Notion and Decagon: an AI support agent, 34% faster resolution and 2x deflection
Notion deployed Decagon's AI customer-experience agent to automate repetitive inquiries and route the rest to the right expert. Per Decagon's case study, Notion saw a 34% improvement in ticket resolution time, a 2x increase in deflection, and a 3.4% ask-for-human rate. Every figure is vendor-published; Notion's Global Head of Customer Experience is named and quoted.
| Metric | Before | After |
|---|---|---|
| Ticket resolution time | pre-AI baseline | up to 34% improvement |
| Deflection | 2x increase | |
| Ask-for-human rate | 3.4% |
The problem
A fast-growing SaaS company accumulates support volume faster than it can hire, and most of that volume is repetitive. Per Decagon’s case study, Notion wanted to automate the repetitive inquiries and route the rest intelligently, consolidating a stack of support tools and freeing its customer-experience team for higher-value work (source).
What was built
Notion deployed Decagon’s AI customer-experience agent, chosen after what its CX lead describes as a rigorous evaluation. Notion’s Global Head of Customer Experience, Emma Auscher, is quoted on record: “We conducted a rigorous” selection process (source). The agent automates repetitive inquiries, does intelligent routing to connect customers with the right expert faster, and consolidates previously redundant platforms.
The outcome
The headline metrics. Per Decagon’s case study, Notion saw a 34% improvement in ticket resolution time, a 2x increase in deflection, and a 3.4% ask-for-human rate (source). In the vendor’s words: “34% improvement in ticket resolution time, 2x increase in deflection, 3.4% ask for human rate”, and “With an average ask for human rate of 3.4%, what once required multiple steps and manual intervention is now” handled by the agent (source).
How this was verified
This case is pending, and the caveat is structural: every figure comes from Decagon’s own case study, which is marketing material, and no independent outlet reports these numbers. What keeps it on the record is that the client is named and its Global Head of Customer Experience is quoted on record. Green requires Notion to confirm the resolution-time, deflection, and ask-for-human figures and how they were measured, on the record.
Sources
Cited in this case file. Tier 3 = vendor or first-party. Each figure was checked against the live source on 2026-07-08.
- Decagon case study, “Notion” (Tier 3, vendor-published with the client CX head named and quoted). https://decagon.ai/case-studies/notion · archived
Decagon AI customer-experience agentIntelligent routing; support-tool consolidation
- Status
- pending
- Method
- Sourced by The Internet Ninja against the public record: every figure below is quoted from the cited source and, on 2026-07-08, checked against the live page over the open network, with a web.archive.org capture taken the same day. This case is pending, it has not yet cleared TIN's green validation bar. The single source is Decagon's own case study, vendor-published marketing material; no independent source reports these figures. The mitigating factor: Notion's Global Head of Customer Experience, Emma Auscher, is named and quoted on the record. A green badge requires Notion to confirm the resolution-time, deflection, and ask-for-human figures and their measurement basis on the record.
- Provider
- Decagon, AI customer-experience agents
- Client
- Notion · SaaS / productivity software (US)
- Disclosure
- named
What results did Notion report with Decagon?
Per Decagon's case study, a 34% improvement in ticket resolution time, a 2x increase in deflection, and a 3.4% ask-for-human rate.
Why is this case file pending, not verified?
Every figure comes from Decagon's own case study, with no independent source. The mitigating factor is that Notion's Global Head of Customer Experience is named and quoted. A green badge requires Notion to confirm the figures on the record.