2024 Year in review: Perspectives on genAI from CX professionals

2024-12-17 · Source: Keatext · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Operations & Process Management · Depth: Intermediate, short

Summary

A review of 2024 perspectives from CX professionals highlights their initial experiences with generative AI (genAI) through chatbots like ChatGPT and Gemini. While businesses recognized genAI's potential for customer experience tasks such as gaining insights from feedback and identifying recurring issues, significant challenges emerged. CX professionals struggled with prompt engineering to achieve consistent output structures and found report generation required substantial manual effort to combine data and add recommendations. Data preparation, particularly for quantifying recurring issues from thousands of support tickets, also proved difficult, as chatbots could list issues but not easily provide frequency counts. These experiences indicate that standalone genAI chatbots often fall short of the comprehensive, trend-monitoring reporting capabilities needed by contact center managers and other CX roles.

Key takeaway

For AI Product Managers evaluating genAI solutions for customer experience, recognize that raw chatbot interfaces like ChatGPT or Gemini are insufficient for robust, trend-based reporting. Your teams will face significant prompt engineering and data integration hurdles. Prioritize platforms that embed genAI within a comprehensive analytics framework, offering automated reporting, multi-source data integration, and specific insight queries to deliver actionable CX intelligence.

Key insights

Standalone genAI chatbots present significant reporting and consistency challenges for CX professionals.

Principles

• Consistent genAI output requires careful prompt engineering.
• Manual effort is often needed to integrate genAI insights into comprehensive reports.

Method

Keatext integrates genAI into its platform to automate reporting, enrich insights beyond sentiment analysis, expand conversational analytics to various data sources, and enhance platform connectivity.

In practice

• Reorganize insights by customer journey stages.
• Automatically identify issue categories from inquiries.

Topics

• Generative AI
• Customer Experience
• Conversational Analytics
• Prompt Engineering
• Data Preparation

Best for: AI Product Manager, Operations Professional, Business Analyst, Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Keatext.