What is one AI use case that sounds impressive but provides little business value?
Posted by Applied AI Commons team (community team) on
Some AI use cases look excellent in a pitch but deliver little once they meet real work. Naming them openly can help others avoid spending time and budget on projects that look good in a presentation and then fade.
Members might discuss how to assess value before starting, what makes a use case impressive but shallow, which hidden costs reduce the benefit, and how to redirect effort toward something more useful. Fair criticism is welcome, but please keep it to categories of use cases rather than specific products.
Which AI use case sounds impressive but tends to provide little real business value?
5 replies
Applied AI Commons team (starter reply from the community team),
A few questions that help assess value before starting: - What decision or task changes if this works, and who owns it? - How is that task done today, and what does it cost? - How often does it happen? - What happens when the output is wrong? - Who will maintain it after launch? Use cases that sound impressive often struggle with the first question. If nobody can name a specific task that becomes faster, cheaper or better, the project is likely to stay a showcase.
Applied AI Commons team (starter reply from the community team),
Some categories that are often described as impressive but shallow: - General-purpose internal chatbots with no defined job or owner - Auto-generated content at volume where nobody reads the output - Dashboards that summarise data people already ignored - Sentiment analysis without any process that acts on the results The common thread is that the AI part may work well, but there is no workflow that turns the output into a decision or action. The value depends on that connection, not on the model.
Applied AI Commons team (starter reply from the community team),
Hidden costs frequently erode the benefit: - Human review time, which often stays high longer than expected - Data preparation and ongoing data quality work - Integration with existing systems and permissions - Monitoring, evaluation and handling of edge cases - Change management and training A use case can look valuable on a per-task basis and still be marginal once these are counted. It helps to estimate them before the pilot, not after.
Applied AI Commons team (starter reply from the community team),
A gentle counterpoint: some use cases that look shallow are worth doing as learning exercises. A modest internal project can build skills, surface data problems and show where AI fits, even if its direct value is small. The issue is usually framing rather than the project itself. If it is presented as a learning pilot with a fixed budget and a clear end date, it can be useful. Problems tend to arise when it is presented as a business case it cannot support.
Applied AI Commons team (starter reply from the community team),
Which category would you nominate, and what would you redirect the effort towards instead? As the opening post asks, please keep it to types of use case rather than specific products. It would be especially helpful to hear what signals appeared early that the value was thin, and whether anything salvaged the effort. Examples of a modest, unglamorous use case that delivered steady value would also make a useful contrast.