Enterprise AI Research & Benchmarks
Empirical benchmarks, production metrics, and industry studies tracking real enterprise AI outcomes.
State of Enterprise AI 2026: The Production Reality
An empirical analysis of enterprise AI adoption across Australian and global enterprises, examining pilot conversion rates, architecture patterns, and actual P&L realization.
- Pilot vs Production Ratio
- Primary Failure Modes
- Enterprise RAG Adoption Rates
- Platform Spend vs Outcomes
Enterprise RAG Accuracy & Cost Benchmark
Benchmarking dense vector search against hybrid sparse-dense retrieval and cross-encoder rerankers across 10,000 synthetic enterprise policy documents.
- Recall@5 and Precision@5
- Latency Overhead (ms)
- Cost per 1,000 Queries
- Impact of Semantic Caching
AI-Enabled SDLC Engineering Productivity Index
Longitudinal study tracking DORA metrics, code review latency, and defect escape rates across engineering teams utilizing AI coding agents.
- PR Cycle Times
- Code Churn & Refactor Rates
- Security Vulnerability Rates
- Developer Cognitive Load
Research Methodology & Standards
All research published under Bhavin Mistry adheres to strict empirical verification: datasets are reproducible, assumptions are documented, and commercial biases are eliminated. We do not publish fabricated survey statistics or synthetic benchmark numbers.