AI Integration Evaluation Framework

Before implementing any AI solution, use this framework to evaluate whether it's appropriate for your context.

Part 1: Prerequisites Check

Before any AI evaluation, confirm these prerequisites are in place:

Process Prerequisites

The process to be automated is documented

The process is stable (not rapidly changing)

Current performance metrics exist

Error handling is defined

Data Prerequisites

Training/input data is available

Data is clean and consistent

Data format is standardized

Data volume is sufficient

Organizational Prerequisites

Someone is accountable for AI oversight

Technical capability exists to implement

Technical capability exists to maintain

Budget for ongoing costs is allocated

If any prerequisites are missing, address them before proceeding.

Part 2: Use Case Evaluation

Suitability Assessment

Rate each factor 1-5 (1 = Poor fit for AI, 5 = Excellent fit for AI):

| Factor | Rating | Notes |

|--------|--------|-------|

| Task is repetitive and consistent | | |

| Inputs are structured and predictable | | |

| Rules/criteria are clear and documentable | | |

| Volume justifies automation investment | | |

| Error tolerance is reasonable | | |

| Speed improvement would be valuable | | |

| Human judgment is minimal | | |

Scoring:

28-35: Strong candidate for AI

21-27: Possible candidate, proceed carefully

Below 21: AI likely not appropriate

Risk Assessment

Evaluate potential downsides:

|-------------|----------------|---------------------|------------|

Part 3: Implementation Planning

If evaluation is positive, plan implementation:

Scope Definition

What specifically will AI do?

Input: _______________

Process: _______________

Output: _______________

Boundaries: _______________

Human Oversight

How will humans stay in the loop?

Review frequency: _______________

Override capability: _______________

Escalation triggers: _______________

Accountability: _______________

Success Metrics

How will you measure success?

|--------|-----------------|--------|-------------------|

| | | | |

Monitoring Plan

How will you monitor AI performance?

Error detection method: _______________

Performance dashboards: _______________

Alert thresholds: _______________

Review cadence: _______________

Rollback Plan

If this fails, how do you revert?

Rollback trigger: _______________

Rollback process: _______________

Data preservation: _______________

Communication plan: _______________

Part 4: Ethical Considerations

Impact Assessment

Who is affected by this AI?

Direct users: _______________

Indirect stakeholders: _______________

Vulnerable populations: _______________

What are the potential harms?

Privacy violations

Biased outcomes

Job displacement

Reduced human agency

Other: _______________

Mitigation measures:

_______________

Transparency Requirements

Users know they're interacting with AI

Decision-making criteria are explainable

Affected parties can request human review

AI limitations are clearly communicated

Part 5: Decision Matrix

Summarize your evaluation:

|----------|------------|--------|-------|

Recommendation

Based on this evaluation:

Proceed with implementation

Proceed with modifications: _______________

Delay until: _______________

Do not implement. Reason: _______________

Part 6: Post-Implementation Review

Schedule reviews at:

2 weeks: Initial functionality check

1 month: Performance review

3 months: Full assessment

Ongoing: Quarterly reviews

Review Questions

Is AI performing as expected?

What errors have occurred?

How have users responded?

Are there unintended consequences?

Should we expand, maintain, or discontinue?

AI Integration Evaluation Framework