Chapter Twelve

Identifying
Opportunities
and User Pain Points

AI doesn't replace talking to users. It lets you talk to all of them at once, at machine speed, and actually hear what they're screaming underneath the noise.

📖 ~14 min readPages 81–88
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Ava's contextSeries B fintech. 8,200 customers. 1 PM, 1 designer, 3 engineers. 12,347 unread tickets.

Ava slumped in her chair at 11:47 PM, the sole PM owning the core dashboard for a Series B fintech serving 8,200 mid-market accounting teams. Zendesk sat at 12,347 unread. Slack from CS pinged nonstop: “this user is about to churn because the export is broken again.”

Her CEO had forwarded a board slide: “AI features or we get left behind — Stripe did it with 12 people.” Ava's tiny team — her, one designer, three engineers — had to ship something that moved the needle.

She exported 90 days of tickets, dumped a sample into Claude, and typed:

Key moveAva asked “what jobs are they failing at, and what workarounds?” — a JTBD framing AI excels at surfacing.
// Ava's midnight experiment
Cluster these into themes.
Ignore feature requests.
Tell me the jobs customers are failing at
and the workarounds they're hacking together.

Forty-three seconds later: five crystal-clear clusters. One jumped out: “Enterprise users on mobile are rage-quitting during reconciliation because the table doesn't scroll horizontally and the keyboard hides half the fields.”

Result
34%
lift in mobile reconciliation after shipping the fix AI identified

Tribal knowledge from three CSMs had whispered about “mobile sucks” for months. Logs showed nothing because mobile sessions weren't instrumented. For the first time in months, Ava felt ahead of the firehose.

AI is your discovery multiplier. It doesn't replace talking to users. It lets you talk to all of them at once, at machine speed.

Chapter 12

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