If AI software is going to take over many repetitive tasks and processes, then what should people do with the free time that creates ?
David Schonthal, a professor at the Kellogg School of Management at Northwestern University, says the answer is in what the Japanese call “shokunin” –
1. Understanding the Friction that New Ideas Face
Key idea : Recognize the main barrier to innovation isn’t bad ideas, but human resistance.
“ Friction Theory ” is about overcoming individuals and organizations saying “ no ” to good ideas. It starts with a core foundational understanding that human beings are hard-wired to resist change for a number of different psychological and evolutionary reasons.
There are 4 types of frictions –
- Inertia Friction – the overwhelming tendency of people to stick with what they know or are familiar with (despite the fact that what they know is insufficient for the future)
- Effort Friction – which is the real or often perceived amount of energy required to effect change (physical and cognitive energy)
- Emotional Friction – the undesired or unexpected negative feelings people cause in those they are trying to help
- Reactance Friction – the psychological phenomenon whereby people don’t want to be changed by other people. This is typically in the form of “ Doesn’t matter how good the idea is, I don’t want to feel like you are changing me.”
2. Use AI to Free Time for Craft and Core Competence
Key idea: As AI automates routine work, reinvest that freed-up time in deepening the craft and quality at the heart of what makes people distinctive and will increase their value.
For example – How could AI enable people to spend more time creating things that are of special interest to them or the group, or to explore new opportunities ?
3. Use AI to Better Frame the Problem – as well as to Generate Ideas
Key idea : In a world where AI can generate many ideas, real differentiation comes from asking a different, deeper first question — what is called “Question Zero” — whereby AI is a partner in framing the problem, providing greater context, to be more open ended, etc.
For example, most innovative companies are solving different problems than everybody else and framed the opportunity differently than others. That stems from the realization that if everybody’s prompting AI with similar or surface level prompts and observations, you’re not going to get anything unique or different !
To begin exploring, figure out the right question to start with and demonstrate curiosity on the innovation journey. And realize questions could be a novel reframe of a problem or opportunity. In addition, be good in asking follow up questions to increase awareness of the options to move forward.
For example, if AI is examining data, it could identify some interesting opportunities, anomalies or behavioral patterns that are worth questioning. From this, do some research to determine if those things are true or valid. How are people framing those things in their minds ? Then feed some of those reframed prompts into an AI and say : ‘ Give me some alternative explanations for alternative ways of framing a problem such as A, B, or C ? ’ Or, is there another problem from the data that other people haven’t considered or explored ?
An example of this approach is Cursor (an AI-powered code editor) that is different than Claude Code or GitHub Copilot which are about “ How do we design or write code faster ?” Cursor took a different tack by analyzing the data and determined the foundational problem wasn’t writing code faster, but that a lot of time was being spent by engineers trying to read and understand existing code. From this, Cursor reframed the problem from – ‘ How do we help people write faster, better code ’ – to – ‘ How do we help designers or engineers understand the code they are working with, make sense of it in a really intelligible and actionable way, and then change, modify or extend it to satisfy the new requirement ?
Aug 11, 2026 IL / CAIL Innovation commentary info@cail.com www.cail.com 905-940-9000