Establish net gains objectively

How can you verify that using AI genuinely saves time without compromising the quality of professional work?

1960 € per session, excl. VAT

In short

This 7-hour day, excluding breaks, enables you to develop a comparative measurement of the performance of an AI-assisted business process. You establish a baseline, account for checking and rework time, verify the quality of the output and create a dashboard that supports decisions based on traceable results.

Why this training?

Faster drafting does not necessarily constitute a productivity gain: corrections, checks and handovers between colleagues can absorb the time saved. Measuring text generation alone conceals these workloads. Generative AI can help structure the protocol and comment on differences. Calculations are still checked independently, and conclusions distinguish between observations, assumptions and limitations to avoid attributing an effect caused by volume or complexity to AI. Ongoing case study: The fictitious organisation Avelcor Équipements prepares responses to requests for quotations. Its team is experimenting with AI to draft proposals. Using fictitious cases of varying complexity, you compare processing with and without assistance, including technical checks, corrections and final approval. Transfer to the workplace: In the following month, apply the protocol to an authorised recurring task, collect time and defect data over two comparable periods, then present the results to the business manager before any wider rollout.

Objectives of this training

  • Define a unit of work and its acceptance criteria
  • Establish a comparable baseline without AI assistance
  • Calculate differences in time and productivity, including rework
  • Assess quality using a common assessment framework
  • Justify a decision based on verified results

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Operational managers and process owners evaluating AI applications
  • AI leads and business analysts responsible for documenting gains and quality

Prior skills

  • Already use a generative assistant for professional tasks.
  • Know the stages and constraints of a recurring business process.
  • Be able to calculate averages, ratios and percentages in a spreadsheet.

What you will work on

  1. Define what counts

    Objective: Define the scope of measurement. Active working time, elapsed time, compliant output volume and productivity; distinction between freed-up capacity and financial savings.

  2. Establish a fair baseline

    Objective: Reduce comparison bias. Case complexity, staff experience, learning effects and order of execution; limitations of a small sample.

  3. Observe the complete work process

    Objective: Capture visible and hidden workloads. Timing each stage, preparing requests, checking, correction, incidents and approval.

  4. Calculate without overinterpreting

    Objective: Quantify the observed differences. Relative time saving: difference divided by the baseline time. Productivity: compliant cases per hour of work used. Initial workloads presented separately.

  5. Compare speed and compliance

    Objective: Verify quality against unchanged requirements. Technical accuracy, completeness, blocking defects and acceptance; thresholds defined before reviewing the results.

  6. Decide and organise monitoring

    Objective: Formulate a recommendation proportionate to the evidence. Spread of results, uncertainty, conditions for sustaining gains and frequency of checks.

What you take away

  • Comparative protocol and indicator dictionary
  • Time, checks and rework log
  • Net gains and quality dashboard
  • Decision paper with limitations and monitoring conditions

Method and assessment

  • Short teaching inputs followed by immediate practice using the ongoing case study.
  • Individual work followed by work in pairs, with peer review of the outputs.
  • Guided group review: what works, what needs correcting and what remains a human decision.
  • Preparation: Identify a repetitive task and describe its expected outcome without sharing confidential data. Check access to the tools authorised by the organisation; the exercises use fictitious or anonymised cases only.

Observable criteria: The groups being compared contain tasks of equivalent complexity. ; Preparation, checking and rework times are included and traceable. ; Ratios are recalculated independently of AI responses. ; The same quality assessment framework is applied to both methods. ; The recommendation states the limitations and the data still required..

Frequently asked questions

Do you need historical measurements?

No. A baseline can be established by observing comparable tasks and documenting its limitations.

Can AI calculate the gains?

It can suggest formulae; the results must be recalculated in the spreadsheet.

Does time saved translate into financial savings?

Not automatically: you must distinguish between available capacity, actual redeployment and demonstrated reductions in expenditure.

How should you handle a negative result?

Document the additional workloads and defects, then review the scope or discontinue the use being evaluated.

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