In a recent LinkedIn post, Jean Ng 🟢 discusses the critical pitfalls businesses face when implementing AI in customer service, particularly emphasizing that viewing it solely as a cost-cutting measure is a recipe for failure. Ng 🟢 distinguishes agentic AI, which can interpret requests, make decisions, and execute actions across workflows, from simpler chatbots.
The potential impact of this advanced AI is significant, with Gartner predicting that agentic AI could resolve 80% of common customer service issues without human intervention by 2029. However, Ng 🟢 raises a crucial question that often gets overlooked in the pursuit of automation: Will these AI-driven resolutions strengthen or weaken the customer relationship?
“AI customer service will fail if brands treat it as a cost-cutting project.”
Ng 🟢 argues that the success of agentic AI hinges on more than just a sophisticated interface. It requires a robust foundation, including accurate product and customer data, seamless access to necessary systems and workflows, clearly defined decision-making boundaries for the AI, and, crucially, a direct pathway to human support when empathy or complex judgment is needed.
The Foundations for Effective Agentic AI
According to Jean Ng 🟢, without these essential underpinnings, AI can inadvertently create more friction for customers. “Without these foundations, AI becomes another layer customers must fight through,” Ng 🟢 states, highlighting the potential for AI to become a barrier rather than a facilitator.
From a marketing standpoint, Ng 🟢 points out that every customer interaction is a brand-shaping event. A rapid response, no matter how quickly delivered, offers little value if it’s inaccurate, lacks personalization, or fails to resolve the underlying issue effectively.
Prioritizing Customer Relationships Over Automation Metrics
The core role of AI in customer service, as outlined by Ng 🟢, should be to manage routine and information-intensive tasks. This frees up human service teams to dedicate their time and expertise to complex cases that demand judgment, care, and accountability. A key point emphasized by Ng 🟢 is that the handover from AI to a human agent should be seamless and context-aware.
“Human handover should never feel like starting again.”
Ng 🟢 elaborates that customers should not be forced to re-explain their issues, resubmit information, or reiterate the urgency of their situation. The human agent, she notes, must be equipped with the complete history of the interaction, relevant data, and details of any actions already taken by the AI.
“The strongest service model will assign each task to the resource best placed to handle it. AI for speed and scale. People for judgement and trust.”
Ultimately, Ng 🟢 suggests that the true measure of success for leaders investing in agentic AI is not the volume of automated conversations. Instead, it is the number of customer problems resolved without detriment to the customer relationship. The fundamental question for businesses, Ng 🟢 poses, is whether AI is being used to genuinely reduce customer effort or simply to shift that effort onto the customer in different ways.
📝 About This Content
This article is based on insights shared by Jean Ng 🟢 on LinkedIn.
📅 Originally posted on July 2, 2026 | View original post on LinkedIn →