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AI Readiness Guide · Appendix B

AI Readiness Leadership Discussion Guide.

Bring leadership perspectives together, interpret your readiness results, identify what matters most, and agree on the actions your organization will take next.

Developed by FridAI Learning Center Published Meeting length 60–90 minutes Format Facilitated leadership workbook
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An AI readiness assessment is most valuable when leadership discusses the results together.

Different leaders may see the organization differently. Technology staff may believe systems are ready. Operations may identify process problems. Human resources may see workforce concerns. Finance may focus on cost and measurable value. Executives may see strategic opportunities others have not considered.

Those perspectives should come together.

This discussion guide is designed to help leadership teams interpret the AI Readiness Assessment, identify the most important gaps and opportunities, and agree on the next steps.

The objective is not to debate every score. The objective is to answer: Where are we today? What matters most? What should we do next?

Recommended Participants

Include the people necessary to understand the organization from multiple perspectives.

Depending on the organization, participants may include:

  • Senior leadership.
  • Operations.
  • Information technology.
  • Finance.
  • Human resources.
  • Legal or compliance.
  • Data or analytics personnel.
  • Department leaders.
  • Selected frontline or operational representatives.

Smaller organizations may have only a few participants. That is fine. What matters is having enough perspective to understand the organization realistically.

Recommended Meeting Length

Recommended duration

60–90 minutes

The discussion can be longer for larger or more complex organizations, but it should remain focused.

Avoid turning the meeting into a technical seminar. This is a leadership decision-making conversation.

Before the Meeting

Each participant should review the AI Readiness Assessment results.

Ideally, leadership should have access to:

Do not hide disagreement

If multiple leaders completed the assessment independently, do not average the scores immediately. Differences in ratings can be extremely valuable.

Technology leadership may score data readiness as a 4 while operations scores it as a 2. The difference may reveal that data technically exists but operational employees do not trust or understand it.

The goal is not to determine who is right. The goal is to understand why the perspectives differ.

Meeting Objective

By the end of the discussion, leadership should ideally have agreement on five things:

  1. Where the organization is strongest.
  2. Where the most important readiness gaps exist.
  3. Which immediate AI risks require attention.
  4. Which organizational problems represent the strongest AI opportunities.
  5. What the organization will do during the next 90 days.

If those five decisions are made, the meeting has been successful.

Part 1

Establish the Context

Begin with one question:

Why are we discussing AI readiness now?

Possible answers might include:

  • Competitive pressure.
  • Employee experimentation.
  • Customer expectations.
  • Productivity opportunities.
  • Workforce challenges.
  • Data opportunities.
  • Technology changes.
  • Leadership interest.
  • A specific operational problem.

This keeps the discussion anchored in organizational purpose.

Part 2

Review the Readiness Profile

Review the scores from Appendix A. Do not begin with the overall score. Begin with the individual dimensions.

Readiness Dimension Score Status
Leadership
%
Workforce
%
Culture
%
Process
%
Data
%
Technology
%
Governance
%
Use Cases
%
Pilots
%
Measurement
%
Scale
%
Internal Capability
%

Then discuss the evidence behind the scores.

Disagreement is useful. Discuss the evidence behind the score.

Part 3

Identify Organizational Strengths

Begin with strengths before focusing on gaps.

Which three readiness areas are strongest?

Strength 1

Strength 2

Strength 3

Treat strengths as resources

  • Strong workforce engagement may help build AI champions.
  • Strong data readiness may make a predictive pilot easier.
  • Strong leadership alignment may help governance develop quickly.
  • Strong process discipline may make automation easier.

Part 4

Identify the Most Important Gaps

Review the lowest-scoring readiness areas, but do not automatically treat the lowest score as the highest priority.

Which three gaps matter most right now?

Avoid vague conclusions. Specific gaps create specific actions.

Gap 1

Gap 2

Gap 3

Part 5

Identify Immediate Risks

Leadership should distinguish readiness improvement from urgent risk management.

Is anything happening today that creates unnecessary AI risk?

Consider:

  • Unapproved employee AI use.
  • Confidential information entered into public tools.
  • AI-generated content used without review.
  • High-impact decisions influenced by AI without oversight.
  • Unknown vendor data practices.
  • Shared accounts.
  • Unclear accountability.

Immediate Risk 1

Immediate Risk 2

If a significant risk exists, address it before waiting for a broader AI strategy.

Part 6

Understand Current AI Use

Where is AI already being used in our organization?

Do not assume leadership knows. Uses may include drafting, summarization, presentations, coding, spreadsheet analysis, marketing, research, customer communication, forecasting, automation, or AI embedded inside vendor software.

Department AI Tool or Capability Current Use Owner

This question often reveals the need for better visibility rather than stricter control.

Part 7

Discuss Workforce Sentiment

Leadership should understand how employees may be experiencing AI.

How do we believe employees currently feel about AI?

Leadership assumptions should not substitute for employee input.

Part 8

Identify Organizational Problems Worth Solving

Ask each participant:

What is one organizational problem you most want to improve?

Do not mention AI initially. Begin with the work and the problem.

Examples might include:

  • Slow reporting.
  • Poor forecasting.
  • Customer churn.
  • Excessive administrative work.
  • Difficulty finding information.
  • Employee burnout.
  • Production downtime.
  • Quality problems.
  • Inventory issues.
  • Slow customer response.
  • Difficulty analyzing organizational data.

Part 9

Ask the Employees' Question

If employees could make one part of their work easier, what would they choose?

If leadership does not know, that is useful information. The next action may be to ask employees directly.

Part 10

Evaluate Potential AI Opportunities

For each problem, ask:

  • Could AI realistically help?
  • Would a simpler solution work better?
  • Is the problem important enough to justify attention?
  • Does it happen frequently?
  • Do we have the required data?
  • Can the outcome be measured?
  • What happens if AI is wrong?
Problem Potential Value Feasibility Risk Measurable? Priority

A strong early opportunity

The ideal early opportunity is often high value, reasonably feasible, manageable in risk, and measurable.

Part 11

Select One Priority AI Opportunity

Do not leave the meeting with 15 equally important AI ideas. Select one leading opportunity for deeper evaluation.

This does not automatically authorize a pilot. It identifies where further evaluation should begin.

Part 12

Determine Whether the Organization Is Ready to Pilot

Pilot Readiness Question Leadership Response
Can we clearly define the problem?
Is the required data available?
Is the process understood?
Can the pilot remain low or manageable risk?
Can human review remain in place?
Can we measure the current baseline?
Can we define success?
Can we assign an owner?
Can participating employees be trained?
Can the pilot be limited in scope?

Part 13

Select the Top Three 90-Day Priorities

These priorities may involve readiness improvement, risk reduction, or practical experimentation.

Examples include:

  • Create AI-use guidelines.
  • Train leadership.
  • Inventory current AI use.
  • Select approved tools.
  • Map a priority process.
  • Clean a specific dataset.
  • Train managers.
  • Identify AI champions.
  • Launch a pilot.
  • Establish measurement.

Priority 1

Priority 2

Priority 3

Part 14

Assign Overall AI Ownership

Who is responsible for coordinating AI readiness and adoption?

This person does not need to:

  • Approve every use.
  • Solve every technical problem.
  • Become the organization's only AI expert.

Their role is to help maintain visibility and coordination. Responsibilities may include:

Part 15

Identify Supporting Roles

Depending on organizational size, identify people responsible for the following areas.

Responsibility Person or Team Notes
Governance
Technology
Data
Workforce Training
Pilot Measurement
Business Process Ownership

In smaller organizations, one person may fill several roles. That is acceptable. The objective is clarity.

Part 16

Agree on AI Principles

Leadership may find it useful to establish several simple principles that will guide AI adoption.

01

We begin with organizational problems, not technology.

02

AI-generated information does not eliminate human accountability.

03

Sensitive information will be protected.

04

We will pilot before we scale.

05

We will measure organizational value, not simply AI activity.

06

Employees will participate in identifying and improving AI use cases.

07

We will stop AI initiatives that do not create sufficient value.

Organizations can adopt these directly or create their own.

Our AI Principles

These principles can help guide decisions when specific policies do not yet address a new situation.

Part 17

Define What Leadership Will Communicate

After the meeting, employees may need to hear from leadership. A useful communication should answer:

Consistent communication matters. Employees should not receive different answers from different leaders.

Part 18

Decide What Leadership Still Needs to Learn

The meeting may reveal questions leadership cannot yet answer. Capture them.

Question 1

Question 2

Question 3

Unanswered questions are not failures. They are the next learning agenda.

Part 19

Establish the Next Review

Do not allow the discussion to end without a follow-up point.

At that meeting, leadership should review:

  • What actions were completed?
  • What remains unfinished?
  • What changed?
  • What did employees learn?
  • Did new risks emerge?
  • Did the priority use case move forward?
  • What should happen next?

A 90-day review is often appropriate for organizations beginning their AI readiness work.

90-Day Leadership Review

A Suggested 90-Minute Meeting Agenda

Organizations may use the following structure.

0–10

Why AI Readiness Matters

Review why the organization is assessing readiness and what leadership hopes to accomplish.

10–25

Readiness Results

Review section-level scores, strengths, gaps, and surprises.

25–40

Risk

Identify immediate governance, privacy, security, workforce, or operational risks.

40–60

Opportunity

Identify organizational problems where AI might create meaningful value.

60–70

Priority Use Case

Select one use case for deeper evaluation.

70–80

90-Day Priorities

Select the top three readiness actions.

80–90

Ownership and Next Review

Assign accountability and schedule the next leadership review.

Protect the final 20 minutes

The meeting should produce decisions, not merely discussion.

Facilitator Notes

The person facilitating the conversation should watch for several common problems.

The Conversation Becomes Too Technical

Return to: What organizational problem are we discussing? Leadership does not need to resolve every technical detail during this meeting.

One Leader Dominates the Discussion

Actively invite other perspectives. Ask what operations, employees, finance, and other functions see differently.

Leadership Jumps Immediately to Vendors

Return to the problem. Before discussing products, clarify the outcome the organization is trying to create.

The Team Tries to Fix Everything

Return to what matters during the next 90 days. Maintain a backlog for additional needs.

Every Idea Becomes a Priority

Use value, feasibility, and risk to force prioritization. Some good ideas must wait.

Leadership Avoids Workforce Concerns

Ask directly what employees will think AI means for their jobs. That conversation will happen whether leadership participates or not.

No One Wants Ownership

Ask who is accountable for making sure the work moves forward. Shared support is useful. Shared accountability often becomes no accountability.

Leadership Decision Summary

At the conclusion of the discussion, complete this page.

The Most Important Leadership Question

At the end of the meeting, ask one final question:

What will be different 90 days from now because we had this conversation?

If the answer is unclear, the meeting is not finished.

The purpose of AI readiness is not to create another report. It is to create action.

Leadership does not need to know exactly what artificial intelligence will look like five years from now. No one does.

Leadership needs to understand enough to make the next responsible decision. Then the next one. Then the next.

That is how organizational capability develops.

Assess honestly Discuss openly Prioritize deliberately Assign ownership Take action

Then return to the conversation with better information than you had before.

That is leadership readiness in practice.

Last reviewed:

This guide is designed to support leadership discussion and organizational decision-making. Adapt the questions and participants to your organization's size, responsibilities, and risk environment.

← Previous appendix Appendix A: AI Readiness Assessment

Assess your organization's readiness across leadership, workforce, culture, processes, data, technology, governance, experimentation, measurement, and scale.

Continue to Appendix C → Data Readiness Checklist and Inventory Worksheet

Evaluate the availability, quality, ownership, sensitivity, and usability of the data required for responsible AI adoption.