Frameworks
Two open-source frameworks for AI adoption — one for strategy, one for execution. Together they cover the full path from "should we use AI?" to "it's running in production."
PAST answers what and why — strategic clarity about what you're trying to achieve. SHAPE answers how and when — systematic execution from assessment through evaluation. Use PAST to define the initiative, then SHAPE to implement it.
The PAST Framework
From Random AI Experiments to Strategic Clarity
Most AI implementations fail before they start — not because of bad technology, but because of unclear strategy. PAST asks four questions that determine success or failure, and works at every level from organizational strategy down to individual prompt engineering.
Purpose
What specific outcome are you trying to achieve?
Prevents vague goals and technology-first thinking
Audience
Who does AI serve?
Prevents designing for buyers instead of users
Scope
What are the realistic boundaries?
Prevents scope creep and trying to solve everything at once
Tone
How should AI align with your culture and voice?
Prevents generic AI slop and adoption failure
Quick Start: The Purpose Clarity Exercise
"Success means [specific measurable outcome] which will [business impact] by [timeline] as measured by [metric]."
If any part is blank, you're not ready to evaluate tools. Return to purpose definition.
Works at Every Level
- Organizational Strategy — Enterprise AI implementation and governance
- Team Workflows — Department-specific process optimization
- Individual Productivity — Personal workflow enhancement
- Prompt Engineering — Creating effective AI interactions
The SHAPE Methodology
From Pilots That Stall to Systematic Implementation
Most AI pilots succeed. Most scaled implementations fail. 95% of AI pilots never achieve enterprise-wide deployment, and organizations lose an average of $1.9 million per failed AI initiative. The problem is execution methodology, not technology.
Situation
Assess current state honestly before changing anything
Prevents bad assumptions and underestimated complexity
Hypothesis
Define measurable success criteria upfront
Prevents unmeasurable goals and drifting pilots
Action
Execute systematic pilots with clear decision frameworks
Prevents analysis paralysis and poor tool selection
Process
Scale what works through systematic phases
Prevents scaling disasters and complexity creep
Evaluation
Measure continuously and iterate based on evidence
Prevents stagnation and sunk cost bias
Key Decision Framework: Takers vs. Shapers vs. Makers
| Approach | Success Rate | Time to Value | When to Use |
|---|---|---|---|
| Takers | 67% | 4–8 weeks | Off-the-shelf solutions (default choice) |
| Shapers | 45% | 8–16 weeks | Customized vendor solutions |
| Makers | 33% | 16+ weeks | Custom-built for competitive differentiation |
Default to Takers unless you have compelling, documented reasons for alternatives. Simple tools that work reliably outperform complex customizations requiring constant maintenance.
Both frameworks are open-source under CC BY-SA 4.0. Use them, adapt them, teach with them — just give attribution and share alike.