This is the part of the module I want you to pay close attention to, because document analysis is an area where the confidence of the output can mask its limitations if you are not watching for them.
The first limitation is accuracy on specifics. AI reads documents well at the summary and synthesis level. It is less reliable on specific details: prices, dates, room category names, exact inclusions and exclusions. If a document analysis produces specific factual information that will influence a client recommendation, verify it against the source document before acting on it. Do not assume that because the tool produced a number or a date confidently, it has read it correctly. Scan the relevant section of the original document yourself.
The second limitation is what is not in the document. If the document does not contain the answer to your question, the tool will sometimes tell you clearly that the information is not present. It will sometimes produce a plausible-sounding answer that is not actually grounded in the document. This is the hallucination risk applied to document analysis specifically. The mitigation is the same as always: high-consequence information goes back to the primary source. The tool surfaces it; you confirm it.
The third limitation is context that the document cannot provide. A supplier brief tells you what a product is. It does not tell you what clients have said about it after they have been. It does not tell you how the product has changed in the past year. It does not tell you what the competitor down the road is doing differently. Document analysis works on what is in the document. Your broader knowledge of the destination, the product and the market is what you bring that the tool cannot replicate from the file alone.
The working principle is this: use AI to process documents faster. Use your expertise to evaluate what it surfaces. The two together produce better outcomes than either alone.
