Setting Rules for AI-Drafted Business Documents

When a small business uses AI to draft quotes, invoices, or proposals, the main risk is that a polished draft can state a false fact or promise something the business has not approved. The same risk applies when AI drafts meeting summaries, management reports, or customer emails. Set rules for each document type based on what could happen if someone relies on an error.

For each document type, set a rule that says what AI may draft, which business records provide the facts, who checks the draft, and who can approve it for release. For example, AI can help explain a quote’s scope, while approved pricing records supply the rates and an authorized person approves any discount. These rules let employees use AI to draft while ensuring that responsible people make business decisions and check factual claims against records.

Classify documents by the consequences of an error

Set a document’s risk level based on what people use it for and what could happen if it contains an error. A meeting summary used for discussion needs less scrutiny than one that records a customer commitment or an approved decision. Require accuracy checks that match how people will use the document. The UK Information Commissioner’s Office guidance on generative AI accuracy reflects this principle.

Treat the table as a practical operating model, not a formal classification required by a standard. Raise a document’s risk when an error could cause financial loss, create a customer expectation, distort a decision, expose sensitive information, or prove difficult to reverse. No universal approval amount fits every business; set any extra sign-off threshold using your own pricing authority, transaction size, and exposure.

AI may produce a confident-sounding statement that is false or unsupported. NIST describes this kind of error as “confabulation” in its Generative AI Risk Management Profile. A polished tone does not verify a price, date, decision, or customer fact.

Set rules for each document

For every document type, name the permitted AI task and the records a reviewer must check. The authoritative record is the business record that controls a fact, such as the accounting system for an invoice amount or an approved price list for a quote.

Quotes. Let AI improve descriptions and formatting. Take quantities and rates from approved pricing or job records. Use those records for discounts and taxes, too. Get delivery terms and expiration dates from those records as well. Before sending, compare the quote with the customer’s request and the approved pricing. A person with pricing authority must approve discounts, unusual scope, or nonstandard terms. Financial-control guidance from the Government Accountability Office describes approval by authorized people and review against supporting records; those federal audit examples inform this recommendation but do not impose a rule specific to small businesses.

Invoices. Use the accounting or billing record for the customer name and invoice number. Take the dates, amounts, and payment instructions from that record. AI should not create or infer line items, tax, payment details, or balances. Match the invoice to the relevant order, delivery, or time record, and send mismatches to an authorized person. The IRS guidance on business records describes invoices and supporting documents as records that help substantiate business transactions.

Proposals. AI can organize a proposal and improve its wording, but approved materials should supply customer-specific facts, capabilities, schedules, pricing, and assumptions. The reviewer checks scope, exclusions, deliverables, dependencies, and deadlines. Require an authorized person to approve prices, contractual language, guarantees, and service commitments before release.

Meeting summaries. Decide whether each summary is an informal recap, draft minutes, or an authoritative record of decisions. Check attendees, dates, decisions, action owners, deadlines, and commitments against notes or an appropriately handled recording. Mark unclear points for confirmation instead of presenting them as settled decisions.

Management reports. Identify the reporting period and the source of each material figure. Check totals and key trends against accounting, operations, or HR records. Label measured results separately from interpretation or forecasts, and have a manager review reports that will guide consequential decisions. Keep the reviewed report with its supporting figures so later corrections can be traced.

Customer emails. Routine acknowledgments and wording edits can receive a lighter check. Require closer review for complaints, refunds, service failures, policy explanations, or claims about performance, price, or timing. The reviewer checks the customer record and approved policy, then escalates uncertain or unusual commitments. If AI sends emails automatically or in large numbers, define who monitors them and who can stop sending; occasional proofreading will not catch every problem.

Separate drafting, review, and approval

Writing or editing a draft does not give someone authority to approve the transaction or promise it describes. A useful document rule names the author or drafter, the reviewer, and the release approver. One person can hold more than one role in a small business, but the approval must still come from someone with the relevant authority.

Ask reviewers to check facts against named records and look for missing qualifications. They must also confirm that they have authority to approve any commitments. A reviewer who only reads for tone can miss a wrong figure or an unsupported promise. The ICO warns that people can over-rely on automated outputs; its guidance says reviewers need authority to challenge and override them. That guidance concerns UK data-protection contexts, so treat it as a review-design lesson rather than a statement of U.S. legal duties.

For each document type, write down five things. State what AI may do and which record supplies key facts. Specify what the reviewer checks. Name who approves release, and say what evidence to retain. Keep the rule in a template, workflow checklist, or approval field employees already use. Retaining the source records, final version, reviewer, and approval makes it easier to explain and correct a decision later.

Define exceptions before a draft is sent

Pause the normal approval path if a material fact cannot be verified or records conflict. Also pause it when the draft adds an unsupported guarantee or the document includes a sensitive or unusual commitment. Send the draft to a named manager or the person responsible for that subject. If the question remains unresolved, use a non-AI method until someone confirms the right answer.

Also review the rule when the business changes the template or data source. Review it as well when the AI tool or intended use changes. A workflow approved for internal summaries does not automatically fit summaries that record customer commitments. NIST’s AI Risk Management Framework recommends defined responsibilities, ongoing monitoring, and plans for response and recovery. It is voluntary guidance, not a document-specific mandate.

Correct drafts that are wrong or incomplete

When an error gets through, stop further distribution or reliance first. Then identify which documents, recipients, and decisions the error affected. Compare the disputed statement with the authoritative record, correct the underlying record if needed, and issue a corrected document through the usual approval path. Notify recipients or decision-makers when the error could change what they believe, pay, do, or decide.

Record what went wrong, the document type, how widely the error spread, the correction, and any near miss. Then change the control that failed: update the template or review instructions, restrict a data source, or add an escalation step. NIST’s generative AI guidance supports incident tracking, response, recovery, and post-incident review. A generic disclaimer does not fix inaccurate content.

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