A non-profit operating a network of regional food banks receives thousands of grant application narratives each cycle. Program staff spend weeks manually reading applications to match applicant organizations with the funding priorities of specific grants. Which AI use case maps most directly to faster, more consistent grant-matching decisions?

The correct pick works because it targets the actual bottleneck named in the scenario: staff manually reading long-form narratives to see whether an applicant's mission and plans line up with a specific funder's stated priorities. That's a language-understanding task, and a model trained to compare application text against criteria documents can surface a ranked, consistent recommendation in seconds instead of weeks, while a human still makes the final call. The public-facing FAQ chatbot fails the mapping test because it serves website visitors, not the internal review team, so it never touches the backlog at all. The inventory-forecasting model is a legitimate use case, but it's a numeric prediction tool solving a supply-chain problem, not a text-comparison problem, and it lives in a completely different part of the organization. The donor thank-you letter generator is generative AI too, but it's aimed at donor relations, a different audience, workflow, and outcome than matching applications to grants. The test to apply here: does the AI's core capability sit directly on top of the specific manual task and metric named in the stem? Only the grant-matching model does; the others are good AI ideas that simply belong to different problems.