Corporations and startups have different superpowers:

• Corporations execute with operational excellence top of mind.
• Startups move quickly, experiment often, and learn on the go.

In other words, corporations are execution machines. Large enterprises set a plan and stick to it. They put governance systems, metrics, and processes in place, and optimize for efficiency, safety, and predictability.

(High Alpha Innovation CEO Elliott Parker talks in greater detail about what that looks like today.)

These are the ingredients that help these scaled organizations succeed. However, they’re also the ones that have made these companies ripe for disruption by startups. It's not hard to find real-life examples:

• TV networks and film studios were disrupted by Netflix.
• The taxi industry was disrupted by Uber.
• Hotel chains were disrupted by Airbnb.
• Legacy video-call providers were disrupted by Zoom.

Technology has become easier, faster, and less expensive to build. This evolution has democratized innovation.

That means the risk of disruption to corporations has risen dramatically.

Corporations are, in many ways, better positioned to innovate than startups. They have access to capital, industry expertise and partnerships, and personnel at their disposal.

But startups' superpower forces them to have to learn more with less money and in less time.

That's why mantras like “Fail fast,” “Build for the customer,” and “Iterate quickly" have become synonymous with startups: They're able to test, learn, and act at a pace corporations simply cannot.

An assumption-testing case study: How quick, low-cost learning helped Netflix outpace Blockbuster’s head start

This difference in superpowers — the efficient corporation and the fast-learning startup — is perhaps best illustrated by Blockbuster, the now-bankrupt, movie-rental retailer:

• Blockbuster CEO John Antioco was given the chance to partner with Netflix. Blockbuster would advertise Netflix in-store. Netflix would advertise Blockbuster on its website.
• However, John rejected the opportunity. In his view, this gave the Blockbuster brand, which the company had worked so hard to build, away for free. It felt like a bad deal.
• John also rejected the offer to buy then-startup Netflix for roughly $50 million. He thought the company would be better off building its own Netflix-esque platform in-house.

Fast-forward several years. Financial disincentives and red-tape stopped Blockbuster's innovation ambitions.

Creating a Netflix competitor meant stepping away from Blockbuster’s $200 million in late fees, a key part of Blockbuster’s revenue model, and spending $200 million to launch the platform.

John lost the CEO position, after activist investor Carl Icahn got involved, and Blockbuster went bankrupt in 2010.

While Blockbuster was stuck in analysis paralysis, Netflix did what startups do best:

Execute fast, cheap, and "weird" experiments to test and validate assumptions, then act on unmet customer demands and market opportunities quickly.

What does that mean? It’s likely best summarized on the first step in Netflix’s own corporate timeline:

“Reed Hastings and Marc Randolph have an idea to rent DVDs by mail. They test the concept by mailing themselves a DVD. The DVD arrives intact, and the idea for Netflix is born.”

It’s an incredibly simple statement. But it's one that's actually the epitome of why startups get to move fast.

Behind that statement is a critical assumption (from 1997) that the entire mail-order version of Netflix rests on: that DVDs can be shipped to customers, who can then be entrusted to send them back.

Tasking an enterprising business team with resolving that assumption, it's easy to see several paths to an answer:

• Interviewing mail carriers to determine if the model is logistically feasible
• Estimating the losses from broken DVDs and modeling the financial impact
• Assessing whether the necessary customer-support function could be built
• Deciding the barrier to launch is too big and stopping the test in its tracks

You may imagine several smart people spending a lot of time solving this problem from various angles. But Netflix’s founders spent 32 cents to validate their assumption: They mailed each other a DVD, and noted it survived.

Their test showed the business model could work, and they acted accordingly.

‍Assumption testing in practice: Conducting fast, cheap, and weird experiments at scale

Behind every innovation — including the creation of new startups — there are hundreds (if not thousands) of assumptions. They’re the series of “But what if this goes wrong” sentiments responsible for the graveyard of good ideas that never get launched.

But what does testing these assumptions look like in practice?

There are many tests founders looking to launch a single startup and scaled organizations with venture studios aiming to launch multiple new companies can run inexpensively and quickly.

‍Speaking with customers

Customer interviews are a vital part of the assumption-testing toolkit.

Talking to your prospective user and subscriber base — whether it's friends and family, LinkedIn connections, cold email outreach, or walking down Main Street — is a great way to learn about the problem you’re trying to solve.

There's a lot of science behind the best interview techniques. ("The Mom Test" by Rob Fitzpatrick is a particularly effective resource for learning how to master the art of customer interviews.)

A great question to ask before even mentioning the problem you're interested in solving is, "What's the hardest part of your day?"

Of all the problems your customer faces, if they bring up the one you’re interested in solving, you can get some pretty good conviction that you’re onto something and should continue exploring.

This was a key early customer interview question that led Catalyst by Wellstar, in partnership with High Alpha Innovation, to launch vflok, an AI-powered shift-scheduling solution for nurses.

More than half the nurses our analyst team spoke with identified scheduling and rearranging shifts as their biggest work-related pain point — before we even told them that’s the problem we wanted to solve.

Creating a product waitlist

Imagine having thousands of users signed up to use your product before the product is even launched. With that large of a sample size, you’d probably have pretty good conviction that your tech is worth launching.

What's more, you could validate some important assumptions around demand.

That’s exactly what Robinhood did — except its waiting list reached 1 million users for its product, well before it was released. All it had was a straightforward sign-up landing page, marketing campaign, and referral strategy that allowed users to move up on the Robinhood waitlist if they referred other users.

Using 'Wizard of Oz' Tests

Many startups, especially with the rise of artificial intelligence, promise to automate something. But actually doing so requires technical expertise, time, and capital.

The Wizard of Oz Test entails pretending that some activity is automated from the user's perspective, even though, behind the scenes, it's actually being done manually:

• In 2020, a group of Berkeley students decided to test a “health coaching chatbot."
• They enrolled users and manually chatted with them, encouraging healthy behaviors.
• It looked like a bot to the user, but it was actually one of the founders testing the idea.
• These chats helped the students learn what a natural-sounding chatbot could look like.
• The group also gained insights that ultimately informed the design and UX of its tech.
  

This direct customer interaction allows for assumption validation before spending time, money, and resources into developing a platform that there may not be an ideal product-market fit or need for.

Building an early MVP

The most misunderstood word in "minimum viable product" is “minimum." A good question to ask when an MVP is drawn up is how to make it more minimum ("Do we really need to code anything at all?").

Let’s revisit Airbnb:

• It's not hard to imagine Airbnb's MVP looking quite similar to their existing website — a marketplace is relatively straightforward. But it turns out, Airbnb’s actual MVP was much more minimal than that.
• Specifically, the founders bought some airbeds, set them up in a San Francisco apartment, and posted their offering on a barebones landing page website: $80/night, airbed, breakfast, and WiFi.
• And, like that, the first three customers of Airbnb were found.

Nothing about Airbnb's MVP is scalable, but it's a cheap (and, in fact, revenue-making) experiment that validates some key assumptions about demand and feasibility for the Airbnb product:

• Is anyone actually interested in this?
• What demographics are interested?
• What are these people willing to pay?
• What does a host need to prepare for?
• What questions do customers have?

Building a true MVP meant that the team could learn a lot quickly (and with minimal financial risk). When it came time to start coding up a platform, they had more confidence the solution had the right features.

Partnering with a venture builder to test and validate your business assumptions

Corporations have a deep-rooted expertise in operational efficiency and experience tackling big problems. However, they can better unlock growth and transformation by partnering with, investing in, or launching startups.

Why? Because all three options enable them to learn more with less quickly.

Internal CVC teams and innovation labs often own the initiatives tied to the first two options. But building new companies requires scaled enterprises to work with an external venture builder.

That's because they are far more likely to engage in an illusion of innovation by doing so on their own.

Identify North-Star themes for startup creation. Use a problem-led ideation approach to narrow in on challenges to solve for. Test assumptions tied to concepts around those issues. Synthesize research and analyze data and findings. Advance the top business idea(s) to the launch phase.

That's the approach we use with venture-building partners: corporations as well as universities, states, and non-profits. And it's an essential relationship enterprises must enter into to realize their own startup-creation ambition