Reference Catalog

Applied AI Use-Case Catalog

A practical catalog of AI use-case patterns, data readiness markers, and low-risk pilot recipes to help teams select, scope, and run pilots that deliver measurable value and scale safely.

What this catalog is for

This catalog helps teams find realistic, low-risk AI pilots that balance potential value, data readiness, and operational risk. Each pattern below includes a concise description, explicit data needs, a short risk checklist, a sample ROI sketch, and scaling criteria. Use the included pilot recipe and readiness checks to move from idea to a measurable pilot with clear acceptance criteria.

How to use this catalog

Scan patterns for ones that match a real operational pain or measurable opportunity.
Score each candidate on Value, Data Readiness, and Risk using the quick prioritization guidance below.
Pick one to two low-risk, high-value pilots and apply the Pilot Recipe to get results quickly.

Quick prioritization

Score each candidate 1–5 for: Value (business impact), Data Readiness (accessibility, quality, volume), and Risk (safety, compliance, reputational). A simple prioritization score:

Priority Score = Value × (Data Readiness / 5) − Risk

Starter filter for pilots: Value ≥ 4, Data Readiness ≥ 3, Risk ≤ 2. These tend to be achievable, meaningful, and safe.

Catalog categories

Catalog categories: automation, insight, augmentation, and experience. Below are representative patterns for each category with the standard fields you can copy for proposals and pilot plans.

Automation (repeatable work done without human decision)

Invoice ingestion and matching

Automatically extract invoice fields (vendor, amounts, dates), validate against PO and GRN, and route exceptions.

Data needs: 6–12 months of historical invoices and matched POs, labeled exceptions for initial training, access to ERP for verification.
Risk checklist: accuracy threshold for automatic posting (e.g., ≥ 98%); manual review workflow for exceptions; audit trail and explainability for finance auditors.
Sample ROI (quarterly): Time saved = 120 labor-hours saved × $30/hr = $3,600. Error reduction and earlier payment discounts ≈ $1,000. Total ≈ $4,600.
Scaling criteria: model performance stable across vendors; integration with ERP API; exception rate <10%.

Routine IT incident triage

Classify incoming tickets, suggest root-cause articles, and auto-assign to the right team.

Data needs: Ticket history with categories and resolutions, service catalog metadata, email/chat logs for context.
Risk checklist: human-in-the-loop for high-priority incidents; confidence threshold to avoid misrouting critical tickets.
Sample ROI: Faster assignment reduces Mean Time to Acknowledge by 30%; estimate on SLA penalties avoided and labor reuse.
Scaling criteria: consistent taxonomy, integration with ticketing system, periodic retraining cadence.

Insight (analytics and forecasts that reveal opportunities)

Demand forecasting for SKU families

Short-term demand forecasts using historical sales, promotions, and seasonality to reduce stockouts and overstock.

Data needs: POS/sales history, promo/calendar data, lead-time, inventory snapshots. 12–24 months preferred for seasonality.
Risk checklist: guardrails for conservative replenishment; explainability for planners; rollback plan for poor performance.
Sample ROI: 5% reduction in stockouts × average margin uplift or lost-sales avoided; 3–8% inventory reduction improves working capital.
Scaling criteria: clear uplift during pilot (e.g., reduced stockouts or inventory), integration with replenishment workflows, trust from planners.

Predictive maintenance alerts

Predict likely equipment failures from sensor and maintenance logs to schedule repairs proactively.

Data needs: time-series sensor data, maintenance history, failure labels. At least several months of continuous sensor readings per asset class.
Risk checklist: failure false-positive cost vs. missed-failure cost; safety-critical asset validation; human decision gate for maintenance scheduling.
Sample ROI: avoided downtime hours × contribution margin per hour − additional preventive maintenance cost.
Scaling criteria: model generalizes across similar assets; actionable lead time; maintenance team acceptance.

Augmentation (assist humans to make better, faster decisions)

Clinical decision support summaries

Summarize patient data and surface likely diagnoses or guideline-based recommendations to clinicians.

Data needs: structured EHR fields, de-identified notes, coding/diagnosis history, clinical guidelines.
Risk checklist: strict privacy/compliance review, human override required, provenance and explainability on each suggestion.
Sample ROI: reduced diagnostic time per case, fewer unnecessary tests, improved guideline adherence.
Scaling criteria: controlled trials showing non-inferior or improved outcomes, clinician trust, regulatory alignment.

Legal document summarization for review

Extract obligations, deadlines, and risky clauses to accelerate legal review.

Data needs: historical contracts with redlines, annotated clauses, taxonomy of obligations.
Risk checklist: final legal decisions remain with lawyers; clear flagging of low-confidence extracts.
Sample ROI: lawyer-hours saved × hourly rate; faster contract turnaround increases deal throughput.
Scaling criteria: high precision on obligations; integration into contract lifecycle tooling.

Experience (customer or user-facing interactions)

Customer support virtual assistant (tier-1)

Handle routine inquiries, surface knowledge-base articles, and escalate complex issues to humans.

Data needs: past chat transcripts, ticket-resolution pairs, up-to-date knowledge base, channel metadata.
Risk checklist: clear escalation rules, branded tone controls, privacy handling of personal data.
Sample ROI: decreased human handle time and improved first-contact resolution; cost-per-contact reduction.
Scaling criteria: containment rate ≥ target, CSAT unchanged or improved, seamless handoff to humans.

Personalized learning paths for employees

Recommend training modules based on skills gaps and career goals to speed up onboarding and upskilling.

Data needs: skill assessments, role profiles, training completion and outcomes, performance metrics.
Risk checklist: fairness and bias review, opt-in and privacy controls, manager visibility.
Sample ROI: reduced time-to-competency; higher productivity and retention among trained cohorts.
Scaling criteria: measurable uplift in competency assessments and accepted integration into LMS.

Pilot recipe (repeatable low-risk approach)

Choose: pick a pattern that meets the starter filter and has an identified owner and sponsor.
Discover (1–2 weeks): confirm data availability, map the workflow, list success metrics, and identify stakeholders.
Design (1–2 weeks): build a minimal model or rule set, design human-in-the-loop gates, and define acceptance criteria.
Pilot (4–8 weeks): run in shadow mode or with limited production scope, collect outcomes vs. baseline, log exceptions and failure modes.
Measure: evaluate against pre-defined metrics (accuracy, time saved, cost avoided, CSAT). Require statistically meaningful improvement or clearly documented operational benefit.
Decide: scale, iterate, or retire based on outcomes and scaling criteria. If scaling, plan integration, monitoring, retraining cadence, and governance handoff.

Pilot acceptance checklist

Clear owner and sponsor assigned.
Baseline metrics and target improvements documented.
Data access and sample extraction verified.
Risk mitigations and human-in-the-loop policies defined.
Monitoring and rollback plan in place.

Next steps and customization

Copy any pattern into a Pilot Plan and adapt the data needs, risk checklist, ROI sketch, and scaling criteria to your context. Consider packaging high-value patterns into a site-specific Applied AI Toolkit for reuse across teams.

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