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Self-Service Resources

Not every organization is ready for a formal advisory engagement — and that's fine. Self-service resources give you structured tools to start evaluating AI readiness on your own terms, using the same principles that underpin our advisory methodology.

What's Available​

Free Resources​

Practical frameworks for initial self-evaluation and planning:

  • Readiness self-assessment guides
  • AI opportunity identification templates
  • Common pitfall checklists for AI adoption

Entry Tools​

Lightweight prioritization and evaluation instruments:

  • Opportunity scoring worksheets
  • Readiness dimension frameworks
  • Quick-reference guides for evaluating AI vendors and solutions

Core Toolkits​

Deeper preparation materials for teams getting serious about AI:

  • Process documentation templates
  • Stakeholder alignment frameworks
  • Baseline measurement guides

How Self-Service Connects to Advisory​

Self-service resources are designed as a natural entry point. Organizations that work through self-service materials arrive at formal advisory engagements with:

  • Clearer internal alignment on goals and priorities
  • Better baseline documentation of current operations
  • More realistic expectations about readiness and timeline

This means advisory engagements start faster and go deeper — because the foundational preparation is already done.

The MA Connection​

Even our self-service resources reflect Mathematical Autopsy's principles. The templates and frameworks are structured around the same concepts — explicit intent, measurable criteria, evidence-based evaluation — that drive our formal engagements.

The difference is depth: self-service gives you the structure; advisory applies the full mathematical discipline.

When to Move to Advisory​

Self-service works well for initial exploration. Consider moving to a formal Express Assessment when:

  • You've identified potential AI opportunities but need objective validation
  • Internal stakeholders aren't aligned on priorities or readiness
  • You need defensible, board-ready analysis rather than internal estimates
  • You want the mathematical rigor that self-service frameworks can't provide on their own