by Jonathan A. Handler, MD, FACEP, FAMIA
The Problem
Consider an innovator (let’s call him “Al”) working in a highly-matrixed company. Al has a very promising new idea and wants to develop it into a commercial product. Naturally, this initiative must get funded in the company’s budget. There are 5 manager stakeholders who decide whether Al’s product development will go forward. Al must garner alignment among these stakeholders for his project. Each stakeholder makes decisions independently — just because one says yes doesn’t mean the others will. Luckily, all 5 stakeholders are “pro-innovation” folks who know and trust Al. The company is strongly supportive of innovation, and all 5 stakeholders are financially incentivized to support innovation. Therefore, with Al’s best efforts, each stakeholder is very likely to say “Yes” to Al’s innovation. Specifically, each is 85% likely to say yes. In this matrixed organization, all 5 stakeholders must ALL approve Al’s project and its funding in order for it to proceed. With a single “no,” the innovation is dead, probably forever. Al is in an “ALL YES” innovation game, because ALL stakeholders must approve his project in order for it to proceed.
In this pro-innovation company with pro-innovation stakeholders, each of whom are far more likely than not to say “yes” to Al’s project, what is the probability of Al’s project getting the green light?
It’s only 44%! (see Appendix)
On top of that, Al must re-convince all 5 managers to fund his project every year. In any year, some of those managerial roles may be filled with new people who are unfamiliar with the project and may have different priorities and interests. Each year is a new opportunity for someone to say “no” and kill Al’s project. So, Al must get 10/10 “yes” votes over the two years. If the chance of each stakeholder’s approval remains at 85% every year, then the likelihood of Al’s project getting funded for 2 years is only 20%.
Now consider an innovator (let’s call her “Amy”) with a new idea. Amy is a researcher at a university. Amy wants to develop the idea, and she needs funding to do it. Amy’s academic department chair can choose to fund her project. Alternatively, Amy can apply for one or more grants to fund the project. Amy can also seek funding from individual philanthropists, or even from companies that wish to invest in her project in return for equity rights, or purely for philanthropic purposes. Whenever Amy needs funding, if ANY one of these sources provides the funding she needs, her innovation can continue. Amy only needs any one funding source to say yes in order for her project to continue. Amy is in an “ANY YES” innovation game, because getting a yes from ANY funding source means her project can continue.
Amy has applied for 5 grants to try to ensure her project gets funded. Since she is an excellent grant writer with a great track record of success, each grant has an 85% chance of getting funded. If even 1 grant application is successful, her project goes forward. In Amy’s situation, what is the probability of her project getting the funding needed to proceed?
It’s 99.99%! (see Appendix)
In other words, just considering the first year of development, the likelihood that Al’s project goes unfunded is 5600 times greater than Amy’s, simply because Amy is playing the “ANY YES” innovation game and Al is playing the “ALL YES” innovation game (see Appendix).
OK, maybe you think it’s unrealistic to think Amy has an 85% chance of getting each grant. So… let’s pick a more “realistic” number. Let’s say Amy has only a 30% chance of getting each grant. Her likelihood of at least one getting funded is still 83%! Al is still 3.3 times more likely to go unfunded than Amy in the first year, even though each of Al’s stakeholders is 85% likely to say yes, while each of Amy’s grant applications is only 30% likely to be successful. That’s the power of the ANY YES game.
This explains why so much innovation comes out of the academic world, and big companies often struggle to produce new innovations. Academics typically can make use of a vast array of funding sources, and only one or a few sources need to say yes for an innovation to proceed. In the business world it’s often closer to the ALL YES situation, where many managers have a say, and just one “no” is fatal to the project. The Innovator’s Dilemma, a groundbreaking book, notes that good managers often made wrong decisions when faced with disruptive innovation. However, I would posit that you are already on the path to innovation failure if a single manager (or anyone else) has the power to kill your innovation. It’s not that you need managers who are more likely to support your innovation. Rather, you need to be in a situation where anyone, in or out of the company, can say yes to supplying the resources needed to proceed.
Even in the best ALL YES situations, groundbreaking innovation will often struggle. Imagine you work directly for your best friend. She privately owns the company, serves as its CEO, and will support your project to completion. It’s an ALL YES situation, but “ALL” is only one person, and she has committed to “YES”. But… an economic downturn, a health issue for the CEO, a corporate buyout — any of these could lead to your project’s cancellation. If the yearly risk of cancellation is just 10%, and if your project will take 5 years, then you only have a little better than a 50-50 chance of getting funded to completion.
The math has spoken. To maximize an innovation’s chances to succeed, the innovator must be in an ANY YES innovation game.
Breaking out of the ALL YES innovation game may be difficult for big companies. The ups and downs of markets, employee turnover, and the pain and long timeframes that frequently accompany the most disruptive innovation make it very unlikely for innovation to succeed in a typical business setting. Even when companies recognize an inability to innovate, many still insist on traditional approaches to funding, ownership, and commercialization of innovation. Some do so because they hope for a big payday if an innovation succeeds wildly. The fact that they will probably not foster and fund the work needed to realize that payday often goes unrecognized because they haven’t done the ALL YES math.
Many business leaders think, for example, if they just “fund and incentivize managers” to support innovation, that will do the trick. Imagine the typical development time for an innovation is at least 3 years, and that innovators will have a manager and a manager’s manager who must approve an innovation for all 3 years in a row in order to get the innovation to market. The business leaders are not innovation-focused, so they are each 30% likely to say yes each year. The likelihood the innovation will fail to progress to market simply because someone says no at some point is 99.93%. To spur innovation, the business leader hires pro-innovation managers and provides incentive bonuses for innovation. These efforts succeed, and now the likelihood that each decision will be a yes is up to 85%. Even in this case, any one innovation is still 62% likely to be killed before it gets to market.
But wait! As a business leader, perhaps you think the math turns in your favor if your company pursues a “portfolio of innovations”. If your company funds 5 innovations every year, and each is 62% likely to get a no at some point before getting to market, then the likelihood that all 5 will be killed before getting to market is just 9%. There’s a 91% chance that at least one innovation will make it to market. Yeah! Now you’re playing an ANY YES game and thinking like a venture capitalist!
Except… it’s not at all like the venture capitalists. The venture capitalists take high risks in return for high rewards. Your managers may not be incentivized with rewards as high as venture capitalists get for their successes, and therefore may not be willing to take such big risks. Your managers may not be in the same role, and may no longer even be employees of the company when the returns are realized, and therefore may not expect to be the beneficiaries of those returns. So, they may preferentially pick low-risk, low-return, incremental innovations. Managers may reap innovation bonuses without the attendant risk by redefining “innovation” as “something new to this company” as opposed to “something new to the world.” By applying a company-centric definition of innovation instead of a customer-centric one, the company thinks it’s “doing innovation” even though it’s producing no innovation in the marketplace. In the end, your “innovation incentives” may simply give you a 91% chance of cloning a competitor’s existing feature into your product. Maybe that’s better than what you can accomplish today, but customers and investors may not perceive that as “innovation.”
Finally, look at this from the point of the innovator. The likelihood of an innovator’s individual project going forward at the company is still substantially less than half, not because the innovation fails, but merely because someone in charge says no (e.g., “we have higher priorities” or “we just don’t have the money this year”). On the other hand, if the innovator had actually been dealing with venture capitalists, then if a VC funds one round but chooses not to fund the next, the innovator can often seek funding from other VCs and other sources. Given the low odds of ongoing support for any individual innovation, companies will often have a hard time recruiting and retaining real innovators.
The Solution
Fortunately, there’s a simple solution for companies that struggle to foster innovation: commit to your innovators that, if your company fails to fund and resource an innovation in any year, then you will either open-source the innovation, cede the rights back to the innovator, or allow the innovator to continue to work on the project on company time using external resources and funding. Unless you’ve got a strong track record otherwise, don’t fall for the thinking that someday you’ll fund and execute the project. If you haven’t typically done that before, you almost certainly won’t in the future. You may choose to retain some reasonable access or ownership rights for the innovation. However, if these demands become too onerous, then it will kill the innovation and you will lose the intended benefits. What potential benefits could possibly accrue to your company from ceding the rights to unused innovations?
- Greater ability to recruit and retain great innovators.
- Ability to access the innovation at lower or no cost, and/or to own a portion of the innovation’s success, depending on the innovation agreement.
- Ability for all in the market space, including your company, to benefit from the innovation if it grows the market (“a rising tide raises all ships”).
- Ability for your company to grow market share if the innovation disproportionately benefits your company compared to competitors. For example, your company may disproportionately benefit if the innovation depends upon capabilities specific to your company’s product.
Conclusion
Long ago, my friend, Dr. Craig Feied, taught me the importance of having alternatives, as well as having the agility to take advantage of them when uncertainty is high. Playing an ANY YES innovation game keeps alternative funding and execution options available as you develop and learn. Playing an ALL YES innovation game tends to limit or eliminate alternatives right at the project’s start, when you know the least about it. The math clearly shows that your innovation success depends less on playing the game right, and more on playing the right game.
Appendix: The Math
The likelihood of ALL saying yes is the likelihood of one yes (e.g., 85%, or 0.85) raised to the power of the number of people who must say yes. So, if 5 people must say yes, and the likelihood of each yes is 85%, then the likelihood that all 5 will say yes is 85%5, or 44% (0.855 = 0.44). The likelihood of ALL saying yes is xy, where x is the likelihood that each person will say yes, and y are the number of votes that must be yes. This assumes that each vote is independent of all other votes.
On the other hand, to calculate the likelihood of ANY saying yes, we first calculate the likelihood that ALL stakeholders will say no. If the likelihood of each yes is 85%, then the likelihood of each no is 100% – 85% = 15%. So, the likelihood of all saying no is 15%5 = 0.0076% (let’s round that to 0.01%). Therefore, 100% – 0.01%, or 99.99%, is the likelihood that they will not ALL say no (in other words, at least one says yes). So, the likelihood of ANY saying yes is 100% – (100% – x)y, where x is the likelihood that each vote will be a yes, and y are the number of votes. This assumes that each vote is independent of all other votes.
If Al is 44% likely to get funded, then he is 56% likely not to get funded. If Amy is 99.99% likely to get funded, then she is 0.01% likely not to get funded. 56% equates to 5600/10,000 likelihood not to get funded for Al, and 0.01% equates to 1/10,000 likelihood not to get funded for Amy. Thus, Al is 5600 times more likely to get a “no” than Amy.
If Amy’s chances of getting her grants are only 30% each, then each grant’s likelihood of being a no is 70%. The likelihood that all of her grant applications will be rejected is 70%5, or 17%. Therefore, the likelihood that at least 1 grant will be accepted is 100% – 17%, or 83%. The likelihood that Amy’s grant funding will not go forward is 17%. The likelihood that Al’s funding will not go forward is 56%. 56/17 = 3.3, so Al is 3.3 times more likely to go unfunded than Amy.
All opinions expressed here are entirely those of the author(s) and do not necessarily represent the opinions or positions of their employers, affiliates, or anyone else. The author(s) reserve the right to change his/her/their minds at any time.
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