Why AI Strategy Is Failing in Customer Experience: The Gap Between Executive Deployment and Customer Preference

Enterprise AI adoption is accelerating, but a significant gap is emerging between how companies deploy AI and how customers experience it. According to Shalima Bhalla, an enterprise CX practitioner and host of the "Customer Signal" podcast, the problem is rooted in how AI programs are funded and framed.

"The conversation revolves around shaving seconds, not solving problems," Bhalla told The American Reporter. She argues that companies are optimizing AI for internal efficiency rather than for meaningful customer outcomes.

The data reflects a real tension. Thirty-four percent of companies report actively using AI across all their customer interaction processes, and 91% of executives say they are pushing to deploy AI in automated workflows in 2026. At the same time, 93% of customers say they prefer talking to a human over an AI agent for customer service, and 50% say they would cancel a service that relied solely on AI.

Bhalla traces the problem to the first wave of automation, when companies deployed chatbots and IVR systems oriented around deflection and cost reduction. Customers were pushed into bot loops, could not reach humans when needed, and rated those interactions poorly. The current AI wave risks repeating that pattern if framing stays the same.

The shift Bhalla advocates is from automation to augmentation. Agentic AI, she argues, can summarize customer history, predict intent, and suggest next best actions in real time, removing back-end friction rather than replacing the interaction. She also describes a proactive model in which AI detects a service issue and contacts the customer with a resolution before they call in.

"When you automate the data routing and the back-end steps between systems, you remove all the friction that prevents resolution," she explains. Success, she argues, should be measured by whether an issue was fixed on the first contact and how little effort the customer had to exert.

Key takeaways:

• 91% of executives are deploying AI in automated workflows in 2026, yet 93% of customers prefer humans for service interactions
• The core problem: AI programs are framed around operational efficiency, not customer problem-solving
• First-wave automation created negative CX baselines; current AI risks repeating this
• Agentic AI shift needed: from gatekeeper to augmentor, summarizing history, predicting intent, routing friction
• Proactive CX model: AI can detect issues and reach customers before they call
• Measurement shift required: success equals first-contact resolution, not deflection rate or handle time

Read the full article on The American Reporter