feat(battlecard): card 05 — real-world enterprise deployment with impact metrics chart

output/battlecards/cards/card_05_enterprise_deployment.md

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+# Card 5: Real-World Enterprise Deployment
+
+> Despite the broader bubble narrative, AI has delivered measurable ROI in specific enterprise deployments.
+
+## Fact
+
+- Klarna replaced 853 FTEs with AI agents, saving $60M and reducing resolution time from 11 minutes to under 2 minutes (82% reduction) *(Source: Klarna/LangChain case study, 2025)*
+- JPMorgan COiN saves 360,000 lawyer-hours annually and generates $150M in annual value, processing 12,000 commercial credit agreements *(Source: JPMorgan, 2025)*
+- ServiceNow partner SnowGeek achieved 73% midnight escalation reduction, 65% MTTR improvement, and $2.3M in downtime savings *(Source: ServiceNow partner report, MEDIUM confidence)*
+- Morgan Stanley's DevGen.AI reviewed 9M+ lines of legacy code, saving 280,000 developer hours *(Source: Morgan Stanley, 2025)*
+
+![](mini_enterprise_savings.png)
+
+## Impact
+
+- **Real ROI exists in focused deployments**: Companies with clear use cases, strong data infrastructure, and C-level sponsorship are seeing double-digit percentage improvements.
+- **But success is concentrated**: MIT NANDA research finds 95% of enterprise AI pilots deliver zero measurable P&L impact *(Source: MIT NANDA, July 2025)*. The winning 5% achieve outsized returns that skew averages.
+- **Hybrid models are the practical approach**: Klarna's partial reversal — restoring human agents for complex emotional queries — highlights that full AI replacement is premature for many use cases.
+
+## Act
+
+- **When presenting AI value**: Use specific case studies with verified metrics. General claims about "AI transformation" are easy to dismiss.
+- **Key question to ask**: "What is the specific ROI from your AI deployment, and how does it compare to the 95% of pilots that deliver zero measurable impact?"
+- **Counter-argument anticipation**: "These are cherry-picked success stories." Response: True, but success patterns are identifiable — clear scoping, data readiness, and executive sponsorship differentiate winners from the 95% failure rate.
+
+---
+
+*Last updated: 2026-06-05 | Sources: Klarna/LangChain case study, JPMorgan 2025, SnowGeek Solutions, MIT NANDA 2025, Morgan Stanley 2025*