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Your stakeholder does not ask for analysis. They ask for a decision-ready update. You already have a workflow that turns query results into charts and charts into a narrative. AI can accelerate pieces of that workflow, but it cannot own the consequences of a misleading chart or an overconfident headline. The analyst owns those consequences because the analyst ships the deliverable.
Treat AI as a drafting assistant inside a controlled reporting process. AI can generate candidate chart types, slide structures, and plain-language summaries. The analyst must sign off on the chart honesty, the audience fit, and the interpretation. If those three fail, the report fails, even if every number in it is technically correct.
Explore how an end to end reporting workflow changes when AI is added.
An LLM is a large language model that generates text and code by predicting plausible next tokens given input. A prompt is the instruction you give the model, including constraints and the deliverable format you want. A context window is the maximum amount of text the model can condition on at once, which means long documents can be partially ignored if you do not manage what you paste in.
A hallucination is fabricated content presented as if it came from your data or sources. It is not the same as rounding error, which is a small, explainable numerical difference from formatting or aggregation, and it is not the same as stale data, which is a correct number drawn from the wrong refresh. AI is prone to hallucination when you ask it for specifics it cannot see.
A grounded output is a claim explicitly tied to provided data, definitions, and time windows. The analyst makes it grounded by supplying the sources and then checking the model’s statements against those sources.
See how these terms map to real reporting risks.
AI is strong at producing options quickly. It can draft three alternative headlines for the same chart, rewrite jargon into stakeholder language, and reformat a table into a slide narrative. Those are productivity wins because they compress drafting time.
AI is weak at the parts that require accountability. The model will default to a chart type even when that chart embeds the wrong argument, like a line chart implying continuity when the metric is only measured monthly. The model will also default to visually impactful choices, like truncating a y-axis, unless you tell it not to.
The analyst must explicitly verify three things before anything reaches a stakeholder. The chart uses the correct data window and segment definitions. The axis scaling and aggregation do not distort magnitude or variability. The headline matches what the data actually supports, not what would be persuasive.
Evaluate how defensible AI drafted slide claims are for a specific executive audience.