From AI pilot to production

An AI pilot changes context when it moves into production. In a test, someone chooses an example and observes the response. Daily work brings incomplete documents, repeated data and situations nobody prepared for the demonstration.

The challenge is keeping the process useful when that happens. The team needs to know what it can accept, what requires review and what to do when the system cannot complete a task.

Before expanding a trial, describe the entire job. AI may occupy just one step. Everything around it determines whether the output serves the operation.

Start with a specific task

“Automate administration” is too broad to evaluate a first project. A task such as extracting particular fields from an authorization and linking it to a record lets you discuss specific inputs, outputs and errors.

Define who does that work today, how often and what they need to review before considering it complete. You also need to identify what happens afterwards: whether the data is consulted, feeds an invoice or triggers another action.

That destination changes the requirements. Text that helps prepare a draft can be corrected while reading. An identifier added to the wrong record can carry the error into several subsequent operations.

If you cannot describe how you will recognise a correct result, more definition is needed. Starting there prevents the project from being judged by how convincing a response seems.

The solution can combine different techniques

Not every task requires a generative assistant. A structured file can be processed with rules. A document may need field extraction. An internal enquiry may require a search over authorised information.

RAG combines information retrieval with response generation. MCP defines a way to connect AI applications to tools and data. These are distinct from document extraction, as the guide to generative AI, RAG and MCP explains.

A case that demonstrates one technique does not establish experience in all of them. When selecting a solution, ask for an explanation of what each component does and why it is needed for the task.

Some steps may also be best kept manual. If an exception is rare and automating it adds considerable complexity, giving the team a clear way to resolve it can be a reasonable decision.

Test what actually arrives

A useful test includes representative examples and difficult cases: documents in different formats, missing information, duplicates or references that do not match. The collection should reflect the work you expect to receive, rather than only the examples that perform best.

Separate the documents used to adjust the system from those used to evaluate it. Repeatedly reviewing the same sample makes it easy to improve that demonstration without learning enough about what will happen with new data.

Agree what counts as a correct result for each field or task. An overall percentage can hide important differences: misreading secondary text does not have the same consequence as assigning an operation to another person.

Also record the cases the system rejects or sends for review. Leaving a task unautomated can be appropriate if it avoids introducing an uncertain result. What matters is that the referral is visible and usable.

Measure the whole process

Generation speed is not enough to assess savings. Count data preparation, review, corrections and incidents. If the response arrives quickly but checking it requires doing the work again, the improvement is limited.

Compare equivalent tasks and document the context: period, type of work, people involved and how the measurement was collected. When a detail is unknown, it is better to retain that limitation than fill the account with invented precision.

Alongside time, you can observe how many cases finish correctly, how many need intervention and which errors occur. The criteria should serve the decision you need to make, rather than accumulate indicators for a presentation.

Do not turn an initial result into a promise for every customer. Data formats, rules and the organisation of work can vary considerably between businesses.

Human review that people can actually do

Adding “with human oversight” to a description does not design the process. Reviewers need to see the extracted data, its context and enough original information to check it.

They must also be able to correct, reject or leave an operation pending without finding workarounds outside the system. If the result seems correct but information is missing, the workflow needs a state that represents that situation.

Responsibility must be clear. Decide who reviews each type of issue and which operations require confirmation before continuing. A person does not need to check everything in the same way, but the criteria must be defined and workable in daily operations.

Test this part with the people who will use it. A screen that seems obvious to its developer may require too much interpretation from someone handling many tasks in succession.

Permissions and errors are part of integration

An AI integration must respect what each user can view or modify. A model's technical ability to access information does not mean everyone should receive it.

Distinguish reading from writing. Preparing a proposed change and applying it to a system are different actions with different consequences. Before automating the latter, define conditions, confirmations and a way to investigate what happened.

The connection can also fail for reasons unrelated to the AI result. You must be able to recognise a pending operation, avoid duplicates and recover work when a service becomes available again.

That behaviour deserves its own test. Deliberately interrupting a connection in a controlled test environment can reveal problems that an incident-free demonstration does not show.

Quiralis demonstrates a specific document workflow

In Quiralis, the platform developed by Mecexis incorporates Google Cloud Document AI to extract information from authorizations. Administrative staff review and correct the data, then link it to the corresponding medical procedures and billing.

The case records a reduction from 8 hours to 20 minutes of daily document review per person, rounded to 96%. The company using the platform validated the measurement on different days over months of use, with administrative oversight.

The result applies to that recurring task. It does not describe savings across the centre's entire activity or establish the same outcome for another process. Nor does it make the case a demonstration of RAG assistants or agents that act autonomously.

What matters for this decision is that extraction sits within a workflow with review and a destination for the data. The model contributes part of the result. The platform and the people make it usable.

Deploy with a scope you can observe

An initial production phase can be limited to a document type, a team or part of the journey. That limit should still allow a useful task to be completed and problems to be detected before expanding scope.

Agree when to move forward and when to pause expansion. If errors emerge that the team cannot resolve, or review takes more work than expected, adjust the process before adding more volume.

After launch, formats, rules and components can change. Keep evaluation examples and review behaviour after significant changes. Initial acceptance does not replace maintenance.

A pilot starts becoming a tool when the team can use it on an ordinary day, correct what fails and understand the result. That is a more demanding and more useful test than a demonstration that only needs to go well once.