What would you want from an AI research assistant built for business teams?
Posted by Applied AI Commons team (community team) on
Business teams increasingly use AI to help with research, from market questions to account preparation. A research assistant designed specifically for teams could look quite different from a general-purpose chat tool.
This thread is a chance to describe requirements. Members might consider how sources and citations should be shown, how teams share and reuse findings, what controls are needed around data and permissions, and which features matter most when budget and time are limited.
If you could shape an AI research assistant for business teams, which capabilities would you prioritise first?
5 replies
Applied AI Commons team (starter reply from the community team),
Source handling is arguably the foundation. A team research assistant should probably: - Link every factual claim to a specific source, ideally the exact passage - Show the date of each source so readers can judge freshness - Distinguish between primary sources, secondary reporting and the tool's own inference - Make it easy to open the source and check it directly Without this, findings are hard to reuse, because nobody downstream can tell which parts are verified and which are guesses.
Applied AI Commons team (starter reply from the community team),
Sharing and reuse are where team tools differ most from personal chat tools. Useful capabilities might include saved research briefs that colleagues can comment on, the ability to rerun a previous query with fresh sources, and clear authorship so people know who asked what and when. A common failure pattern is research that lives in individual chat histories and gets repeated by different people. Even a simple shared library of past findings can reduce that duplication significantly.
Applied AI Commons team (starter reply from the community team),
Controls around data and permissions matter more than they first appear. Questions worth asking of any design: - Can the tool see internal documents, and does it respect existing access rights? - Can administrators restrict which external sources are used? - Are queries and results retained, and who can view them? - Can sensitive accounts or projects be excluded entirely? Teams often discover these issues only after a tool is in use, so it helps to settle them before rollout.
Applied AI Commons team (starter reply from the community team),
A sceptical angle: the most valuable feature may be restraint rather than capability. A research assistant that says "I could not find reliable information on this" is more useful than one that produces a confident answer from weak sources. For teams on a limited budget, prioritising accurate citation, clear uncertainty and easy verification may deliver more than adding many data sources or elaborate formatting. Breadth is easy to promise; reliability is harder and worth more.
Applied AI Commons team (starter reply from the community team),
If you were choosing only three capabilities for a first version, which would they be? It would help to hear what kind of research your team does most often, for example market sizing, account preparation, competitor tracking or policy questions, since priorities probably differ by task. We are also curious about the frustrations with current tools that push teams back to doing research manually. Specific scenarios will make this thread more useful for everyone.