eltyoMoving into daily use depends on how the work is organised as much as on the quality of the demo.
The demo works. What comes next?
Explore the three handovers, from prototype to everyday use.
Work with a few representative examples. Check that the need is understood, then record what still needs testing.
Take away: one clear task and known limitations.An illustrative preparation path. Moving to the next stage depends on observed results, not an automatic schedule.
How do you move an AI prototype into everyday use?
First define scope, responsibilities and success criteria. Test information sources, access, routine cases and exceptions with a pilot team. Prepare onboarding and human handover, then expand according to observed quality and the full operating cost. A convincing demo is one input to that decision, not the acceptance test for the whole workflow.
A good demo opens up a possibility
The prototype responds quickly, finds a document and prepares a convincing result. That is a useful first step: the team can see the process and react to something concrete.
Everyday work brings different situations. Sources change, requests arrive less neatly prepared and colleagues use the tool in different ways. Moving into daily use means preparing for that reality, with a scope and support process people can understand.
Define what “ready” means
Choose a task, an owner and an expected result. Describe where the process starts and who receives the work at the end. Note the conditions it requires: available sources, agreed permissions and planned approval steps.
An assistant is easier to evaluate when everyone’s role is clear. The team can distinguish a known limitation from behaviour that needs correcting. They also know where to send observations and who can decide what happens next.
Bring testing closer to real work
Prepare representative examples with the people performing the task. Include incomplete documents, unusual wording and cases where the assistant should ask for help.
Keep the results and corrections. When a rule or source changes, rerun the relevant examples to understand the difference. The aim is to make decisions using comparable evidence, rather than judging each version on a fresh impression.
Check the whole process: finding information, preparing work, approval, delivery and any handover. A quick step may hide additional effort for the next person. Testing the full journey makes that effort easier to see.
Connect only what is needed
Identify the tools and information required for the first use case. Separate reading, proposing a change and applying it. Permissions should match the intended role.
Also define what happens when access is unavailable or a source is missing. An assistant that states a limitation clearly can be more useful than a process that leaves the user guessing what happened.
This preparation gives the team a manageable starting point. More connections can be considered when the existing process is understood and their value is clear.
Prepare people to use it
Show users how to start a task, check the result, correct a suggestion and take over. Use examples from their own work where possible.
Explain known limits too. Someone who understands the assistant’s role can make a better decision about when to use it. Provide a simple feedback route and identify who reviews problems or requests for change.
| Stage | What should be concrete |
|---|---|
| Define | A task, an owner and an expected result |
| Prepare | Sources, access and approval rules |
| Test | Real cases, exceptions and comparable results |
| Adopt | Guidance, feedback and human handover |
| Improve | A decision based on observed use |
Measure before expanding
Compare assisted work with the current process: total time, review, corrections and result quality. Include usage and support costs when assessing the value of the scope.
Speak with the users as well. A metric does not capture everything about finding a source, understanding an exception or passing a case to a colleague. These details can determine whether the assistant earns a place in the working day.
The next step might be better context, another connection or a wider scope. It might also be keeping a limited role. Follow what the work demonstrates: a useful assistant fits into the way your team works.
Explore how we help teams bring AI into everyday work.
The NIST AI RMF addresses design, use and evaluation as connected parts of risk management. To assess a pilot, explore our method for measuring work saved.
Explore the original sources.
The references behind this approach. Examples and exercises in this article are illustrative.