SI helps when the task has a name.
A useful first application starts with an everyday task you can assess. Choose the model afterwards. Optimism needs a way to measure progress.
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Start with the task before choosing a model
The temptation is strong: a new tool, big promises and a use case that will surely emerge eventually. We reverse that order. Look for a recurring step in which people search for, transfer or prepare information. Who does it? What are the inputs? What makes a good result? Once those three questions are answered, it is worth discussing the technology.
For example, a service team searches approved manuals to answer internal questions. A possible form of support is to suggest relevant passages. The team checks, writes and decides. The scope is small and responsibility is clear, which makes it an honest experiment. Only everyday use can show whether it works in your organisation; a demonstration cannot.
Measure everyday work and its results
Before connecting anything, establish how the task works today: time spent searching and reworking, typical errors and waiting times. That is your baseline. A fluently written answer does not yet show that work has become easier.
For the manual example, useful measures include relevant passages, traceable sources and the eventual cost of checking the answer. Decide when the experiment should end or change. A good result may be a decision to stop. That is progress, too.
Clarify data and alternatives before getting carried away
Before the first transfer, establish which information the tool can see, who has permission and where processing takes place. Carefully selected example data is often enough for early experiments. A local model also needs access controls and operation; running it on your own premises is not a complete plan.
Also consider straightforward alternatives: better search, clearer forms or more current documents. Sometimes outdated knowledge is the underlying problem, and SI would merely cover it up. Appoint someone responsible for the subject matter. Changes to a manual or the removal of a permission must reach the supported workflow. Otherwise the apparent convenience rests on an unreliable foundation.
Test the difficult cases
A serious experiment includes the normal case, contradictions and cases with no suitable answer. Decide how the system should express uncertainty. Check the results with people who know the work; their follow-up questions often provide the most useful specification.
Look at the complete process: preparation, use, checking and correction. Speed helps when the checking effort remains proportionate to the benefit. If something goes wrong, people need a clear way back to the previous workflow.
Finish with a decision
The NIST AI Risk Management Framework 1.0 connects context, measurement, responsibility and ongoing risk management. For an initial project, we bring this together in one working question: do we have enough evidence to continue developing this application responsibly?
Record what works, where the limits are and who will address open issues. Then choose a larger experiment, a different solution or an improvement to the underlying data first. SI can provide a substantial boost. Responsible use turns that potential into dependable work; without checking, it remains a performance.
What does this look like in your work?
We can turn the idea into a sensible next step with you.
Workspace & SI