SR
ENExample situation: AI Customer Support Automation
What would an analysis look like if customer support were handed to AI?
Business situation
A service company wants to use AI to reduce response times and support costs. Management is considering automating first-line customer support, including account questions and troubleshooting. The opportunity is significant, but errors, privacy concerns, poor escalation, or impersonal interactions could damage trust.
The decision
How much of customer support should be automated, and where should human control remain mandatory?
What to question
Which requests are genuinely routine, and which carry real risk if the AI gets them wrong? How does the customer know they're talking to AI, and how fast can they reach a human? What gets measured to catch declining trust early?
How the depth changes
Focused → broader → deepest
Four perspectives identify the operating gain, challenge automation assumptions, quantify economics, and frame the strategic role of service quality.
Nine perspectives add customer experience, messaging, failure modes, contractual/privacy exposure, bias toward cost cutting, and practical objections…
Fifteen perspectives examine capital discipline, escalation design, service economics, governance, regulatory/privacy constraints, measurement logic,…
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