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 maintains organisational risk registers and scans for emerging risks across regulatory, operational, and market domains. Includes automated risk identification and impact assessment; distinct from compliance gap analysis which measures against known requirements rather than scanning for emerging risks.
AI-driven risk register maintenance and horizon scanning has crossed from experimental to deployed — but only at forward-leaning organisations. The practice applies AI to continuously identify emerging threats across regulatory, operational, and market domains, going beyond compliance gap analysis by scanning for novel risks rather than measuring against known requirements. Vendor platforms now offer production-grade tooling across the market: Origami Risk, SAI360, LogicGate, and Holistic AI all compete on AI-powered risk identification and continuous monitoring. Board-level attention has surged: 48% of S&P 500 companies cite AI risk oversight, triple the 2024 rate. Market adoption continues accelerating through Q2 2026: 75% of enterprises plan GRC budget increases with AI governance as the top priority (43%); the market for AI governance software is growing at 32.8% CAGR. Regulated insurance markets show even steeper adoption curves: Lloyd's Market Association survey of 39 CROs (60%+ of stamp capacity) finds 93% have AI governance frameworks in place or development, up from 25% a year prior. The defining tension remains an execution gap. Despite high awareness and adoption intent, critical gaps persist: 30% of organisations have experienced AI security incidents despite claiming governance frameworks; only 22% have automated risk monitoring in place; two-thirds require weeks to implement policy changes. Regulatory deadlines and escalating AI-related liabilities are compressing the timeline, but governance maturity has not kept pace with vendor capability or stated organisational intent. The shadow AI blind spot persists: 86% of security leaders claim complete AI inventory visibility, yet 59% admit ungoverned shadow AI operates within their organisations.
Adoption is real but execution is lagging. Moody's January 2026 survey of 600 risk and compliance professionals puts active usage or trialling at 53%, up from 30% in 2023. Yet 46% report only moderate impact, constrained by insufficient expertise (41%), regulatory uncertainty (33%), and legacy system integration (30%). This pattern recurs across independent surveys: KPMG's study of 2,500 tech executives across 27 countries finds 74% confirm AI business value but only 24% achieve return on investment—pointing directly to inadequate risk identification and governance frameworks as the constraint.
Market momentum is unmistakable in Q2 2026. Optro's Q2 governance investment survey shows 75% of enterprises planning GRC budget increases, with AI governance solutions as the top investment priority (43%). The enterprise AI governance software market itself is growing at 32.8% CAGR, indicating mainstream infrastructure investment. Vendor tooling has matured rapidly: SAI360 maintains an AI-Connected Risk Register with KRI monitoring that surfaces trends and anomalies, incident pattern analysis, and emerging risk detection—capabilities that compress manual work from months to days. LogicGate Risk Cloud, recognized by Gartner (MQ) and ISACA, now offers AI Governance modules for use case assessment and continuous monitoring workflows. Origami Risk's Spring 2026 AI Risk and Control Explorer further compresses risk register population. These are production deployments, not proofs-of-concept, but they remain concentrated among early movers and regulated firms.
In regulated insurance markets, adoption has become mainstream. Lloyd's Market Association survey of 39 chief risk officers (representing 60%+ of market stamp capacity) finds 93% have AI governance frameworks in place or in active development—a dramatic shift from 25% adoption one year prior. This market-wide deployment signals that boards and regulators are no longer treating AI risk register maintenance as optional. MOL Group's unified ERM platform deployment across 30 countries, integrating risk, security, and compliance with predictive analytics and real-time monitoring, demonstrates that multi-national enterprises are operationalising the practice at scale.
Yet the shadow AI blind spot reveals structural gaps. Stanford's AI Index 2026 survey identifies security/risk concerns as the #1 blocker (62% of respondents) to scaling agentic AI—a 24-point margin over the next factor—with governance and data-layer control gaps cited as critical adoption barriers. Critically, 30% of organisations have experienced AI security incidents despite claiming governance awareness; only 22% have automated risk monitoring; two-thirds require weeks to implement policy changes. This execution gap persists even as boards demand oversight and regulators tighten deadlines: EU AI Act classification guidelines (August 2026 enforcement), Data Act (September 2026), Product Liability Directive (December 2026), and California AI risk assessment requirement (December 2027) all compress the timeline for governance maturity. Organisations recognise that AI risk belongs on their registers, but most have not yet built the operational muscle to maintain them continuously, hampered by data sovereignty concerns, inadequate third-party risk oversight, and the endemic problem of static, ownership-less spreadsheet registers.
— SAI360 demonstrates AI-Connected Risk Register with centralized risk views, KRI monitoring with AI trend surfacing, incident pattern analysis, and emerging risk detection—operational capability maturity for continuous risk maintenance.
— Lloyd's Market Association survey of 39 CROs (60%+ market representation) shows 93% have AI governance frameworks in place/development; AI adoption surged from 25% to majority in one year, signaling regulated market acceleration.
— Survey data shows critical execution gap: 30% of orgs experienced AI security incidents; only 22% have automated risk monitoring; two-thirds require weeks to implement policy—negative signal on governance maturity despite awareness.
— Stanford survey shows 62% of organizations cite security/risk as #1 blocker to scaling agentic AI (24-point margin), identifying governance and data-layer control gaps as critical adoption barriers.
— Named case study of MOL Group (30 countries) showing deployment of unified ERM platform with predictive analytics for risk forecasting and real-time monitoring—core horizon scanning and register maintenance components.
— KPMG analysis of AI adoption in enterprise risk management (N=1029) documents adoption breadth alongside governance challenges and maturity barriers, providing balanced assessment of operational realities.
— Market research shows enterprise AI governance software market growing at 32.8% CAGR, confirming mainstream adoption trajectory and market maturity for AI governance tools including risk management infrastructure.
— KPMG survey of 2,500 tech executives across 27 countries shows 74% confirm AI value but only 24% achieve ROI, suggesting inadequate risk identification and governance frameworks are primary barriers to value realization.