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AI Readiness Guide · Appendix A

AI Readiness Assessment.

Understand where your organization is today, identify the strengths and gaps that matter most, and decide what to work on next.

Developed by FridAI Learning Center Published Format Organizational assessment Series AI Readiness Guide
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Artificial intelligence readiness is not a single capability.

It is the combined result of leadership, workforce preparation, culture, processes, data, technology, governance, use-case selection, measurement, and the organization's ability to learn.

This assessment is designed to help organizations understand where they are today.

It is not intended to produce a passing or failing grade.

The more useful question

What should we work on next?

Organizations should complete this assessment honestly.

The value comes from identifying both strengths and gaps.

How to Use This Assessment

For each statement, rate your organization using the following scale.

Score Readiness level
1 Not Yet We have little or no capability in this area.
2 Early We have begun discussing or addressing this area, but significant gaps remain.
3 Developing Basic capability exists, but implementation is inconsistent.
4 Established The capability is generally understood, documented, and consistently practiced.
5 Advanced The capability is well developed, routinely applied, measured, and continuously improved.

If you are unsure how to score a statement, choose the lower rating. It is better to identify an area for further discussion than to overestimate readiness.

Section 1

Leadership Readiness

Rate each statement from 1 to 5.

Add the ratings for Questions 1–15.

Statement 1 2 3 4 5
1 Senior leaders can explain in practical terms what artificial intelligence is and how it may affect our organization.
2 Leadership understands that AI includes more than generative AI and chatbots.
3 Leaders understand important AI limitations, including inaccurate outputs and the need for human judgment.
4 Our organization has identified why AI matters to our broader strategy.
5 Leadership can identify organizational priorities where AI might create meaningful value.
6 Employees understand why leadership is exploring AI.
7 Leadership encourages responsible AI experimentation.
8 Leaders actively seek ideas from frontline employees.
9 Leadership openly acknowledges uncertainty and continues learning about AI.
10 Someone has clear responsibility for coordinating our organization's AI efforts.
11 Important AI decisions involve appropriate perspectives from across the organization.
12 Leaders expect measurable outcomes from AI initiatives.
13 Leadership is willing to stop AI projects that do not demonstrate sufficient value.
14 Human accountability remains clear when AI supports decisions.
15 AI is increasingly viewed as an organizational capability rather than simply a technology purchase.

/75

Section 2

Workforce Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 Employees have a basic understanding of artificial intelligence.
2 Employees understand how AI may apply to their specific roles.
3 Employees understand that AI can produce incorrect or misleading information.
4 Employees know which AI tools they are permitted to use.
5 Employees understand what organizational information should not be entered into unapproved AI systems.
6 Employees know when AI-generated work requires human review.
7 Leadership understands how employees are currently using AI.
8 Employees feel comfortable asking questions about AI.
9 Workforce concerns about AI and job changes can be discussed openly.
10 Employees are encouraged to identify potential AI opportunities.
11 Frontline employees participate in AI planning and experimentation.
12 Managers are prepared to support responsible AI use.
13 Employees receive hands-on opportunities to practice with approved AI tools.
14 Employees are developing critical-thinking skills for evaluating AI outputs.
15 Relevant employees possess sufficient data literacy for their roles.
16 AI champions or early adopters exist within the organization.
17 Successful AI practices are shared among employees.
18 Employees have some time available for AI learning and experimentation.
19 AI training is ongoing rather than treated as a one-time event.
20 Employees understand how AI relates to organizational priorities.

/100

Section 3

Cultural Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 Employees feel comfortable asking basic questions about AI.
2 Leaders are comfortable admitting when they do not know something.
3 Employees are encouraged to challenge inefficient processes.
4 Employees can propose new AI use cases or experiments.
5 Responsible experiments that do not succeed are treated as learning opportunities.
6 Employees feel comfortable questioning AI-generated recommendations.
7 Departments regularly share useful knowledge.
8 Successful AI experiments are communicated across the organization.
9 Employees are recognized for identifying better ways of working.
10 Frontline and experienced employee expertise is valued when evaluating AI applications.
11 Employees have permission to experiment within appropriate boundaries.
12 Departments are willing to collaborate across organizational boundaries.
13 AI adoption is understood as an organizational learning process.
14 Leadership avoids creating pressure to adopt AI simply because competitors are doing so.
15 Responsible use is valued more than appearing technologically advanced.
16 The organization is willing to change longstanding processes when better approaches are found.
17 AI knowledge is becoming organizational knowledge rather than remaining with a few individuals.
18 Employees can raise AI concerns without fear of negative consequences.
19 Leadership behavior reinforces responsible AI expectations.
20 Continuous learning is already part of the organizational culture.

/100

Section 4

Process Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 We understand how our most important operational processes actually work.
2 Employees who perform the work participate in process-improvement discussions.
3 We can identify processes that consume excessive employee time.
4 We know where repetitive manual work occurs.
5 We understand where information is manually transferred between people or systems.
6 We can identify major bottlenecks.
7 We understand where errors and rework frequently occur.
8 We know where employees spend significant time searching for information.
9 We understand where important decisions occur within major workflows.
10 We know which decisions require substantial human judgment.
11 We understand common exceptions to standard processes.
12 We consider eliminating or simplifying work before automating it.
13 Similar work is reasonably standardized across departments or locations.
14 Important workflows have basic documentation.
15 We understand which data and information important processes require.
16 We can measure current process performance before introducing AI.
17 Employees are encouraged to identify inefficient work.
18 We distinguish problems requiring AI from problems that can be solved more simply.
19 AI use cases are connected to specific process improvements.
20 We can describe what better performance would look like before implementing AI.

/100

Section 5

Data Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 We know where our most important organizational data is stored.
2 We know which employees or departments understand our major datasets.
3 Important datasets have identifiable owners.
4 We can access the information needed for priority AI use cases.
5 We understand significant quality problems in our data.
6 Important organizational terms and metrics are defined consistently.
7 We can identify material duplicate records.
8 We understand where important information is frequently missing.
9 We know how far back important historical data extends.
10 We understand how frequently major datasets are updated.
11 We can distinguish reliable data from data requiring caution.
12 We understand which categories of organizational information are sensitive.
13 Employees understand which information should not be entered into unapproved AI systems.
14 Access to sensitive information is appropriately controlled.
15 Privacy and security are considered before organizational data is used with AI.
16 We recognize documents and other unstructured information as potential data assets.
17 We are working to preserve important institutional knowledge.
18 We can trace important AI-supported conclusions back to underlying data where appropriate.
19 Data quality is evaluated in relation to specific AI use cases.
20 Employees with relevant expertise help interpret AI-supported analysis.

/100

Section 6

Technology and Infrastructure Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 We understand the major technology systems used throughout the organization.
2 We know which current platforms already contain AI or advanced analytics capabilities.
3 Technology purchases are connected to clearly defined organizational needs.
4 Employees have appropriate devices and connectivity for priority AI use cases.
5 We understand where system integration may be required.
6 We avoid building complex infrastructure before proving AI value.
7 Access to organizational technology is appropriately managed.
8 Employee access can be removed promptly when roles or employment change.
9 We understand security implications associated with AI access to organizational information.
10 Employees know which AI platforms are approved.
11 AI vendors are evaluated beyond product demonstrations.
12 We understand where important vendors store and process our information.
13 We consider data portability when selecting AI platforms.
14 We understand how AI pricing may change as usage grows.
15 Employees know how to report technology or AI system problems.
16 Important AI applications have clear technical or administrative support.
17 We can monitor whether significant AI systems continue performing appropriately.
18 We understand which legacy systems may limit future AI applications.
19 We intentionally evaluate whether to buy, configure, integrate, partner, or build.
20 We can realistically maintain the AI systems we are considering.

/100

Section 7

Governance, Privacy, Security, and Responsible AI

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 We have basic written guidance for employee AI use.
2 Employees know which AI tools are approved.
3 Employees understand what information should not be entered into unapproved AI platforms.
4 We have identified major categories of sensitive organizational information.
5 Vendor privacy practices are evaluated before sensitive information is used.
6 Vendor security practices are reviewed before important implementations.
7 Access to significant AI systems is appropriately controlled.
8 Employees understand that AI-generated information may be inaccurate.
9 Human review increases as the potential consequence of error increases.
10 Someone remains accountable for important AI-assisted decisions.
11 Higher-risk AI applications receive additional review.
12 Potential bias and unfair outcomes are considered when AI affects people.
13 We can explain important AI-supported decisions where appropriate.
14 We consider when stakeholders should know that AI is being used.
15 Intellectual property and confidentiality concerns are addressed.
16 AI-generated code receives appropriate review.
17 Important AI use cases are documented.
18 Leadership has visibility into where AI is being used.
19 Employees know how to report AI-related concerns or incidents.
20 AI governance practices are reviewed and updated periodically.

/100

Section 8

AI Opportunity and Use-Case Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 We begin AI discussions with organizational problems rather than products.
2 Potential AI opportunities are connected to organizational priorities.
3 Employees help identify potential use cases.
4 We look for repetitive, high-volume work when identifying opportunities.
5 We look for information-heavy processes.
6 We identify situations where better prediction could create value.
7 We identify situations where earlier detection could create value.
8 We identify knowledge-access problems.
9 We consider simpler non-AI solutions before selecting AI.
10 Potential use cases are defined specifically rather than broadly.
11 We understand who experiences the problem.
12 We understand how frequently the problem occurs.
13 We can describe the current impact of the problem.
14 We know what better would look like.
15 We understand what data would be required.
16 We consider the consequence of AI error.
17 We evaluate potential use cases based on value, feasibility, and risk.
18 We prioritize a manageable number of opportunities.
19 Strong use cases have measurable success criteria.
20 We are willing to reject AI ideas that do not create sufficient organizational value.

/100

Section 9

Pilot Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 AI pilots begin with a clearly defined problem.
2 We develop a specific hypothesis about expected improvement.
3 We establish a baseline before testing.
4 Success criteria are defined before the pilot begins.
5 Pilot scope is intentionally limited.
6 Pilot participants represent realistic future users.
7 Participants receive appropriate training.
8 Required data is available.
9 Data limitations are understood.
10 Privacy and security risks are reviewed before testing.
11 Human review remains appropriate to the use case.
12 Every pilot has a clear owner.
13 Technical support responsibilities are understood.
14 We identify quantitative measures for pilots.
15 We collect qualitative employee feedback.
16 Errors and corrections are documented.
17 Stop conditions are established for higher-risk pilots where appropriate.
18 Pilots have defined evaluation points.
19 Leadership is willing to scale, modify, pause, or stop based on evidence.
20 Lessons from pilots are documented and retained.

/100

Section 10

Measurement and Value Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 Significant AI initiatives begin with clearly defined outcomes.
2 We establish baselines before implementation.
3 Success measures are identified before pilots begin.
4 We distinguish AI activity from organizational outcomes.
5 We measure time savings when relevant.
6 We distinguish employee capacity gained from actual payroll savings.
7 We measure direct cost savings accurately.
8 Revenue impact is evaluated where appropriate.
9 Quality and accuracy are measured where relevant.
10 Predictive AI is compared with actual outcomes.
11 False positives and false negatives are evaluated where appropriate.
12 Employee experience is included in AI evaluation.
13 Customer experience is considered when AI affects customers.
14 Risk reduction is recognized as a potential form of value.
15 We understand the total cost of ownership of significant AI systems.
16 ROI is calculated when credible estimates are available.
17 We monitor unintended consequences.
18 We consider how recovered employee capacity will be used.
19 Successful and unsuccessful AI results are communicated honestly.
20 Operational AI systems are periodically reevaluated.

/100

Section 11

Scale and Operational Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 We scale AI only after measurable pilot success.
2 Successful AI-enabled workflows are documented.
3 Common AI errors and limitations are understood before scaling.
4 Exceptions requiring human intervention have clear procedures.
5 Important AI workflows do not depend entirely on one employee.
6 Employees outside pilot groups can be trained effectively.
7 Managers are prepared to support broader adoption.
8 Required data can support increased volume.
9 Technology infrastructure can support additional users and activity.
10 Temporary pilot workarounds are addressed before large-scale rollout.
11 Support capacity can handle additional users.
12 Privacy and security risks are reassessed before expansion.
13 Governance is strengthened as AI use expands.
14 Human oversight remains meaningful at greater scale.
15 Total costs at scale are understood.
16 Expected value remains attractive after scale costs are included.
17 Ongoing operational ownership is clear.
18 Monitoring responsibilities are established.
19 Business-continuity considerations are addressed for critical AI systems.
20 AI expansion occurs in stages where appropriate.

/100

Section 12

Internal AI Capability and Learning Readiness

Rate each statement from 1 to 5.

Statement 1 2 3 4 5
1 Employees share a baseline level of AI literacy.
2 We have identified AI skills relevant to organizational strategy.
3 Training varies appropriately by role.
4 AI champions or knowledgeable early adopters exist.
5 AI champions have opportunities to learn from one another.
6 Successful workflows are documented.
7 Useful lessons from unsuccessful experiments are preserved.
8 Employees can easily locate internal AI guidance and resources.
9 Significant AI systems have internal owners.
10 Business and technical responsibilities are distinguished where appropriate.
11 Relevant employees possess sufficient data literacy.
12 We can independently evaluate whether AI creates value.
13 We understand our major AI vendor relationships.
14 External partners are expected to transfer knowledge.
15 Critical AI knowledge does not reside with only one employee.
16 Leadership AI capability continues to grow.
17 Existing employees have opportunities to develop deeper AI skills.
18 AI-related hiring decisions are connected to clearly identified capability needs.
19 We can make informed build-versus-buy-versus-partner decisions.
20 Organizational dependence on outside expertise decreases as internal capability grows.

/100

Overall calculation

Calculating Your Overall AI Readiness Score

The assessment contains 1,175 possible points.

Readiness dimension Maximum Your score
Leadership 75 possible points /75
Workforce 100 possible points /100
Culture 100 possible points /100
Process 100 possible points /100
Data 100 possible points /100
Technology 100 possible points /100
Governance 100 possible points /100
Use Cases 100 possible points /100
Pilots 100 possible points /100
Measurement 100 possible points /100
Scale 100 possible points /100
Internal Capability 100 possible points /100
Maximum Possible Score 1,175 points /1,175
Readiness percentage Total Score ÷ 1,175 × 100
%

Interpreting Your Overall Score

The overall score provides a general picture. It should not be interpreted as a certification, guarantee, or definitive judgment about your ability to use AI.

The section-level scores are usually more valuable.

0–20%

Beginning

AI readiness is at an early stage.

The organization may have limited formal AI activity, little governance, minimal workforce preparation, or limited understanding of potential use cases.

Focus on:

  • Leadership education
  • Basic governance
  • Current AI-use discovery
  • Identifying organizational problems AI might eventually help solve

21–40%

Exploring

The organization has begun engaging with AI.

Some employees may be experimenting. Leadership may understand the opportunity. Initial policies or training may exist. But activity remains inconsistent or fragmented.

Focus on:

  • Ownership
  • Basic workforce literacy
  • Use-case prioritization
  • Data visibility
  • One controlled pilot

41–60%

Developing

The organization has meaningful AI readiness foundations.

Several capabilities exist but may not yet be consistent across departments. Pilots may be underway. Governance, data, process, or measurement capabilities may still need development.

Focus on:

  • Standardizing successful practices
  • Strengthening measurement
  • Building internal capability
  • Converting experimentation into repeatable workflows

61–80%

Established

AI adoption is becoming systematic.

Leadership, employees, governance, processes, data, and technology generally support responsible implementation. The organization can increasingly identify, pilot, measure, and scale useful AI applications.

Focus on:

  • Expanding what works
  • Improving cross-functional learning
  • Deepening applied AI capability
  • Identifying more strategic opportunities

81–100%

Advanced

The organization has developed strong AI adoption capability.

AI is increasingly treated as an organizational capability rather than a collection of individual tools or projects. The organization can repeatedly identify opportunities, manage risk, measure value, and adapt as technology changes.

Focus on:

  • Continuous improvement
  • Strategic differentiation
  • Proprietary data advantages
  • Advanced applied AI
  • Workforce redesign
  • Maintaining governance as AI capabilities evolve

Do Not Let the Overall Score Hide Important Gaps

An organization can have a strong overall score and still possess a serious weakness.

Example one

Strong adoption, weak governance

Leadership, workforce, and technology readiness may be high while governance readiness is very low. That could create substantial risk.

Example two

Safe systems, little value

Governance and technology may be strong while use-case readiness is weak. That could result in safe AI systems that create very little value.

Calculate Your Section Percentages

For Leadership Readiness, divide the Leadership Score by 75 and multiply by 100. For every other section, divide the section score by 100 and multiply by 100.

Readiness Dimension Score Percentage
Leadership /75 %
Workforce /100 %
Culture /100 %
Process /100 %
Data /100 %
Technology /100 %
Governance /100 %
Use Cases /100 %
Pilots /100 %
Measurement /100 %
Scale /100 %
Internal Capability /100 %

Create Your AI Readiness Profile

Do not focus only on the lowest number. Look for patterns.

Strong foundations

Which three dimensions received the highest scores?

These are organizational strengths you can build upon.

Highest-priority gaps

Which three dimensions received the lowest scores?

These deserve closer examination. But low scores alone do not determine priority.

Now ask

Does this gap prevent or increase the risk of something we want to do next?

Identify Immediate Risks

Review the assessment and identify any issue that could create significant near-term risk.

  • Employees entering confidential data into public AI platforms.
  • Unapproved tools containing organizational information.
  • High-stakes AI decisions without human oversight.
  • Weak access controls.
  • Unclear accountability.
  • AI systems being used without validation.

Immediate Risk #1

Immediate Risk #2

Identify Your Biggest Opportunities

Readiness assessment should not focus only on risk.

Ask

What becomes possible because of the capabilities we already have?

  • Strong data readiness may create forecasting opportunities.
  • Strong workforce readiness may support rapid adoption.
  • Strong process readiness may reveal automation opportunities.
  • Strong internal capability may allow more sophisticated experimentation.

Opportunity #1

Opportunity #2

Opportunity #3

Choose Your Top Three Readiness Priorities

After reviewing the entire assessment, identify three priorities for the next 90 days.

Keep the list focused

Do not choose 12. Choose three.

Priority 1

Priority 2

Priority 3

Identify One AI Opportunity to Explore

Do not finish the readiness assessment with only improvement actions. Readiness should eventually lead to application.

Identify one organizational problem worth exploring.

Readiness Before Pilot Checklist

Before moving the opportunity into a pilot, confirm:

If several boxes cannot be checked, identify what needs to happen first.

Leadership Discussion Questions

After completing the assessment, leadership should discuss the results together.

The AI Readiness Heat Map

Organizations may find it useful to convert section scores into a simple visual heat map.

Red · Priority Attention 0–40%

Significant readiness gaps exist.

Yellow · Developing 41–60%

Basic capability exists but needs development.

Green · Established 61–80%

The organization has a meaningful readiness foundation.

Blue · Advanced 81–100%

The capability is mature and consistently applied.

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

The visual pattern can often reveal more than the overall score.

Reassess Over Time

AI readiness changes.

The organization learns. Employees gain experience. Policies improve. Data improves. Technology changes. New use cases emerge.

For that reason, consider repeating this assessment periodically.

01

Initial Assessment

Establish the baseline.

02

Six-Month Review

Measure progress and identify new gaps.

03

Annual Assessment

Evaluate organizational maturity and establish new priorities.

Organizations moving rapidly may choose to review individual readiness areas more frequently.

The objective is not to continually chase a higher score. It is to determine whether organizational capability is keeping pace with AI adoption.

Track Progress

Record your results over time.

Readiness Dimension Initial 6 Months 12 Months
Leadership % % %
Workforce % % %
Culture % % %
Process % % %
Data % % %
Technology % % %
Governance % % %
Use Cases % % %
Pilots % % %
Measurement % % %
Scale % % %
Internal Capability % % %
%
%
%

What Your Score Cannot Tell You

A readiness score cannot tell you:

  • Which AI tool to purchase.
  • Whether a particular vendor is appropriate.
  • Whether every AI project will succeed.
  • What future technology will look like.
  • Whether AI should be used for a specific high-stakes decision.

The assessment is a decision-support tool.

Human judgment remains necessary. Context matters.

The organization must still determine:

  • What problems matter.
  • What risks are acceptable.
  • What resources are available.
  • What responsible adoption means in its own environment.

The Most Important Result

The most important outcome of this assessment is not the number at the bottom of the page.

It is the conversation the assessment creates.

  • Perhaps leadership discovers employees are much further ahead with AI than expected.
  • Perhaps the organization realizes its data is stronger than it assumed.
  • Perhaps governance needs immediate attention.
  • Perhaps several high-value use cases emerge.
  • Perhaps the organization discovers it has been purchasing technology without clearly defining problems.
  • Perhaps employees identify operational frustrations leadership never knew existed.

Those discoveries are the real value.

Visibility Better decisions Better experiments Organizational learning Capability

Do not ask only, “What is our score?” Ask, “What did we learn?” “What matters most?” and “What should we do next?”

Those are the questions that move an organization from AI interest to AI readiness—and ultimately, from AI readiness to meaningful adoption.

Last reviewed:

This assessment is a decision-support tool. Human judgment remains necessary, and organizational context matters.

← Previous chapter From AI Readiness to AI Adoption

Bring the lessons of the readiness journey together and move from organizational preparation toward sustained, responsible, and measurable AI adoption.

Continue to Appendix B → AI Readiness Leadership Discussion Guide

Use structured questions to help leaders interpret results, align priorities, and decide what should happen next.