Executing Innovation starts from learning to understand frame conditions for an application of proven Technologies from other sectors and/or new inventions in new combinations. It is often a multidisciplinary context requiring open collaboration between different disciplines – and if you want the best result, teams on the job will have to be given the required autonomy how to spend their time budgets to achieve goals, plus allowance for failures to learn from. Most managers, spending their time in their businesses won’t be able to mentor such processes – only leaders of vision how to work on their business will be able to do so. It is about enticing creativity in service of the shared core values of the intended innovation. People understanding the meaning of their potential contributions to this world will follow, collaborate, co-learn with colleagues or even competitors of same spirit.
Oh, by the way, very few people understand that in the phase of the Implementation of an Innovation there is no crowding out competition – because it will create new added value from which everyone who contributes could get his fair share. Even if the Innovation might cannibalize someone’s existing business model, a participation by contributing all expertise on the field of application will make him part of future. While trying to retard might just call his inferior competitors on the agenda of the Innovation Team leading to a change in market leadership at some point. We’ve seen start-ups overleap big players on and on.
So effective Innovation Implementation needs excellently competent rebels open to question existing solutions in the market and readiness for co-petition under a visionary mentorship mediation with state of the art risk-minimization discipline. The team must adopt a small child’s practice of asking questions, trying things out, looking for guide or patterns, review previous experiences and develop the causal model of its task’s matrix of risks for a consensual prioritization of the needed investigations and solutions. Exemplarily for determination of final Capital Expenditure requirement for Carbotopia™ it may look like:
Mr. Bayes general laws of chance aimed to allow that an expectation depending on the truth of any past fact, or the happening of any future event, ought to be estimated so much the more valuable as the fact is more likely to be true, or the event more likely to happen. Following a Bayesian probability assessment of priorities to hypothetically mitigate the risks where “there is a 65% chance of meeting project expectations. That probability will rise to 90% after the first 100-days research and 97% after 160 days” by challenging the team with the right questions at the right time.
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