The AI landscape doesn't move in one direction — it lurches. Some techniques leap from experiment to table stakes in a single quarter; others stall against regulatory walls, technical ceilings, or organisational inertia that no amount of hype can dislodge. Knowing which is which is the hard part. The State of Play cuts through the noise with a rigorously maintained index of AI techniques across every major business domain — classified by maturity, evidenced by real-world adoption, and updated daily so you always know where you stand relative to the field. Stop guessing. Start knowing.
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AI that analyses legacy systems to document behaviour, identify dependencies, and assist migration to modern platforms. Includes COBOL-to-Java migration and mainframe modernisation; distinct from code refactoring which improves existing code within its current platform.
AI-assisted legacy code migration has solidified at leading-edge maturity: vendors are shipping competitive agentic platforms, consulting firms are scaling AI practices at industrial scale (EPAM certifying 1,300+ architects with 10,000+ target), and deployments span multiple vertical markets. The practice uses AI to analyse, document, and transform systems written in older languages—primarily COBOL on mainframes—into modern platforms like Java and cloud-native microservices. Its urgency is demographic and economic. With 10% of COBOL developers retiring annually, 43% of US banking still COBOL-reliant, and the modernization services market projected to grow from $22.1B (2026) to $50.7B (2033) at 12.6% CAGR, the economics are forcing action. Yet production deployment remains at ~13-14% (per 2025 surveys), and the binding constraints are organisational—semantic validation expertise, behavioral equivalence assurance, and change management—not technical capability. The tools work. Real-world evidence now shows AI accelerates the discovery phase by 2-3x and handles 30-60% of migration work, but the final 40-70% (business logic validation, regulatory compliance, zero-trust testing) remains human-intensive. Scaling them means solving the human problem first.
IBM dominates the vendor landscape through watsonx Code Assistant for Z, which reached v2.8.0 in December 2025 with agentic capabilities that orchestrate multi-step analysis and transformation across mainframe codebases. Its Project Bob initiative consolidates RPG and COBOL assistants into a single platform. That dominance faces intensifying competitive pressure: Anthropic's February 2026 announcement of Claude for COBOL modernisation triggered a 13.2% single-day drop in IBM stock. AWS launched Transform service (GA April 2026) with agentic AI for code analysis and PL/I modernization, demonstrating real-world deployment velocity—a software firm migrated 12 weeks' worth of Control-M workflows to Apache Airflow in 2.5 weeks, achieving 3-5x delivery acceleration and 100% validation success. BMC shifts to agentic architecture, capturing institutional knowledge from historical resolutions to produce AI-analyzed application narratives. CAST Imaging and OpenLegacy round out the ecosystem with documented results: 110M COBOL lines analyzed in four weeks by a leading insurer; Thoughtworks delivering financial services firms 4M lines of COBOL/HLASM modernization in four weeks using agentic AI with 80% code comprehension. However, validation overhead persists. A 2026 survey of 200 enterprise SRE/DevOps leaders found 43% of AI-generated code requires manual debugging in production—developers spend 38% of weekly time fixing AI output. Gartner predicts 70% of 2026 mainframe exit projects will fail. The Stack Overflow survey (49K+ developers) shows 80% AI adoption but only 29% confidence in accuracy; 66% spend extra time fixing near-correct output. Independent testing confirms all mainstream AI tools produce semantically incorrect COBOL-to-Java transformations without expert validation. The UK government's experience illustrates organisational headwinds: legacy systems consume £2.3 billion of £4.7 billion IT budget, with high-risk systems growing 26% annually despite remediation efforts.
— Named global services firm (FPT) with 300+ systems and 200M+ LOC transformed; 30% effort reduction in assessment phase for major steel manufacturer case, documenting real deployment economics.
— Market sizing: $22.1B (2026) → $50.7B (2033) at 12.6% CAGR, with application modernization 34% of market, BFSI dominant, broad vertical adoption signaling category maturation.
— AWS and Anthropic official documentation demonstrating integrated workflow for legacy mainframe modernization using reverse engineering and agentic code generation.
— Major consulting firm (EPAM) publicly commits to certifying 10,000+ architects on Claude with 1,300 already certified and 5,000 by Q3 2026; signals enterprise-scale consulting shift toward vendor-specialized practices.
— IBM SVP critical assessment: code translation ≠ modernization. Real work is system-level engineering (data architecture, runtime, transaction integrity). Includes three named customers with metrics.
— Market signal: IBM stock dropped 13.2% (worst day since 2000) after Anthropic announced AI-driven COBOL analysis, indicating investor perception that AI automates legacy discovery cost bottleneck.
— AWS-IBM hybrid cloud collaboration with specific named deployment (Toyota Motor NA): 40M+ LOC COBOL to Java in 50% less time with AI, demonstrating deployment velocity and ecosystem acceleration.
— Named case study (ZK Fiddle, 13-year-old app) with specific problem (lost institutional memory), solution (Claude Code + CaseFoundry knowledge base), outcome (unblocked 2-year stalled migration).