This presentation includes an overview of PARC, of Innovation Services at PARC and our use of social science, and a description of a process we use, experience probes, to reduce the risk of adopting novel technologies while still making breakthrough innovations.

1. Good afternoon. It’s a great pleasure to be here today. My name is Mike Kuniavsky and I’m
a user experience designer at PARC’s Innovation Services Group. Today I would like to
talk about two things: I would like to give you an overview of PARC and I’d like to tell you
about a process we use to reduce the risk of adopting novel technologies while still
making breakthrough innovations. As any research lab, we regularly find ourselves in the
situation where we have technologies, great solutions, but without a clear idea of what
the problems. Our group, Innovation Services, has come up with several methods for
reducing risk in such situations and today I’d like to share one with you.
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2. But first I would like to review a little bit about the history of this research lab.
Many people know of PARC as the birthplace of the personal computer, and
the ecosystem of technologies that surrounded that concept: the laser printer,
ethernet networking, graphical user interfaces, object oriented programming
were all created here during a particularly fertile period in the 1970s. Every
time one of your Chrome Web browser runs JavaScript, there’s a little bit of
Smalltalk code in there that originated here 40 years ago. Since then we’ve
continued to innovate in ways that keep pushing the boundary forward. Our
research into lasers is in every Blue Ray player, Apple paid a nice nod to the
PARCPad, one of the first portable networked tablet computers, when they
named the iPad. When you hold an Amazon Kindle, the display is a cousin of
the electronic ink displays we developed here, and when you use Microsoft
Bing, PARC’s information indexing and AI technology powers some of that.
Today we do a lot of work in energy, much of it for ARPA-E, and of course
printed electronics.

3. • Applying a custom combination -- of the deep knowledge and diverse experience of the best
minds in the business -- to each client engagement
• An industry – not academic -- perspective that has launched more than 30 new companies…
• Our people aren’t just the brightest but they’re the leading minds in their fields. We’ve
authored over 2,500 patents – now, we’re averaging 100 per year – and over 4000 papers and
100 books in the physical, computational, and social sciences.
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4. PARC has 4 Labs: System Science, Interaction & analytics, Hardware Systems and
Electronics Materials & Devices
Each Lab has several technology areas, including printed electronics, data analytics,
networking, security, energy, interactive intelligence, and many more

5. In addition, because we’re essentially a kind of consulting company, and not a pure
academic or corporate research lab, we provide the infrastructural services necessary to
ensure that new technology we develop has the greatest opportunity of market success.
That’s where the group I belong to, Innovation Services, and our global business
development group come in.

7. • Due to the confidentiality of our client relationships and work, this is just a
partial list of the companies and organizations we are working with (or have
worked with).
• This includes Fortune 500 and Global 1000 companies; startups we have
enabled and in some cases spun out; and government agencies or partners.
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8. We are also part of the original Silicon Valley DNA. PARC has been part of
Silicon Valley counterculture since the beginning. Perhaps because of the
distance from established East Coast institutions, and certainly because of
direct influence by the 1960s countercultural movement in San Francisco and
Berkeley, the philosophy here has always been to take transformative risks
and to use a combination of social science and hard science to challenge not
just what a technology does, but why it exists in the first place to solve human
problems and maximize human potential. Rather than working within existing
boundaries and with existing constraints, the Silicon Valley counterculture
philosophy, which I certainly buy into, has been to find new ways to do old
things and, when necessary, to replace those old things, whether they’re
technologies, assumptions, ways of working, or entire industries. The
Facebook philosophy of moving fast and breaking things started here.
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9. And the secret to PARC’s success over the last 40 years is that we focus on
creating business impact and not just inventions.
We especially focused on breakthrough impact.
We believe this BI requires these 3 areas.
1. Need work class expertise – we have the top scientists in the world
2. But also we develop tech that is used by people. So we need to understand
people and their context. So we need to not only solve right problems but
also solve them in the right way. Such in a way that people will want to use
it.
3. Lastly, we need to make sure to deliver value from our innovations. This
requires thinking of new business models. We have a biz team at PARC
that thinks about how we deliver value thru new business models enabled
by new technologies.
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10. Which brings me back to social science. When we’re trying to reduce the risk of
adopting novel technologies so they’re successful for our clients and their
customers, we have found that applying social science methods is very
effective. We pioneered this research in the 1980s and we’re still doing it
every day.
At lot of these methods get lumped under the heading of design ethnography, or
corporate ethnography, but there are a number of methods we regularly use
to research how people and technologies mix.
We do both qualitative observation work and quantitative big data behavior
research. We work online and offline and we have a pretty big toolbox.
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11. This work also dovetails with our open innovation model.
Open Innovation means embracing internal and external capabilities to design a wider range
of new offerings than that suggested by traditional R&D, which is focused primarily on
existing markets and customers. OI widens the lens, encouraging active consideration of
a mix of strategies that includes adjacencies and new market entry supported by organic
(develop new capabilities) and inorganic (partner for technology and/or market access)
means.

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13. Let me give you a an example of how this works in practice. Many of our client
engagements start with what we call the ENGAGE process.
Engage is a structured approach to exploring new business models and manage
disruptive innovation through a repeatable process...
—It starts with a 2-day program that starts PARC where we get multidisciplinary
teams of researchers to help develop technology and IP strategies and
roadmaps to help solve business challenges
—We then execute on the roadmap that’s developed through a rapid, iterative,
highly collaborative process that pulls in all of the relevant technical, social,
product design and business strategy talent.
We’d love to tell you more about this, so please catch us at some point and we
can give you details.
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14. Now I’d like to switch gears and walk you through a specific technique we use as
part of the work we do after an ENGAGE project to explore where the right fit
for a technology is, from a business and customer perspective.
The core question we try to answer is: How do you know whether there's a
market for something, that people will want it and use it, without building it?
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15. Software used to be a physical product, too, with the same kinds of linear
waterfall manufacturing and distribution dynamics…
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16. …but about 15 years ago the Internet fundamentally changed the way that
software is deployed, and now there is a robust ecosystem to quickly design,
develop and deploy software, websites and apps to test product market fit.
The Web and app stacks are tailored around tools for rapidly evaluating how
well products work and whether they fit the needs and behaviors of their
market.
Every major digital product you use, whether it’s a website or an app, is heavily
instrumented and data flows from every one of your actions back to the
company, which uses it to create real-time experiments about how to
maximize factors such as engagement and shopping cart size. Every time you
use a Google product, for example, you’re participating in something like 40
experiments. You probably rarely notice that it’s happening, but subtle
changes are made constantly in response to huge volumes of information
about people’s actions. This is what makes Amazon so successful at selling
you things and Candy Crush Saga so addictive. It’s all instrumented.
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17. In fact, this technology has led to the dominant philosophy about how you create
successful Silicon Valley startup, Eric Ries/Steve Blank Lean Startup model.
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18. Unfortunately, this model doesn’t yet work for hardware. If you try it for a physical
product, you realize that it is based on Internet and software assumptions that
just don’t apply to today’s hardware manufacturing ecosystem. For example,
maybe you can cobble together an MVP with an Arduino on a breadboard and a
3D printed plastic enclosure, but you still can’t deploy it quickly or get accurate
feedback about it to measure usage. The barrier to getting past the very first part
of the very first iteration is enormous.
The best we get is this (arduino) and this (Kickstarter). Crowdfunding is
essentially only a very crude tool for evaluating the desirability of an idea, and it’s
a pretty messy tool where there’s very little clear signal.
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19. The problem is that this is very hard to do with hardware.
Physical product design is still very much a slow, old waterfall development
model. After 20 years dismantling it in software, hardware today is worse than
software ever was. Product design is actually LESS agile than the LEAST
agile software waterfall model.
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20. Sure sometimes people will draw it as a circle, but that doesn’t fix the underlying
problem.
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21. Developing a new connected hardware product today, the process is little
different than when Michael Porter defined it like this in 1980 for traditional
manufactured goods like coffee makers.
You get one shot and if you’re lucky and all the stars align, you get to roll the dice
again, and they don’t, then you shut down and have to start over. That sucks!
I want to change it.
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22. This waterfall-style environment has lead into a situation where I think product
development happens in one of four ways, none of which is satisfying.
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23. And when you go down that “technology, manufacturing, launch and market”
route, which is kind of like saying Ready-Fire-Aim, you typically get one of
three outcomes: 1. kinda pointless gadgets that exist because they can, not
because anyone wants them, such as this Quirky egg thing. In this situation it
doesn’t matter what the question is, the answer is always “Let’s put a chip in
it” or whatever your pet technology of choice is. This is product design by
willful ignorance. It’s essentially saying: “Figuring out what people actually
need and want is hard, so we’ll just work on the stuff we know and we’ll spend
a lot of time and money, and we’ll cross our fingers and hope that we don’t
have to sell the overstock to woot.”
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24. …2. or you get superficial formal variation with very little change of core
functionality. This is product design by fear. It’s saying “Figuring out what
people want and need is hard, so we’ll make small changes to things that we
already know they want and hope that our tweaks will bring them to us instead
of a competitor.”
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25. Or 3., the worst situation is that even if you hit on a success, it’s impossible to
replicate it because you don’t know what you did right or wrong. That’s
product design by random walk and the big consumer electronics companies
are notorious for this. After the success of the Gameboy, Nintendo created the
first consumer virtual reality gaming system in 1995. Using 1990 technology. It
was visionary, but it was a legendary failure for the company and cost them
perhaps billions of dollars and valuable market share. Why did they make
this? Why did they go this far? Perhaps it’s because they didn’t even know
why the Gameboy had succeeded. They had thrown things against the wall
and one of them stuck, perhaps, like in a hit movie, they treated everyone
involved in that project, especially the project’s leaders, like geniuses with
some kind of magic insight and those people believed the hype. Regardless,
they were unable to recreate their earlier success because perhaps they
didn’t have a process for understanding why it had been successful.
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26. The best option we have today is the traditional customer-centered new product
development model derived from industrial design and popularized by
companies like IDEO and frog. In the model you use a combination of
ethnographic observation, ideation and a lot of talking and analysis to identify
what sounds like a good idea; then you define personas, build storyboards,
models, customer journeys and perhaps make some looks-like and works-like
prototypes that you show key stakeholders and perhaps a handful of users.
This is as effective a process as we have right now, and most companies don’t
even go through these relatively simple steps. Still, I think this approach only
produces a weak signal at best and it doesn’t get at the hard part, rapidly
identifying whether an idea is even interesting to people, and throwing it away
as soon as you can if it isn’t, while iterating on the parts that ARE interesting.
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27. So how do we design not from positions of ignorance and fear to create products
that we have a high degree of confidence that people will want? I’ve been
thinking about this for a while.
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28. And here’s the process I’m currently using to help us push the bounds of existing
products and services with some idea of what people value and what’s
technologically feasible. It’s explicitly based on the Lean Startup approach,
but geared toward hardware.
The top layer describes a highly iterative exploratory phase that exists solely to
help us understand what experiences provide value to people and what
technology our team can reasonably deliver. Probes, which I will go to in more
detail later, are things that resemble finished products—perhaps they’re
nonfunctional apps or boxes with lights—but exist to explore hypotheses
about what kinds of products and services people will respond well to. We
give them to people for a couple of days or a week and then we interview
them when they’ve had a chance to live with them. Proofs of concept exist to
test the core technological ideas. I don’t care if we have to throw a $3000
server at the problem when the end product has to cost $10, is it possible for
us to do that thing AT ALL? This kind of technical proof-of-concept is what
Alan Kay used to call a time machine. If we can do it for $3000 today, we’ll be
able to do it for $10 in five years, so it’s as if we’re paying a bunch of money to
get a device from the future.
After this phase is done, we throw everything we did away and start designing
the real product from scratch, and that’s where the prototyping process starts.
We iteratively develop prototypes with additional user feedback until we’re
pretty sure it represents a valuable and technologically feasible service. Then
we deliver that to our client who throws away all of our work, but uses it as a
reference design that they can then tear apart and rebuild using their own in- 28

29. Our approach to probes approach owes a lot to the technology probes approach
that Hillary Hutchison, who’s now at Google, and others developed in the
early 2000s, and then mix it with with the hypothesis testing of Lean Startup
minimum viable products.
The Hutchinson model explicitly makes a distinction between probes and
prototypes, and defines probes as disposable explorations. I feel this is quite
liberating because it forces us to rapidly identify our assumptions and figure
out ways to invalidate those assumptions. In other words, it helps us ask
better questions.
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30. In summary…
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31. Here’s our current probe process. It’ll look familiar from various new product
development and user centered design practices. I’ve tried to emphasize
explicit hypothesis generation and evaluation because when you’re making an
unknown product in a novel category for an untested market, which is where
we work, one of the only points of stability is an clear statement about what
you think the value of the idea is, and that’s the hypothesis, so that when the
probe process calls it into question, that provides guidance about how to
iterate it.
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33. Recommend using Lean Business Model
Canvas as a quick 1 page checklist to make
sure that at least all of the key aspects of a
potential business model have been
discussed.
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38. So here are our results. We obviously can’t tell you how this project went, but we
were able to rapidly identify a number of directions where the ideas were
valuable and, mostly importantly, a number of directions where they weren’t.
For our clients, I believe this information was valuable, and, I feel this is
important, we were able to get it to them quickly and without expending the
resources required to actually build the products. With this information, we
could go back and iterate on these ideas to create concepts that we felt were
much closer to what people would want,
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