Two short example audits: a quick win and a strategic pilot

Example A — Quick win: Automated invoice triage

Problem: Accounts Payable spends 10 hours per week classifying vendor invoices before routing. Errors cause payment delays.

Desired outcome: Reduce manual triage time by 70% and cut routing errors by 50%.

Intake evidence: 5,000 invoices/year, structured fields available, sample dataset exists. Data quality: good. Estimated effort: low.

Scores: Value 4, Feasibility 5, Risk 1 → Priority = (4*5)/1 = 20 (high). Confidence level: 4/5.

Readiness gaps: Minor normalization work, need a small UI for human review. No regulatory concerns.

Next steps: 2-week discovery to train a lightweight classifier, measure triage time and error rate against baseline, then pilot with two AP users.

Example B — Strategic pilot: Predictive patient no-shows for a clinic

Problem: Missed appointments cost clinic staff time and reduce revenue; some patients need targeted reminders.

Desired outcome: Reduce no-show rate by 15% through targeted outreach.

Intake evidence: Historical scheduling data exists but includes missing fields and inconsistent timestamps. Data quality: partial. Privacy considerations: patient data—requires privacy review.

Scores: Value 5, Feasibility 3, Risk 4 → Priority = (5*3)/4 = 3.75 (moderate). Confidence level: 3/5.

Readiness gaps: Need de-identified dataset, legal approval for outreach, and clinician sign-off on intervention strategy.

Next steps: Fund a 4-week discovery with data engineering to prepare a de-identified sample and a small randomized pilot design. Engage compliance and clinical champions before pilot execution.

These examples show how the same rubric and checklist guide different outcomes: quick automation vs. a careful, multidisciplinary pilot with regulatory considerations. Use the same scoring language across ideas so stakeholders can compare apples to apples.