The Muskrat Ramble

The following are some thoughts on innovation. They came to me while musing on the theories of thinkers far greater than me.

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The Problem with Problems (Einstein)

All good ideas solve at least one good problem

• It’s much easier to have a good idea if you have a good problem

Relativity applies to problems – there are different solutions for different frames of reference

• An outsider will see different solutions to an insider

• There can be many “layers” of outsiders and insiders. The directly affected, the participants, the observers within the company, the external consultants paid to observe, people looking through the window, etc. (diagram this)

A solution based solely on outside observations will often be less valuable, or unworkable, when applied to, or by, an insider (and vice-versa)

• The solution you sell must be relevant and workable to the frame of reference of the buyer

• Insider experience + outsider perspective = successful & innovative solution

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Guaranteed System Failure (Gödel)

There are many “systems” for capturing and developing innovations…

…Gödel inadvertently proved these systems are bound to fail

Given: Innovations are exceptions

• Something that is innovative is new and different, thus the exception to the established rule

Given: Gödel proved that no consistent system can be complete, and vice-versa.

• “This statement is unprovable”
• In a complete system of logic the above statement is possible, but yields inconsistent results when analysed
• A consistent logic system would have to discard the above, and therefore it would not be complete.

Given: Innovation systems are designed to capture identifiable ideas – but you can only identify based on what you know, or what can be defined within the rules

Thus: Innovation systems will miss all the exceptions to the rules, which means they will miss all the innovations.

Q.E.D.

PS: Please note the large bump in my cheek. That’s my tongue.

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Innovative Voyeurism (Heisenberg)

Heisenberg says certain pairs of physical properties, like position and momentum, cannot both be known to arbitrary precision.

• This is because the act of observing affects the measurement

Innovation seems to be rife with uncertainty: how much to invest, how long to invest for, etc.

• “Common knowledge” says: Many innovations fail because they were undercapitalised – the plug was pulled right before success (but how do we know that?)
• “Common knowledge” says: Many innovations cost far more money than they should because the innovators are incapable of letting go of their failures (but many of the successes would have never been if the innovators had let go)

The act of observing and analysing the innovation process often has an effect on the process itself

• Effects can be positive or negative, depending on the tools, methods and objectives of the observations
• Introspection is also observation – the innovators themselves are often observers

Do more observations lead to consistently better or worse outcomes, or is the sum effect neutral?

• This is a constant debate between VCs (or program managers) and innovators
• Innovators prefer less observation; however external feedback is often key to preventing long journeys down dead-ends. Of course, external feedback can also lead to short-sighted decisions that can kill an innovation

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Three Steps beyond the 1st Horizon

1) Getting beyond 1st Horizon innovation requires a 3-5 year capability for investment

• Organisations managed on 6-12 month performance targets (most) don’t have KPIs that encourage long-term investment of time and capital

2) Recruit, management, and reward strategies need to match the structure that implements this capability

• 90% of ideas will fail at some stage; how do you performance manage someone who does excellent work for 6 months on a failure?
• Project KPIs should match the innovative process – # of prototype revisions, # of potential customers engaged at each round of development, project management skills, etc.

3) An innovation capability must be fed with qualified problems

• Qualified: real customers must be willing to pay real money for solutions to the problem
• Qualified: the parameters of “acceptable solution” should be fairly clear
• Problems: Do we know the current top 10 problems faced by our clients?
• Problems: Have we defined the problems well enough, or are we reverse-engineering problems from the solutions we pulled out of thin air?