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 classifies, prioritises, and routes incoming support tickets to the right teams based on content, urgency, and customer value. Includes skill-based routing and priority auto-assignment; distinct from incident triage in IT ops which routes technical infrastructure issues rather than customer queries.
AI-driven ticket routing is a solved problem with an execution problem. The core ML task — classifying incoming support requests by intent, urgency, and customer value, then assigning them to the right agent — has been production-ready since the late 2010s, and every major helpdesk vendor now ships it as a GA feature. Deployments that reach maturity consistently deliver strong results: 95%+ routing accuracy, 60-80% reductions in classification time, and measurable savings running into seven figures annually. The challenge has shifted from whether the technology works to whether organisations can operationalise it. Only about 10% of organisations report mature, fully integrated deployments, even as investment intent runs above 80%. That gap — between proven capability and stalled rollouts — defines the practice today. The tooling is accessible and the ROI is documented; the bottleneck is governance, data quality, and change management.
Salesforce, Zendesk, and Freshworks all ship GA routing with intent detection, skill-based assignment, and workload balancing. Freshworks added Intelligent Routing in January 2026; Zendesk and Salesforce continue iterating on established capabilities. A Kustomer comparison catalogues twelve vendors offering AI triage features, confirming a competitive, commoditised tooling market. Deployment evidence is specific and repeatable. An MSP case study reports 95%+ first-assignment accuracy versus 75-80% manual, with 80% faster response times. A Salesforce telecom deployment saved 1,100 hours per quarter with 28% response-time improvement. Unity's Zendesk rollout delivered $1.3M in annual savings from 8,000 ticket deflections. These are not outliers; named results from Fox Communities Credit Union, Tata Consumer Products, and Hinge Health tell a consistent story of efficiency gains on routine ticket volume.
The difficulty is getting there. An Intercom survey of 2,400 professionals found 82% had invested in AI for support in 2025, yet only 10% described their deployment as mature. Nearly 40% of new AI deployments fail due to governance and oversight gaps, and AI-powered customer service fails at four times the rate of other AI categories. Vendor lock-in anxiety compounds the problem: 94% of IT leaders express concern, while willingness to pay a premium for AI has dropped to 29%. The technology is proven; the organisational machinery to adopt it is not.
— Critical assessment: Salesforce data shows only 33% of AI initiatives meeting ROI targets, 72% failed to scale, 20% abandoned; documents specific routing failure modes (intent misinterpretation, context loss, escalation design).
— Documents triage evolution to agentic era with quantified misrouting costs ($329k annually for 2k tickets/month at 35% misroute rate) and deployment case (Descope 54% faster resolution).
— Deployment data shows AI triage accuracy at 95% vs 77% manual, with routing time dropping from 5-12 minutes to under 2 seconds and misrouting rates falling from 40% to 4%.
— BMC Helix ITSM GA feature for automated ticket categorization and classification confirms practice adoption in enterprise ITSM platforms.
— Named-organization deployments show customer support as most mature ROI category: Klarna 700 FTE-equivalent automation, Salesforce 84% autonomous resolution, ServiceNow 410k hours saved.
— Benchmark aggregating 150+ data points shows median tier-1 deflection of 41.2% (top quartile 58.7%), with AI cost-per-resolution at $0.62 vs $7.40 for human agents.
— Critical finding: 81% of customer service teams run AI as disconnected tools; only 1 in 5 report systems working together, showing implementation orchestration as major barrier despite capability maturity.
— Enterprise-scale synthesis from McKinsey, Deloitte, Gartner shows 33% median productivity gains with 40-60% resolution time improvements and 45% AHT reduction through AI ticket automation.