{
  "$schema": "https://www.slavin.ai/data/ai-use-case-catalog-schema.json",
  "dataset": {
    "name": "Enterprise AI Use Case Catalog with ROI & Risk Classification",
    "version": "2026-06",
    "publisher": "Slavin AI (SLAtech LTD)",
    "publisherUrl": "https://www.slavin.ai/",
    "license": "CC-BY-4.0",
    "lastUpdated": "2026-06-14",
    "description": "Catalog of 24 enterprise AI use cases organized by industry, with realistic ROI timeline bands, complexity rating, regulatory classification under EU AI Act (Annex III high-risk vs low-risk), and recommended starting architecture (RAG / fine-tuning / prompt-engineering).",
    "audience": ["AI Strategy Lead", "CIO", "CTO", "Product Manager", "Procurement"],
    "methodology": "Compiled from 150+ Slavin/SLAtech engagements 2022-2026. ROI timelines reflect observed range from production launch to verified business impact, not pilot results. Complexity rating accounts for data preparation, model selection, governance overhead, and ongoing ops. EU AI Act classification follows Article 6 + Annex III as of 2026-06."
  },
  "use_cases": [
    {
      "id": "UC-01",
      "name": "Customer Support Auto-Reply (Tier 1)",
      "industry": ["any"],
      "complexity": 2,
      "roi_band_months": [6, 12],
      "primary_kpi": "deflection rate, AHT reduction",
      "starting_architecture": "RAG over knowledge base + prompt-engineering",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Pilot on easy questions only; production traffic is 60% edge cases."
    },
    {
      "id": "UC-02",
      "name": "Document Extraction (Invoices, Contracts)",
      "industry": ["finance", "legal", "insurance", "manufacturing"],
      "complexity": 3,
      "roi_band_months": [4, 9],
      "primary_kpi": "labor hours saved, accuracy vs human baseline",
      "starting_architecture": "fine-tuned multimodal LLM + structured output",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Underestimating layout variability across vendors / counterparties."
    },
    {
      "id": "UC-03",
      "name": "Code Assistant for Dev Teams",
      "industry": ["any tech-enabled"],
      "complexity": 2,
      "roi_band_months": [3, 9],
      "primary_kpi": "PR cycle time, dev satisfaction",
      "starting_architecture": "managed vendor (GitHub Copilot / Cursor) — buy",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Inconsistent adoption; ROI lives or dies on enablement."
    },
    {
      "id": "UC-04",
      "name": "Marketing Content Generation",
      "industry": ["any"],
      "complexity": 1,
      "roi_band_months": [3, 6],
      "primary_kpi": "content velocity, cost per piece",
      "starting_architecture": "prompt-engineering on managed LLM",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Brand voice drift; quality regression after first month enthusiasm."
    },
    {
      "id": "UC-05",
      "name": "Lead Qualification & Routing",
      "industry": ["B2B SaaS", "professional services", "real estate"],
      "complexity": 3,
      "roi_band_months": [6, 12],
      "primary_kpi": "SDR pipeline efficiency, MQL→SQL conversion",
      "starting_architecture": "classification model + RAG over CRM",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Sales team rejection of black-box routing; explainability matters."
    },
    {
      "id": "UC-06",
      "name": "Internal Knowledge Search (Enterprise RAG)",
      "industry": ["any"],
      "complexity": 4,
      "roi_band_months": [9, 18],
      "primary_kpi": "search-to-resolution time, employee satisfaction",
      "starting_architecture": "RAG over multi-source corpus + ACL-aware retrieval",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Access control complexity dominates; corpus hygiene more important than model choice."
    },
    {
      "id": "UC-07",
      "name": "Clinical Documentation Assistant",
      "industry": ["healthcare"],
      "complexity": 5,
      "roi_band_months": [12, 24],
      "primary_kpi": "documentation time per encounter, physician burnout",
      "starting_architecture": "domain-fine-tuned + RAG over guidelines, HITL",
      "eu_ai_act_classification": "high_risk",
      "typical_pitfall": "Hallucination in medication / dosage; HITL is non-negotiable."
    },
    {
      "id": "UC-08",
      "name": "Compliance Officer AI Assistant",
      "industry": ["finance", "insurance", "healthcare"],
      "complexity": 5,
      "roi_band_months": [12, 24],
      "primary_kpi": "case review throughput, audit pass rate",
      "starting_architecture": "RAG over policy + case law, citation enforcement, full audit log",
      "eu_ai_act_classification": "high_risk",
      "typical_pitfall": "Without citations, every output requires re-verification — no time saved."
    },
    {
      "id": "UC-09",
      "name": "Resume Screening / Candidate Ranking",
      "industry": ["any"],
      "complexity": 3,
      "roi_band_months": [6, 12],
      "primary_kpi": "time-to-hire, screening cost",
      "starting_architecture": "structured-output LLM + bias monitoring, mandatory HITL",
      "eu_ai_act_classification": "high_risk",
      "typical_pitfall": "Bias amplification; EU AI Act Annex III mandates human oversight + transparency."
    },
    {
      "id": "UC-10",
      "name": "Credit Decisioning Augmentation",
      "industry": ["banking", "fintech"],
      "complexity": 5,
      "roi_band_months": [18, 36],
      "primary_kpi": "approval rate, default rate, regulatory pass",
      "starting_architecture": "ensemble traditional + LLM rationale generation, full explainability",
      "eu_ai_act_classification": "high_risk",
      "typical_pitfall": "Regulator approval is the long pole; technical work is the smaller half."
    },
    {
      "id": "UC-11",
      "name": "Demand Forecasting",
      "industry": ["retail", "logistics", "manufacturing"],
      "complexity": 4,
      "roi_band_months": [12, 24],
      "primary_kpi": "forecast accuracy, inventory turn, stock-out rate",
      "starting_architecture": "time-series specialist model + LLM for narrative analysis",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Confusing AI accuracy with business impact; small accuracy gains rarely move P&L."
    },
    {
      "id": "UC-12",
      "name": "Personalization & Recommendation",
      "industry": ["retail", "media", "fintech"],
      "complexity": 4,
      "roi_band_months": [12, 24],
      "primary_kpi": "click-through, conversion, lifetime value",
      "starting_architecture": "collaborative filtering + LLM re-ranking",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Cold start; privacy / data minimization requirements."
    },
    {
      "id": "UC-13",
      "name": "Predictive Maintenance",
      "industry": ["manufacturing", "utilities", "transport"],
      "complexity": 4,
      "roi_band_months": [12, 24],
      "primary_kpi": "downtime reduction, maintenance cost",
      "starting_architecture": "anomaly detection on sensor streams + LLM for diagnostic narrative",
      "eu_ai_act_classification": "low_risk (high_risk if part of critical infrastructure)",
      "typical_pitfall": "Sensor data quality dominates; model is the easy part."
    },
    {
      "id": "UC-14",
      "name": "Fraud Detection",
      "industry": ["banking", "insurance", "fintech", "e-commerce"],
      "complexity": 4,
      "roi_band_months": [12, 24],
      "primary_kpi": "fraud loss reduction, false positive rate, customer friction",
      "starting_architecture": "graph + traditional ML + LLM for case narrative",
      "eu_ai_act_classification": "high_risk (creditworthiness/insurance)",
      "typical_pitfall": "False-positive cost > false-negative cost above a threshold; calibration is everything."
    },
    {
      "id": "UC-15",
      "name": "Sales Coaching from Call Recordings",
      "industry": ["B2B SaaS", "professional services"],
      "complexity": 3,
      "roi_band_months": [6, 12],
      "primary_kpi": "win rate, rep ramp time",
      "starting_architecture": "ASR + LLM analysis + dashboard",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Privacy / consent under GDPR; sales rep resistance to perceived surveillance."
    },
    {
      "id": "UC-16",
      "name": "Legal Contract Review",
      "industry": ["legal", "any with high contract volume"],
      "complexity": 4,
      "roi_band_months": [9, 18],
      "primary_kpi": "review time, risk flag precision",
      "starting_architecture": "RAG over precedent + structured clause extraction",
      "eu_ai_act_classification": "low_risk (high_risk if affecting access to essential services)",
      "typical_pitfall": "Lawyers reject black-box flags; show source clauses inline."
    },
    {
      "id": "UC-17",
      "name": "Adverse Event / Pharmacovigilance Triage",
      "industry": ["pharma", "healthcare"],
      "complexity": 5,
      "roi_band_months": [12, 24],
      "primary_kpi": "triage time, false negative rate",
      "starting_architecture": "domain-fine-tuned + RAG over reference, mandatory HITL",
      "eu_ai_act_classification": "high_risk",
      "typical_pitfall": "Regulatory acceptance varies by region; document model card extensively."
    },
    {
      "id": "UC-18",
      "name": "AI for Insurance Underwriting",
      "industry": ["insurance"],
      "complexity": 5,
      "roi_band_months": [18, 36],
      "primary_kpi": "loss ratio, expense ratio, regulatory pass",
      "starting_architecture": "traditional actuarial + LLM rationale + explainability",
      "eu_ai_act_classification": "high_risk",
      "typical_pitfall": "Fairness across protected classes; data is rarely as clean as expected."
    },
    {
      "id": "UC-19",
      "name": "Public Sector Citizen Service Chatbot",
      "industry": ["government"],
      "complexity": 4,
      "roi_band_months": [12, 18],
      "primary_kpi": "case deflection, satisfaction, accessibility",
      "starting_architecture": "RAG over policy + bilingual / multilingual support",
      "eu_ai_act_classification": "high_risk (essential services)",
      "typical_pitfall": "Accessibility / multilingual requirements; political pressure for transparency."
    },
    {
      "id": "UC-20",
      "name": "Educational Assistant / Tutoring",
      "industry": ["education", "EdTech"],
      "complexity": 3,
      "roi_band_months": [9, 18],
      "primary_kpi": "engagement, learning outcomes, completion rate",
      "starting_architecture": "RAG over curriculum + LLM tutoring loop",
      "eu_ai_act_classification": "high_risk (Annex III — education access)",
      "typical_pitfall": "EU AI Act Annex III applies to education-determining systems; many overlook this."
    },
    {
      "id": "UC-21",
      "name": "AI-Assisted Translation / Localization",
      "industry": ["any global"],
      "complexity": 2,
      "roi_band_months": [3, 9],
      "primary_kpi": "throughput, post-edit time",
      "starting_architecture": "specialized NMT + LLM post-edit",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Legal / medical content needs domain models; consumer translators fail."
    },
    {
      "id": "UC-22",
      "name": "Meeting Note Taking & Action Item Extraction",
      "industry": ["any"],
      "complexity": 2,
      "roi_band_months": [3, 9],
      "primary_kpi": "meeting productivity, follow-through rate",
      "starting_architecture": "ASR + LLM summarization + integration to task system",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Privacy / consent; integration to existing task systems is the hard part."
    },
    {
      "id": "UC-23",
      "name": "AI Security Operations (SOC) Triage",
      "industry": ["any with mature SOC"],
      "complexity": 4,
      "roi_band_months": [9, 18],
      "primary_kpi": "MTTR, analyst productivity, alert noise",
      "starting_architecture": "LLM over SIEM data + RAG over playbooks",
      "eu_ai_act_classification": "low_risk",
      "typical_pitfall": "Prompt injection from log data; SOC analyst trust requires explainability."
    },
    {
      "id": "UC-24",
      "name": "Predictive Patient Risk Stratification",
      "industry": ["healthcare", "health insurance"],
      "complexity": 5,
      "roi_band_months": [18, 36],
      "primary_kpi": "readmission rate, cost of care, equity metrics",
      "starting_architecture": "specialized ML + LLM narrative, mandatory HITL, fairness monitoring",
      "eu_ai_act_classification": "high_risk",
      "typical_pitfall": "Fairness across demographics; regulatory and clinical acceptance both required."
    }
  ],
  "summary_distribution": {
    "by_complexity": { "1": 1, "2": 4, "3": 5, "4": 8, "5": 6 },
    "by_eu_ai_act_classification": { "low_risk": 14, "high_risk_or_conditional": 10 },
    "by_industry_top": {
      "any": 6, "finance/banking/insurance/fintech": 7, "healthcare/pharma": 4,
      "manufacturing/logistics/utilities/transport": 4, "education/government": 3
    },
    "roi_quick_wins_under_12_months": ["UC-01", "UC-02", "UC-03", "UC-04", "UC-05", "UC-15", "UC-21", "UC-22"],
    "roi_long_horizon_above_18_months": ["UC-10", "UC-18", "UC-24"]
  },
  "see_also": {
    "build_vs_buy_framework": "https://www.slavin.ai/Article-Build-vs-Buy-AI.aspx",
    "rag_vs_finetune": "https://www.slavin.ai/Compare-RAG-vs-Fine-tuning.aspx",
    "governance_baseline": "https://www.slavin.ai/data/ai-governance-baseline.json",
    "eu_ai_act_checklist": "https://www.slavin.ai/EU-AI-Act-Checklist.aspx",
    "interactive_assessment": "https://www.slavin.ai/AI-Maturity-Assessment.aspx"
  }
}