Beyond Automation
Why Customer Problems Keep Coming Back
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From Insights to Action: What Changes Now?
These problems existed before AI, and beyond Korea. Gartner has studied avoidable contacts caused by product issues. In Medallia's 2026 global CX study, 30–40% of departments took no action on customer insights. While 66% of CX practitioners saw improvement, only 17% of consumers agreed. Medallia research report Analyzing thousands of unstructured conversations used to be difficult. LLMs make patterns easier to find, especially when transcripts connect to product journeys and service data. But it won't decide which issues matter most or who needs to work on them.
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What Can We Finally Decide with AI?
Reading thousands of conversations to understand why each customer called wasn't realistic. So we counted broad inquiry types and often chose automation priorities from the largest groups. That works for reporting, but it doesn't explain why customers needed help.
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Who Owns the Customer's Problem?
Customers don't experience problems according to our org chart. When a fix involves several teams, it has to compete with the work each team has already planned. Even when everyone agreed about the cause, it was hard to find room for the work on another team's roadmap.
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Who Gets to Define the Problem?
Why Contact Center Investments Fall Short (2/2) Part 2 looked at what we chose to automate. Here I want to ask who decides what to automate, and why. When a contact center plans automation, it usually starts with the inquiries customers make most often. The shortlist often comes straight from its existing call categories.
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What Were We Actually Trying to Solve?
Why Contact Center ROI Falls Short (1/2) Part 1 asked whether we solve customer problems or simply handle calls better. In practice, it's easy to pick a technology before answering that question.
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Why Do Customers Still Call?
I've worked in support, built chatbots and voicebots, and now design contact-center AI products. We've gone from IVR and self-service to agent assist and LLMs. The technology changes, but we keep asking similar questions. How many calls can we automate? How much handling time can we save? How much can we reduce operating costs?