleinfrom NPR said something along the lines of, you know, that idea where you're sitting at
(45:54):
one level and then, you have great taste and your craft or your art is not at the level ofyour taste yet.
But there's that constant, that's constantly what you're trying to do.
And I think it's very hard in what you and I do in the world because
There is an artistry towards what we're trying to do, but then there's also very much thebusiness side of it as well.
(46:16):
And one of the hardest things, last night I watched a great movie again, because I wastalking to someone about it called The Big Night with Stanley Tucci about a great meal.
And the movie is basically really about this idea, yeah, this idea like this tension wehave between creating great art, something that's gonna live beyond us and really
influence other people to doing the stuff that pays the bills, as you would say in theStates.
(46:40):
Yes.
And there's that constant tension that we have to do.
And I guess, know, where I see amazing companies sometimes is that they have thisaspiration to create great artistry in what they're doing with the technology just
disappears.
You're not really thinking about the technology, but then they have the execution and thequality of execution and planning and strategy behind that to be able to make that happen.
(47:05):
And that's where it gets really exciting when you see that as well.
Where is the best place for people if they want to learn more about you, want to bring youin to come speak at their event, want to bring you in to consult, where's the best place
for to go and do that?
My website is KOinsights.com.
My company is KOinsights.
KOinsights.com is the best place to find me, my work, the book, other books, and speakinginformation.
(47:30):
I also spend a lot of time on LinkedIn, so I'm Kate O'Neill there, on Blue Sky, KateO'Neill there, and so on, so you just look for me.
There's a lot of Kate O'Neills in the world, but if you search Kate O'Neill tech humaniston Google, you will usually find me.
Well, what matters next?
A leader's guide to making human-friendly tech decisions in a world that's moving too fastis out now.
(47:54):
Kate O'Neill, thank you so much for coming and being a guest on the Super CreativityPodcast.
Hey, thank you, James.
I really appreciate it.
Welcome to the Super Creativity Podcast where we explore the intersection of technology,humanity and innovation.
Today we're honored to have with us Kate O'Neill, renowned as the tech humanist.
Kate is a leading expert in aligning business success with human-centric technology.
As the founder and CEO of KO Insights, she has guided organizations like Adobe, Google,Microsoft and the United Nations towards more meaningful and effective digital
(00:37):
transformation.
Her latest book, What Matters Next?
A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving TooFast, offers a roadmap for leaders to navigate the rapid pace of technological change
while keeping humanity at the forefront.
So let's delve and dive into Kate's insights on making technology work better for businessand importantly for people.
(01:01):
Kate, welcome to the Super Creativity Podcast.
Thank you, James.
Great to see you.
Well nice that we've met a few times before now and it's just wonderful because I knowthis is your, is this your fourth book I'm guessing?
Third, fourth.
book in the business and tech space.
I have six overall and I've contributed to a few others, but so it's always kind of afunny question.
(01:21):
Like how many are there?
Well, it depends on how you count them.
But yes, four in the business and tech space.
Now, what were you doing before you became known as a tech humanist?
Because I've always known you as a tech humanist, and that's the title that you often get,you is used.
What were you doing before you were out there giving speeches, writing books, consulting?
(01:41):
You know, so my career has been in technology for...
30 years now, but it's been in different fields of technology.
in different fields that didn't have names at the time.
before we were calling it information architecture, I was doing information architecture.
Before user experience and before customer experience, was doing that content management,content strategy.
(02:03):
So all these different fields that are, in some ways, the interesting thing about them isthat what they all have in common is that they are this interesting synthesis of
understanding language.
and the way humans organize in our brains and the way technology best organizes.
And so it's kind of always bringing those things together.
(02:23):
And then over time, realizing that I was always, in every organization, the person who wasthe sort of fiercest advocate for the customer or the user or the people on the other side
of the equation.
And that became sort of my go-to role.
I just became the person who
who realized like, need to make sure the business is successful, that the businessobjectives succeed, but we also need to make sure that in doing that, we're providing for
(02:51):
human success as well.
And so that became that morphed into this field over the last 15 years of speaking andresearching and writing around tech humanism.
Well, it's interesting, you use that, the tech humanist, it's an interesting phrasebecause I don't think of you as a futurist in that way.
(03:15):
I think I've seen you talk about futurist adjacent, which I thought is quite nice becausewhen I often think of traditional futurists, it's like intellectual Red Bull.
It's just like a high, a sugar high, and there's nothing really much there.
But you kind of go deeper.
this, tell me about this, the tech, the humanist start, was there a particular point thatyou decided, because you could have gone and just done the typical futurist thing and gone
(03:46):
down that route and break shany thing syndrome, but you chose a slightly different path toface it a little bit more around ethics, around humanity, was there a particular point
that you went that this is the direction I want to go with this?
Yeah, I think like you, I see the whole futurist space.
as one that's filled with a lot of conjecturing and a lot of posturing that doesn't reallyfeel like it pays off in many respects.
(04:10):
It doesn't necessarily feel like it benefits business leaders either.
You know, the people who most need to consume that content are the people who are tryingto make the high stakes decisions and want the most guidance.
And what it feels like is much more relevant is what I would think of as more likeforesight strategy.
And so that's one of the things that I really lean into in this book is the differencebetween futures
(04:33):
them in foresight, really trying to use a model where we're using insights thinking thatyields foresight along the way so that we can triangulate our decisions for what we need
to do now with what we need to do next.
But that came about because I did work around the future space for a good bit of mycareer.
(04:53):
I worked around fields that were very much trying to be on the cutting edge of things.
And sometimes it worked and sometimes it didn't.
And the times when it worked, like
You know, I know that in my bio you probably have for For viewers to have seen by now thatI was one of the first hundred employees at Netflix It was a really incredible experience
But what was for me the most incredible part of it was getting to see the visionaryleadership at that company So for example, this is in 99 2000 2001 Netflix is still very
(05:27):
much the very tiny company for compared to blockbuster, which is the 800 pound gorilla
in the video rental space in the US and largely internationally.
And even at that time, Reed Hastings and the executive leadership team made decisions toinvest research and development, money and resources into what we were then calling
(05:50):
set-top boxes, which was the predecessor to streaming as we now know it.
And so you, just, it's so mind blowing to me, the confidence and the vision to be able tosay, here we are struggling to actually gain any kind of market share whatsoever.
But we're also going to be doing this research and putting money into what we're going tobe doing if and when we succeed in sort of dethroning the, the king and becoming the
(06:18):
dominant player in the space.
We know that there's probably going to be new technologies, new platforms, and we have tobe ready for that.
And to me, that's far more useful than sort of an intellectual exercise in futurism.
It's really trying to think ahead and trying to put your resources where they most willmake an impact.
Now there's a lot of, I guess, people kind doing what you do, but internally within anorganization.
(06:42):
You mentioned your work at Netflix, and there's a documentary I remember seeing years agocalled 16 Feet from Stardom, I think it was called.
And it was about the backing singers in Famous, and there was a line, I think it was maybeBruce Springsteen said, that 16 feet from the back of the stage where you're the backing
singer or the drummer or the bass player to the front of the stage where you are the mainperson.
(07:06):
where the spotlight is on you, that's quite a distance to go.
What gave you the confidence to go from that backstage hero within organizations likeNetflix to going onto the front of the stage and to really being that thought leader we
know you today?
think you just called me Darlene Love and I like that.
(07:26):
I'll take it.
I'll take it.
No one gets those vocals like she does.
But I think for me it was just the exposure to good leadership and good decision makinglike what I saw with Reed Hastings.
I saw a number of other instances of good leadership.
I also saw some bad leadership and I think you learn even more from a bad example thanfrom a good example.
(07:48):
So getting to see some choices being made that I would like to have seen made different.
had a kind of a...
life-altering interaction with a CEO I was working with one time who who was frustratedwith my Consists constantly trying to guide the decisions on what I how I saw the the
market and the field playing out And he sat me down once and he said Kate I know you thinkyou could lead this company better than I do I said no I think you could lead this company
(08:16):
better than you're doing and that's really where this comes from.
It's not it's not arrogance I don't think it's it's really the sense that
In my last company, Before KO Insights, which I've now run for 11 years, I had an agencybefore that was an analytics and strategy agency.
And we evolved our values through actual work with clients.
(08:38):
And one of those values that we came to was speak truth to power, but confront withcompassion.
And I really feel like that second piece of that,
that idea is an important one because I think people are just doing their best sometimes.
They're really trying to figure out how to solve the problems in front of them and they'reintractable problems and especially today and that's really what comes into fruition with
(09:01):
What Matters Next.
I think a lot of leaders are really struggling with how much complexity there is, how muchacceleration there is, how much they feel like the field is changing all the time, how
much it seems like technology is just constantly in flux.
And there's a lot of anxiety about, what if I make this investment in this technology andtry to, you know, update my customer service function and three to six months from now,
(09:24):
there's an entirely new technology space and we've made the wrong investment.
And that's a, that's a valid concern.
It's one that I have an answer to.
And that answer usually is you're going to be so much better off because you're not stuckin inertia.
Just having some momentum around change actually makes you more agile and you can begin toswap out for whatever you
(09:45):
find that the more important latest technology needs to be much more readily than if youwere still stuck in your previous implementation in most cases.
So I think the compassion though for that situation is what I learned along the way is tobe able to really empathize with the fact that these are complex decisions and leaders are
(10:06):
in a really difficult role.
And I like to find myself in a role where I can actually help.
affect that change in ways that scale, in ways that make pretty significant impactsbecause it's downstream from those decisions that most people experience what the company
has to offer and what the impacts of those decisions are going to be.
(10:27):
Now, one of the things that you do in this book, I know you have a love of language, theuse of language.
We were speaking earlier, you speak ridiculous number of languages as well.
You are definitely beating me on the Duolingo stakes at the moment.
One of the things you talk about in this book, which is a useful thing, and I think it'sgood someone with an understanding of language really calls us out, is the difference
(10:52):
between innovation is that term that we hear all the time gets used.
but you kind of make a definition between transformation versus innovation and how theseare not necessarily the same things.
We think of digital transformation being used all the time, innovation, and they're usedin the same phrase, same thing often, but you make a distinction about that.
(11:14):
can you just talk to about when did that idea come to you?
Where did you see that as a problem in a lack of clarity in what people were trying to do?
Yeah, I think that I was seeing that in the questions and answers and interactions I washaving with with executives and business leaders after keynotes, you know, having these
sessions where in many of it, and this probably happens for you too, where in many of theconferences I speak at, one of those sort of perks that an organizer might set up is you
(11:42):
get a 45 minutes alone with with the VIP attendees and you get to sort of field theirquestions in a much smaller group.
And those kinds of things are just their goal.
They're so
useful.
And I think the attendees really enjoy that too.
So note to the event planners, that is a really nice feature.
But in those kinds of sessions, I would often hear much more candid kinds of questionsthan I would hear, you know, in a 3000 room audience, 300,000 person audience, because you
(12:11):
don't want to speak up with those those personal questions.
But the kind of thing that I was hearing, eventually made me realize that what washappening was that leaders who came up to the CEO role or to executive
leadership roles in a field other than technology, which is most of them.
We have only recently begun to see technology executives make their way into the topsenior roles in organizations.
(12:34):
So most CEOs come from a field other than technology.
And the way that they're thinking about the technology imperatives was conflating whatneeded to happen in terms of bringing the organization up to current standards with what
they needed to do to look ahead and
be ready for the future.
(12:55):
And I just found it very useful to make this distinction to say, look, what you're talkingabout most of the time when you're talking about digital transformation, you're talking
about catching up.
You're talking about looking at what the market already expects you to do.
What you're talking about, what your competitors already do.
You're talking about what there is an sort of existent set of models and technologies thatyou can use to get to where you need to be.
(13:22):
Look at COVID, for example, digital transformation was incredible during COVID, but it wasa lot of things that existing companies could already do, like, you know, the mobile
ordering and things like that.
so retail and fast food and these kinds of services had to quickly implement those kindsof things where lead
in the space already had them, like Starbucks or Dunkin' Donuts or those kinds ofcompanies were already ahead of the game.
(13:46):
So that's a catch up.
But innovation is different.
Innovation is looking ahead.
Innovation is standing firmly in this moment and looking ahead and saying, again, like theNetflix example, what might the next series of changes be?
If we ask the meaningful questions about this moment, what people need, what looks likeit's changing, what kinds of trends are actually sort of changing and evolving the
(14:09):
landscape?
around us, where do we probably need to be or where might we likely need to be in 10 yearsor so?
And how do we think about the kind of meaningful and significant changes that we need tomake over the next few moves that position us there?
And I actually have a few models that help people conceive of that better, and some ofthem are in the book and some of them are more conceptual consultative models, but...
(14:35):
I think that distinction is one of the clearer, more useful ones in the book and that fora lot of, especially for non-technology CEOs, I hope that's going to be a really
disambiguating, very helpful clarifying type of model that will help them give betterheadspace to the kind of decisions that they need to make.
(14:55):
I guess that's one of the benefits of bringing someone like you in, whether it's inconsulting or speaking, is there's that phrase, you can't, you don't know what's inside,
you can't see the label from the inside.
There's something like that, that phrase, that type of phrase where I think withtransformation, it seems to be a lot of the time that people within the organization, they
(15:15):
kind of know the questions that they should be asking.
They might not have a process to think about that, about how you use those.
but they kind of know because they're quite close to it and they're quite close to thecustomer versus the innovation part where they don't know what they don't know, they don't
know the questions that they don't know.
guess bringing someone like you in allows them to ask, help them ask better questions.
(15:37):
Or you, right?
I mean, you find that as well, I would imagine.
Don't you find that transformation and innovation to be conflated ideas that happen sooften when the discussions you encounter?
Yeah, it's funny, it often comes back down, I think it comes back down to the curiosity,having curiosity about asking questions.
I think it's really hard to innovate within an industry.
(16:02):
If you're embedded, if you look at Nobel Prize winners, many Nobel Prize winners areboundary crosses.
They have expertise in chemistry, but then they kind of, I've got this interesting biologything just now.
And I think, know,
my brother-in-law owns a taxi company.
And taxi companies were changed dramatically with Uber because the average age of a taxidriver was 58.
(16:26):
And they're not sitting around making apps really.
So I think it's quite hard to innovate within an aside.
And I've seen different companies try to attempt different ways of doing this almost likehaving a kind of a, almost a separate part of the business that's focused on
growth, going 100 % for growth while they have another team that's focused on that kind ofjust transformation, getting the business to work more efficiently, more effectively,
(16:52):
because it's slightly different head spaces.
And I think it's very difficult sometimes to be boundary cross in that way.
Right, right.
But the value is there.
think it's so right.
The one thing that you and I have in common is that we bring these different fields ofinterest to the work that we do, know, music and language and everything else.
And you and I both have an interest in animal rights.
(17:15):
And, you know, I think just having those those radar, those antenna up for differentfields and what's meaningful and relevant in those fields, you know, this ties into the
title of this book, What Matters Next is that I think one of the things that we miss isthat
what is fundamentally human is our sense of meaning.
(17:36):
What is meaningful is what matters.
Meaning is always about what matters at every level.
So whether we're talking about semantic meaning, how we communicate through language orpatterns or purpose or significance or truth or relevance, or all the way out to the
biggest, most macro, big picture, what's it all about?
Why are we here cosmic, existential kinds of meaning?
(17:57):
It's always at every level we're talking about what matters.
And I think that distillation is really handy as a shortcut for asking the right kinds ofquestions in an environment, whether you're crossing boundaries in an organization or not,
inside of a silo in an organization or crossing across many layers of the growth people,the retention people, the people who are thinking about the two moves ahead, what matters
(18:23):
and what's going to matter.
And what I advocate for in this book is really about
not timid incrementalism, where you're too timid to take bigger steps, but the right sizedincrementalism where you're moving yourself a step at a time into the next thing and the
next thing, because you're making very sensible choices about how to navigate between whatyou know matters now and you're balancing that perspective with what you can see about the
(18:49):
future, what you can see about what is likely to be the priority, what's likely to matterwell ahead of you.
And I think that's
That's what helps make those blended perspectives really helpful too.
When you're bringing together people from across the organization who have differentmetrics for success, different OKRs, different priorities and ways that they're gauged for
(19:12):
success.
But if they're able to bring it together at a higher level and say, what matters acrosseverything that we're talking about is this fundamental concept, then you start having
really meaningful conversation.
that word idea of meaning, I always find it interesting talking with clients sometimes andthey'll say we're thinking about this, this is what we want.
(19:33):
And then just asking the follow up question, what do you mean by that?
Because me being the outsider, I can ask dumb questions and I have no problem about askingdumb questions.
And what it does is it makes them just have to pause and to think about what do they meanby that?
And I guess this is the great thing that you and I get to do, we're not academics.
(19:55):
So we are getting to have real deep relationships often with clients and have thatiterative process on our work and finding what's working, how do we need to refine that,
how do we need to define that in a slightly better way.
As you've been working with clients, I'm interested to get an understanding of, in thebook you have these set of tools to really help leaders make decisions specifically about
(20:22):
the future.
and you mentioned the kind of insight foresight.
Was there a particular occasion that when you were working with a client, it really helpedyou distill any of these particular tools or it made you kind of rethink actually what
they think I'm thinking, I'm saying is not that thing, but actually that's interesting orjust that kind of little quick feedback loop that you meant, actually this is, I'm gonna
(20:42):
change this tool because it's gonna be more useful for them.
Yeah, yeah, it's something that's evolved over the years and it-
It's my own insights, my own process that I talk in the book about keeping an insightsinventory, you something where you're, you're keeping a log of the most meaningful
insights and revelations that you've had about your business, about your life, about yourpriorities, you know, whatever they may be.
(21:08):
But it was something that over the years of working in different industries, differentcountries with different types of leaders, um, I would just hear the, these kinds of
patterns.
of feedback.
For example, one of the observations is that we think that we learn by asking questionsand getting answers and that's how we make decisions, but that's actually only the very
(21:30):
first step and that usually results in when you ask a question, I'll ask this of yourlisteners and viewers right now, when you ask a question, how often do you get a different
answer from every single person in your team or in the room, right?
It's probably every time.
And the reason for that is not that everybody's wrong, but it's that proverbial elephantthat we're all on different sides of, right?
(21:56):
We're all seeing the trunk or the nose or the tail or whatever.
And it's really important that what we do is synthesize that, that we take all thosepartial answers, which are part of a truth, and look for what the whole truth is.
At the best we can.
We're still human.
We're only going to be able to see what we can see.
But what we do is we try to make some kind of whole list
(22:19):
sense out of that and then use that kind of clarity that we get from that insight.
That is an insight, know, the sort of compressed truth of those partial vantage pointsthat then becomes this prism, this lens that lets us see more clearly into the decisions
we're making.
So we may be able to then take it and say, look, this decision we have to make todayabout, you know, implementing this call center software, we have to decide what our
(22:49):
taxonomy is going to be.
Like, are we deciding between this breakdown and that breakdown?
It's going to help us to be able
to say, well, one of the insights we have about our company is that we truly value designand experience.
Branded design is a really important facet to us.
So we're going to skew toward this decision over here.
It helps us make decisions more readily.
What happens along the way, though, is so interesting is that we sort of have this exhaustof
(23:14):
foresight I call them bankable foresight because what you don't have to do anything withthem right now you can set them aside but it starts to give you a clue about things that
are going to matter like even in that decision that taxonomy decision it's totally made upand not
not very vividly depicted, but you can even see how maybe saying, well, we're going tomake this decision toward this organization of our content because we're a design and
(23:41):
experience oriented company starts to say, well, but also we need to reconcile at somepoint, what do we do about this other organization?
If people have a more natural inclination to that, we need to address that at some pointand figure out, does this mean a partnership?
Does this mean, you know, a referral type of situation?
Those are just the
types of opportunities that become much clearer and more cogent by working through thisprocess diligently.
(24:07):
And I've just observed that through road work, right?
Like you said, it's just that the routine of being in the room and being in boardroomsand...
having the privilege of being on a whiteboard with customers again and again, and being ina room, being asked the questions and being able to turn the questions back around to
(24:28):
people and ask for clarification, ask for what that means to you, et cetera.
And you just start to see these patterns.
And so hopefully the distillation of these patterns to be able to share them with othersthrough the book is going to help there be a lot more clarity as people are making these
important decisions about.
technology deployment that affect us all.
(24:51):
sounds almost a little bit like Charlie Munger has his mental models, you're creatingthese kind of mental models that you can see.
it's, I'm reading a great book just now about Vienna during the, I guess, the end of theWeimar Republic before and then before kind of early 1940s, it went through this, it was
called Red Vienna and then Black Vienna.
(25:12):
And it's one of the things that was pushed back on because it was known as beingempirical, you know,
Can we test this?
Because that, and that was probably the greatest thing that Vienna gave to other parts ofthe world.
It's a sense of, is this testable?
Is it repeatable?
And if it's not, then it's just, that's a nice idea, but it's not real.
(25:32):
It's not like a mental model.
So what you're saying is you're able to kind of create those insights from that, and thenyou're able to test them with different clients and say, is this true at the end of it?
Is the response we're getting, is that correct?
Right, right.
Or does it lead to clarity?
think, you know, some things are testable in a quantitative way and some things aretestable in more of a qualitative way.
(25:58):
And the qualitative sense of, you know, does a leader sort of sit up more in his or herchair and go like, that makes so much sense.
I suddenly have a new insight.
I was doing a guest lecture for my friend at Harvard who teaches a class there withexecutive leaders.
And so, you know, the really wonderful thing about
(26:19):
doing guest lectures in schools where executives are going to get further education isthey're coming already with experience and business projects and some examples that they
start to think about as you talk through these scenarios.
And I was sharing this process.
so at each step of the process, I was asking these executive learners, do you haveexamples in your organization that you can see how this might apply?
(26:44):
And every time somebody would go like, my gosh, yes.
share an example and how it was immediately helping the clarity of their thinking to beable to put these these steps in place.
And I just I remember having this incredible sense of being able to affect change likethrough that moment and witnessing people coming back with very vivid examples, in some
(27:10):
cases, you know, way too long a story, but very helpful to get to hear, like here's the
Here's the way that we're gonna be able to take the questions to partial answers anddistill it into insights.
Immediately I can see how the many respective peoples on my team, that their perspectiveis going to be at odds with one another unless they have.
(27:35):
This was something we did at Netflix.
each had our own area of measurement for what we needed to affect as a team, sort of atour team level.
But those needed to roll up holistically into a metric that we all cared about as adepartment or as a company and so on.
It's almost like a taxonomy of metrics.
And I really found that process to be so helpful.
(27:58):
It's one that I share a lot with clients, you know, to be able to have that one unifyingmetric, but then everybody has their own version of it or something they feel contributes
to it.
So you're not only looking at your one metric for your own team, you have to have some waythat that resolves to something more significant than your own work, something that has
(28:19):
more wholism than that.
But it is, so wonderful to hear the real life
lived experiences of executive learners in those kinds of situations who are in the momentapplying these insights into their world and figuring out how they're going to solve
problems and take them back to their organization.
I guess one of the other interesting things we're starting to see now, I you have a, Iknow you've written a lot and you've spoken a lot about the use of data for good and for
(28:47):
bad.
And a few years ago, I was introduced to this idea of synthetic data.
And I thought, oh, this is fascinating.
Where, let's say San Francisco, where I I used to live and I used to be based as well.
If we want to change something around, let's say policing in this area, then there's acertain amount of
data that we have, we could think about how could that affect the crime rate in thatplace.
(29:12):
Or what we can also do is we can add synthetic data in order to get a wider data pool inorder to test it.
And then we get some different results.
So I guess what's interesting, what you're talking about now is, some of these things, youjust have that conversation with that CEO or that senior leader and trying different
things.
But I guess what we can also, going a few steps forward,
(29:35):
is you can take some of these ideas and you say, listen, rather than actually test it outon the real business or this real department, let's create the digital twin of that with
some synthetic data and let's see what actually happens.
Let's test our assumptions.
Yeah, that's interesting.
I had seen that kind of thing in practice in a couple of different areas.
(29:55):
So when I read my previous company, the analytics and strategy company, we were doing alot of experimental kind of modeling and helping companies figure out how to model their
company in data.
sort of say, are the meaningful things we need to capture here?
We need to know about inputs and we need to model what's happening kind of internally andthen we need to know about outputs and what the overall system looks like.
(30:20):
And along the way, in order to synthesize or to be able to simulate that, to know if achange over here would affect the outcome over here, you would often have to create
synthetic data.
You would have to create kind of bogus use cases and inject synthetic data into differentplaces
of the model.
yeah, as it relates to the insights and foresight, I hadn't thought about that parallel.
(30:44):
It's pretty interesting.
I also think, you know, synthetic data is something that comes up a lot when you'redealing with AI ethics and, you know, counterbalancing some of the bias in data sets and
so on.
So that, again, circles back to many of the underlying
sort of dilemmas that we're trying to deal with when it comes to using these models in thefirst place, using the insights and foresights and the now next continuum and the harms of
(31:11):
action and harms of inaction.
All the other models that I've introduced in this book are very much intended to deal withthe reality of the challenges that a data-based and algorithmically optimized and
technologically advanced.
a society is leading us to.
(31:31):
You know, we're leading, we're being led into or leading ourselves into a world where manyof these, decisions that we make have scale and capacity and consequence well beyond what
many leaders were ever taught to deal with in business school.
Or if they didn't go to business school, what they learned through years and years,decades of being in the trenches.
(31:56):
But the consequences are just so much bigger.
So they need new models.
They need clearer thinking and they need the ability to have some sense that what comes inover here is going to have this kind of an echoing impact over there.
And the only way we can really do that is by trying to demonstrate it with different usecases, different examples as we've done throughout
(32:19):
the book, showing with some clarity how some of these past to present to futureconsequences play out, trying to give some clarity around some of these ideas around like
transformation and innovation like we've already talked about.
You know, all of it just comes down to clearer thinking.
And at the end of the day, if someone is approaching these technology decisions withclearer thinking, I'm really hoping that they're going to be making better decisions
(32:46):
overall.
So you're brought in by these companies to help them with that clearer thinking, thatdecision making.
Where do your ideas come from?
Where do you go to get inspired?
How do you go about developing your ideas?
there things that have tried and tested for you that you kind of caught you in your kindof box of tricks that you tend to go to?
(33:08):
Yeah, you know, I, like you, I am drawn to many fields and I agree with the notion thatinnovation or, you know, new ideation comes often from the juxtaposition of ideas from
different spaces.
Like you were talking about Charlie Munger's sort of mental models and I also am a fan ofthe Zettelkasten, you know, the idea of kind of a thought process, like a thought
(33:33):
cataloging process.
So I read a lot of across a
lot of different fields, economics, history.
physics and so I'm constantly recognizing these meaningful sentences or something thatsomeone says that I go like, that's really interesting.
I wonder if that's true in another space.
I wonder if that would be true if you applied it over here.
(33:55):
And just keeping a set of interesting observations that maybe sometimes you encounterrandomly as you're looking for something else.
A lot of that is cataloged in my notion.
I use notion as many people probably do who are listening here.
And so you go to search for something if I'm looking for if I just search for the wordinsights in Notion, I might come back with a whole lot of different documents that I
(34:21):
wasn't looking for, but it might go like, yeah, the Amsterdam engagement that I had, thatwas an interesting one.
And thinking about smart cities and AI ethics and, you know, the responsible deployment oftech as it relates to multiple stakeholders, that could really apply in this health care
company example that I'm thinking about right now.
And so I think you do.
(34:41):
you just kind of, if you design your workflow well, you sort of build a certain amount ofserendipity into it.
And you get some of these cross-pollinization opportunities sort of just by the way ithappens.
And so that's what I look for.
And sounds like that's probably somewhat similar to your approach too, is it not?
(35:02):
Yeah, I mean, I have a lot of fun.
I used to have, I'm listening to a book just now, The History of the Notebook, going wayback into the Egyptians and then the Venetians and the Tuscans.
And so this idea of like just keeping a note, things are obviously important you want toreflect on.
(35:23):
So I've always done this.
I've always an idea, a thought, or I read something or I see something, I carry mynotebook everywhere with me.
But then I decided to go with AI, because I'm fascinated about how we can use AI to becomemore super creative.
I then said, okay, so this is the concept, this is the idea.
(35:43):
So I then I use an AI to then say, take this idea, this concept, this phrase, and turn itinto a visualization, a visual representation of this idea.
That could be a graph, it could be something else.
Okay, now give me something in terms of a metaphor that I could use.
that relate to that story.
Then give me a phrase that pays.
(36:05):
Then give me the data that supports that.
So what I'm trying to do is because often we're speaking to different types of learnersand different types of audiences, I'm trying to figure out at what stage am I better, like
just with a real sharp phrase that really just kind of lands.
And then what point am I better actually with something visual that represents that or ametaphor or...
(36:29):
a key data, a key graph or something as well.
So that's why I have a lot of fun using AI just now to help take that idea, that notion,that thing, and then kind of go across.
And then that kind of builds that intellectual property, like kind of database that I cango.
And sometimes then that feeds on something else and I can say, actually that image, I cando something else.
(36:52):
I know that a lot of, you know, especially like you and I, speak to lot of globalaudiences and
One of the downsides, we have a mutual friend called Sylvie Di Giusto, and she gets bookedall the time.
And one of the benefits being a German speaker, a native German speaker speaking English,is often she's much more direct in her language.
(37:13):
Whereas as us, as native English speakers, we can get a little bit overly flowery, a bitfancy, I guess, which is a detriment sometimes when you're speaking to people whose
English isn't their first language.
So if I'm speaking in parts of the world with English, like Vietnam,
I knew it was much easier to get my message across with a very strong visual rather thanlike two paragraphs on a particular idea.
(37:37):
So I do kind of what you do, like taking these notions, finding a way to capture them andthen thinking how can I expand that so it's multimodal in some way.
Yeah, that's great.
I love it.
That's so fun.
And I love that it ties back to this history of the notebook thing, because that is verymuch my kind of nerdery.
we're very aligned on that.
(38:00):
fascinating reading all the what the Venetians used to do.
They used to have like five different notebooks and how they would keep these notebooks.
And if you've ever read any of Leonardo da Vinci's notebooks, we used to write right toleft.
So you didn't think anyone would copy his work.
So a lot of things are particularly new.
But what I find so amazing just now is how, especially with generative AI, we're able tohelp that.
(38:25):
even richer, even fuller as a way to be able to do it.
And for me, that's the exciting thing.
And I hope that we go a little bit more towards that because my biggest concern, talkingabout the ethics, is we end up going the same way that we went with social media, where it
had so much optimism, know, when it initially started many of these social medias, and wekind of went to a slightly different place with it.
(38:51):
There probably wasn't, and a lot of people that were starting those businesses didn'treally
envisage.
And that's my thing with AI is like having people like you speaking events, talking aboutthe human side and talking about what are the questions that we're not maybe asking that
we should be asking.
think that I think is a really powerful thing to do.
So you mentioned Notion, are there any other tools or apps that you just findindispensable for the creative work that you do?
(39:18):
Yeah, yeah, mean, the writing obviously takes place across its own set of tools, Scrivenerand Notion and Evernote and so on.
But I'm a big mind mapper.
I love MindNode and I love the ability to sort of sketch out in MindNode, whether it's tojust get at a crude level, you know, sort of like organize, what are the component parts
(39:42):
of this thought?
Let me break them up and see them, you know, visually.
Or in some cases, it's for really trying to catalog something that I'm not as familiarwith.
Like for example, if there's an opposing viewpoint to my own, like if I see someone putout an idea that they're advancing that I think ideologically, philosophically, like we're
(40:06):
not on the same page, but I need to understand what their perspective is.
in order to refute it even, you know, or if I want to have some standing, if we're on adebate panel together or something like that, I want to be able to understand their
perspective.
And so I may just have to take it apart in my note, I find as a really useful tool for meto do that and just plot different aspects of it.
(40:30):
But I use AI a lot as well.
Sorry, go ahead.
that's great.
Someone told me the other day when they write a book, they do the first draft and thesecond draft and then the third draft that they actually show to people and then they also
do another draft.
Well, what they do is they use AI to basically strong man, I guess, against the ideas inthe book and read it from the perspective of someone who is the absolute opposite who is
(40:55):
going to destroy the book and the ideas in the book.
Yeah.
with What Matters Next.
Strawman, I think, is what you're looking for, right?
The idea like here, walking through this, there were a few places where there are people Iname who have different ideas.
Mark and Andresen, for example, holds different philosophies to mine, and they're opposingphilosophies in some respects.
(41:19):
And so, yeah, I would use generative AI sometimes to say,
pretend you're Mark Andreessen and you're reading what I've just written, how would youpush back on this?
And it's so helpful.
It's so helpful.
Because you can think what that might be, what the objections might be.
But when the tools that have actually been trained on lots and lots of documents thatunderstand Mark Andreessen's point of view, know, like that come from a...
(41:46):
come from myriad perspectives of Marc Andreessen's writings over the years, it's justgonna be better.
It's just gonna be that much more able to distill what the pushback is likely to be.
And so it helped me to say either I need to shore those things up or I just need to notcare.
And one way or the other, it doesn't matter which way you choose to go.
(42:07):
It's just very helpful to understand what those objections are likely to be.
So yeah, that's a really helpful tool in the process.
But also just using generative AI, like you described a really wonderful rich process formodeling different types of learning.
I also use it to distill many times across the sort of digital Zettelkasten where I mighthave the...
(42:28):
the physics clips and the economics clips and politics clips and all sorts of things allin one space and have AI kind of summarize.
If you go through and look at all these notes that I've saved, what seemed to be some ofthe common themes that maybe I'm missing, that I seem to be drawn to?
And it might say like, you seem to be drawn to the concept of entropy across all thesedifferent systems.
(42:50):
Like, yeah, that's true.
I am really drawn to that.
Good observation, AI.
So maybe now I'm actively going out and reading about entropy.
more directly so that I understand what it is my mind is instinctively drawn to.
I just think that there's such opportunities, as you say, for us to use these tools inways that just make us better at what we're already doing and take us leaps and bounds,
(43:14):
you know, fields down the road so that we can just do what we're doing and do it much moreeffectively.
Now we're going to have a link to your new book, but I'd also love to know, is there abook you think our listeners should be also checking out, maybe some that's been
influencing you over the course of the past few months, you just kind of been returningto, it's kind of, maybe you reassess an area or got really interested in a particular
(43:40):
area.
There's so many.
I think that in this book, the ones that, these are classics, but the ones that I taped upthe tables of contents of Good to Great and Blue Ocean Strategy as two of the really most
effective business strategy books of the last few decades.
(44:03):
And I wanted to make sure that,
Conceptually, there was a flow that was happening in the book that would bring readersthrough that transformative process of, you know, sort of starting at a more abstract
argument and working your way through to how do you implement, how do you operationalizeand how are we going to take step by step through that?
(44:25):
So for me, those were books I returned to many times.
I just flipped good to great or bluish and strategy open to any page and sort of read aparagraph over it.
and thought, all right, I'm not hitting that level yet.
I need to level up what I'm doing.
And it's funny, because it actually sort of, for me, I know you and I have music in commonand songwriting in common, and it was similar to a process I learned from Pat Patterson,
(44:51):
the really wonderful songwriting instructor at Berkeley University in New England.
He has this idea that if you've written a verse and a chorus,
And then another verse in the chorus that you might look at one of those verses, maybeyour second verse, because the second verse usually is weaker than the first, and you'll
(45:11):
kind of go like, oh shoot.
He usually says something different than oh shoot, but oh shoot.
Like now I've written a better verse and I need to go and write a better second verse.
So it's always whatever is your level that you've hit with one section, your othersections need to level up too.
And that's what I find across my writing.
(45:32):
It's a trick I've learned from my songwriting that if you really hit it out of the park,it's like, shoot, now I just made my job harder across the entire rest of the book.
I have to level the rest of it up.
that's great.
I mean, I think it's, you know, because there was an, I remember there was an Ezra Kleinfrom NPR said something along the lines of, you know, that idea where you're sitting at
(45:54):
one level and then, you have great taste and your craft or your art is not at the level ofyour taste yet.
But there's that constant, that's constantly what you're trying to do.
And I think it's very hard in what you and I do in the world because
There is an artistry towards what we're trying to do, but then there's also very much thebusiness side of it as well.
(46:16):
And one of the hardest things, last night I watched a great movie again, because I wastalking to someone about it called The Big Night with Stanley Tucci about a great meal.
And the movie is basically really about this idea, yeah, this idea like this tension wehave between creating great art, something that's gonna live beyond us and really
influence other people to doing the stuff that pays the bills, as you would say in theStates.
(46:40):
Yes.
And there's that constant tension that we have to do.
And I guess, know, where I see amazing companies sometimes is that they have thisaspiration to create great artistry in what they're doing with the technology just
disappears.
You're not really thinking about the technology, but then they have the execution and thequality of execution and planning and strategy behind that to be able to make that happen.
(47:05):
And that's where it gets really exciting when you see that as well.
Where is the best place for people if they want to learn more about you, want to bring youin to come speak at their event, want to bring you in to consult, where's the best place
for to go and do that?
My website is KOinsights.com.
My company is KOinsights.
KOinsights.com is the best place to find me, my work, the book, other books, and speakinginformation.
(47:30):
I also spend a lot of time on LinkedIn, so I'm Kate O'Neill there, on Blue Sky, KateO'Neill there, and so on, so you just look for me.
There's a lot of Kate O'Neills in the world, but if you search Kate O'Neill tech humaniston Google, you will usually find me.
Well, what matters next?
A leader's guide to making human-friendly tech decisions in a world that's moving too fastis out now.
(47:54):
Kate O'Neill, thank you so much for coming and being a guest on the Super CreativityPodcast.
Hey, thank you, James.
I really appreciate it.
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