Why Agentic and Conversational AI Products Are Not What CX Leaders Think - with Baker Johnson of UJET

2026-01-05 · Source: The AI in Business Podcast · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Customer Experience Technology · Depth: Intermediate, extended

Summary

Baker Johnson, Chief Business Officer at UJET, discusses why conversational and agentic AI often fail to deliver expected gains in customer experience (CX). He argues that decades of treating CX as a cost center have led to siloed processes, misaligned metrics, and poor returns, rather than issues with the AI agents themselves. Johnson emphasizes that enterprises have heavily invested in AI without achieving anticipated CX improvements because they attempt to retrofit AI into existing, often broken, processes. He advocates for redesigning workflows before automation, aligning real-time interaction data with systems of record, and establishing a healthier balance between human and AI agents. The discussion highlights the "delight fallacy" in CX, where customers prioritize painless, efficient outcomes over "delightful" journeys, and stresses the importance of data governance and knowledge management for effective AI deployment.

Key takeaway

For Product Managers overseeing CX initiatives, recognize that simply automating existing workflows with AI will not yield significant ROI. Your focus should be on fundamentally redesigning customer journeys and ensuring robust data governance across all interaction channels before deploying AI. This approach will enable AI to act as a true collaborator with human agents, leading to more efficient, outcome-driven customer experiences and measurable business value.

Key insights

Effective CX transformation requires redesigning processes and integrating data before deploying AI, focusing on outcomes over "delight."

Principles

• Automating broken processes accelerates existing problems.
• CX should be outcome-focused, not merely efficient.
• AI and human agents must collaborate, not replace each other.

Method

Redesign workflows from a clean sheet, align real-time interaction data with systems of record, and intentionally integrate human and AI agents based on specific tasks and desired outcomes.

In practice

• Prioritize process redesign over direct AI automation.
• Reconcile systems of record with real-time interaction data.
• Treat AI as a co-worker, not a wholesale human replacement.

Topics

• Customer Experience (CX) AI
• Conversational AI
• AI Workflow Design
• Data Governance
• Human-AI Collaboration

Best for: Product Manager, Director of AI/ML, Executive, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.