My name is Mike, and my goal is to help all of you achieve your innovation research outcomes faster, less expensively, with less bias, and fewer assumptions. The obvious next step is giving you a way to sort through strategic options so you can accelerate into experiments quickly, cheaply, easily and productively. This blog documents many of the thought exercises I go through as I continually strive to take existing knowledge and emerging technology and blend them together to address our unmet needs.
If you want a deeper dive, here are a few options:
* My courses and AI prompting technology
* My Community (free)
Note: Paid subscribers get this a week early. Everyone else will get an email when it releases to the world.
For the “Too Long; Didn’t Read” Folks 🤫
My approach to using AI - in some cases - requires that we generate a quantity of outputs that is not necessarily conducive to survey construction. It just makes them too long.
Not too long ago I published an article and video (Blogcast) that demonstrated an approach you can use - after the fact - to shrink those lists down to a level that you can actually use.
The approach requires a review of other factors in the model - such as situational factors and contexts, in combination with stakeholder collaboration - to filter the list down to only those metrics that are relevant to a subset of those factors. It explained the reasoning for both inclusion and exclusion in the output. Still useful, but …
There is now a better way. In order to emulate what really happens when humans synthesize a large set of metrics, I decided to
