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Reducing validation time for AI-assisted production by 57%

When the generation goes from weeks to days, validation can become the new bottleneck. This project has shown that by structuring rules, exceptions and responsibilities, human attention can be concentrated where it really brings value.

  1. 3 daystechnical production
  2. 3.5 → 1.5 weeksvalidation period
  3. ≈ –57%of validation time, with comparable volume

This feedback comes from a project prior to CAIAC. It illustrates the operational mechanisms that contributed to the platform's design. The results are specific to the project context and do not constitute a performance guarantee.

The challenge

Why the business result was out of reach

Generative AI has greatly reduced the time needed to produce text, variants or pages. But if each result still needs to be read, compared, corrected and approved manually, the cost has not disappeared: it has moved to validation.

This is a business problem before being a technical problem. A company can multiply its theoretical production while slowing down its launches, mobilizing more experts and increasing the risk that an error will pass between controls.

The right indicator is therefore not the number of content generated, but the cost and time required to obtain accepted, usable and compliant content.

The starting point

Data, processes and constraints

  • A production device assisted by AI used in 2022 to prepare a large volume of pages: image selection, text generation, preparation of titles.
  • About three days for the technical part of the production, then three and a half weeks of full validation.
  • A maturity of the models of the time such that a significant part of the generations required in-depth verification or resumption.
  • Adding people would have absorbed the burden, but would have made the cost of control proportional to the volume produced.
The approach

What was implemented

  1. 01
    Define acceptance criteriaExplain before the generation what makes content acceptable, rather than discover it at rereading.
  2. 02
    Automate repeatable controlsSeparate what is mechanically true from what requires judgment.
  3. 03
    Classify exceptionsArrange content by level of risk and priority, so as not to reread a standard case as a sensitive exception.
  4. 04
    Climb to the right expertsMake mistakes and ambiguities for those whose opinions really change the decision.
Results

What has been measured, and what it means

  1. Observed result

    For a comparable volume of work, the validation period increased from three and a half weeks to one and a half weeks, a reduction of approximately 57%.

  2. Interpretation

    The problem was not to generate more. It was to make the cost of control less proportional to the volume produced. This project suggests that a validation organized by risk and by exception can contribute to this — without suppressing human intervention.

Why does it matter

How this translates into business impact

  • Revenue available sooner

    A page or campaign does not create any value as long as it remains in a queue.

  • Better use of specialists

    Brand, legal and product are scarce resources: mobilising them on exceptions reduces the cost per accepted content.

  • A production that can change scale

    If the cost of review increases as fast as the generation, AI does not change the production economy.

  • Explicit risk exposure

    A lack of style, an erroneous legal claim and false product information must not follow the same path.

The principle

What this project inspired in CAIAC

This project has taught us that AI brings capacity, and that it is workflow that brings scalability.

CAIAC today industrializes this principle: acceptance criteria, automatic controls, classification of exceptions, targeted validation, traceability and return loops are part of the workflow itself. No code, model, content or parameterization of the historical project is included: it is the operational principle that is rebuilt.

About this feedbackShow less

This feedback comes from a project prior to CAIAC. It illustrates the operational mechanisms that contributed to the platform's design. The results are specific to the project context and do not constitute a performance guarantee.

Items to be documented:

  • Volumes compared between the two periods.
  • Resumption definition.
  • Time actually recorded in validation.
  • Composition of the teams involved on both sides.
  • The distribution between directly satisfactory generations and generations to be resumed is not published: the evaluation protocol has yet to be documented.

Anonymized project · pre-CAIAC · unsecured results. Deployment is adapted to the data, systems and levels of autonomy available in the company environment.

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