By SupportHQ Team · September 25, 2026 · Metrics

Case Study Template: What to Collect After Your First AI Rollout

The first AI support rollout produces two things: a result, and a story about the result. Most teams keep the story and lose the numbers. Three months later, when leadership asks whether it worked or a prospect asks for proof, there is a feeling but no evidence.

This template fixes that. It lists what to collect before, during, and after the rollout, so the case study writes itself and the numbers are real.

Why bother with a case study

Two reasons, one internal and one external:

• Prove return, not just an experiment. “We tried AI support” is a project. “Escalations on billing questions fell by a measured amount over eight weeks, and here is the sample” is a decision.
• Build trust with future customers. Prospects believe specifics from a team like theirs more than any feature list. A short, honest case study with real numbers is the most persuasive marketing asset a small company can produce.

The case study only works if it is honest about what did not work too. Collect that as carefully as the wins.

Before launch: the baseline

Nothing after this matters without a baseline. Collect two weeks minimum, four if you can.

• Ticket volume per week, by channel.
• First response time and, if you track it, resolution time.
• Top ticket categories, with counts. Ten categories is enough.
• Knowledge base coverage snapshot: for each top category, is there an article that answers it? Yes, partial, or no.
• Team hours on support per week, even as an estimate. This becomes the “time saved” denominator.

Benchmarks for response time are in customer support response times: benchmarks and improvement plan if you want context for where you start.

During the rollout

Collect weekly. The trends matter more than any single week.

• Deflection rate by category, split by the same categories as the baseline. Deflection on billing and deflection on setup will differ, and the difference is the story.
• Escalation rate and failure clusters: how often a person was needed, and the grouped reasons. The clusters are your content backlog and, later, the “what we fixed” section.
• Time saved, both ways. Quantitative: hours on support per week versus baseline. Qualitative: ask the team what changed. “I stopped answering password resets” is a quote worth keeping.
• Resolution quality spot checks, ten a week, graded pass or fail. A deflection number without a quality number is not credible, to leadership or to prospects.

Definitions for all of these are in KPIs for AI-assisted teams; this post does not repeat them.

After launch: the results

At eight weeks, or whenever the numbers have settled, collect the comparison.

• Change in ticket volume reaching a person, overall and by category, against the baseline.
• Change in first response time. With an agent answering first, this usually collapses. Report it for agent-answered and for escalated conversations separately.
• Customer feedback. Ratings if you have them. If not, quotes from conversations, with permission. Include a negative one; it makes the positive ones believable.
• Biggest lessons learned. What you would do differently. Common answers: “we should have written the billing articles first”, “we escalated too late in week one”, “the mobile widget needed testing”. This section is what other teams actually read.

Turning the data into a case study

Keep it short. One page. Five parts:

1. Context. Team size, channels, volume, what the support problem was.
2. Baseline. Three or four numbers from before.
3. What was done. Scope, content built, escalation rules, the review cadence.
4. Results. The same three or four numbers after, plus one quality measure and one quote.
5. Lessons. Two or three, honestly stated.

If a number did not move, say so and say why. That paragraph is what makes the rest credible. For the internal version, this is also the document that settles the buy-in discussion described in how to get buy-in for AI support inside your team.

A collection checklist

Copy this into a doc on day one:

• Baseline: volume, response time, top categories, coverage, team hours
• Weekly: deflection by category, escalation rate, failure clusters, quality grades, time saved
• Week 8: volume change, response time change, feedback, lessons
• Quotes from the team and from customers, with permission
• One thing that did not work, and what you changed

Where SupportHQ fits

Be precise about what the tool gives you and what you collect by hand, because the case study will be judged on that honesty too.

SupportHQ’s insights provide, for the rollout and post-launch sections: total conversations, auto-replies (resolved with no human), escalations, whether anyone answered, all with period-over-period comparison, plus the list of questions the agent could not answer. Conversations export as CSV or JSON, and the unified inbox holds every conversation across web, Telegram, and Discord, so the sample for quality spot checks is in one place.

You collect by hand: the entire baseline (it predates the tool), volume by category, team hours, quality grades, and quotes. Deflection by category means tagging a sample of conversations yourself.

See the unified inbox page, or start a free trial once your baseline is recorded.