An AI can write a contract proposal in 90 seconds. A lawyer can review it in 10 minutes. A mistake can cost you six figures. The gap between speed and safety is where most teams fail. They treat AI as a writer, not a researcher. They skip the review step or treat it as a box to tick. Then a term slips through, a number doesn't match the quote, a threshold is set wrong, and the deal either stalls or you eat the loss. The businesses that win with AI on documents aren't faster at drafting. They're faster at reviewing. They've built a workflow that catches errors before they matter, uses the right people at the right step, and actually scales without adding risk. Where AI drafting breaks down AI is good at structure and pattern. It's bad at context and numbers. Show it a hundred proposals and it will write the 101st. Show it a specific deal with unusual terms, and it will confidently invent details. The real failures are usually three types: Math that looks right but isn't. A discount structure, a milestone payment, a volume tier—AI copies the shape but gets the variables wrong. The proposal looks fine until the customer tries to use it. Terms that contradict your last agreement. You offered different payment terms to a similar customer last month. AI doesn't know that. It generates language that conflicts, and now you have competing obligations. Missing the actual deal. The customer wants net-60 payment, but the AI draft says net-30 because that's what it learned from your templates. The proposal gets signed. Then the disagreement happens. None of these are AI hallucinations in the dramatic sense. They're failures of context. And they're completely predictable if you design for them. Build the review layer, not the generation layer The mistake is treating AI as a replacement for a drafting step. It's not. It's a starting point that saves typing, not thinking. Your workflow should be: AI drafts from a template and the deal data. Feed it real numbers: the quoted price, the deal term, the customer's name, any custom terms you've already agreed verbally. Don't ask it to invent anything. The salesperson or account owner reviews for accuracy and fit. This is not a skim. They're checking: Does the price match the quote? Are the terms what we promised? Did we miss any custom requests? Finance or legal reviews for risk. Depending on deal size and complexity, this might be a partner, a legal advisor, or your finance lead. Their job is to catch term conflicts and threshold problems. One final check before send. A simple rule: if anything has changed from step 1 to step 3, someone re-reads the whole thing once more. This isn't bureaucracy. It's just making explicit what should happen anyway. Most teams skip step 3 or combine step 2 and 3 with one person in a hurry. That's where errors live. The data quality problem that AI exposes Here's what really happens: You feed AI your CRM data, and the draft comes back wrong because the CRM data was already wrong. A customer contact is listed as the CFO but they're actually the controller. The product SKU in the quote doesn't match the line item code. The discount is flagged as 15% but the system stored it as 0.15 and AI multiplied it again. AI doesn't tolerate bad data the way humans do. A person can infer and correct. AI propagates the error into the document and makes it look authoritative. Before you try AI on contracts, invoices, or proposals, audit your data: Pull 20 recent deals. Check: Do the prices in your CRM match the actual quotes sent? Are customer names consistent? Are terms complete? Run a test: Generate a proposal with AI using that data. Does it look right? If not, go back to the CRM and fix it, not the AI output. Make this data hygiene a standing rule. Bad data + AI = fast errors at scale. If your CRM doesn't keep deal data clean , start there. AI amplifies CRM problems; it doesn't fix them. When to use AI and when to stop Not every document should be AI-drafted. Some documents need a human start, not an AI start. Good use cases for AI drafting: Proposals and quotes from templates (standard products, familiar terms) Follow-up invoices and recurring invoices (same structure, different numbers) Email cover letters and brief contract covers (low risk, high volume) Internal briefs and summaries from structured data Use AI as an editor, not a drafter: Complex multi-party agreements (start with legal framework, AI polishes language) Contracts with novel terms or custom riders Anything involving a lawyer or regulatory check anyway Don't use AI at all: One-off, high-value deals where the terms are still negotiating Anything with IP, equity, or liability risk that needs counsel Agreements that set precedent for future deals (write once, right, then use as template) The rule is: AI is good at reproducing what you've done before. It's bad at inventing what you should do once. Tooling: Where the review happens The platform matters because review is where humans re-enter the w