Tool · Benchmark
What is a realistic AI benchmark for your industry and where do you stand?
Pick your industry to see the realistic AI resolution rate and CSAT range — with the median and the top-10% world-class mark. Then see the four factors that decide where a given deployment actually lands.
Highest-volume intents — order status, returns, shipping — are structured and data-rich, so genuine resolution runs high.
Enter a value to place it on the scale.
Ecommerce & Retail
Genuine resolution and AI-handled CSAT, on a 0–100 scale.
These bands are Aissist’s own synthesis — published vendor and cross-program figures read against deployments we run — not an audited third-party dataset. Two things to know before reading a number off the chart. First, “resolution” is not one metric: deflection counts conversations that never reached a human, resolution counts issues actually fixed, and vendors publish both under the same word (Lorikeet, updated 7 August 2026). Second, the fleet-wide vendor averages you see quoted — clustering around 65–76% (The Context Company, 18 January 2026) — are averages across mature deployments, not a starting point. Treat the band as a realistic target, not a guarantee. Full sourcing and the vendor-claimed vs. verified gap: AI Customer Service Benchmark 2026.
See where your operation could land.
Book a working session — we will map these factors onto your real traffic and show you an achievable resolution and CSAT target.
What determines the benchmark
Industry sets the range. Three things decide where you land.
Your industry fixes the ceiling and the shape of the range above. Whether a specific deployment sits near the median or pushes toward the world-class mark is decided by the next three factors — all within your control.
Industry — complexity
The intrinsic difficulty of your intents — how ambiguous, regulated, emotional, or multi-step they are. This sets the ceiling: structured, data-rich intents resolve high; ambiguous or regulated ones resolve far lower. The industry-by-industry evidence sits in the AI Customer Service Benchmark 2026.
System capability
How capable the AI platform is — multi-agent reasoning, backend actions, retrieval, and guardrails. A stronger system reaches higher within the same industry band and holds quality as complexity rises.
Maturity of assets & playbooks
The quality of your knowledge base, SOPs, and escalation rules — and the biggest reason two teams on the same platform land in different places. Intercom’s Fin team puts industry-average resolution at 40–60% on initial deployment, rising past 60% within 6–12 months of optimization (Fin, “ROI of AI Customer Service”, 19 March 2026). The climb comes from assets maturing, not from the model changing.
Transparency of data
How accessible the data and systems needed to resolve a ticket are. When the AI can see orders, accounts, and history, it resolves; when data is siloed or missing, even simple intents stall.