Home›AI Agency Academy›Lesson 06
Module 02 · Lesson 06

Research conversations, not just competitors

Turn customer language and workflow evidence into a useful problem inventory.

Last updated August 5, 2026•15–25 minutes•Free AI agent course
What you will learn

Create the interview evidence log

You will be able to collect evidence from operators and buyers, separate a stated preference from an observed problem, and use it to improve an offer.

Why this matters

Good agent work is useful before it is impressive.

Competitor sites show what companies say they sell. They rarely show where a team hesitates, what data is missing, or which exception makes a workflow painful. Real language from calls, reviews, tickets, and demos is more useful.

Core concepts

The language that keeps the work clear.

Problem evidenceA concrete example of delay, rework, loss, or customer friction—not a vague opinion about AI.
Verbatim languageWords a buyer or operator uses to describe the problem, outcome, fear, or current workaround.
PatternA theme that appears across several independent conversations or records.
HypothesisA testable explanation of how a bounded workflow could improve a named problem.
The practical method

How to create the interview evidence log

Collect raw examples

Ask for a recent case, then trace what happened, who acted, and what made it hard.

Capture the language

Write short phrases exactly as said. Do not translate everything into product jargon.

Group evidence carefully

Separate pain, desired outcome, current workaround, risk, and buying constraint.

Test a small statement

Turn the strongest pattern into an offer sentence and ask whether it matches the buyer’s actual situation.

Worked example

Worked case: research conversations, not just competitors

A property-management team says it needs ‘an AI assistant for tenants.’ In six conversations, the repeated issue is more specific: maintenance requests arrive incomplete, so vendors cannot be dispatched quickly.

The useful phrases are ‘we spend all morning chasing photos’ and ‘we cannot send a contractor without the unit and access detail.’

The offer becomes a guided intake and routing workflow that requests missing details, records consented contact information, and flags urgent cases for a person. It is based on observed work, not a generic chatbot category.

Build it in practice

Complete the working artifact

Evidence source: [call, ticket, review, observation]. Exact phrase: ‘[quote]’. Observed situation: [case]. Current workaround: [workaround]. Pattern count: [number]. Hypothesis: [tested change].
Practice

Before you move on

  • Collect five pieces of raw workflow evidence.
  • Group them without forcing them into a solution.
  • Write one offer sentence using the customer’s language.
  • At least one operator supplied a recent real example.
  • The offer language has not been invented from competitor headlines.
  • Patterns are separated from one-off complaints.
  • The hypothesis can be disproved or refined.

Lesson progress

Finished this lesson?

Save your place on this device so it is easy to pick up where you left off.

Not marked complete yet.