Support leaders spend years optimizing for resolution time, only to watch the same bugs resurface across hundreds of tickets while engineering drowns in incomplete reports. The metric that was supposed to indicate customer satisfaction actually incentivizes superficial fixes, masks systemic product issues, and creates an adversarial relationship between support and

Support leaders are rushing to adopt AI tools, driven by executive pressure and competitor moves, only to watch their initiatives stall or fail within months, with 70-85% of AI initiatives failing to meet expected outcomes. The pattern repeats across orgs: promising pilots that never scale, teams that resist using new

A customer reports a checkout bug at 2pm. By 2:15pm, support has logged a ticket with a vague description. By 3pm, engineering sends it back asking for browser details, console logs, and reproduction steps that were never captured. By EOD, the bug still isn't fixed and three

A customer success manager at a B2B SaaS company once told me she spent 15 hours each week manually updating health scores, pulling usage data from three different dashboards, and trying to predict which accounts might churn. By the time she identified at-risk customers, they were already halfway through their

A customer writes "the checkout button isn't working" and your support team sends back five clarifying questions. Three days and a dozen emails later, you're still not sure which button they meant or what browser they were using. Screen recording tools let customers show