Why Your AI Strategy Needs Negative Results
Source: Mike Kentz Substack
Author: Mike Kentz
Published: 2026-01-17
Source type: essay
Original source: https://mikekentz.substack.com/p/why-your-ai-strategy-needs-negative
Private backup: the full article text is archived in the private repository at archives/articles/mikekentz-substack-com-why-your-ai-strategy-needs-negative.source.md. It is not published on the public Quartz site.
Summary
Mike Kentz argues that schools and universities should not wait for generic AI research to dictate their AI strategy because institutional context matters too much. Instead, institutions should run controlled, mission-aligned experiments that collect local data about whether AI helps or harms specific students, tasks, and workflows. A key contribution is the argument that negative or null results are strategically valuable: failed AI pilots can prevent bad investments, reveal risks, and lead to better approaches. Kentz illustrates this with his own failed AI tutoring/mentorship idea and with ongoing experiments at the University of Washington, University of Central Florida, and Dayton Public Schools.
Pull quotes
Negative data can create positive data
“It was the negative data that created the positive data.”
Do not wait for distant research
“Why wait for research universities to tell you what’s right and wrong?”
No one-size-fits-all AI strategy
“AI will never offer a one-size fits all framework.”
Big ideas
- AI tools should be judged by the work they will actually do
- Schools should start with learning values before choosing AI tools
- District AI work is a long-term redesign project
Claims
- AI tool choices should be judged against stated learning values
- Failed AI pilots are useful evidence
Key evidence and examples
- Kentz’s failed upperclassman AI tutoring/mentorship pilot revealed the need for more formal structure and risk controls.
- University of Washington graduate communication students test transcript analysis, peer transcript annotation, and metacognitive AI reflections.
- University of Central Florida tests AI use in business model creation through student surveys and iterative experiments.
- Dayton Public Schools’ planned SEL Check-In Bot avoids therapy and functions as a signal flare for counselors using frequency data and dashboards.
Education relevance
High relevance for school and university AI strategy, pilot design, research-practice partnerships, governance, implementation planning, and evaluation culture.
Durability note
The named pilots and institutional examples may date, but the core lesson is durable: AI strategy improves when schools run local, controlled experiments and preserve negative results as evidence.