October 2, 2024 • 59 mins
Steve Rader is a leader in the thinking behind open innovation, open talent, and the future of work. In this episode of The Evolving Leader podcast, Steve joins Evolving Leader cohosts Jean Gomes and Scott Allender to share how leaders can learn from his important work and use their learnings to solve complex problems. Steve tells us that open innovation and open talent are key strategies for organisations to stay competitive in a rapidly changing world.
References from this episode:
"Co-Intelligence: Living and Working with AI" by Ethan Mollick (April 2024)
RapidMiner (https://altair.com/altair-rapidminer)
Other reading from Jean Gomes and Scott Allender:
Leading In A Non-Linear World (J Gomes, 2023)
The Enneagram of Emotional Intelligence (S Allender, 2023)
Social:
Instagram @evolvingleader
LinkedIn The Evolving Leader Podcast
Twitter @Evolving_Leader
YouTube @evolvingleader
The Evolving Leader is researched, written and presented by Jean Gomes and Scott Allender with production by Phil Kerby. It is an Outside production.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jean Gomes (00:03):
How will your organization continue to keep up
with the rising pace ofinnovation and disruption being
driven by exponentialtechnologies and the 50 million
startups that appear every yearwith little or no barriers to
entry? Many leaders stillbelieve that their new product
development and partnershipmodels will sustain their long
term growth despite the growingevidence to the contrary. In
(00:27):
this show, we talk to a pioneerin open innovation who has built
the strategic capability ofharnessing external resources,
crowds and communities toaccelerate innovation and growth
in one of the world's mostchallenging systems, space
exploration. Steve radarhighlights that 90% of the
scientists who have ever livedlive today. Think about that for
(00:52):
a moment and its implications.
If your organization isstruggling to own its future,
this is an important Show totune into. The
Scott Allender (01:21):
Hi friends, welcome to the evolving leader
the show born from the beliefthat we need deeper, more
accountable and more humanleadership to confront the
world's biggest challenges. I'mScott Allender.
Jean Gomes (01:32):
And I'm Jean Gomes.
Scott Allender (01:33):
How are you feeling on this Friday? Mr.
Gomes,
Jean Gomes (01:36):
I'm feeling pretty good. The weather is amazing.
I've got a great weekend linedup of rest and renewal and time
friends and family, and sothat's good, and I've got a
couple of really interestingbooks I'm reading. I've just
spent a couple of hours talkingto a pretty remarkable guy about
AI and some of the things thatare happening there in his work.
(01:57):
So that's left me both slightlybewildered but excited. So yeah,
I'm pretty good. How are youfeeling, Scott?
Scott Allender (02:06):
I'm feeling rested and curious today. I'm
feeling a bit playful, and I'mfeeling thrilled to have Steve
Rader on the show today. A fewmonths ago, you and I heard
Steve talk about his work, andit was incredible. And I know we
were both having thoughtsbouncing around in our heads for
(02:28):
days and days following thattalk. And so Steve is a thought
leader in the open innovationspace, working to infuse both
challenge and crowdsourceinnovation approaches in the
teams that he works with he ispassionate about the power of
open collaboration, specificallyas it applies to leveraging
communities and adjacentindustries to drive growth
(02:50):
within a more traditionalorganization. We're delighted
he's here. Steve, welcome to theevolving leader.
Steve Rader (02:56):
Oh, thanks for having me on.
Jean Gomes (02:58):
Steve, great to see you again. Welcome to the show.
How are you feeling today?
Steve Rader (03:02):
Feeling really good, very optimistic. There's a
lot of good stuff going on, alot of bizarre things going on,
but I think, generally, reallygood. Yeah.
Scott Allender (03:13):
So Steve, you've spent a big, big portion of your
career working in the innovationspace. It's a word that I hear
used a lot, but I don't, notsure it's universally understood
in the same way. So let's saywe're at a dinner party with a
group of people who know nothingabout innovation. How do you
(03:36):
answer when your guest asks,Steve, what do you do?
Steve Rader (03:39):
Yeah, I get that a lot, and it's, it's a, I'm not
sure I give very satisfyinganswers it. You know, innovation
is taking the best tools andcapabilities that we have in
solving hard problems and bysolving problems, that includes
just making things better,right? So it's not just the
(04:00):
problems that you're like, oh mygosh, I've got to solve this.
It's getting from where we areto where we can be. A lot of my
time has been spent at NASA inthe space industry. And, you
know, we can get into space, butwe need to go farther, right? So
it's, how do you make thingseven lighter and more reliable
and things like that. So I thinkin the world we have we're not
(04:23):
going to run out of any problemsanytime soon, but a lot of that
is, how do we do things saferand better? And the innovation
comes when you take these newtechnologies or tools or new
ideas or new approaches and andmake those work for you, to make
those problems go away, or makethose make the progress on
(04:45):
those, those issues and thosethings that that we really need
to do.
Jean Gomes (04:49):
And in this show, we want to focus on something where
you've been at the vanguard ofinnovation and a particular type
of innovation. And we wouldreally love to just start.
Getting a deeper understandingof what this term open
innovation means, because youdid start it at NASA, and you
got to shepherd the organizationthrough its adoption and do some
pretty remarkable things here,but we stand back and understand
(05:11):
what it is. Yeah,
Steve Rader (05:13):
yeah. I was introduced to open innovation
back in 2010 when I read JeffHowe's book on it, which is
really just coming into being inthe early 2000s and open
innovation is is crowdsourcingand that. You see it around in
lots of different forms. You seeit in crowdfunding, with, you
(05:34):
know, Kickstarter and Indiegogo.
You see it in sites likeWikipedia, where people in the
public can come and contributeand be part of that community
that has now created the, youknow, the largest encyclopedia
in the world, right? We don'teven have any competitors to
that anymore, but it'sspecifically in the innovation
(05:56):
space. Is where we use contestslargely to reach out to many,
many people to try to findsolutions to problems. So we'll
actually post what we call achallenge out there, and often
we'll offer a cash reward. Butthere's actually for
crowdsourcing. There's fourkinds of incentives. People have
(06:19):
gold Guts, glory and good. Wecall them the four G's, right?
And people will actuallyparticipate if they think
they're doing good for theworld, or they some people just
really like a hard problem, andother people are trying to build
a reputation that they can thenuse to go make money somewhere
else. And then some people justinterested in the prize. And
(06:39):
there's a lot of what we callcurated communities out there
now, which has made it reallyinteresting. The internet really
enabled platforms to be builtthat could gather people around.
So, you know, Wikipedia is agreat example of that, but there
are over 700 different platformsout there where they've stood up
(07:00):
a website, they've startedinviting people in, and often
they'll invite them aroundcommunity, connecting with other
people that have that passion.
So TopCoder is this 1.8 millionsoftware developers and data
scientists, and they they jointhat community to connect with
(07:20):
other people who have thatpassion and to learn. And then
these challenges get layered ontop of that as a way to learn
even more, start to build areputation, start to have an
impact. And so there's this kindof lattice work of these
communities that do differentkinds of problem solving.
GrabCAD, for instance, is run byStratasys out of Estonian, and
(07:46):
it's, I think they're up to 11or 12 million mechanical
engineers and designers on asingle platform, and they're
there to trade CAD models and 3dmodels, but they will run
contests, and they'll runchallenges to help that
community kind of try out theirskills against each other and
(08:08):
see see what they can come upwith. And we get amazing work
out of them. It's amazing. Someof the designs that you get on
we actually had when NASA did,when they had a Nobel Prize
winning scientist that ran acontest on there for $10,000 to
build these starshades, to kindof find planets, ended up with
(08:31):
50 or 60 amazing designs, notsimple designs, very complex
designs. And I think from thegovernment side. It's a great
way to involve the public. Butwhat it really comes down to is
we live in a world that isreally, really complicated, and
(08:51):
we're evolving to a pace ofchange that is really hard to
keep up with, and I call it thetsunami of technology that has
resulted in my talks, I talkabout, since the population's
done this, the number ofscientists and engineers,
especially now that theircountries are more wealthy and
(09:14):
there's a lot more education outthere, the number of scientists
has just grown exponentially,kind of with the population, to
the point that right now, 90% ofall scientists that have ever
lived are estimated to be alive,right? Everyone who created the
(09:35):
science and technology that weall take for granted, you know,
starting with like Socrates andworking your way up to Einstein
and the folks that lived in thiseven last century, there's nine
times as many people as all ofthem put together, all working
with new technologies likeblockchain and advanced
software, APIs and machinelearning and 3d printing and
(09:57):
nano material and and all of.
These are working acrossmultiple industries
simultaneously, and so if you'rein an industry and you're trying
to solve problems and innovate,you have this kind of unlimited
buffet of new technologies. Theproblem is you can't find it
like not posted on the internet,like people think you can just
(10:19):
google anything, no newtechnologies, especially
solutions that grow and maturein one industry, they remain
kind of hidden because they areproprietary, or they have jargon
around the the problem they'resolving. You know, if you go to
John Deere right now, they'reone of the world leaders in
automation and machine learningkind of weird farm equipment,
(10:42):
but they do. But if you were tosee some of their most advanced
work, you might go, Well, that'sthat's not really applicable to
over here in say, aerospace, butsomeone who understands their
problems and their nomenclaturesand what they're doing, and they
understand what you're trying todo over nearest they can
actually connect the dots andsay, no, no, this new thing
(11:03):
they're doing, if we just dothese mods to it, can be our new
solution that can give us a 5x10x improvement in what we're
doing. And this is where openinnovation comes in, because if
you're in your industry, youcan't see that you can't
understand those problems, thosesolutions. You don't know it's
out there. You see these thingslike blockchain and
(11:25):
nanomaterial, but you don't knowthat it's actually getting
combined with other solutions.
And so crowdsourcing and openinnovation is this way that you
can kind of cast a statisticaldragnet across all the
industries and all that world.
And this is where it kind ofwarps, warps the mind a little
(11:48):
bit, because humans, we aren'tgreat at large numbers, right?
But some of the communities youknow are millions of people, and
we don't reach millions ofpeople when we run a campaign,
but a fraction of many, manypeople is still many people. And
so I actually use stadiums,pictures of stadiums when I do
(12:10):
talks, because they say, youknow, Texas, A and M stadium
happens in Texas, happens to bea good one to use, because its
capacity is like 102,000 people.
And I said, here's a picture ofme at a game looking at 100,000
people. And whenever you go tothose events, it kind of blows
(12:30):
your mind a little bit like ifyou're if you're nerdy like me,
you look around and you think,how many doctors Am I looking
at? How many, how many topscientists? Am I looking at how
many, you know, innovators, amI? And the thing is, anytime you
got 100,000 people, there,statistically, are a little bit
(12:51):
of everyone. But when you startto look at these crowds, there
are multiples of that. You know,11 million people in GrabCAD.
That's 110 of those stadiumslike that's that's some
significant numbers. And so whenyou start to think about that
statistics, someone that canconnect the dots goes way up,
(13:14):
compared to me trying to sit inmy cubicle with my team. Even if
I have some of the world'sexperts, they're never going to
be able to connect the dots tothose other things. And this is
where open innovation becomes.
This, in my mind, anindispensable tool these days.
It's not even an option anymore.
You have to find the time to goout to the crowd sometime, if
(13:37):
you're just doing everythinginternal, you probably are going
to fall behind. So I'm
Scott Allender (13:52):
thinking about leaders listening right now, who
are thinking, Well, I'm notgonna be able to run a contest
in my organization, right? Sohow can I leverage the crowd,
and really do it in a way thatis truly innovative and builds
on solving the problem versuswhat I see a lot of which is,
let me look externally, findwhat I call a quote, unquote,
best practice, and try tooverlay that onto my problem,
(14:13):
and then it doesn't work, right,
Steve Rader (14:15):
right? Here's the thing, it's a super effective
tool. It does take some learningand some investment to go out
and find it. A lot of peoplefind it's really hard to get
over the idea that your expertscan somehow have people that
aren't the experts help, right?
That there's ego there. There'sa little bit. And so one of the
(14:38):
first things is maybe do aninternal crowd right, where you
actually can get a platformwhere you say, hey, look, we've
got several 1000 people in ourcompany. Let's at least go
outside our individual silos tomake sure that we're harnessing
the power of the group we havethat gets people a little bit
used to that. Now you have to bespecific. With the challenges
(14:59):
you put out there, you can't dothese general give us all your
ideas and make it a suggestionthat will create a disaster. You
have to have the challengeowner, the person that owns the
problem in your organization andcan do something with the
solution. They have to be theowner. And then you can ask,
does anyone have ideas if you'regoing to go outside, really,
(15:21):
it's what are you trying to do?
Because there's really differentkinds of crowds. You know, I
mentioned TopCoder and GrabCAD.
There's herox that does a bunchof innovation challenges.
There's Kaggle and driven datathat do algorithm contests along
with TopCoder. There are tongilWho does videos, you know, in
(15:42):
this creative work, creativework is great, by the way, I
should just pause on that forone second. If you think about
any kind of work that you dowhere it's a complex kind of,
you know, set of options, andyou might actually ask, like an
artist, give me five renditionsof this. The crowd is a great
(16:03):
way to get lots of different,varied input to help you kind of
narrow that down. So we do a lotin the video and graphics and
things like that. Gen AI isstarting to mess with that a
little bit. So I get it. Butwhen it comes to design, you
know, getting lots of differentdesigns for something,
(16:24):
especially in like actualengineering designs, you start
to get some, some stuff that youcan can really get better
performance, because what you'rereally doing in innovation,
right, failure is an importantcomponent, right? Like you have
to be able to you have to trysomething and fail. Try
something fail. Crowdsourcing issimply taking that in in certain
(16:45):
instances and making itmassively parallel to where you
get 150 people working onsomething. Well, 140 something
of those are going to actuallyfail when you compare the
results in the performance, butthose top five to 10 that
actually outperform the rest,they're they're actually the
(17:09):
ones that give you what youneed. And it's not that that
failure is bad. I tell people,open innovation as a
participant, is one of the onlyplaces in the world, in your
entire life that you can fail,and nobody cares. You can just
do it for the sake of learningand the possibility of what it
(17:31):
might mean. And that's a goodthing, because we learn a lot
from failure. But if you're atschool, you can't fail. You have
to get a good grade. If you'reat work, you can't fail, and
you're just wasting thecompany's money. And they'll
they'll get there'll berepercussions. But in the
challenge world, in your owntime, you can actually fail. We
actually had had an experienceonce where a lead researcher in
(17:54):
this area participated in achallenge, and she ended up
winning, and she said, I'm soglad that that we did this as a
challenge, because my boss, myPI, never would have let me
pursue this idea I had, rightbecause they're trying to get
grant money, and they're tryingto be conservative, and in The
(18:17):
research world, you've got to,you've got to have that
conservancy sometimes. But weknow innovation is about risk
taking and failure, so openinnovation starts to open up
that that idea a little bitmore. I think I got away from
your original question. It was,how do you how do people go
implement a lot of it islearning, learning about
(18:37):
platforms and realizing that alot of those platforms can do
this largely for you, youcontract it to them so they
define the channel. You bringthem your problem, they will
form it into a challenge. Hostit. Tell you how much the prizes
should be. Can work with you onyour budget. One of the things
that NASA did that was kind ofsmart was they created a
(19:01):
contract that had 30 differentcrowdsourcing companies, and
then they could basically say,here's our problem. Compete on
those. And that was a reallygreat construct. It's kind of
harder for some folks to do, butget to know those that you need.
If you need technology searches,you know yet two is a great
company if you're doing a lot ofalgorithm work driven data,
Kaggle Pop Code, or those aregreat companies. If you're just
(19:25):
doing general science with Zokuhero X, you know, and, and you
can start relationships withthose companies and, and they
can help you do it. One of thethings I advise people not to do
is stand up your own website andtry to post it. Post a job, it
is harder than you think, and ittakes skills that you probably
don't have, even the work I'vedone in the past. And that's a
(19:50):
we rarely do stuff direct to thepublic. It's almost always
through these curated crowds,because they've already worked.
Through the barrier of entry.
These folks that haveparticipated have participated
on multiple challenges. TheyIt's not scary to them anymore,
and they still do big adcampaigns to try to bring in
(20:12):
even new people, to try to makesure that that crowd that you're
using has both the depth ofexpertise, if it's a technical
problem, as well as the breadthacross multiple industries,
never a short answer. Sorry,
Jean Gomes (20:26):
what have you learned about helping the
Problem Owner actually set thereally good brief. What are the
kind of mistakes people mightmake and sometimes maybe confuse
a solution with a problemstatement?
Steve Rader (20:38):
I would say the biggest mistake technical people
especially make is not spendingenough time understanding your
problem. You know, what do we doin an organization? If somebody
says we've got this problem,let's work on it. Well, everyone
stands up at the whiteboard andstarts brainstorming. Well,
there's science that says thatthat's the absolute worst thing
you can do, because as soon asone person puts an idea up on
(21:00):
that board, you've eliminatedsomething like 70% of the
original ideas that couldactually have been innovative.
And so there's some techniquesaround that, but we encourage
folks to really take the time tolist out all their assumptions,
list out all their constraints,really understand the
performance parameters. Whereare you today? Where are you
(21:21):
trying to get stakeholders? Ohmy gosh, that's the one thing I
see people forget all the time,is they think, Well, I can just
implement something. Well, ifyou better involve all the
stakeholders, because if it'ssoftware and the IT department's
not involved, they're going totell you why it won't work after
(21:42):
you've spent a lot of moneytrying to get it to work right,
because you didn't know you hadto have some security
certificate, or you had, youknow, that kind of thing becomes
really important. And so whenwe're formulating problems, we
always like to take a lot oftime to find out everything we
can about the problem. And thenif you can find out everything
(22:05):
you can about what exists inyour organization, knowledge
around that, that's where wehave used our internal platform
before to kind of pull to say,Have we already solved this? And
we just don't know? And then youcan actually use technology
searches to search for, arethere products or companies
already out there that I justdon't know about? And often the
(22:25):
company, like yet two or ninesigma is really good at that
kind of work. And then if you'restill not seeing, hey, the kinds
of solutions I need and you oryou really want to be on the
cutting edge, then you can run achallenge to try to find that
really unique innovation, but ittakes more money. Each of those
things is a little more money.
And so if you're trying to beefficient about this first
(22:48):
figure out, if you've alreadysolved it inside you
Scott Allender (23:04):
what's the what's the mindsets that leaders
need to adopt to do this?
Because as you're talkingthrough this, I can almost
imagine that some people aresitting here thinking, This
sounds like a long process,right? I feel a lot of pressure.
Innovation seems like it'sdistracting from or detracting
from the day to day pressures ofme delivering now. So what's the
sort of shift, and do you see alot of resistance when you go
(23:27):
into organizations where peopleare like, this sounds great,
Steve, but it's not going towork here.
Steve Rader (23:33):
Yeah, lots of resistance. There's Harvard
Business scale school casestudies, one on Victors and
spoils and Havas, where, youknow, John Windsor took his
crowdsourced advertising andtried to plug it in to a
traditional, large scalemarketing and advertising
company. And literally, youknow, he's like the technology
(23:57):
head of the company, and as hewalks down the hall of the
headquarters, people are closingtheir doors because they didn't
want to talk to them. Becausefor them, advertising was this
experience that they had sloggedtheir way through, and the crowd
could never be a thing, right?
There's a great case study aboutNASA actually called Houston. We
have a problem where NASAinitially rejected it, because
if you come to an organization,you feel like you're there to be
(24:20):
the innovator. Why would youever because you also find out
when you get there that you onlyget to do that 1% of the time,
right? It only comes along everyfew years, and then you're
getting to do that, which, bythe way, you have to change your
messaging to say, Look, you arethe innovator, but you need the
absolute best, most up to datestarting point to innovate, and
(24:43):
that's what crowdsourcing bringsyou, is those ideas, expertise,
technologies, that you justdon't know about, and then you
have to assemble them into asolution that is the new cutting
edge, I would say, the big.
Thing for CEOs and heads oforganizations is ambidexterity,
(25:05):
right? So Michael tushman at HBShas some great literature on
that. I think there's a book outon this idea of balancing two
competing priorities in yourorganization. So one is the
exploitation, right? You've gotto go make money. You've got to
be producing something thatthere's no doubt about, that.
You've got to keep the machinegoing. And oftentimes that's
(25:30):
that's the result of someearlier innovation, right? That
really produced a product andmade value. But there's this
other piece that is thestrategic investment, and you've
got to strategic investment atits core runs sideways to the
exploitation, to the moneymaking, right? If you're
(25:51):
spending money trying toinnovate, there's no profit
today. There's profit in fiveyears or 10 years. It depends
on, you know, how big your scaleis. But if you don't actually
make this investment, if youdon't actually make that a
priority, then in five years,this product is no longer valid,
and you have nothing to replaceit with. So that in you know
(26:13):
this, this strategic investmentcan't be chasing all the flash,
right? So it's got to begrounded in real performance and
real understanding of whatproblem you're trying to solve.
And right now, it's very that'sa difficult conundrum, right?
Because there are a tsunami oftechnology you know, just just
(26:38):
evaluating the impact of 3dprinting is a task. If you have
big manufacturing base, justlooking at nano materials, if
you've got some really complexsystems, is a thing quantum in
quantum computing, networkingand quantum sensing, they're all
about to change everything insome complex systems. And so one
(27:00):
of the big organizationalproblems right now is that the
complexity of the technology isrequiring, and the change, the
rapid change in it is requiringorganizations to have to access
expertise that they don't have.
And that the this is, this goesto a whole area that I talk a
lot about, which is open talent,which is kind of the flip side
of open innovation, which is, ifcompanies have this problem, and
(27:24):
almost every company does, wherethey don't have all the
expertise they need, the recruitand retain strategy that we've
had for 100 years, recruit allthe best people and retain them
and get them going on all Yourstuff that starts to break,
because as things change fasterand faster and starts to
fragment the expertise you can'thire your way out of that you
(27:47):
can't hire fast enough becauseyou don't have a way to exit
people, which you know you own.
There's a lot of companies rightnow where you go and talk to
them, they're like, yes, we havea skills shortage and a hiring
freeze. Well, that's becauseyou're trying to hire your way
(28:09):
out of this. And so this, thisbig shift that happens to have
been coming since about 2015where, kind of by happenstance.
I can't figure out exactly whyit started happening, but people
started moving to the gigeconomy. Well, that happens to
be a really convenient thing fororganizations now, because
there, there's a lot of people,and a lot of people just think
(28:31):
about Uber drivers and, youknow, food delivery. But if you
look at the full landscape ofwhat's happening out in
independent work, most of thetop experts are now freelance.
They see that they can actuallymake a better living kind of
farming out their skills to thecompanies they need them. And
there's NDAs and ways to protectall of the fears that companies
(28:55):
have about sharing theirinformation that's actually
getting easier to deal with. Butwhat you're finding is companies
are having to now hybridizetheir expertise, because even
the best companies can't hireall the experts they need. And
it's both they can't find them,find people willing to move to
your to your city and to workfull time, remote work in the
(29:20):
pandemic really showed people,Hey, there's another there's
another path here. They love theagency of it. They love the fact
that it's not beholden to asingle industry that might have
ups and downs. And so there'sthis kind of new work concept
that's out there. And if youtalk to a lot of startups, this
is what they do, native. Theydon't go hire a staff of 10
(29:42):
people. They go hirefreelancers, and then they get
to know them, and that theystill work with them as a team.
It's not a one off all everytime. But they don't own them,
right? They don't own all oftheir time, and that's great for
a startup, because yourmarketing person. You don't need
a full time. You need somebody athird time. And the fact that
(30:03):
they can make a living makesthem a more stable team member,
not a less stable and then it's,it's you still have to bring
them into your team. So this isa thing I've been working with
open assembly. John Windsor, whoI mentioned earlier, is the
founder of that, and it's awhole group where they're just
talking with the communitiesthat supply talent, along with
(30:24):
the enterprises that are tryingto adapt and use talent. And
it's it is every time I thinkthey're two different things of
open innovation and open talent,I realize, no, it's all crowds
of hundreds of 1000s or millionsof people that are being used to
match a need to a capacity,right? Kind of like Uber is
(30:47):
someone over here needs a rideand somebody over here has a car
down the block. Well, I can putthose people together and meet
needs for both, right? The samekind of thing in these
platforms. I've got thisexpertise in quantum sensing.
Somebody needs that for twoweeks, two months, two years. I
put them together, right? Andthe most exciting part of this,
(31:10):
this is brand new, is AI matchedhigh performing teams. So
there's a new company calledTeam lift that has already
started doing this in terms ofbeing able to take a roster of
people, match them into small,high performing teams based on
their skill sets and personalitytraits. But you can imagine
(31:33):
taking your problem to a crowdof the future, and they maybe
have half a million peoplethere? Well, they can stand up
50 high performing teams eachthat has a task to take two
hours and decompose your problemor two hours and come up with
new ideas, like all in parablein parallel, all kind of
(31:55):
competing with each other, andorchestrate that into an entire
product development cycle. Sothere's some really interesting
stuff. And I would say now eachof those teams also trained and
equipped on using generative AI.
So, you know, being able tooperate lightning fast. So it
gets to be really exciting,terrifying in some ways, by the
(32:16):
way, because everything'schanging. But
Jean Gomes (32:19):
yeah, well, it kind of challenges the fundamental
notions of what a company is andhow it operates at a business
model level. And it kind of sumsup my mind this idea that a
company could become like abrokerage between problems and
talent and resources, which iskind of what you're describing
there. So if you take a stepback for a moment and go, right,
(32:42):
I'm listening to all of this,and as a CEO or as a finance
director, or somebody who's kindof thinking, this is really
exciting, can you kind of walkus through how you would start
in this process, beyond just thetournaments or the challenges,
and how that might play out overseveral years from starting to
(33:02):
build something to the systemthat you might create, yeah,
Steve Rader (33:06):
what I recommend is really putting together kind of
a center of excellence, right?
So a core team that one goes outand learns all of the piece part
learns, you know, there's somegreat books like platform
revolution, which is almost atextbook on how to stand up a
curated crowd. There's, I thinkit's platform computer, no
(33:28):
machine, platform crowd. Greatbook. Also is a primer open
talent John's books, reallygood, competing in the age of
AI, so just kind of doing thehomework of what this is, and
then running pilots right. Havea pilot on putting together an
(33:49):
internal crowd, simultaneouslyrunning your first tech search
and your first challenge to seewhat does that look like, how
does that feel? And then reallytaking stock of your technology
portfolio management, right? Andtrying to start to understand
what that ambidexterity mightlook like, so that you
(34:10):
understand what you have tocarve out, because this does
doesn't come for free, butremarkably open innovation
tools, when you compare themwith using just traditional go
out and get a consultant, thingslike that. At NASA, they were
seeing 75% of the projects hadcost savings, and the average
cost savings was 50% sosignificant kinds of savings can
(34:34):
actually be had, but you've gotto have the program. But just
start piloting these pieces andsee which ones fit best. You
know, if you're software centriccompany, what's the right? You
know, if you have highperforming software teams, you
don't need to bring crowds intothat. They're doing great. But
if you're struggling, andthere's some areas where you
(34:56):
know you need expertise in.
Languages or hardware that youdon't have. Well, that might be
a really good place to say, oh,let's find a crowd that can
actually help us do that, orlet's find open talent to bring
in and help our team come up tospeed. But really trying to look
at the landscape of what am Itrying to do as a company, and
(35:18):
where's technology a threat forme. So where do I need good
surveillance and good kind ofnew ideas? And then in what kind
of investment can I? Can I makeand putting the right framework
around that, so that the teamthe Center of Excellence really
knows how far you want to go? Inmy opinion, open methods are
(35:40):
kind of a core skill thattechnology and tech folks need.
So if you have a techorganization training people all
around the organization, notevery single person, but enough
people that you've got someonewho's looking at what's going
on, and when they see a problemor they see a risk, they say,
you know, that's a goodcandidate. Let's this is the
(36:01):
right tool to go do that. I tellpeople, you'll get really
excited about open innovation,sometimes, none more than me,
but it's not a tool foreverything, and it's not a tool
for all the time. It is fit forcertain phases of product
development in certain phases ofcreativity. So it's a tool you
need, but you need to find outwho needs it when, and that's
(36:23):
where the center of excellencecan kind of be a broker and an
orchestrator of those, so thatthey're working with various
folks around the organization tofigure out when the right need
is to get their buy in fromthose problem owners, so that
that they actually buy in.
Because you don't get that,they'll never implement what you
bring them, right? So, kind ofworking that aspect and just
(36:44):
just created along the way. Butyou have to start slow, and it
has to, you know, have you haveto have people that know, big
mistakes people make is they'llput their marketing or HR folks
in charge of over their openinnovation program with no
training. And you know they'llthere are some, I will tell
(37:05):
people. And I think you guysprobably know this, innovation
is the most dangerous initiativeyou can partake in in a company.
It's the most crucial. But ifyou're trying to tell your
organization, we need toinnovate and you're not serious
about enabling innovation, itwill backfire on your entire
(37:28):
organization. Will become thisDilbert cartoon where they just
roll your eyes every time yousay you want innovation, because
they'll try to bring it to youin lots of different ways. And
if you don't have a frameworkwhere you have the strategic
investment ready, they basicallywill see you saying, well, we
can't do that. And it's like,well, you want to innovate, but
(37:48):
you're not willing to do any andover and over organizations. And
you know, we call thatinnovation theater, right? We
see it all the time. And I thinkopen innovation is no different.
It's a hard tool to just pick upand go use now, you can find
people who are experts in this.
(38:08):
This model has been done acrossProcter and Gamble and General
Mills, and there's a ton ofcompanies that use this. They
just don't talk about it becauseit's their competitive edge,
right? And I think we're moreand more seeing some of the
startups and some of the early,the younger folks, they just,
they know it's out there, andthey use it as if it's, you
(38:31):
know, yeah, of course, use this.
This is just what you do. Youknow, you need a graphic, go out
and run a quick contest, or useGen AI, or, you know, you need a
piece of software. I don't wantto go hire a bunch of software
people. I'm just going to spinthat over here to TopCoder to,
you know, see, outsource. Andthat's, I think, you know, it's
a different way of working. AndI think it's something that's
(38:53):
not always easy, but it's, it'snecessary, unfortunately, I
think the other thing is avoidthe black and white thinking of,
I need to hire all freelancers,or I need to do everything in
open innovation, like it's onepiece of what you're already
doing. Like, don't try to upsetthe entire apple card for a long
(39:14):
time. Even though open talent isgoing to be a necessary thing,
it's not going to take all ofyour your full time employees,
right? It's you still need that.
What I tell people is things arechanging so fast. If you really
want to get on board, upskillingis your, is your the thing you
(39:35):
need most, and why you have tobring in experts is because it's
really hard to upskill internallet your workforce go
participate, train them on whatopen innovation is, and open
talent. Let them spend 5% oftheir time doing these things,
learning something new with aplan to tell you how it's going
(39:55):
to help you bring back a valueto the company and say, Hey, I'm
going to let you do this. You.
To keep any profit you make, anyprize you win, right cash
incentives for them to go learn.
And then every six months, say,tell me what you learned from
these other industries youworked with, or other things,
and tell us how that's helpingus. It starts to give people a
little bit of agency, a littlebit of visibility into what's
(40:17):
going on, gets them out of thebubble. And it's a free training
program like That's right now,training is everything, and if
you even augment that with alittle bit of Hey, and if you
find a Coursera course, we'llpay for that, you start to get a
workforce that is transitioningto the new technology with
(40:39):
visibility of what's going onout there. And I think that's a
really valuable thing.
Scott Allender (40:45):
If you can, could you share a little bit
about the successes that you andyour team have led through open
innovation? I'd love to hearabout, like, what's your when
your standout sort ofinnovations that have happened
as a result of doing this workand, and, and I'd be curious to
just a bolt on a sort of subquestion around that I'm curious
as a leader, how has leadingthis changed you
Steve Rader (41:10):
so success? You know, NASA's had a lot of
successes over the years. Youknow, Jeff Davis and Jason
Cruzan started that program andand that program has run over
850 projects. They they actuallyrun projects for the other
federal agencies as well.
They've worked with 30 differentother federal agencies, and I
think at any one time now,they're running about 80 to 100
(41:33):
projects. So it's there's a lotto choose from there. Some of
the more fascinating ones thatthat I've seen. There was a
really interesting one on forHomeland Security, on, like,
increasing the accuracy ofscanners, and they spent two and
a half million dollars on thatchallenge. But they the results
(41:55):
they got were like, 98%detection rates, which were just
blew away what they had beenable to do before. There's been
work in the medical areas withthings like kidney disease and
Lyme disease that have beenreally outstanding. Gosh, again,
800 projects, so there's a wholelot. One of the most fun ones I
(42:19):
ever did was one called Spacepoop. Turns out, in space, you
got to handle some things. Andwhen the Orion spacecraft was
going to do that burn to themoon, you know, and it gets rid
of its last big rocket that'sjust stuck with the service
module, well, once you make thatburn, you're not coming back for
(42:39):
four to six days, right? So yougot to go all the way around the
moon and back, and if at thatpoint, the cabin gets a leak,
you have to get back into youryour pressurization suit and
live there for six days. Andthat, you know, if you've ever
had a kid with diaper ash, youknow, within hours, that can be
a really painful thing. Well,within days, it can be a really
(43:03):
dangerous thing. So thatchallenge was to look at, how
could you do this? It was reallyinteresting that the winner of
that challenge was a flightsurgeon out of San Antonio for
the Air Force, and he didn'tactually do design drive. He
just, like, did these sketches,but he built these prototypes.
And one of the things he builtwas a little prototype of a
(43:24):
little airlock. He said, In inlaparoscopic surgery, we inflate
the belly to like, 15 psi,right, which is about the same
as the pressure differential ona space, a little bit more,
actually, and they insert thislittle airlock into the abdomen
so that they can pass wipes andlittle instruments and and,
(43:44):
well, that's exactly what youneed in this case of trying to
deal with Facebook. What'sinteresting is herox Did that
challenge. They had 20,000registrants and 5000
submissions, and they actuallyin the contracts NASA had with
them, they they had to actuallyonly provide for judging
(44:06):
submissions that were met alltheir criteria. Well, there were
only 87 out of those 5000 soherox actually had to call those
down to 87 but it wasfascinating. Well, I
Scott Allender (44:22):
think you've just given us a title for this
episode, so that's great.
Steve Rader (44:28):
Yeah, it's funny. I actually, in my early part of my
career at NASA, one of the firstjobs I had was as a flight
controller for the life supportsystem, and one of the first
assignments they gave me was,okay, the main thing you're
responsible for, it is the wastemanagement system. So I very
quickly had to learn how to tobe comfortable with talking
about human waste to my familyand friends who are like, Oh,
(44:51):
you work at NASA. I'm like,yeah. They're like, what do you
work on? I'm like, wow, butspace, that's one of the hard
things about. Space as you go,it's
Scott Allender (45:00):
real deal with all. Yeah, yeah. We
Jean Gomes (45:04):
talked a little bit about AI and how it's changing
things. How do you think this isgoing to play out into open
innovation over the coming Yeah,five or six years,
Steve Rader (45:13):
I spent a lot of my time talking to academia about
that, because we're actuallytrying to study what the
crossovers are and what theimpacts are, I think what it's
basically going to be is, ifyou're not monitoring Gen AI,
then you may as well just packup, because it's changing
everything. But I think for thetime being, it's going to
(45:38):
provide better and betterresults, right? Because all of
the folks out there that are inthe crowd, a good chunk of them
are going to start usinggenerative AI to increase their
performance, and that meanswe're going to get better
submissions, and it can kind ofreally condense the timeline of
what they can find, what theycan produce. And so I think
(46:01):
you'll, you'll see increasing,uh, quality of submissions as we
get better and better. There'sit depends on the area. Open
Innovation can be used, like, Isay, for for multimedia, for
technical challenges, forprototypes all the way through.
And so the things we think willdrop off first are, if they
haven't already, or graphic andmultimedia, right? We're just
(46:25):
watching and the different kindsof challenges. Again, I think
it'll you'll see a steadyincrease. I do think that the
big gear change for openinnovation is going to be when
we get the capacity toorchestrate high performing
teams rapidly. Because highperforming teams, where each
team member is trained on usinggenerative AI, they're going to
(46:46):
be able to tap into that uniquehuman creativity that generative
AI is going to take a long time.
I mean, generative AI isliterally predicting the next
word to say it is not beingcreative. It is finding the
creativity out there that wemost have tapped into and giving
it back to you. So it's not asgreat on finding things at the
edges, because they don'tstatistically. You know,
(47:08):
extracting that statistically ishard, and so I do think once you
start getting high performingteams really using generative AI
to augment, you can get somereally fantastic result results.
Right now, open innovation isactually only good for narrow
problems, because you're askingindividuals, unless you well,
(47:29):
you can have higher and higherprize, price prizes, right? A
million dollar prize, and you'llget a team to assemble. But if
you're doing $100,000 prizes,kind of harder to get a team to
form. Well, that means you don'thave all the disciplines to do a
complex problem, so it has to benarrowed down to a piece of a
problem with these highperforming teams. Now you can
(47:51):
put teams that have the entiredesign team, that have an
electrician or an electricalengineer and a chemist, and, you
know, all the people necessaryfor that team to really build
something more complex. And Ithink that's going to be a key
thing. Now, there may be a timewhen generative AI, you know,
just gets so good and so cheapthat that's what you go to. I
(48:16):
think we're a ways away there.
In so it'll it'll be a path, butyou definitely want to be
watching both of those things,because they're both really
important.
Scott Allender (48:30):
What else should we be asking you Steve in the
time that we have left?
Steve Rader (48:36):
I want to go back a little bit to the open talent
thing, because when I firststarted working all this, I was
seeing a lot of new technologyand a lot of automation and Gen
AI. And the first thing it goesthrough your chest, really, not
even through your mind, butthrough your chest, is, oh my
gosh, everyone's gonna losetheir job. And what do people
(48:58):
do? Right? And what was reallyinteresting is a couple years
into that, I started seeing thisopen talent movement, where
people were moving into kind ofworking through platforms to
access global need in that newmatching. And what I found there
was some real hope, becausepeople have agency. They're
(49:20):
encouraged to do lifelonglearning, which is ultimately
the secret to the adaptation tothese new tools. Because the new
tools, we're not going to runout of problems like in fact,
they create some new problems,right? And we have to go solve
those, but it does up the game,right? So I tell people when
they made Disney's Snow Whiteright back in it's like one of
(49:45):
the first ones they did rightback in the 40s. They had
something like 250 animatorswork for like two years to make
2 million different frames at.
That had to be pieced together,and most people agree that a
team of about three to fivecould recreate that in a few
(50:05):
days now with the technologieswe have. And so you'd think,
wow, filmmaking super cheap now,but it's not, it's it's much
more expensive. But what do wedo? You if you look at Iron Man
three, right? That is actuallyan animated film. Looks totally
(50:25):
real, but it's animated. And theanimations we're able to do are
super high fidelity. They looklike reality, but it took
something like 3500 animators todo that. So when we get tools
that allow us to do thingsfaster, it doesn't necessarily
mean we just get rid of everyonewho who's not keeping up we do
(50:46):
harder things. And that's kindof where the hope comes, for me
is we have more accessibility toeducation than ever before. I
think education is going tochange drastically and and you
know, especially now withgenerative AI tutors. I don't
know if you've started to watchthis, but tutoring is a
massively useful tool ineducation, and there are now
(51:09):
generative AI tutors. Andinterestingly enough, people
learn better when they don'thave a human, frustrated
teacher, right, where there'snot somebody who's like, why
aren't you getting this right?
So it's kind of a good thing inthat regard. Now there's
(51:31):
cognitive offloading that worksthe opposite effect there, but,
but if people start to actuallytrain and learn and start to
find those, those jobs that theyneed. Through these new
platforms, you start to get areally malleable workforce that
can keep up with that cuttingedge and that can get
(51:52):
redeployed. And eventually, yes,the hours per work we might
shrink, but if you're, if you'reworking in a freelance
environment where most peopleare kind of working as
independent workers, that thatactually changes in a way that's
easier to adapt, right? Becausethe rates adjust to where you
can still maintain a living wageprovided the supply demand works
(52:14):
out. So gets really interesting.
There's a lot of theory there. Idon't think it's all worked out.
There is a rate issue there, andthis gets kind of nerdy, but at
the same time, there's hopethere. It's not all gloom and
doom and everyone's just goingto be out of a job tomorrow. So
Jean Gomes (52:32):
no, I love that. And I think the notion that there's,
there's kind of, there's hardwork to do, because the
technology kind of pushes you tohave to actually add human
ingenuity at a higher level. Alot of what people do in
organizations now is shufflearound other bits and pieces of
information, which is very
Steve Rader (52:50):
ironic, if you think about it, this new
technology that's that'sbasically doing things that took
us hours and hours, but the onusthen becomes on us to learn how
to use those tools and do more.
It's it. You'd think it justallow us to go sit around
sipping my ties. But turns out,we haven't really oriented
ourselves to a world wherethat's that's the case, right?
(53:15):
So
Sara Deschamps (53:18):
If the conversations we've been having
on the evolving leader havehelped you in any way. Please
head over to Apple podcasts andleave us a rating and review.
Thank you for listening. Nowlet's get back to the
conversation.
Jean Gomes (53:32):
It seems like there might be an inversion of all
that about to take place. Yeah.
What given? Given that? And Iknow you, you, you teach on MBA
programs and so on. What wouldyou say to younger generations
now in terms of the skill setsthey should be thinking about?
Steve Rader (53:47):
Yeah, no doubt.
Establish yourself as a lifelonglearner. Be curious. Learn how
to be curious, learn how tolearn, learn how to learn
quickly. There's so many freetools out there like I think
everyone should go, forinstance, download. I think it's
RapidMiner, which is a freemachine learning tool, and take
the few hours course it takes tolearn how to actually write a
(54:11):
machine learning algorithm. Youliterally only have to
understand statistics, which Iactually don't really, I'm not a
great statistician, and yet Iwas able to do that and learn
how to actually create a machinelearning algorithm. Now, does
that mean I'm gonna go be amachine learning algorithm guy?
No, but the fact that Iunderstand how that works now
helps me to know when I shouldgo build an algorithm, right? So
(54:35):
same with a CAD model, likethere's free CAD models out
there. Learn how to go create aCAD model. Go learn how to do
something new. Go buy a 3d or goto a 3d printing lab. They're
all over these maker spaces. Golearn how to use one and learn
how to print out some goofything. But these new skills are
(54:56):
the building blocks. Yes, andcompanies are going to need
people who know how to use thebuilding blocks of the future.
And that's, you know, go spend$20 to get a month on chatgpt,
not the free version, but theactual four. Oh, and see what
you can do. There's a great bookby a guy out of North,
(55:19):
Northwestern whose name isescaping me, I apologize, but he
basically says, look, go spendthree. Ethan Malik, sorry. Go
spend three or four hours justasking Gen AI questions and
seeing how far you can push it.
You can literally tell it tocreate a PowerPoint deck, or
(55:42):
create it the code for a site,and it'll just go do it, but
you've got to kind of embedyourself with it for three or
four hours and get lost in it toreally start to clue in. Oh,
this is how I need to use thisas an extension of myself to go
do things right and do my work.
So if you're young, there'snever been more opportunity to
(56:05):
plug in and go do things. Goestablish yourself on some of
these freelance sites. Startbuilding a reputation. Do a gig
for practically free just to seewhat it's like. Participate in a
contest. Start doing you know,you can literally the world is
your oyster right now, and it'sall really hard, right? You've
(56:26):
got both of these thingshappening at once. What's really
great about it is most of thegatekeepers are gone. So when it
comes to equity, as long as longas people can get in onto the
digital and there's still divideon equity, on digital platforms
and the rest, but if you can getonline, man, there is a ton of
(56:47):
opportunity there for peoplefrom every country, culture,
walk of life. The most excitingto me is some of the folks that
are on the spectrum that couldnever make it through an
interview now have a chance ofhaving a really rich life and
making a living, because theycan actually a lot of people on
(57:10):
the spectrum are really good atlike, math and some data stuff.
Well, now they can get thosejobs and do stuff so people with
severe disabilities, it juststarts opening up the job market
in ways that the interviewprocess and the whole people
factor kind of, you know,created along with in person,
doesn't have to all go away,but, but it is this new world we
(57:34):
live in where we really haveaccess to some increasingly
special resources that we didn'thave access to before.
Jean Gomes (57:44):
That's really helpful. And give Scott nice
some homework as well. I think
Scott Allender (57:49):
Steve, how can people connect with you if they
want to learn more? Bring youinto their organization to help
them out. Yeah,
Steve Rader (57:55):
LinkedIn is usually the easiest way, because you
just connect to me, and then shecan message me, I'm out there,
you should put Steve Rader.
Usually, that will get to methat's not finding me the word,
sometimes that'll hit me, butjust shoot me a message, and I'm
happy to I have a side gig,approved side gig at NASA. So
today, for instance, I'm notrepresenting NASA. I'm
(58:17):
representing my my crowdresources consulting, but I I do
talks for organizations toreally, kind of explain this
stuff and kind of get themmotivated around it. I do
consulting where I'll come inpretty light, kind of to help
folks get oriented on this. Andthen sometimes I'll do workshops
or teaching or lecturing,excellent
Scott Allender (58:40):
well, we'll put all that in the show notes. And
thank you so much for joining ustoday and sharing all of your
amazing insights. Super, superuseful, really good stuff.
Steve Rader (58:49):
Thanks so much. You guys been great.
Scott Allender (58:52):
And folks, until next time, remember the world is
evolving. Are you?
How will your organization continue to keep up
with the rising pace ofinnovation and disruption being
driven by exponentialtechnologies and the 50 million
startups that appear every yearwith little or no barriers to
entry? Many leaders stillbelieve that their new product
development and partnershipmodels will sustain their long
term growth despite the growingevidence to the contrary. In
(00:27):
this show, we talk to a pioneerin open innovation who has built
the strategic capability ofharnessing external resources,
crowds and communities toaccelerate innovation and growth
in one of the world's mostchallenging systems, space
exploration. Steve radarhighlights that 90% of the
scientists who have ever livedlive today. Think about that for
(00:52):
a moment and its implications.
If your organization isstruggling to own its future,
this is an important Show totune into. The
Scott Allender (01:21):
Hi friends, welcome to the evolving leader
the show born from the beliefthat we need deeper, more
accountable and more humanleadership to confront the
world's biggest challenges. I'mScott Allender.
Jean Gomes (01:32):
And I'm Jean Gomes.
Scott Allender (01:33):
How are you feeling on this Friday? Mr.
Gomes,
Jean Gomes (01:36):
I'm feeling pretty good. The weather is amazing.
I've got a great weekend linedup of rest and renewal and time
friends and family, and sothat's good, and I've got a
couple of really interestingbooks I'm reading. I've just
spent a couple of hours talkingto a pretty remarkable guy about
AI and some of the things thatare happening there in his work.
(01:57):
So that's left me both slightlybewildered but excited. So yeah,
I'm pretty good. How are youfeeling, Scott?
Scott Allender (02:06):
I'm feeling rested and curious today. I'm
feeling a bit playful, and I'mfeeling thrilled to have Steve
Rader on the show today. A fewmonths ago, you and I heard
Steve talk about his work, andit was incredible. And I know we
were both having thoughtsbouncing around in our heads for
(02:28):
days and days following thattalk. And so Steve is a thought
leader in the open innovationspace, working to infuse both
challenge and crowdsourceinnovation approaches in the
teams that he works with he ispassionate about the power of
open collaboration, specificallyas it applies to leveraging
communities and adjacentindustries to drive growth
(02:50):
within a more traditionalorganization. We're delighted
he's here. Steve, welcome to theevolving leader.
Steve Rader (02:56):
Oh, thanks for having me on.
Jean Gomes (02:58):
Steve, great to see you again. Welcome to the show.
How are you feeling today?
Steve Rader (03:02):
Feeling really good, very optimistic. There's a
lot of good stuff going on, alot of bizarre things going on,
but I think, generally, reallygood. Yeah.
Scott Allender (03:13):
So Steve, you've spent a big, big portion of your
career working in the innovationspace. It's a word that I hear
used a lot, but I don't, notsure it's universally understood
in the same way. So let's saywe're at a dinner party with a
group of people who know nothingabout innovation. How do you
(03:36):
answer when your guest asks,Steve, what do you do?
Steve Rader (03:39):
Yeah, I get that a lot, and it's, it's a, I'm not
sure I give very satisfyinganswers it. You know, innovation
is taking the best tools andcapabilities that we have in
solving hard problems and bysolving problems, that includes
just making things better,right? So it's not just the
(04:00):
problems that you're like, oh mygosh, I've got to solve this.
It's getting from where we areto where we can be. A lot of my
time has been spent at NASA inthe space industry. And, you
know, we can get into space, butwe need to go farther, right? So
it's, how do you make thingseven lighter and more reliable
and things like that. So I thinkin the world we have we're not
(04:23):
going to run out of any problemsanytime soon, but a lot of that
is, how do we do things saferand better? And the innovation
comes when you take these newtechnologies or tools or new
ideas or new approaches and andmake those work for you, to make
those problems go away, or makethose make the progress on
(04:45):
those, those issues and thosethings that that we really need
to do.
Jean Gomes (04:49):
And in this show, we want to focus on something where
you've been at the vanguard ofinnovation and a particular type
of innovation. And we wouldreally love to just start.
Getting a deeper understandingof what this term open
innovation means, because youdid start it at NASA, and you
got to shepherd the organizationthrough its adoption and do some
pretty remarkable things here,but we stand back and understand
(05:11):
what it is. Yeah,
Steve Rader (05:13):
yeah. I was introduced to open innovation
back in 2010 when I read JeffHowe's book on it, which is
really just coming into being inthe early 2000s and open
innovation is is crowdsourcingand that. You see it around in
lots of different forms. You seeit in crowdfunding, with, you
(05:34):
know, Kickstarter and Indiegogo.
You see it in sites likeWikipedia, where people in the
public can come and contributeand be part of that community
that has now created the, youknow, the largest encyclopedia
in the world, right? We don'teven have any competitors to
that anymore, but it'sspecifically in the innovation
(05:56):
space. Is where we use contestslargely to reach out to many,
many people to try to findsolutions to problems. So we'll
actually post what we call achallenge out there, and often
we'll offer a cash reward. Butthere's actually for
crowdsourcing. There's fourkinds of incentives. People have
(06:19):
gold Guts, glory and good. Wecall them the four G's, right?
And people will actuallyparticipate if they think
they're doing good for theworld, or they some people just
really like a hard problem, andother people are trying to build
a reputation that they can thenuse to go make money somewhere
else. And then some people justinterested in the prize. And
(06:39):
there's a lot of what we callcurated communities out there
now, which has made it reallyinteresting. The internet really
enabled platforms to be builtthat could gather people around.
So, you know, Wikipedia is agreat example of that, but there
are over 700 different platformsout there where they've stood up
(07:00):
a website, they've startedinviting people in, and often
they'll invite them aroundcommunity, connecting with other
people that have that passion.
So TopCoder is this 1.8 millionsoftware developers and data
scientists, and they they jointhat community to connect with
(07:20):
other people who have thatpassion and to learn. And then
these challenges get layered ontop of that as a way to learn
even more, start to build areputation, start to have an
impact. And so there's this kindof lattice work of these
communities that do differentkinds of problem solving.
GrabCAD, for instance, is run byStratasys out of Estonian, and
(07:46):
it's, I think they're up to 11or 12 million mechanical
engineers and designers on asingle platform, and they're
there to trade CAD models and 3dmodels, but they will run
contests, and they'll runchallenges to help that
community kind of try out theirskills against each other and
(08:08):
see see what they can come upwith. And we get amazing work
out of them. It's amazing. Someof the designs that you get on
we actually had when NASA did,when they had a Nobel Prize
winning scientist that ran acontest on there for $10,000 to
build these starshades, to kindof find planets, ended up with
(08:31):
50 or 60 amazing designs, notsimple designs, very complex
designs. And I think from thegovernment side. It's a great
way to involve the public. Butwhat it really comes down to is
we live in a world that isreally, really complicated, and
(08:51):
we're evolving to a pace ofchange that is really hard to
keep up with, and I call it thetsunami of technology that has
resulted in my talks, I talkabout, since the population's
done this, the number ofscientists and engineers,
especially now that theircountries are more wealthy and
(09:14):
there's a lot more education outthere, the number of scientists
has just grown exponentially,kind of with the population, to
the point that right now, 90% ofall scientists that have ever
lived are estimated to be alive,right? Everyone who created the
(09:35):
science and technology that weall take for granted, you know,
starting with like Socrates andworking your way up to Einstein
and the folks that lived in thiseven last century, there's nine
times as many people as all ofthem put together, all working
with new technologies likeblockchain and advanced
software, APIs and machinelearning and 3d printing and
(09:57):
nano material and and all of.
These are working acrossmultiple industries
simultaneously, and so if you'rein an industry and you're trying
to solve problems and innovate,you have this kind of unlimited
buffet of new technologies. Theproblem is you can't find it
like not posted on the internet,like people think you can just
(10:19):
google anything, no newtechnologies, especially
solutions that grow and maturein one industry, they remain
kind of hidden because they areproprietary, or they have jargon
around the the problem they'resolving. You know, if you go to
John Deere right now, they'reone of the world leaders in
automation and machine learningkind of weird farm equipment,
(10:42):
but they do. But if you were tosee some of their most advanced
work, you might go, Well, that'sthat's not really applicable to
over here in say, aerospace, butsomeone who understands their
problems and their nomenclaturesand what they're doing, and they
understand what you're trying todo over nearest they can
actually connect the dots andsay, no, no, this new thing
(11:03):
they're doing, if we just dothese mods to it, can be our new
solution that can give us a 5x10x improvement in what we're
doing. And this is where openinnovation comes in, because if
you're in your industry, youcan't see that you can't
understand those problems, thosesolutions. You don't know it's
out there. You see these thingslike blockchain and
(11:25):
nanomaterial, but you don't knowthat it's actually getting
combined with other solutions.
And so crowdsourcing and openinnovation is this way that you
can kind of cast a statisticaldragnet across all the
industries and all that world.
And this is where it kind ofwarps, warps the mind a little
(11:48):
bit, because humans, we aren'tgreat at large numbers, right?
But some of the communities youknow are millions of people, and
we don't reach millions ofpeople when we run a campaign,
but a fraction of many, manypeople is still many people. And
so I actually use stadiums,pictures of stadiums when I do
(12:10):
talks, because they say, youknow, Texas, A and M stadium
happens in Texas, happens to bea good one to use, because its
capacity is like 102,000 people.
And I said, here's a picture ofme at a game looking at 100,000
people. And whenever you go tothose events, it kind of blows
(12:30):
your mind a little bit like ifyou're if you're nerdy like me,
you look around and you think,how many doctors Am I looking
at? How many, how many topscientists? Am I looking at how
many, you know, innovators, amI? And the thing is, anytime you
got 100,000 people, there,statistically, are a little bit
(12:51):
of everyone. But when you startto look at these crowds, there
are multiples of that. You know,11 million people in GrabCAD.
That's 110 of those stadiumslike that's that's some
significant numbers. And so whenyou start to think about that
statistics, someone that canconnect the dots goes way up,
(13:14):
compared to me trying to sit inmy cubicle with my team. Even if
I have some of the world'sexperts, they're never going to
be able to connect the dots tothose other things. And this is
where open innovation becomes.
This, in my mind, anindispensable tool these days.
It's not even an option anymore.
You have to find the time to goout to the crowd sometime, if
(13:37):
you're just doing everythinginternal, you probably are going
to fall behind. So I'm
Scott Allender (13:52):
thinking about leaders listening right now, who
are thinking, Well, I'm notgonna be able to run a contest
in my organization, right? Sohow can I leverage the crowd,
and really do it in a way thatis truly innovative and builds
on solving the problem versuswhat I see a lot of which is,
let me look externally, findwhat I call a quote, unquote,
best practice, and try tooverlay that onto my problem,
(14:13):
and then it doesn't work, right,
Steve Rader (14:15):
right? Here's the thing, it's a super effective
tool. It does take some learningand some investment to go out
and find it. A lot of peoplefind it's really hard to get
over the idea that your expertscan somehow have people that
aren't the experts help, right?
That there's ego there. There'sa little bit. And so one of the
(14:38):
first things is maybe do aninternal crowd right, where you
actually can get a platformwhere you say, hey, look, we've
got several 1000 people in ourcompany. Let's at least go
outside our individual silos tomake sure that we're harnessing
the power of the group we havethat gets people a little bit
used to that. Now you have to bespecific. With the challenges
(14:59):
you put out there, you can't dothese general give us all your
ideas and make it a suggestionthat will create a disaster. You
have to have the challengeowner, the person that owns the
problem in your organization andcan do something with the
solution. They have to be theowner. And then you can ask,
does anyone have ideas if you'regoing to go outside, really,
(15:21):
it's what are you trying to do?
Because there's really differentkinds of crowds. You know, I
mentioned TopCoder and GrabCAD.
There's herox that does a bunchof innovation challenges.
There's Kaggle and driven datathat do algorithm contests along
with TopCoder. There are tongilWho does videos, you know, in
(15:42):
this creative work, creativework is great, by the way, I
should just pause on that forone second. If you think about
any kind of work that you dowhere it's a complex kind of,
you know, set of options, andyou might actually ask, like an
artist, give me five renditionsof this. The crowd is a great
(16:03):
way to get lots of different,varied input to help you kind of
narrow that down. So we do a lotin the video and graphics and
things like that. Gen AI isstarting to mess with that a
little bit. So I get it. Butwhen it comes to design, you
know, getting lots of differentdesigns for something,
(16:24):
especially in like actualengineering designs, you start
to get some, some stuff that youcan can really get better
performance, because what you'rereally doing in innovation,
right, failure is an importantcomponent, right? Like you have
to be able to you have to trysomething and fail. Try
something fail. Crowdsourcing issimply taking that in in certain
(16:45):
instances and making itmassively parallel to where you
get 150 people working onsomething. Well, 140 something
of those are going to actuallyfail when you compare the
results in the performance, butthose top five to 10 that
actually outperform the rest,they're they're actually the
(17:09):
ones that give you what youneed. And it's not that that
failure is bad. I tell people,open innovation as a
participant, is one of the onlyplaces in the world, in your
entire life that you can fail,and nobody cares. You can just
do it for the sake of learningand the possibility of what it
(17:31):
might mean. And that's a goodthing, because we learn a lot
from failure. But if you're atschool, you can't fail. You have
to get a good grade. If you'reat work, you can't fail, and
you're just wasting thecompany's money. And they'll
they'll get there'll berepercussions. But in the
challenge world, in your owntime, you can actually fail. We
actually had had an experienceonce where a lead researcher in
(17:54):
this area participated in achallenge, and she ended up
winning, and she said, I'm soglad that that we did this as a
challenge, because my boss, myPI, never would have let me
pursue this idea I had, rightbecause they're trying to get
grant money, and they're tryingto be conservative, and in The
(18:17):
research world, you've got to,you've got to have that
conservancy sometimes. But weknow innovation is about risk
taking and failure, so openinnovation starts to open up
that that idea a little bitmore. I think I got away from
your original question. It was,how do you how do people go
implement a lot of it islearning, learning about
(18:37):
platforms and realizing that alot of those platforms can do
this largely for you, youcontract it to them so they
define the channel. You bringthem your problem, they will
form it into a challenge. Hostit. Tell you how much the prizes
should be. Can work with you onyour budget. One of the things
that NASA did that was kind ofsmart was they created a
(19:01):
contract that had 30 differentcrowdsourcing companies, and
then they could basically say,here's our problem. Compete on
those. And that was a reallygreat construct. It's kind of
harder for some folks to do, butget to know those that you need.
If you need technology searches,you know yet two is a great
company if you're doing a lot ofalgorithm work driven data,
Kaggle Pop Code, or those aregreat companies. If you're just
(19:25):
doing general science with Zokuhero X, you know, and, and you
can start relationships withthose companies and, and they
can help you do it. One of thethings I advise people not to do
is stand up your own website andtry to post it. Post a job, it
is harder than you think, and ittakes skills that you probably
don't have, even the work I'vedone in the past. And that's a
(19:50):
we rarely do stuff direct to thepublic. It's almost always
through these curated crowds,because they've already worked.
Through the barrier of entry.
These folks that haveparticipated have participated
on multiple challenges. TheyIt's not scary to them anymore,
and they still do big adcampaigns to try to bring in
(20:12):
even new people, to try to makesure that that crowd that you're
using has both the depth ofexpertise, if it's a technical
problem, as well as the breadthacross multiple industries,
never a short answer. Sorry,
Jean Gomes (20:26):
what have you learned about helping the
Problem Owner actually set thereally good brief. What are the
kind of mistakes people mightmake and sometimes maybe confuse
a solution with a problemstatement?
Steve Rader (20:38):
I would say the biggest mistake technical people
especially make is not spendingenough time understanding your
problem. You know, what do we doin an organization? If somebody
says we've got this problem,let's work on it. Well, everyone
stands up at the whiteboard andstarts brainstorming. Well,
there's science that says thatthat's the absolute worst thing
you can do, because as soon asone person puts an idea up on
(21:00):
that board, you've eliminatedsomething like 70% of the
original ideas that couldactually have been innovative.
And so there's some techniquesaround that, but we encourage
folks to really take the time tolist out all their assumptions,
list out all their constraints,really understand the
performance parameters. Whereare you today? Where are you
(21:21):
trying to get stakeholders? Ohmy gosh, that's the one thing I
see people forget all the time,is they think, Well, I can just
implement something. Well, ifyou better involve all the
stakeholders, because if it'ssoftware and the IT department's
not involved, they're going totell you why it won't work after
(21:42):
you've spent a lot of moneytrying to get it to work right,
because you didn't know you hadto have some security
certificate, or you had, youknow, that kind of thing becomes
really important. And so whenwe're formulating problems, we
always like to take a lot oftime to find out everything we
can about the problem. And thenif you can find out everything
(22:05):
you can about what exists inyour organization, knowledge
around that, that's where wehave used our internal platform
before to kind of pull to say,Have we already solved this? And
we just don't know? And then youcan actually use technology
searches to search for, arethere products or companies
already out there that I justdon't know about? And often the
(22:25):
company, like yet two or ninesigma is really good at that
kind of work. And then if you'restill not seeing, hey, the kinds
of solutions I need and you oryou really want to be on the
cutting edge, then you can run achallenge to try to find that
really unique innovation, but ittakes more money. Each of those
things is a little more money.
And so if you're trying to beefficient about this first
(22:48):
figure out, if you've alreadysolved it inside you
Scott Allender (23:04):
what's the what's the mindsets that leaders
need to adopt to do this?
Because as you're talkingthrough this, I can almost
imagine that some people aresitting here thinking, This
sounds like a long process,right? I feel a lot of pressure.
Innovation seems like it'sdistracting from or detracting
from the day to day pressures ofme delivering now. So what's the
sort of shift, and do you see alot of resistance when you go
(23:27):
into organizations where peopleare like, this sounds great,
Steve, but it's not going towork here.
Steve Rader (23:33):
Yeah, lots of resistance. There's Harvard
Business scale school casestudies, one on Victors and
spoils and Havas, where, youknow, John Windsor took his
crowdsourced advertising andtried to plug it in to a
traditional, large scalemarketing and advertising
company. And literally, youknow, he's like the technology
(23:57):
head of the company, and as hewalks down the hall of the
headquarters, people are closingtheir doors because they didn't
want to talk to them. Becausefor them, advertising was this
experience that they had sloggedtheir way through, and the crowd
could never be a thing, right?
There's a great case study aboutNASA actually called Houston. We
have a problem where NASAinitially rejected it, because
if you come to an organization,you feel like you're there to be
(24:20):
the innovator. Why would youever because you also find out
when you get there that you onlyget to do that 1% of the time,
right? It only comes along everyfew years, and then you're
getting to do that, which, bythe way, you have to change your
messaging to say, Look, you arethe innovator, but you need the
absolute best, most up to datestarting point to innovate, and
(24:43):
that's what crowdsourcing bringsyou, is those ideas, expertise,
technologies, that you justdon't know about, and then you
have to assemble them into asolution that is the new cutting
edge, I would say, the big.
Thing for CEOs and heads oforganizations is ambidexterity,
(25:05):
right? So Michael tushman at HBShas some great literature on
that. I think there's a book outon this idea of balancing two
competing priorities in yourorganization. So one is the
exploitation, right? You've gotto go make money. You've got to
be producing something thatthere's no doubt about, that.
You've got to keep the machinegoing. And oftentimes that's
(25:30):
that's the result of someearlier innovation, right? That
really produced a product andmade value. But there's this
other piece that is thestrategic investment, and you've
got to strategic investment atits core runs sideways to the
exploitation, to the moneymaking, right? If you're
(25:51):
spending money trying toinnovate, there's no profit
today. There's profit in fiveyears or 10 years. It depends
on, you know, how big your scaleis. But if you don't actually
make this investment, if youdon't actually make that a
priority, then in five years,this product is no longer valid,
and you have nothing to replaceit with. So that in you know
(26:13):
this, this strategic investmentcan't be chasing all the flash,
right? So it's got to begrounded in real performance and
real understanding of whatproblem you're trying to solve.
And right now, it's very that'sa difficult conundrum, right?
Because there are a tsunami oftechnology you know, just just
(26:38):
evaluating the impact of 3dprinting is a task. If you have
big manufacturing base, justlooking at nano materials, if
you've got some really complexsystems, is a thing quantum in
quantum computing, networkingand quantum sensing, they're all
about to change everything insome complex systems. And so one
(27:00):
of the big organizationalproblems right now is that the
complexity of the technology isrequiring, and the change, the
rapid change in it is requiringorganizations to have to access
expertise that they don't have.
And that the this is, this goesto a whole area that I talk a
lot about, which is open talent,which is kind of the flip side
of open innovation, which is, ifcompanies have this problem, and
(27:24):
almost every company does, wherethey don't have all the
expertise they need, the recruitand retain strategy that we've
had for 100 years, recruit allthe best people and retain them
and get them going on all Yourstuff that starts to break,
because as things change fasterand faster and starts to
fragment the expertise you can'thire your way out of that you
(27:47):
can't hire fast enough becauseyou don't have a way to exit
people, which you know you own.
There's a lot of companies rightnow where you go and talk to
them, they're like, yes, we havea skills shortage and a hiring
freeze. Well, that's becauseyou're trying to hire your way
(28:09):
out of this. And so this, thisbig shift that happens to have
been coming since about 2015where, kind of by happenstance.
I can't figure out exactly whyit started happening, but people
started moving to the gigeconomy. Well, that happens to
be a really convenient thing fororganizations now, because
there, there's a lot of people,and a lot of people just think
(28:31):
about Uber drivers and, youknow, food delivery. But if you
look at the full landscape ofwhat's happening out in
independent work, most of thetop experts are now freelance.
They see that they can actuallymake a better living kind of
farming out their skills to thecompanies they need them. And
there's NDAs and ways to protectall of the fears that companies
(28:55):
have about sharing theirinformation that's actually
getting easier to deal with. Butwhat you're finding is companies
are having to now hybridizetheir expertise, because even
the best companies can't hireall the experts they need. And
it's both they can't find them,find people willing to move to
your to your city and to workfull time, remote work in the
(29:20):
pandemic really showed people,Hey, there's another there's
another path here. They love theagency of it. They love the fact
that it's not beholden to asingle industry that might have
ups and downs. And so there'sthis kind of new work concept
that's out there. And if youtalk to a lot of startups, this
is what they do, native. Theydon't go hire a staff of 10
(29:42):
people. They go hirefreelancers, and then they get
to know them, and that theystill work with them as a team.
It's not a one off all everytime. But they don't own them,
right? They don't own all oftheir time, and that's great for
a startup, because yourmarketing person. You don't need
a full time. You need somebody athird time. And the fact that
(30:03):
they can make a living makesthem a more stable team member,
not a less stable and then it's,it's you still have to bring
them into your team. So this isa thing I've been working with
open assembly. John Windsor, whoI mentioned earlier, is the
founder of that, and it's awhole group where they're just
talking with the communitiesthat supply talent, along with
(30:24):
the enterprises that are tryingto adapt and use talent. And
it's it is every time I thinkthey're two different things of
open innovation and open talent,I realize, no, it's all crowds
of hundreds of 1000s or millionsof people that are being used to
match a need to a capacity,right? Kind of like Uber is
(30:47):
someone over here needs a rideand somebody over here has a car
down the block. Well, I can putthose people together and meet
needs for both, right? The samekind of thing in these
platforms. I've got thisexpertise in quantum sensing.
Somebody needs that for twoweeks, two months, two years. I
put them together, right? Andthe most exciting part of this,
(31:10):
this is brand new, is AI matchedhigh performing teams. So
there's a new company calledTeam lift that has already
started doing this in terms ofbeing able to take a roster of
people, match them into small,high performing teams based on
their skill sets and personalitytraits. But you can imagine
(31:33):
taking your problem to a crowdof the future, and they maybe
have half a million peoplethere? Well, they can stand up
50 high performing teams eachthat has a task to take two
hours and decompose your problemor two hours and come up with
new ideas, like all in parablein parallel, all kind of
(31:55):
competing with each other, andorchestrate that into an entire
product development cycle. Sothere's some really interesting
stuff. And I would say now eachof those teams also trained and
equipped on using generative AI.
So, you know, being able tooperate lightning fast. So it
gets to be really exciting,terrifying in some ways, by the
(32:16):
way, because everything'schanging. But
Jean Gomes (32:19):
yeah, well, it kind of challenges the fundamental
notions of what a company is andhow it operates at a business
model level. And it kind of sumsup my mind this idea that a
company could become like abrokerage between problems and
talent and resources, which iskind of what you're describing
there. So if you take a stepback for a moment and go, right,
(32:42):
I'm listening to all of this,and as a CEO or as a finance
director, or somebody who's kindof thinking, this is really
exciting, can you kind of walkus through how you would start
in this process, beyond just thetournaments or the challenges,
and how that might play out overseveral years from starting to
(33:02):
build something to the systemthat you might create, yeah,
Steve Rader (33:06):
what I recommend is really putting together kind of
a center of excellence, right?
So a core team that one goes outand learns all of the piece part
learns, you know, there's somegreat books like platform
revolution, which is almost atextbook on how to stand up a
curated crowd. There's, I thinkit's platform computer, no
(33:28):
machine, platform crowd. Greatbook. Also is a primer open
talent John's books, reallygood, competing in the age of
AI, so just kind of doing thehomework of what this is, and
then running pilots right. Havea pilot on putting together an
(33:49):
internal crowd, simultaneouslyrunning your first tech search
and your first challenge to seewhat does that look like, how
does that feel? And then reallytaking stock of your technology
portfolio management, right? Andtrying to start to understand
what that ambidexterity mightlook like, so that you
(34:10):
understand what you have tocarve out, because this does
doesn't come for free, butremarkably open innovation
tools, when you compare themwith using just traditional go
out and get a consultant, thingslike that. At NASA, they were
seeing 75% of the projects hadcost savings, and the average
cost savings was 50% sosignificant kinds of savings can
(34:34):
actually be had, but you've gotto have the program. But just
start piloting these pieces andsee which ones fit best. You
know, if you're software centriccompany, what's the right? You
know, if you have highperforming software teams, you
don't need to bring crowds intothat. They're doing great. But
if you're struggling, andthere's some areas where you
(34:56):
know you need expertise in.
Languages or hardware that youdon't have. Well, that might be
a really good place to say, oh,let's find a crowd that can
actually help us do that, orlet's find open talent to bring
in and help our team come up tospeed. But really trying to look
at the landscape of what am Itrying to do as a company, and
(35:18):
where's technology a threat forme. So where do I need good
surveillance and good kind ofnew ideas? And then in what kind
of investment can I? Can I makeand putting the right framework
around that, so that the teamthe Center of Excellence really
knows how far you want to go? Inmy opinion, open methods are
(35:40):
kind of a core skill thattechnology and tech folks need.
So if you have a techorganization training people all
around the organization, notevery single person, but enough
people that you've got someonewho's looking at what's going
on, and when they see a problemor they see a risk, they say,
you know, that's a goodcandidate. Let's this is the
(36:01):
right tool to go do that. I tellpeople, you'll get really
excited about open innovation,sometimes, none more than me,
but it's not a tool foreverything, and it's not a tool
for all the time. It is fit forcertain phases of product
development in certain phases ofcreativity. So it's a tool you
need, but you need to find outwho needs it when, and that's
(36:23):
where the center of excellencecan kind of be a broker and an
orchestrator of those, so thatthey're working with various
folks around the organization tofigure out when the right need
is to get their buy in fromthose problem owners, so that
that they actually buy in.
Because you don't get that,they'll never implement what you
bring them, right? So, kind ofworking that aspect and just
(36:44):
just created along the way. Butyou have to start slow, and it
has to, you know, have you haveto have people that know, big
mistakes people make is they'llput their marketing or HR folks
in charge of over their openinnovation program with no
training. And you know they'llthere are some, I will tell
(37:05):
people. And I think you guysprobably know this, innovation
is the most dangerous initiativeyou can partake in in a company.
It's the most crucial. But ifyou're trying to tell your
organization, we need toinnovate and you're not serious
about enabling innovation, itwill backfire on your entire
(37:28):
organization. Will become thisDilbert cartoon where they just
roll your eyes every time yousay you want innovation, because
they'll try to bring it to youin lots of different ways. And
if you don't have a frameworkwhere you have the strategic
investment ready, they basicallywill see you saying, well, we
can't do that. And it's like,well, you want to innovate, but
(37:48):
you're not willing to do any andover and over organizations. And
you know, we call thatinnovation theater, right? We
see it all the time. And I thinkopen innovation is no different.
It's a hard tool to just pick upand go use now, you can find
people who are experts in this.
(38:08):
This model has been done acrossProcter and Gamble and General
Mills, and there's a ton ofcompanies that use this. They
just don't talk about it becauseit's their competitive edge,
right? And I think we're moreand more seeing some of the
startups and some of the early,the younger folks, they just,
they know it's out there, andthey use it as if it's, you
(38:31):
know, yeah, of course, use this.
This is just what you do. Youknow, you need a graphic, go out
and run a quick contest, or useGen AI, or, you know, you need a
piece of software. I don't wantto go hire a bunch of software
people. I'm just going to spinthat over here to TopCoder to,
you know, see, outsource. Andthat's, I think, you know, it's
a different way of working. AndI think it's something that's
(38:53):
not always easy, but it's, it'snecessary, unfortunately, I
think the other thing is avoidthe black and white thinking of,
I need to hire all freelancers,or I need to do everything in
open innovation, like it's onepiece of what you're already
doing. Like, don't try to upsetthe entire apple card for a long
(39:14):
time. Even though open talent isgoing to be a necessary thing,
it's not going to take all ofyour your full time employees,
right? It's you still need that.
What I tell people is things arechanging so fast. If you really
want to get on board, upskillingis your, is your the thing you
(39:35):
need most, and why you have tobring in experts is because it's
really hard to upskill internallet your workforce go
participate, train them on whatopen innovation is, and open
talent. Let them spend 5% oftheir time doing these things,
learning something new with aplan to tell you how it's going
(39:55):
to help you bring back a valueto the company and say, Hey, I'm
going to let you do this. You.
To keep any profit you make, anyprize you win, right cash
incentives for them to go learn.
And then every six months, say,tell me what you learned from
these other industries youworked with, or other things,
and tell us how that's helpingus. It starts to give people a
little bit of agency, a littlebit of visibility into what's
(40:17):
going on, gets them out of thebubble. And it's a free training
program like That's right now,training is everything, and if
you even augment that with alittle bit of Hey, and if you
find a Coursera course, we'llpay for that, you start to get a
workforce that is transitioningto the new technology with
(40:39):
visibility of what's going onout there. And I think that's a
really valuable thing.
Scott Allender (40:45):
If you can, could you share a little bit
about the successes that you andyour team have led through open
innovation? I'd love to hearabout, like, what's your when
your standout sort ofinnovations that have happened
as a result of doing this workand, and, and I'd be curious to
just a bolt on a sort of subquestion around that I'm curious
as a leader, how has leadingthis changed you
Steve Rader (41:10):
so success? You know, NASA's had a lot of
successes over the years. Youknow, Jeff Davis and Jason
Cruzan started that program andand that program has run over
850 projects. They they actuallyrun projects for the other
federal agencies as well.
They've worked with 30 differentother federal agencies, and I
think at any one time now,they're running about 80 to 100
(41:33):
projects. So it's there's a lotto choose from there. Some of
the more fascinating ones thatthat I've seen. There was a
really interesting one on forHomeland Security, on, like,
increasing the accuracy ofscanners, and they spent two and
a half million dollars on thatchallenge. But they the results
(41:55):
they got were like, 98%detection rates, which were just
blew away what they had beenable to do before. There's been
work in the medical areas withthings like kidney disease and
Lyme disease that have beenreally outstanding. Gosh, again,
800 projects, so there's a wholelot. One of the most fun ones I
(42:19):
ever did was one called Spacepoop. Turns out, in space, you
got to handle some things. Andwhen the Orion spacecraft was
going to do that burn to themoon, you know, and it gets rid
of its last big rocket that'sjust stuck with the service
module, well, once you make thatburn, you're not coming back for
(42:39):
four to six days, right? So yougot to go all the way around the
moon and back, and if at thatpoint, the cabin gets a leak,
you have to get back into youryour pressurization suit and
live there for six days. Andthat, you know, if you've ever
had a kid with diaper ash, youknow, within hours, that can be
a really painful thing. Well,within days, it can be a really
(43:03):
dangerous thing. So thatchallenge was to look at, how
could you do this? It was reallyinteresting that the winner of
that challenge was a flightsurgeon out of San Antonio for
the Air Force, and he didn'tactually do design drive. He
just, like, did these sketches,but he built these prototypes.
And one of the things he builtwas a little prototype of a
(43:24):
little airlock. He said, In inlaparoscopic surgery, we inflate
the belly to like, 15 psi,right, which is about the same
as the pressure differential ona space, a little bit more,
actually, and they insert thislittle airlock into the abdomen
so that they can pass wipes andlittle instruments and and,
(43:44):
well, that's exactly what youneed in this case of trying to
deal with Facebook. What'sinteresting is herox Did that
challenge. They had 20,000registrants and 5000
submissions, and they actuallyin the contracts NASA had with
them, they they had to actuallyonly provide for judging
(44:06):
submissions that were met alltheir criteria. Well, there were
only 87 out of those 5000 soherox actually had to call those
down to 87 but it wasfascinating. Well, I
Scott Allender (44:22):
think you've just given us a title for this
episode, so that's great.
Steve Rader (44:28):
Yeah, it's funny. I actually, in my early part of my
career at NASA, one of the firstjobs I had was as a flight
controller for the life supportsystem, and one of the first
assignments they gave me was,okay, the main thing you're
responsible for, it is the wastemanagement system. So I very
quickly had to learn how to tobe comfortable with talking
about human waste to my familyand friends who are like, Oh,
(44:51):
you work at NASA. I'm like,yeah. They're like, what do you
work on? I'm like, wow, butspace, that's one of the hard
things about. Space as you go,it's
Scott Allender (45:00):
real deal with all. Yeah, yeah. We
Jean Gomes (45:04):
talked a little bit about AI and how it's changing
things. How do you think this isgoing to play out into open
innovation over the coming Yeah,five or six years,
Steve Rader (45:13):
I spent a lot of my time talking to academia about
that, because we're actuallytrying to study what the
crossovers are and what theimpacts are, I think what it's
basically going to be is, ifyou're not monitoring Gen AI,
then you may as well just packup, because it's changing
everything. But I think for thetime being, it's going to
(45:38):
provide better and betterresults, right? Because all of
the folks out there that are inthe crowd, a good chunk of them
are going to start usinggenerative AI to increase their
performance, and that meanswe're going to get better
submissions, and it can kind ofreally condense the timeline of
what they can find, what theycan produce. And so I think
(46:01):
you'll, you'll see increasing,uh, quality of submissions as we
get better and better. There'sit depends on the area. Open
Innovation can be used, like, Isay, for for multimedia, for
technical challenges, forprototypes all the way through.
And so the things we think willdrop off first are, if they
haven't already, or graphic andmultimedia, right? We're just
(46:25):
watching and the different kindsof challenges. Again, I think
it'll you'll see a steadyincrease. I do think that the
big gear change for openinnovation is going to be when
we get the capacity toorchestrate high performing
teams rapidly. Because highperforming teams, where each
team member is trained on usinggenerative AI, they're going to
(46:46):
be able to tap into that uniquehuman creativity that generative
AI is going to take a long time.
I mean, generative AI isliterally predicting the next
word to say it is not beingcreative. It is finding the
creativity out there that wemost have tapped into and giving
it back to you. So it's not asgreat on finding things at the
edges, because they don'tstatistically. You know,
(47:08):
extracting that statistically ishard, and so I do think once you
start getting high performingteams really using generative AI
to augment, you can get somereally fantastic result results.
Right now, open innovation isactually only good for narrow
problems, because you're askingindividuals, unless you well,
(47:29):
you can have higher and higherprize, price prizes, right? A
million dollar prize, and you'llget a team to assemble. But if
you're doing $100,000 prizes,kind of harder to get a team to
form. Well, that means you don'thave all the disciplines to do a
complex problem, so it has to benarrowed down to a piece of a
problem with these highperforming teams. Now you can
(47:51):
put teams that have the entiredesign team, that have an
electrician or an electricalengineer and a chemist, and, you
know, all the people necessaryfor that team to really build
something more complex. And Ithink that's going to be a key
thing. Now, there may be a timewhen generative AI, you know,
just gets so good and so cheapthat that's what you go to. I
(48:16):
think we're a ways away there.
In so it'll it'll be a path, butyou definitely want to be
watching both of those things,because they're both really
important.
Scott Allender (48:30):
What else should we be asking you Steve in the
time that we have left?
Steve Rader (48:36):
I want to go back a little bit to the open talent
thing, because when I firststarted working all this, I was
seeing a lot of new technologyand a lot of automation and Gen
AI. And the first thing it goesthrough your chest, really, not
even through your mind, butthrough your chest, is, oh my
gosh, everyone's gonna losetheir job. And what do people
(48:58):
do? Right? And what was reallyinteresting is a couple years
into that, I started seeing thisopen talent movement, where
people were moving into kind ofworking through platforms to
access global need in that newmatching. And what I found there
was some real hope, becausepeople have agency. They're
(49:20):
encouraged to do lifelonglearning, which is ultimately
the secret to the adaptation tothese new tools. Because the new
tools, we're not going to runout of problems like in fact,
they create some new problems,right? And we have to go solve
those, but it does up the game,right? So I tell people when
they made Disney's Snow Whiteright back in it's like one of
(49:45):
the first ones they did rightback in the 40s. They had
something like 250 animatorswork for like two years to make
2 million different frames at.
That had to be pieced together,and most people agree that a
team of about three to fivecould recreate that in a few
(50:05):
days now with the technologieswe have. And so you'd think,
wow, filmmaking super cheap now,but it's not, it's it's much
more expensive. But what do wedo? You if you look at Iron Man
three, right? That is actuallyan animated film. Looks totally
(50:25):
real, but it's animated. And theanimations we're able to do are
super high fidelity. They looklike reality, but it took
something like 3500 animators todo that. So when we get tools
that allow us to do thingsfaster, it doesn't necessarily
mean we just get rid of everyonewho who's not keeping up we do
(50:46):
harder things. And that's kindof where the hope comes, for me
is we have more accessibility toeducation than ever before. I
think education is going tochange drastically and and you
know, especially now withgenerative AI tutors. I don't
know if you've started to watchthis, but tutoring is a
massively useful tool ineducation, and there are now
(51:09):
generative AI tutors. Andinterestingly enough, people
learn better when they don'thave a human, frustrated
teacher, right, where there'snot somebody who's like, why
aren't you getting this right?
So it's kind of a good thing inthat regard. Now there's
(51:31):
cognitive offloading that worksthe opposite effect there, but,
but if people start to actuallytrain and learn and start to
find those, those jobs that theyneed. Through these new
platforms, you start to get areally malleable workforce that
can keep up with that cuttingedge and that can get
(51:52):
redeployed. And eventually, yes,the hours per work we might
shrink, but if you're, if you'reworking in a freelance
environment where most peopleare kind of working as
independent workers, that thatactually changes in a way that's
easier to adapt, right? Becausethe rates adjust to where you
can still maintain a living wageprovided the supply demand works
(52:14):
out. So gets really interesting.
There's a lot of theory there. Idon't think it's all worked out.
There is a rate issue there, andthis gets kind of nerdy, but at
the same time, there's hopethere. It's not all gloom and
doom and everyone's just goingto be out of a job tomorrow. So
Jean Gomes (52:32):
no, I love that. And I think the notion that there's,
there's kind of, there's hardwork to do, because the
technology kind of pushes you tohave to actually add human
ingenuity at a higher level. Alot of what people do in
organizations now is shufflearound other bits and pieces of
information, which is very
Steve Rader (52:50):
ironic, if you think about it, this new
technology that's that'sbasically doing things that took
us hours and hours, but the onusthen becomes on us to learn how
to use those tools and do more.
It's it. You'd think it justallow us to go sit around
sipping my ties. But turns out,we haven't really oriented
ourselves to a world wherethat's that's the case, right?
(53:15):
So
Sara Deschamps (53:18):
If the conversations we've been having
on the evolving leader havehelped you in any way. Please
head over to Apple podcasts andleave us a rating and review.
Thank you for listening. Nowlet's get back to the
conversation.
Jean Gomes (53:32):
It seems like there might be an inversion of all
that about to take place. Yeah.
What given? Given that? And Iknow you, you, you teach on MBA
programs and so on. What wouldyou say to younger generations
now in terms of the skill setsthey should be thinking about?
Steve Rader (53:47):
Yeah, no doubt.
Establish yourself as a lifelonglearner. Be curious. Learn how
to be curious, learn how tolearn, learn how to learn
quickly. There's so many freetools out there like I think
everyone should go, forinstance, download. I think it's
RapidMiner, which is a freemachine learning tool, and take
the few hours course it takes tolearn how to actually write a
(54:11):
machine learning algorithm. Youliterally only have to
understand statistics, which Iactually don't really, I'm not a
great statistician, and yet Iwas able to do that and learn
how to actually create a machinelearning algorithm. Now, does
that mean I'm gonna go be amachine learning algorithm guy?
No, but the fact that Iunderstand how that works now
helps me to know when I shouldgo build an algorithm, right? So
(54:35):
same with a CAD model, likethere's free CAD models out
there. Learn how to go create aCAD model. Go learn how to do
something new. Go buy a 3d or goto a 3d printing lab. They're
all over these maker spaces. Golearn how to use one and learn
how to print out some goofything. But these new skills are
(54:56):
the building blocks. Yes, andcompanies are going to need
people who know how to use thebuilding blocks of the future.
And that's, you know, go spend$20 to get a month on chatgpt,
not the free version, but theactual four. Oh, and see what
you can do. There's a great bookby a guy out of North,
(55:19):
Northwestern whose name isescaping me, I apologize, but he
basically says, look, go spendthree. Ethan Malik, sorry. Go
spend three or four hours justasking Gen AI questions and
seeing how far you can push it.
You can literally tell it tocreate a PowerPoint deck, or
(55:42):
create it the code for a site,and it'll just go do it, but
you've got to kind of embedyourself with it for three or
four hours and get lost in it toreally start to clue in. Oh,
this is how I need to use thisas an extension of myself to go
do things right and do my work.
So if you're young, there'snever been more opportunity to
(56:05):
plug in and go do things. Goestablish yourself on some of
these freelance sites. Startbuilding a reputation. Do a gig
for practically free just to seewhat it's like. Participate in a
contest. Start doing you know,you can literally the world is
your oyster right now, and it'sall really hard, right? You've
(56:26):
got both of these thingshappening at once. What's really
great about it is most of thegatekeepers are gone. So when it
comes to equity, as long as longas people can get in onto the
digital and there's still divideon equity, on digital platforms
and the rest, but if you can getonline, man, there is a ton of
(56:47):
opportunity there for peoplefrom every country, culture,
walk of life. The most excitingto me is some of the folks that
are on the spectrum that couldnever make it through an
interview now have a chance ofhaving a really rich life and
making a living, because theycan actually a lot of people on
(57:10):
the spectrum are really good atlike, math and some data stuff.
Well, now they can get thosejobs and do stuff so people with
severe disabilities, it juststarts opening up the job market
in ways that the interviewprocess and the whole people
factor kind of, you know,created along with in person,
doesn't have to all go away,but, but it is this new world we
(57:34):
live in where we really haveaccess to some increasingly
special resources that we didn'thave access to before.
Jean Gomes (57:44):
That's really helpful. And give Scott nice
some homework as well. I think
Scott Allender (57:49):
Steve, how can people connect with you if they
want to learn more? Bring youinto their organization to help
them out. Yeah,
Steve Rader (57:55):
LinkedIn is usually the easiest way, because you
just connect to me, and then shecan message me, I'm out there,
you should put Steve Rader.
Usually, that will get to methat's not finding me the word,
sometimes that'll hit me, butjust shoot me a message, and I'm
happy to I have a side gig,approved side gig at NASA. So
today, for instance, I'm notrepresenting NASA. I'm
(58:17):
representing my my crowdresources consulting, but I I do
talks for organizations toreally, kind of explain this
stuff and kind of get themmotivated around it. I do
consulting where I'll come inpretty light, kind of to help
folks get oriented on this. Andthen sometimes I'll do workshops
or teaching or lecturing,excellent
Scott Allender (58:40):
well, we'll put all that in the show notes. And
thank you so much for joining ustoday and sharing all of your
amazing insights. Super, superuseful, really good stuff.
Steve Rader (58:49):
Thanks so much. You guys been great.
Scott Allender (58:52):
And folks, until next time, remember the world is
evolving. Are you?
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