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.
A daily newsletter distilling the past two weeks of movement in a domain or two — delivered to your inbox while the index updates in the background.
Each dot marks the weighted maturity of practices within a domain — hover for a brief summary, click for more detail
AI that monitors equipment health through vibration, acoustic, thermal, and visual sensors and alerts on anomalies. Includes multi-sensor fusion and threshold-based alerting; distinct from remaining useful life estimation which predicts future failure rather than detecting current conditions. Scope covers ML-based anomaly detection and sensor fusion; traditional threshold alarms and statistical process control without ML are out of scope.
ML-based condition monitoring is a solved technology problem with an unsolved adoption problem. Multiple GA platforms from major vendors deliver proven ROI -- 30-50% downtime reduction, 40% maintenance cost savings, payback within 18 months -- for organisations with the digital maturity to absorb them. The practice uses vibration, acoustic, thermal, and visual sensors to detect equipment degradation in real time, distinct from remaining-useful-life estimation, which predicts future failure rather than flagging current conditions.
The vendor ecosystem is consolidated, the analyst coverage is deep, and independent case studies span steel, energy, automotive, and manufacturing with consistent positive outcomes. That places this firmly in good-practice territory: the question is how to roll it out, not whether it works. Yet only about 22.5% of organisations report their programmes as "effective" -- a figure unchanged since 2023 -- and 60-80% of implementations underperform or stall within two years. The bottleneck is organisational, not technical: data quality, workflow integration, technician adoption, and change management determine success far more than algorithm sophistication. A sharp bifurcation persists between digitally mature sectors achieving production-scale returns and commodity industries still blocked by infrastructure and readiness gaps.
Three platform-scale vendors dominate: Siemens Senseye (new wins including Octapharma; 80% failure forecast accuracy in automotive welding), GE Vernova (SOCAR Turkiye achieving 20% reactive maintenance reduction; deployments at Xcel Energy and Sasol; May 2026 reports covering 350+ equipment types with $1.6B cumulative customer losses avoided and 3.41-month average ROI), and AWS IoT SiteWise with native multivariate anomaly detection. AWS discontinued standalone Lookout for Equipment (October 2026 end-of-life), signaling that even GA production services lack sufficient adoption momentum for standalone viability. Market projections remain aggressive -- USD 70.7B by 2032 at 26.5% CAGR -- and named deployments at BlueScope Steel (1,950 avoided downtime hours), Cepsa refineries, Nissan (10,000+ assets), and a North American refinery (USD 1.89M losses prevented) show 40-50% cost reductions at digitally mature sites. Real-world case studies validate deployment value: coal plant avoided $1.84M emergency repair (19-day early detection); 310 MW hydroelectric station avoided $2.2-3.1M generator replacement via partial discharge detection (94% accuracy, 67% false-positive reduction).
Adoption is accelerating among digitally mature organizations but remains blocked at mainstream entry. Latest independent survey (Fluke, 600+ manufacturers across US/UK/Germany, May 2026) shows UK predictive maintenance adoption more than doubled from 9% to 22% year-over-year, while reactive maintenance dropped 42% to 26%, confirming growth trajectory but revealing critical workforce barriers: 77% cite skills gaps and expertise shortages as primary implementation obstacles, not technology maturity. Aviation sector deployments demonstrate sector-specific ROI: 30-40% reduction in unplanned AOG (Aircraft on Ground) events and 15-25% per-aircraft maintenance cost reduction vs. scheduled maintenance, with implementation costs of USD 95K-USD 1.7M and 12-24 month payback. However, a critical consulting firm assessment (KGT Solutions, May 2026) documents systemic deployment failures: 60-70% of PdM implementations miss ROI in 18 months despite correct algorithms, with root causes being organizational workflow failures—sensor strategy overcapitalization, data quality drift causing false alerts, CMMS disconnection preventing action, and manual alert handoff delays—rather than model sophistication. Successful closed-loop architectures achieve 4-5x lower repair costs and 27% clear payback in 12 months. A broader practitioner analysis (ManWinWin, May 2026) reveals 79% of manufacturers report recurring unplanned downtime despite decades of reliability engineering and digital tool investment; best-in-class organizations maintain 90% planned maintenance ratio vs. average 55%—a 35-point gap rooted in organizational discipline and work management maturity, not technology readiness.
The research-practice gap persists as a critical deployment constraint. Peer-reviewed systematic review (20 studies, Applied Sciences, May 2026) documents rapid publication growth (11 papers in 2017 → 38 in 2023) but unresolved robustness and interpretability challenges: most academic studies rely on standardized benchmark datasets or short-term controlled experiments, not real-world production variability and multi-site integration complexity. Real-world failures documented across the sector are data engineering, operational integration, and model-drift management problems—not algorithm sophistication. Aviation sector research confirms deep learning dominance in prognostic algorithms but highlights deployment constraints: data heterogeneity across aircraft fleets, explainability requirements for regulatory bodies, and certification timelines prevent transition from experimental models to operational use. Recent multimodal AI deployments (DreamzTech specialty-chemicals production case study, May 2026) show promise: a six-agent manufacturing platform achieved $850K annual savings and 47% unplanned-downtime reduction when condition monitoring was integrated within broader operational orchestration architecture, illustrating that isolated sensor data is insufficient without closed-loop action integration. Edge-based acoustic anomaly detection has achieved 91.80% accuracy on industrial motor signals, while multi-modal sensor fusion architectures are producing 94%+ fault detection with 30-90 day advance warning in production settings, yet false-positive management remains the unresolved bottleneck: threshold-based detection generates 60%+ false positives, causing operator distrust and system disablement in organizations lacking mature CMMS integration.
— Consultancy technical analysis with independent deployment examples: BlueScope (1,950 hours unplanned downtime prevented), Södra (300→20 alarms/week via ML), Omya (bearing fault detection at 0.5-1mm/s), SCG Chemicals (turbine cooling anomaly detection). Four-layer architecture analysis with human-AI hybrid emphasis.
— Aviation MRO survey (78% respondents 10+ years experience, cross-sector sample): 53% rank predictive maintenance as single highest technology priority; deployment context: 20K technician shortage, 17K aircraft backlog—positioning PdM as competitive necessity in capacity-constrained MRO sector.
— Unilever Indaiatuba plant deployed AI condition monitoring across 50,000+ IoT sensor data points (compressors, HVAC, packaging equipment); achieved 45% maintenance cost reduction ($2.3M saved), 40% downtime reduction (8.2% to 4.9%), with sub-7-month payback on $1.2M investment.
— Independent journalist synthesis of 24 confidential interviews across Chinese industrial sectors. Predictive maintenance identified as fastest ROI: petrochemical case achieved 92% accuracy, 30% downtime reduction, ¥1.8B annual savings with 12-18 month payback. Battery manufacturer saved ¥1.8B in single year.
— Critical assessment of predictive maintenance failure modes: sensor drift, out-of-distribution events, late detection windows. Per-prediction confidence scoring approach documented reducing unplanned downtime 20-40% and maintenance costs 15-30% by detecting model uncertainty in real time.
— POSCO Gwangyang Steelworks deployed AWS-based AI agent platform (InnoPIMS) enabling field engineers to develop anomaly models without coding. Development time reduced 80% (2 weeks→2 days). Pilot operation complete, expanding to broader enterprise-wide facility monitoring rollout.
— Named food-beverage manufacturers deployed Boston Dynamics Spot robots for autonomous thermal+vibration condition monitoring (Q2-Q3 2025). AB InBev prevented 6 failures ($2.1M avoided downtime), Cargill prevented 8 failures ($2.7M). Combined year-one ROI $4.8M; multi-modal fusion improved accuracy 28-35%.
— Consulting firm critical assessment: 60-70% of PdM deployments miss ROI in 18 months despite correct algorithms. Root cause analysis identifies workflow failures (sensor strategy inflation, data quality drift, CMMS disconnection) not model limitations; closed-loop architectures report 4-5x lower repair costs.