AI Analysis: Turn Source Material Into Checked Findings

AI analysis uses an AI tool to organize source material, identify possible patterns and explain findings you can check. A useful workflow keeps every important claim attached to evidence and verifies calculations outside the generated explanation.

You might compare project proposals, summarize public survey responses or inspect a table of service requests. fenbs can keep the source links, agreed definitions and reviewed conclusions with the project, so the next assistant starts from the checked record. Your analysis tool still does the document or spreadsheet work.

How do you start an analysis you can check?

1. Write one question that could change an action. For example: did requests increase because each workday became busier, or because the reporting period was longer?
2. Choose material you are allowed to use. Record its date, version, coverage and any missing pages, rows or responses before asking for findings.
3. Define the terms and calculation rules. State what counts as a request, which dates apply and whether repeated entries should be removed.
4. Ask for a small evidence table first. Each proposed finding should name its source, the exact supporting passage or rows, and an uncertainty.
5. Check the evidence and reproduce the arithmetic. Only then write the conclusion and save what a reviewer accepted, rejected or left unanswered.

This order makes a wrong answer easier to locate. If the evidence table points to the wrong document, there is little value in polishing its summary. For the surrounding workflow, see using AI at work; the AI at work hub covers other everyday tasks.

What should you give the assistant besides the files?

Supply the decision you are considering, who will read the findings and what evidence would change your mind. Add a short inventory of the material, including its owner and the period it covers. A filename alone rarely explains whether a report is final or a partial export.

For a table, define the unit behind each column. Counts of tickets, people and conversations are different things even when their totals look similar. Explain whether an empty cell means zero, unknown or not applicable, and preserve that distinction during cleanup.

For documents, identify the approved version and provide page or section labels. Ask the assistant to report a missing attachment instead of filling the gap from general knowledge. Keep an unchanged original so you can compare any extracted text or cleaned table against it.

The Census Bureau publishes Statistical Quality Standards↗ (opens in a new tab) for its information products. Those standards are a useful reference when you want to improve your own approach to quality; a workplace AI summary is not a substitute for following the methods required by your organization.

How can you separate evidence from interpretation?

Use three labels: observed, inferred and unresolved. Observed means you can point to something in the supplied material. Inferred means you have an explanation that goes beyond that observation. Unresolved means the available material cannot answer the question.

This example uses invented numbers to show the method. Ask the assistant to offer a competing explanation for each interpretation, then check whether you have evidence to distinguish the alternatives. A plausible explanation should stay a hypothesis until the material supports it.

Save accepted definitions and unresolved questions with the source links in fenbs, using its shared work record, so a later session can tell an observation from an approved conclusion.

Which checks catch confident but unsupported findings?

Open the original source for every material claim. A citation can lead to a real document that does not support the sentence beside it. Confirm the wording, population, reporting period and any qualifications before using the claim in a recommendation.

Recalculate important totals and percentages in a spreadsheet or calculator. Check denominators, duplicates, filters, units and rounding. Inspect both typical rows and awkward cases such as missing dates, negative adjustments and records that sit exactly on a cutoff.

NIST identifies confidently stated false content as a generative AI risk in its Generative AI Profile↗ (opens in a new tab). Treat the assistant’s confidence as writing style; the original material and reproducible checks provide the evidence.

• For summaries, compare several original passages with the summary and look for omitted exceptions.
• For classifications, review examples in every category, especially ambiguous cases and items assigned no category.
• For calculations, keep the formula, input range and result together so another person can repeat the check.
• For recommendations, state the missing information that could change the proposed action.

When does the question need a specialist workflow?

A general document comparison cannot establish every kind of conclusion. Use the focused guides for financial analysis, competitive analysis, contract analysis, data analysis review and sentiment analysis when those are your actual tasks.

Escalate when the result needs professional judgment, specialized statistical methods or access to material the tool cannot properly handle. An assistant can help list the questions for that reviewer. It should not turn missing expertise into a confident final answer.

How to do this with fenbs

1. Create a free account and add a project named for the question you want to answer.
2. Add source links in Links and listings, including enough context to distinguish the approved version from an earlier draft. Keep restricted originals in their approved storage.
3. Create tasks for extraction, calculation checks and review. Put the proposed method in each task’s plan and the checks actually performed in its test notes.
4. Record the person’s chosen definitions and accepted conclusions in Decisions and rules. Leave unanswered choices open, and ask your supported connected assistant to read the context before continuing.
5. Save progress with the remaining uncertainties and the next reviewer action. When new material arrives, identify what needs checking again.

fenbs is not the right fit for running statistical models or storing sensitive source datasets. It holds the work record around an analysis. The AI brain guide explains why preserving that record helps when a different assistant takes the next session.