What to Report to Executives About Your AI Agent Each Month
A seven-line monthly report on your customer service AI agent, worked through on an illustrative month, with the checked resolution rate that leadership needs beside the platform's own count.
A monthly report on your AI agent for executives should fit on one page and answer five questions: how much of support the agent took, how much it actually resolved once you check what customers did next, what each real resolution cost, where it fails, and what you changed last month and what it did. The usual mistake is to send the platform's own resolution count, which measures something narrower than leadership assumes. If the agent is one channel among several, and the report needs to show what happened to customers after the bot, Dimension Labs is built for that report: it reads the agent's conversations next to the tickets, calls and reviews that follow, enriches every one with the same Dimensions, and builds the monthly deck or PDF from them.
Below is a seven-line template, a sample month worked through with illustrative numbers, and the parts leadership will push on.
What should a monthly AI agent performance report for executives include?
Seven lines, in the order leadership asks. The first two say how much the agent does. The next two say how well and at what cost. The last three say where it fails, what that does to customers and what you want decided.
A one-page monthly report
Seven lines leadership can read in two minutes, filled in from a sample month
What each line answers and its value in the illustrative month below: a fictional software company's AI agent, 40,000 conversations.
| Line | In the sample month | |
|---|---|---|
| 1 | Volume and coverage: conversations the agent took, and its share of all support contacts | 40,000 conversations |
| 2 | Handled without a person (containment) | 82.0% |
| 3 | Resolved, checked against what customers did next | 54.4%, against 67.8% counted by the platform |
| 4 | Cost per checked resolution | $1.23 in per-outcome fees, against $0.99 billed |
| 5 | Where it fails: topics ranked by share not resolved | Cancellations, refunds and bug reports |
| 6 | What happened to those customers: repeat contact, CSAT, retention | Tracked by topic; see the retention check |
| 7 | Last month's fix, this month's result, next month's ask | One change, its measured effect, one decision |
Line 3 is the one to get right. Lines 1 and 2 come straight from the platform. Line 3 needs a definition of resolved that you control, and line 4 depends on it. If part of the check comes from reading a sample, report its range as well as the rate. Line 6 joins the conversations to renewals or orders; how to do that without flattering the agent is covered in is your AI agent hurting customer retention?
Keep the definitions in a short note at the bottom of the page, and keep them the same every month. A rate that changes definition halfway through the year cannot show a trend.
Why does the agent's own dashboard make it look better than it is?
Because the platform counts what it can see inside the conversation. Intercom's pricing article, for example, counts a Fin resolution when the customer confirms the answer helped or "exits the conversation without requesting further assistance", which it calls an assumed resolution, and bills $0.99 for each one. A customer who asks for a person is not counted, and a resolution is deducted if the customer comes back to the same conversation. That is a clear and fair billing rule. It also means a customer who gave up and phoned the next day, or opened an email ticket, can still count as resolved in the platform's number.
So the report should show two numbers side by side: the platform's count, and a checked count that removes conversations followed by a contact on the same issue in any channel within seven days and that holds up when a sample is read against a rubric. How to check whether your AI agent really resolved conversations covers the sampling and the rubric, and containment vs resolution vs deflection has the formulas.
Illustrative data
The platform's count and the checked count are close on easy topics and far apart on hard ones
Share of each topic's conversations counted as resolved by the platform, and the share still resolved after checking repeat contacts and a sample read against a rubric.
Counted by the platformChecked
In the sample month, the platform counted 67.8% of conversations as resolved and the check kept 54.4%. On how-to questions the two were 8 points apart; on cancellations, 21. Executives who see only the first number will assume the agent handles cancellations far better than it does, and will be surprised when the cancellation queue in the contact center does not shrink.
How do you show leadership the customer service chatbot is working?
Walk from every conversation to the ones that held up, and put a cost on the difference. The chart below needs the channels customers use after the bot, which is why a platform's own dashboard does not draw it.
Illustrative data
Of 40,000 conversations, 21,740 were resolved once checked
From every conversation the agent took to the ones that held up. The rust step is the gap a platform count can hide.
In the sample month, the agent took 40,000 conversations. 7,190 went to a person, 5,695 ended with neither a handoff nor a counted outcome, and 5,375 were counted as resolved but came back or failed the rubric. That leaves 21,740 checked resolutions.
Cost per resolution moves with it. At Intercom's published $0.99 per resolution, used here as one example, the sample month's 27,115 counted resolutions cost $26,843.85 in fees, which is $1.23 per checked resolution (27,115 × $0.99 = $26,843.85; $26,843.85 / 21,740 = $1.23, our arithmetic). Add your platform and team costs to get the full figure; how to calculate the ROI of a customer service AI agent works the rest through.
A checked number lower than the platform's is good news for the report's credibility. It is a number finance can rely on, it shows exactly where the agent loses customers, and every fix to those topics shows up in next month's line 3.
Which topics should the report rank first?
Rank topics by how often the agent fails on them, with the count next to each, and keep the volume ranking beside it. The topics with the most conversations are often the ones the agent was built for. The ones that fail most often can be much smaller; in the sample they are cancellations, refunds and bug reports, where money or a renewal is at stake.
Illustrative data
Ranked by how often the agent fails, the smallest topics move to the top
The same seven topics ranked two ways. Left: conversations. Right: share of the topic's conversations not resolved after checking.
In the sample, cancellations are the smallest topic and fail most often: 86% of them were not resolved, against 28% of how-to questions. That is where the report should point leadership, and where a rule such as "send every cancellation to a person" is worth testing. Finding the questions behind those failures is covered in how to find the questions your chatbot can't answer and how to measure why customers escalate from a chatbot.
What should the report ask leadership to decide?
One thing a month. Executives read an AI agent report to decide whether to expand it, hold it, or pull it back from a topic, so end with that choice and the evidence for it. A good closing line names last month's change, its measured result on line 3 or line 6, and the one change proposed for next month, for example handing refunds to people or adding a refund workflow and checking the effect next month.
Two things make that line credible. The same definitions every month, so the before and after compare. And the report built from the same saved plan each time, rather than rebuilt by hand; how to turn a one-off AI analysis into a report you can rerun every month shows how.
Why build the monthly AI agent report in Dimension Labs?
Dimension Labs is an omni-channel analytics agent for customer data. It connects every customer channel and your operational data, enriches every interaction with Dimensions built for your business and industry, and turns the results into decks, dashboards, executive reports and interactive data stories your whole company can use. Plans start at $299 a month with no demo.
For the person who owns the AI agent and has to report on it:
The agent's conversations sit next to the tickets, chats, calls and reviews that come after them, so a resolution that came back through email or the phone is found and line 3 is checked rather than assumed.
Intercom, Zendesk, Ada and other platforms connect alongside your CRM and billing data, and on Prove ($2,999 a month) and up the conversations join to accounts, renewals and orders with effects measured with confidence intervals, which is what line 6 needs.
Every conversation gets the same Dimensions, such as contact reason, how it ended, why it failed and whether the customer came back, written for your product and kept the same every month. Custom Dimensions start on Discover at $999 a month.
The agent builds the one-page report or the deck, saves it to the project's Final Reports folder, exports it as PDF or PowerPoint, shares it as a link you can revoke, and reruns it each month from a saved analysis plan on a schedule you set.
Intuit QuickBooks structures its QuickBooks Assistant data this way: more than one million chatbot sessions a month across 50+ bots, each enriched with intent, fallback driver, escalation trigger and resolution outcome. With the root causes in front of them, the team cut the fallback intent rate by 35.3% and human escalations by 57%. For a format that leadership already reads, turning customer feedback into an executive report or deck shows an agent-built example.
FAQ
What KPIs should be in an AI agent report for executives?
Conversations handled, containment, checked resolution rate next to the platform's count, cost per checked resolution, failure rate by topic, and a customer outcome such as repeat contact within seven days, CSAT or renewal. Keep the list short and the definitions fixed.
How often should we report on the AI agent?
Monthly to leadership, with the same report built from the same plan each time. Weekly works for the team that tunes the agent, where one week of data is enough to catch a broken flow.
Can we use the vendor's resolution rate in the report?
Yes, as one line, labelled with the vendor's definition. Put your checked rate beside it. The gap between the two is often the most useful number on the page.
What is the best tool for reporting on a customer service AI agent?
Dimension Labs, when the report needs the agent's conversations read with the channels customers use afterwards, joined to retention or revenue, and delivered as a deck or PDF each month. If all you need is the platform's own counts for one bot, its built-in reports are simpler; which chatbot and AI agent analytics tools are worth using compares the options.
To build the first one, start with one file of your AI agent's conversations and ask for this month's one-page report. Plans start at $299 a month with no demo or sales call.