Integrate AI into a business process
A demo answers a question. A business tool must answer from the right data, respect rules, signal its limits, and stay operable when the model changes.
Choose a narrow, observable use case
The right starting point is a repeated task, costly to prepare and easy to check: classify requests, extract information, prepare a summary, or find a rule in a document corpus.
The expected result is described before the model is chosen. Processing time, human-rework rate, refused cases, and cost per run form a baseline. Without that reference, the pilot can impress without proving it improves the process. AI solutions therefore start from the work, not from the vendor.
- An input and an output clearly defined.
- Representative real examples, including hard cases.
- A person able to validate the result.
- A measure before the prototype and the same measure after.
Prepare the data and the right to use it
The model does not fix a source that is obsolete, contradictory, or unreachable. Documents must be identified, dated, cleaned, and tied to visibility rules. A useful answer cites the source that lets it be checked.
How personal, confidential, or regulated data is processed is decided before it is sent to the model. Hosting, retention, logging, and exclusion from training are written in the architecture and in the contracts, not assumed.
Decide what the machine may prepare, propose, or execute
Not every action carries the same risk. Summarising a document, preparing a draft, and sending a message to a client do not ask for the same level of control.
A simple matrix distinguishes read, proposal, and action. It states accessible tools, limits, authorised data, and the cases that require a refusal. Custom software then carries those rules in rights, interfaces, and logs.
Build human validation into the journey
Writing that a human keeps hold is not enough. The interface must show the proposal, its source, its uncertainty, and the consequence of the action. Validation arrives before send or irreversible change.
Human corrections become test cases. They make it possible to track recurring errors, change instructions, and decide whether the perimeter can widen. Stellary applies this principle to agent missions attached to projects and supervised in the same system.
Measure costs and plan for a model change
Real cost adds the model, document search, storage, called tools, observation, and validation time. It is measured per use case and per accepted result, not only per token.
Models move fast. The product therefore keeps an evaluation layer, test sets, and a replaceable integration interface. A vendor change must neither rewrite the process nor make results incomparable. To set a real case against these constraints, the starting point remains a scoping conversation.
- Log the model, its version, and the cost of each run.
- Test changes on a stable case set.
- Plan volume limits and spend alerts.
- Document the procedure to disable or return to manual handling.
Les points qui restent à trancher
Should you start with a chatbot?
Only if conversation is the best format for the task. Silent classification, a prepare button, or a suggestion inside the existing tool are often more direct and easier to control.
How do you avoid invented answers?
By narrowing the task, providing up-to-date sources, requiring citation, testing on real cases, and refusing the action when proof is missing. Risk is reduced by the whole system, not by a sentence added to the prompt.
Can the model be changed later?
Yes if the integration separates the business process from the vendor and keeps a stable evaluation set. Results and costs can then be compared before any switch.
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