Quick Answer: You can use AI in client reports if you treat it like a fast junior analyst: ground it in your real data, check every number, avoid causal claims the data cannot support, and have a person approve anything the client sees.
AI can write a report summary in seconds. It can also get a number wrong, state a cause as fact, or leave something out.
In a client report, those mistakes land in front of someone who pays you. This guide covers what is known about AI errors, a checklist you can use, and how to judge any AI reporting tool.
What the data says about AI errors
NP Digital, a digital marketing agency, ran a study of 600 prompts across six large language models and surveyed 565 U.S. digital marketers. It was published as sponsored content on Adweek Wire, and Adweek states it does not review or edit that content. Treat it as one agency's findings, not neutral research.
According to that report:
- 47.1% of marketers run into AI errors several times a week
- 36.5% say inaccurate or hallucinated AI content has already gone live
- More than 70% spend one to five hours a week checking AI output
- 23% say they are comfortable using AI output without human review
- Reporting was among the higher-risk tasks, alongside schema creation and full content development
- The most common errors were omissions, outdated information, fabrication and misclassification
You can read it at Adweek.
The takeaway is not "avoid AI". It is "do not send AI output to a client unchecked".
A 7-point review checklist
This list adapts the QA rules published by Prose Media (March 2026), which are worth reading in full on their blog.
- Source. Can every claim be traced to a metric, a source and a date range?
- Math. Recalculate percentage changes, conversion rates and blended figures
- Definitions. Do terms like lead, pipeline and ROI match what the client means?
- Scope. What is left out, such as offline sales or branded search?
- Cause. Avoid stating a cause as fact unless the evidence supports it
- Action. Does the summary say what to investigate, decide or change?
- Sign-off. A named person approves anything that affects budget, targets or what the client is told
What to keep with a person
Prose Media's view, which we share, is that AI should summarize approved data and draft questions, while people decide anything that affects money, targets or narrative. That includes:
- Budget shifts between channels
- Forecasts and targets
- How attribution works
- What counts as a KPI
- Why conversion rate changed, or whether a channel is really working
Questions to ask any AI reporting tool
Before you let an AI tool write for your clients, ask:
- How are numbers checked? Does it compare what it writes with the source data, or just trust the model?
- Can I review before the client sees it? Is there a draft stage, or does it publish automatically?
- What happens when data is missing? Does it say so, or fill the gap?
- Does it avoid overclaiming? Does it say "likely" or "proves"?
- Who receives my data? Which AI providers does the tool send information to, and is that listed?
How LedgeSpace's RUSHA handles this
Here is what RUSHA does, and what it does not.
What it does:
- Number checking. Numbers in an answer are checked against the raw integration data. If one cannot be matched, the response is rejected and retried
- Internal and client versions. It writes a blunt internal diagnosis and a diplomatic client version, and a programmatic check confirms every number in the internal version appears in the client version
- Careful wording. It is barred from absolute words like "confirms", "proves" and "definitively"
- Drafts, not auto-publishing. Proactive alerts arrive as drafts, and a client sees nothing until you approve it
- Honest fallbacks. If RUSHA cannot produce a verified answer, it returns a "not enough data" message, and that response does not use up an insight
What it does not do:
- It does not guarantee the reasoning is right. The number check confirms figures match your data, not that the conclusion is correct, so a person should still read what the client will see
- It is not a replacement for knowing the client's business
Where your data goes: RUSHA uses third-party AI providers. Summarized account metrics and your question are sent to them to produce answers, and our sub-processors page lists the vendors we use.
RUSHA is an optional add-on from $26/mo, billed monthly, on top of a platform plan from $39/mo billed annually. The 14-day free trial includes 20 insights and needs no credit card.
You can read more in the guide to how RUSHA's guardrails work.
