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AI Readiness Assessment

Before diving into AI implementation, organizations need to honestly assess their readiness. This guide helps you evaluate where you stand and what foundations need strengthening.

The Four Pillars of AI Readiness​

Successful AI adoption rests on four interconnected pillars:

Self-Assessment Framework​

Rate your organization on each dimension (1 = Not Ready, 5 = Fully Ready):

People Readiness​

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Leadership understands AI beyond buzzwords
Staff are open to changing how they work
You have internal champions for new technology
Training budgets exist for upskilling

Process Readiness​

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Core workflows are documented
You know where time is being wasted
Decisions have clear owners
You measure process outcomes

Data Readiness​

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Key data is digitized and accessible
Data quality is actively managed
You know where your data lives
Privacy and security policies exist

Technology Readiness​

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Systems can integrate with new tools
IT can support new implementations
Cloud infrastructure is in place
Security protocols are established

Interpreting Your Score​

64-80 points: You're ready for sophisticated AI implementations. Focus on strategic use cases.

48-63 points: Strong foundation with gaps to address. Start with simpler automation while building capabilities.

32-47 points: Foundational work needed. Prioritize the lowest-scoring pillar before major AI investments.

16-31 points: Significant preparation required. Focus on basic digital transformation before AI.

Common Readiness Gaps by Organization Type​

Small Businesses & Startups​

  • Typical Strength: Agility and willingness to experiment
  • Common Gap: Limited data history and documentation
  • Focus Area: Start capturing data now, even simple spreadsheets

Non-Profits & Associations​

  • Typical Strength: Mission clarity and volunteer enthusiasm
  • Common Gap: Technology budgets and IT capacity
  • Focus Area: Leverage free/low-cost tools and seek tech-focused grants

Chambers of Commerce & Member Organizations​

  • Typical Strength: Networks and relationship data
  • Common Gap: Siloed information across committees
  • Focus Area: Centralize member data before AI implementation

Corporate Teams & Departments​

  • Typical Strength: Resources and infrastructure
  • Common Gap: Cross-functional coordination and change resistance
  • Focus Area: Secure executive sponsorship and identify internal champions

School Districts & Educational Institutions​

  • Typical Strength: Clear mission and improvement focus
  • Common Gap: Privacy concerns and stakeholder alignment
  • Focus Area: Establish clear AI governance policies first

Building Your Readiness Roadmap​

Phase 1: Foundation (Months 1-3)​

  • Document top 5 time-consuming processes
  • Inventory existing data sources
  • Identify 2-3 internal AI champions
  • Establish basic data quality practices

Phase 2: Pilot (Months 4-6)​

  • Select one low-risk, high-visibility use case
  • Train core team on AI fundamentals
  • Implement first automation or AI tool
  • Measure and communicate early wins

Phase 3: Scale (Months 7-12)​

  • Expand successful pilots
  • Develop internal expertise
  • Create AI governance framework
  • Build sustainable training programs

The "Feature vs. Strategy" Test​

Before any AI investment, ask:

  1. Is this solving a real problem? Can you articulate the specific pain point?
  2. Do you have baseline metrics? How will you know if it's working?
  3. Who owns the outcome? Is there clear accountability?
  4. What happens if it fails? Is the risk acceptable?
  5. Does this fit your strategy? Or is it just chasing a trend?

If you can't answer these questions clearly, you're buying a feature, not implementing a strategy.

Next Steps​

  1. Complete the self-assessment above honestly
  2. Share results with your leadership team
  3. Identify your lowest-scoring pillar
  4. Create a 90-day plan to address the biggest gap
  5. Revisit this assessment quarterly