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0 → 1 and 1 → N

by Nis Frome
January 22, 2019

To succeed in the digital age, your organization needs to be excellent at both innovating and scaling. Here’s our blueprint.

It’s almost cliche to say that massive companies born in the industrial age struggle to evolve and innovate in today’s digital world. Regardless of eagerness, developing a mobile app by applying the same resource allocation and planning process you would for a widget factory almost always results in disappointment. But best-in-class approaches to digital product management don’t just elude traditional blue chips.

Even so-called ‘Millennial’ brands born in the digital age struggle to keep up with rapidly changing consumer tastes — look no further than the struggles and stock prices of Snap and Blue Apron. In fact, the ability to invent new things becomes exponentially more difficult quite early in a company’s lifecycle. That’s a problem because in today’s fast-paced landscape, remaining relevant and achieving sustained growth requires being excellent at both innovating and scaling.

Two operating systems are better than one

Recently, a number of serial entrepreneurs have written about the nuanced differences between innovation and scale. In Zero to One, Peter Thiel, a renowned investor and founder of Paypal and Palantir, frames innovation and scale as distinct operations with unique applications:

  • Going from 1 → N or post-product-market-fit: “Horizontal progress is easy to imagine because we already know what it looks like…If you take one typewriter and build 100, you have made horizontal progress.”
  • Going from 0 → 1 or pre-product-market-fit: “Vertical progress is harder to imagine because it requires doing something nobody else has ever done…If you have a typewriter and build a word processor, you have made vertical progress.”

Historically, most large organizations could focus almost exclusively on going from 1 → N, or ‘globalization’ as Thiel refers to it. Today, the market demands that companies shift the ratio considerably toward focusing on 0 → 1 via developing ‘technology.’ That’s because solutions are more quickly reaching maturity and becoming irrelevant as underlying problems and preferences are rapidly changing. In order to scale tomorrow’s solutions, you need to discover and validate them (i.e. go from 0 → 1) today.

There’s reason to be optimistic. If we define innovation as the creation or commercialization of uniquely differentiated value, then virtually every company that has turned a profit has at some point innovated. Pinpointing when the ability to innovate deteriorated is critical to understanding how to regain it.

Wearing blinders

If you evaluate initiatives to simultaneously innovate and scale, you’ll notice that few teams are excellent at both for a number of reasons:

  • Unrelated specialties: The skills required to be excellent at user research, validation, and iteration are substantially different than those required to increase efficiency and optimize supply chains.
  • Unrelated resources: Going from 0 → 1 involves a broad range of one-off activities, from legal paperwork to spinning up a web server to creating an initial sales deck. Scaling requires managing more predictable and static resources.
  • Unrelated context: Even if an individual or team was competent at both innovating and scaling activities, they’d lose significant momentum from context switching between the two.
  • Unrelated incentives: There are widespread practices for measuring performance in a 1 → N environment (e.g. quotas, output, etc.). Going from 0 → 1 is intrinsically uncertain and indeterminable, so traditional incentives are inadequate.

Perhaps this last point is the most informative since, over the long-term, incentives generally dictate activities. In my own experience, I’ve seen that to be precisely the case: the moment a leader or team assumes responsibility to optimize a key performance indicator (such as ‘engagement’) marks the peak of the entire company’s ability to innovate. From there, it’s rapidly downhill.

1 → N misapplied to 0 → 1

Optimization of a defined metric is at odds with innovation. If you measure the former, you forego the latter. To maintain any expectations otherwise means first redefining success and then adopting turnkey user insights solutions, like Alpha, that seamlessly enable employees to circumvent the aforementioned constraints to explore new market opportunities.

Such a hybrid approach has led to meaningful and documented successes at organizations like Northwestern MutualAARP, and The Advertising Council. Organizations like Google even encourage employees to diversify their time away from core competencies toward side projects (sometimes even resulting in game changing ideas like Gmail).

Of course, companies are rightfully eager to scale such efforts after they get a taste of initial success. By default, many spin up innovation labs to ‘own’ such initiatives. If you’ve been following along though, you not only aren’t surprised that most innovation labs underperform, but you can also pinpoint exactly why.

To begin, innovation cannot be optimized, at least not in the traditional sense. To reiterate Thiel’s quote from earlier, innovation “requires doing something nobody else has ever done.” How can a metric be optimized if it has yet to be defined? The very term innovation lab is in and of itself a paradox — likely the misguided illusions of a rigid 1 → N mindset, if one consultant’s observation is any indication:

[Innovation labs] are failing in their primary mission. This is not for lack of trying, but because they unwittingly apply patterns of behavior that destine them to underperform.

Further, siloing innovation activities away from scaling activities ironically does the exact opposite of what’s intended. Going from 0 → 1 is counterproductive if there isn’t systematic alignment for then going from 1 → N. That’s how a company like Xerox wound up inventing the graphical user interface but failed to commercialize it, while Apple leveraged it into being the world’s first trillion dollar company.

Innovation and scaling may be distinct activities but separating the former from the latter is a near guarantee that you’ll never apply the latter to the former.

Scaling 0 → 1

It cannot be overstated: organizations today needs to be excellent at both innovating and scaling. Complacency with anything less is a one-way ticket to irrelevance. By studying past innovation efforts and making a few critical adjustments, we are creating a new team and defining a more sustainable blueprint.

Instead of optimizing for innovation, we are making a subtle but meaningful distinction that many recent studies have noted: innovation is not an outcome but rather the byproduct of learning and experimentation. Our new team isn’t focused on optimizing innovation, but instead on facilitating experimentation and accelerating learning.

To do that, we’re enabling teams and individuals throughout our organization to efficiently test and learn incrementally within traditional 1 → N environments. Of course, all of our employees have access to and use our on-demand insights platform, but we’re also continuously evangelizing an experiment-driven mindset. That begins by relentlessly reiterating how quickly market assumptions become outdated, and then providing insights for how to operate in a rapidly changing world.

Building the Future is our new publication dedicated to doing exactly that. We’ll be sharing our own insights as well as collaborating with entrepreneurs, intrapreneurs, and researchers to uncover best practices for developing cultures of innovation.

We’ll also be managing a portfolio and backlog of strategic assumptions or ‘bets’ that are outside the scope or domain of a single employee or team. Unlike a separate innovation lab or skunkworks, any strategic bet for which we initiate experimentation must have a designated team or stakeholder to provide guidance during the 0 → 1 phase and ownership thereafter, if evolving success criteria are met. This ensures alignment between 0 → 1 initiatives and 1 → N accountabilities.

Our sourcing and evaluation process means that any initiated experiment originates from a team on the front lines with actual customers or prospects. That’s quite different from many innovation labs which have freedom to explore the outer realms of possibility and divert resources from commercialized initiatives. Our alignment not only mitigates risk of infighting over resource constraints but keeps leaders excited about the opportunity to be involved in the strategic experimentation process focused on solving problems they’ve prioritized.

Over the next few weeks, months, and years, our experimentation team will be showcasing what we’re learning, what we’re learning from learning, and the nearly inevitable innovations that result.

This post was originally published on Medium and has been republished here with permission.

Nis Frome

Nis Frome is the co-founder of Alpha, the platform that enables management teams to make data-driven decisions about users, products, and new markets.