Make your numerical analyses more reliable

How can you turn a business table into well-reasoned decisions without carrying over AI calculation errors?

1960 € per session, excl. VAT

In short

“Analysing tables with AI” is a private in-person session of a full day for operations managers, sales professionals and administrators producing periodic reports or business analysts and management accountants working with tabular data exports. Groups of up to 12 participants, in French, with exercises on your own work cases.

Why this training?

A seemingly clean table may mix orders and order lines, gross and net amounts, and planned and actual dates. A fluent AI-generated summary sometimes conceals these ambiguities and leads to comparisons between incompatible indicators. AI helps formulate lines of enquiry and comment on discrepancies, but its proposals require checks. The course combines structured requests, independent calculations and traceability to make every conclusion verifiable. Running case study: The fictitious organisation Avelune Conditionnement examines its quarterly orders: reference, workshop, quantity, amount, promised delivery date and actual delivery date. Duplicates, missing dates and credit notes complicate interpretation. The operations manager requests a verified analysis of delays and the amounts involved to prioritise investigations. Transfer to the workplace: At the next operational review, apply the protocol to an authorised data export, have the indicators validated by their business owner and track the number of discrepancies detected before distributing the summary.

Objectives of this training

  • Define the scope, granularity and indicators of an analysis
  • Correct structural anomalies without altering the source data
  • Formulate exploratory requests suited to the available columns
  • Verify proposed results through independent calculations
  • Write a summary that distinguishes findings, hypotheses and decisions

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Operations managers, sales professionals and administrators producing periodic reports
  • Business analysts and management accountants working with tabular data exports

Prior skills

  • Be able to filter, sort and create a simple pivot table
  • Have already used generative AI with structured instructions
  • Know the indicators of the business process being studied

What you will work on

  1. Frame the operational question

    Objective: Define an unambiguous measure of delays. Granularity, period, open orders and the denominator of the delay rate.

  2. Prepare the columns

    Objective: Obtain a table that can be analysed and traced. Data types, duplicates, missing values, credit notes and preservation of the source.

  3. Guide the exploration

    Objective: Identify discrepancies relevant to operations. Instructions specifying columns, filters, aggregations and the expected format; processing limitations depending on the tool.

  4. Check the results

    Objective: Measure the reliability of the proposed figures. Reconciliation of totals, weighted average, denominators and rounding.

  5. Interpret without drawing excessive conclusions

    Objective: Separate observed anomalies from assumed causes. Volume effect, small sample sizes, missing data and comparison of periods.

  6. Report to support decisions

    Objective: Produce a note that can be used in an operational review. Prioritisation of findings, numerical evidence and action tracking.

What you take away

  • Column dictionary and indicator specification sheet
  • Prepared table with a transformation log
  • AI request file and verification register
  • Decision-oriented summary with actions and limitations

Method and assessment

  • Short instructional sessions followed by immediate practice on the running 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: Arrange a spreadsheet application and access to generative AI authorised by the organisation Identify a reporting question; the exercises use only fictitious or anonymised data

Observable criteria: Each indicator specifies its scope, unit and denominator; Each transformation is documented and the source remains intact; All figures in the summary are recalculated; discrepancies are explained or corrected; Each finding refers to an evidence table and distinguishes hypotheses; Each action specifies a business owner and a monitoring indicator.

Frequently asked questions

Do you need programming skills?

No, the checks rely on formulae and pivot tables.

Can you import a business file?

Only after anonymisation and confirmation of compliance with the organisation’s rules; no sensitive data export is required.

What if the AI cannot read the files?

Use a fictitious extract or authorised aggregates, then perform the calculations in the spreadsheet.

Does AI identify the causes of delays?

It proposes hypotheses; establishing causes requires additional evidence and business validation.

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