"""Enterprise AI Agent Productivity Case Studies and Failure Modes Source: Company case studies, vendor reports, research studies Retrieved: June 2026 IMPORTANT: This module presents both successes AND failures honestly. Many 'productivity gains' are self-reported by vendors and need independent verification. """ case_studies: list[dict] = [ # Klarna — vendor case study via LangChain { "company": "Klarna", "system": "AI Assistant (LangGraph + LangSmith)", "metrics": { "active_users": 85_000_000, "daily_transactions": 2_500_000, "fte_equivalent": 700, "resolution_time_reduction_percent": 80, "task_automation_percent": 70, "conversations_handled": 2_500_000, }, "source": "LangChain case study (Feb 2025)", "source_url": "https://www.langchain.com/blog/customers-klarna", "date": "2025-02", "confidence": "HIGH", "caveat": "Vendor case study — metrics from LangChain's official blog", }, # JPMorgan Chase — COiN system launched 2017, widely cited { "company": "JPMorgan Chase", "system": "COiN (Contract Intelligence)", "metrics": { "hours_saved_annually": 360_000, "contracts_processed_annually": 12_000, "attributes_per_document": 150, "error_rate_before_percent": 5, "error_rate_after_percent": "~0", "annual_value_usd": 150_000_000, "fte_equivalent": 173, }, "source": "Multiple sources including JPMorgan executive quotes", "date": "2017-launched, metrics current through 2024", "confidence": "HIGH", "caveat": "Metrics are 8+ years old; system has evolved significantly", }, # ServiceNow partner case — SnowGeek Solutions (mid-size manufacturer) { "company": "ServiceNow (Partner Case — SnowGeek Solutions)", "system": "Now Assist + Agentic AI for IT Operations", "metrics": { "midnight_escalation_reduction_percent": 73, "mttr_improvement_percent": 65, "annual_downtime_savings_usd": 2_300_000, "engineering_hours_reclaimed": 1_840, "repeat_incident_reduction_percent": 62, "self_healing_incident_percent": 40, }, "source": "SnowGeek Solutions partner case study (Q4 2025)", "date": "2025-Q4", "confidence": "MEDIUM", "caveat": ( "Partner-reported metrics for mid-size manufacturer — " "not directly from ServiceNow" ), }, # Morgan Stanley — DevGen.AI claim, unverified { "company": "Morgan Stanley", "system": "DevGen.AI Developer Assistant", "metrics": { "developer_hours_saved": 280_000, }, "source": "Widely-reported claim", "date": "Unknown", "confidence": "LOW", "caveat": ( "Could NOT be independently verified. Treat as unconfirmed." ), }, # Amazon Q / CodeWhisperer — no verifiable metrics { "company": "Amazon Q / CodeWhisperer", "system": "Developer Productivity Tools", "metrics": {}, "source": ( "AWS has published various studies but specific metrics " "could not be sourced" ), "date": "Unknown", "confidence": "LOW", "caveat": ( "Could NOT be independently verified. AWS has claimed 55% " "faster task completion but no primary source found." ), }, ] # --------------------------------------------------------------------------- # Failure Modes # --------------------------------------------------------------------------- # Sourced from academic research, consulting reports, and industry analyses. # These rates underscore the gap between AI hype and measurable outcomes. # --------------------------------------------------------------------------- failure_modes: list[dict] = [ # MIT Media Lab 2025 — broad survey of corporate AI pilots { "category": "ai_pilots_zero_roi", "rate_percent": 95, "source": "MIT Media Lab 2025", "confidence": "HIGH", "detail": ( "95% of corporate AI pilots deliver zero measurable return; " "only 5% reach production with impact" ), "scope": "300+ initiatives, 52 org interviews, 153 executive surveys", }, # S&P Global 2025 — corporate AI abandonment trends { "category": "companies_abandoned_ai", "rate_percent": 42, "source": "S&P Global 2025", "confidence": "HIGH", "detail": ( "42% of companies abandoned most AI initiatives in 2025 " "(up from 17% in 2024); 46% of PoCs scrapped before production" ), }, # RAND Corporation 2025 — comparative failure rates { "category": "ai_projects_overall_fail", "rate_percent": 80, "source": "RAND Corporation 2025", "confidence": "MEDIUM", "detail": ( "Over 80% of AI projects fail — twice the failure rate " "of non-AI technology projects" ), }, # Gartner May 2026 — layoffs vs ROI disconnect { "category": "layoffs_unrelated_to_roi", "source": "Gartner May 2026", "confidence": "MEDIUM", "detail": ( "~80% of autonomous-AI deployers cut headcount; " "ZERO correlation between layoffs and ROI" ), "scope": "350 global executives", }, # Gartner prediction — agentic AI project cancellations { "category": "agentic_ai_projects_cancelled_by_2027", "rate_percent": 40, "source": "Gartner prediction", "confidence": "MEDIUM", "detail": ( "Over 40% of agentic AI projects will be canceled by end of " "2027 due to escalating costs, unclear value, or inadequate " "risk controls" ), }, # McKinsey State of AI 2025 — pilot purgatory { "category": "pilot_purgatory", "source": "McKinsey State of AI 2025", "confidence": "HIGH", "detail": ( "88% AI adoption but only 31% scaling — vast majority " "stuck in pilots" ), }, # MIT Media Lab 2025 — build vs buy outcomes { "category": "build_vs_buy_success", "source": "MIT Media Lab 2025", "confidence": "MEDIUM", "detail": ( "External partnership deployments succeed at ~67% " "vs ~33% for internal builds" ), }, # Multiple sources — shadow AI adoption { "category": "shadow_ai_adoption", "source": "Multiple sources", "confidence": "MEDIUM", "detail": ( "90%+ of companies have employees using personal AI tools; " "only 40% have official licensing" ), }, ] # --------------------------------------------------------------------------- # Additional Known Successes (from failure-mode research sources) # --------------------------------------------------------------------------- # These surfaced while researching failure rates but are not # among the primary case studies above. # --------------------------------------------------------------------------- known_successes_outside_main: list[dict] = [ {"company": "Lumen", "savings_usd": 50_000_000, "metric": "research_time_4hrs_to_15min", "source": "WorkOS article"}, {"company": "Air India", "metric": "97%_automation_on_4M_queries", "source": "WorkOS article"}, {"company": "Microsoft", "savings_usd": 500_000_000, "metric": "call_center_ai_savings", "source": "WorkOS article"}, ] # --------------------------------------------------------------------------- # Metadata # --------------------------------------------------------------------------- case_studies_meta = { "total_cases": 5, "high_confidence_cases": 2, # Klarna, JPMorgan "medium_confidence_cases": 1, # ServiceNow partner "low_confidence_cases": 2, # Morgan Stanley, Amazon Q "sources": [ "LangChain case study", "JPMorgan executive quotes", "SnowGeek Solutions", "widely-reported claims", ], "retrieved": "2026-06-04", }