Selecting AI Use Cases:

A simple scoring matrix to balance value and risk

Summary

Choosing the right use cases determines most of your AI outcome. This scoring matrix helps you start with high-value, low-risk, measurable workflows. It includes criteria, weights, a 1–5 scale, a simple formula, examples across Finance/Legal/HR, and decision thresholds.

The scoring matrix (criteria and weights)

Rate each criterion 1–5. Multiply by the weight and sum.

1. Business impact – 30%

   Time/cost savings, risk reduction, revenue influence.

2. Data readiness – 20%

   Volume, scan quality, access, local language, schema.

3. Process fit – 15%

   Stability, clear rules, well-defined outputs.

4. Risk & compliance – 15%

   Legal criticality, PII, auditability.

5. Integrations & effort – 10%

   Complexity to connect to ERP/CRM/HRIS; APIs/webhooks available.

6. Sponsorship & ownership – 10%

   Executive sponsor, product owner, IT/legal alignment, resourcing.

Scale 1–5 (definitions)

• 1: Low/immature/high risk/poor data.
• 3: Medium/partially ready/controlled risk.
• 5: High value/mature data/low risk/easy integrations/clear ownership.

Computation

Total Score = Σ(criterion score x weight). Max 5.00.

Decision thresholds

• ≥ 4.0: Pilot now (2–6 weeks).
• 3.0–3.9: Prepare prerequisites; pilot in 4–8 weeks.
• 2.0–2.9: Backlog with requirements; revisit in 90 days.
• < 2.0: Park for now.

Example scoring

1. Invoice Automation (Finance/AP)

• Impact: 5
• Data: 4
• Process: 5
• Risk: 4
• Integrations: 4
• Sponsorship: 5

Score: 4.55 → Pilot now.

2. Contract Search with Citations (Legal)

• Impact: 4
• Data: 4
• Process: 4
• Risk: 3
• Integrations: 3
• Sponsorship: 4

Score: 3.75 → Prepare and pilot.

3. Digital Employee File & Alerts (HR)

• Impact: 4
• Data: 3
• Process: 4
• Risk: 4
• Integrations: 3
• Sponsorship: 4

Score: 3.65 → Prepare and pilot.

Pilot exit criteria

• Extraction accuracy > 95% on critical fields.
• Citation-accepted answers ≥ 85%.
• Cycle time reduction ≥ 50%.
• No-touch rate ≥ 60% on standardized steps.
• Integration with 1 core system (ERP/CRM/HRIS).
• Clear exception/runbook and support handover.

Common pitfalls

• “Model first, data second.”
• Pilots without ownership and sponsorship.
• No KPIs or exit criteria.
• Skipping governance (RBAC, audit, retention).
• No integrations → manual steps creep back.

KPIs to set on day one

• Cost per document and cost per answer.
• Time-to-answer and time-to-post (e.g., into ERP).
• Exception rate and mean time to resolve.
• User adoption (MAU/DAU, internal NPS).
• Audit findings and off-policy access.

Suggested internal links

• AI Invoice Automation
• From manual contract skimming to automated clause retrieval
• HR Document Automation
• Procurement & Vendor Management with AI

Want this scoring matrix as a ready-to-use spreadsheet?
Book a short demo and we’ll tailor it to your data with 2–3 live examples.