Rigid scripts failed on edge cases. Reasoning agents now plan workflows and adapt to context to resolve complex requests.
A global service platform powering more than 4.6 billion customer resolutions annually.
Traditional intent-based bots relied on rigid scripts that failed when customers asked clarifying questions or changed their minds. These static...
“The old world was message in, response out. Real customers change their minds, ask clarifying questions, and expect the AI to follow along naturally. In service, the only outcome that matters is resolution, and until now, bots have been somewhat limited in their ability to achieve it.”
Zendesk's Customer service automation is part of this use case:
AI-powered customer service and employee experience software platform.
AI research and deployment company developing generative models and tools.
Related implementations across industries and use cases
Fragmented AI pilots slowed support. Now, staff-sourced agents handle 89% of self-service requests and automate 37% of case workflows.
Complex refunds once required human review. Rigorous testing now lets AI resolve millions of multi-step queries monthly.
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Fragmented AI pilots slowed support. Now, staff-sourced agents handle 89% of self-service requests and automate 37% of case workflows.
AI support risked hallucinations. Now, 20 specialized models fact-check every answer against strict company policies.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Accountants manually scoured mailboxes to assemble 15 subsidiary workbooks. Now, staff-built AI agents pull invoice data for instant review.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
Rigid scripts failed on edge cases. Reasoning agents now plan workflows and adapt to context to resolve complex requests.
A global service platform powering more than 4.6 billion customer resolutions annually.
Traditional intent-based bots relied on rigid scripts that failed when customers asked clarifying questions or changed their minds. These static...
“The old world was message in, response out. Real customers change their minds, ask clarifying questions, and expect the AI to follow along naturally. In service, the only outcome that matters is resolution, and until now, bots have been somewhat limited in their ability to achieve it.”
Zendesk's Customer service automation is part of this use case:
AI-powered customer service and employee experience software platform.
AI research and deployment company developing generative models and tools.
Related implementations across industries and use cases
Fragmented AI pilots slowed support. Now, staff-sourced agents handle 89% of self-service requests and automate 37% of case workflows.
Complex refunds once required human review. Rigorous testing now lets AI resolve millions of multi-step queries monthly.
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Fragmented AI pilots slowed support. Now, staff-sourced agents handle 89% of self-service requests and automate 37% of case workflows.
AI support risked hallucinations. Now, 20 specialized models fact-check every answer against strict company policies.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Accountants manually scoured mailboxes to assemble 15 subsidiary workbooks. Now, staff-built AI agents pull invoice data for instant review.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.