AI Chatbot Failure Analysis

CLIENT

Various Clients

SERVICES

App Development

STORY

SJ Design partnered with a large organization to solve a growing challenge: turning thousands of chatbot conversations into meaningful improvements. Although AI could flag potentially problematic interactions, employees still spent hundreds of hours investigating what went wrong. We created an AI-powered review system that identifies root causes, consolidates recurring issues, and transforms scattered reports into a prioritized action plan.

The Challenge

Our client receives thousands of chatbot conversation reports. Another AI system flags conversations that may need attention, but employees still have to manually review hundreds of cases to determine:

  • Whether the chatbot actually made a mistake
  • Whether the correct content existed but was not retrieved
  • Whether new content, logic, routing, or escalation changes were needed
  • Whether multiple conversations were caused by the same underlying issue

Flagging the problem was not enough. The client still needed experienced employees to identify the root cause and decide what to fix.

Solution:

We developed a comprehensive AI review agent that analyzes each conversation, compares it with existing support content, and determines what actually went wrong. The system then groups repeated issues into a prioritized action plan, identifying:

  • The root cause and recommended correction
  • The affected conversations and supporting evidence
  • Whether the issue involves content, retrieval, chatbot logic, or routing
  • How many conversations could be resolved by each action
  • Which team should complete the work

From Analysis to Action

The results are organized in a dedicated workstation where employees can review each recommendation, complete the required task, and mark it as resolved.
Because the investigation and analysis are already complete, the client can use a broader operational team instead of relying on senior specialists to review every conversation. The result is a faster, more consistent process that transforms chatbot reports into a clear and manageable improvement plan.

The Impact at Scale

4,000

conversations

analyzed each month

667 – 1000

review hours saved

manual review each month

4–6

full-time reviewers

equivalent capacity released

Repeated issues consolidated
Prioritized action plan
Direct resolution

*Estimated using an average manual review time of 10–15 minutes per conversation.