AI Traction

AI TRACTION

Your team has AI tools. They’re not using them.

AI Traction is a diagnostic-driven, cohort-based program that closes the gap between deploying AI tools and getting value from them.

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Six reasons AI adoption stalls — and none of them are technical

Understanding the human factors behind deployment failure
1

Permission is unclear

Employees don’t know what’s allowed with AI tools

2

Manager bottleneck

Managers don’t model the behavior they’re asking for

3

“I tried it once” wall

One bad experience becomes permanent abandonment

4

No shared language

Team members have wildly different mental models of AI

5

Identity threat

Expertise feels devalued, not augmented

6

No feedback loop

Nobody measures what happens after deployment

How AI Traction works

A four-phase program designed around human behavior, not just technology
0

Phase 0: Diagnosis

Julie leads
Human factors research: contextual inquiry, interaction analysis, workflow observation. Understand WHY the human-AI interaction is failing.

A paid standalone engagement ($5K-$10K) that maps the behavioral landscape before anything else happens. The diagnostic determines what the learning program needs to address, how organizational conditions need to shift, and what baseline measurements look like. Everything downstream is designed from these findings.

1

Phase 1: Leadership Alignment

Cynthia leads
Executive alignment, manager enablement, AI use policy clarity.

Before the cohort program starts, leadership needs to be on the same page. This phase ensures managers model AI use, policies are clear, and the organizational environment supports change — not just permits it.

2

Phase 2: Cohort Program

Susan leads
10-15 people, 8-12 weeks, biweekly sessions. Community-of-practice methodology.

Each 90-minute session has three parts: a skill module, an experiment share where participants discuss what they tried, and accountability pairs who commit to the next experiment. This is the WinAI methodology, adapted for enterprise.

3

Phase 3: Measurement

Julie leads
Pre/post behavioral data, manager-reported change, ROI modeling.

Every engagement produces evidence. We measure what changed, not just what people say changed. Behavioral data, manager reports, and ROI modeling that makes the case for continued investment.

“We went from 12% weekly AI usage to 74% in four months. But the real shift was that people started teaching each other.”
Maria Rodriguez
Operations Director, Financial Services Firm

Ready to find out what’s actually happening with AI in your organization?

Schedule a diagnostic conversation to uncover the human factors blocking adoption—and what we can do about it together.

Schedule a diagnostic