Technology & AI · ML & AI

Human + AI Creative Studio — Ship the Aurore Relaunch

A four-round, intermediate creative-technology simulation set inside Lumen & Co., the in-house studio of French cosmetics group Florette Beauté (Paris, 9th arrondissement; €620M revenue, 13% margin; a 62-person studio on an €11M budget).

4 rounds MBA & Masters English, Español

Preview

About this simulation

A four-round, intermediate creative-technology simulation set inside Lumen & Co., the in-house studio of French cosmetics group Florette Beauté (Paris, 9th arrondissement; €620M revenue, 13% margin; a 62-person studio on an €11M budget).

As Creative Director you must ship the make-or-break relaunch of the €88M Aurore skincare line — defending against a D2C insurgent that has taken 6 share points in 18 months — across six channels, in a 5-week deadline that is 40% shorter than normal, on a fixed €1.8M production budget, with the board watching how the studio uses AI after last spring's over-automated launch tested 18% below the brand's distinctiveness baseline.

Grounded in Margherita Pagani's work on human-centered AI and marketing creativity, the simulation operationalises the core argument that AI amplifies creativity when humans and machines are orchestrated complementarily — AI for divergence, scale and analysis; humans for judgement, meaning and brand sense — and flattens it when machines displace the human creative core.

Round 1 diagnoses the brief and rates creative tasks for AI-suitability (the creative core versus scalable production). Round 2 is the core decision: deploy generative versus non-generative AI per task, costing it within €1.8M and defending each placement against distinctiveness risk.

Round 3 orchestrates the 5-week human-AI workflow, places review gates for distinctiveness and rights/likeness/disclosure, and locks the six-channel delivery plan. Round 4 reveals launch telemetry — distinctiveness versus baseline, time-to-delivery, engagement, the realised human-AI cost split and any legal flags — and recalibrates the operating model.

The math rewards genuine human-AI complementarity and punishes the five classic errors: automating the creative core, all-human refusal (over budget, misses the date), confusing generative with non-generative AI, one fixed human-AI ratio across all tasks, and skipping the rights/legal review gate.

Final KPIs track Distinctiveness vs baseline, Delivery (weeks to ship against the 5-week date), Engagement, and Budget committed against the €1.8M cap, with a live Legal-risk flag.

Who it is for

An intermediate simulation for participants who already know the core concepts of the subject, designed for master's and MBA programmes.

How a session runs

  1. The instructor creates a session from the Eureka dashboard and invites the participants.
  2. Participants play 4 rounds. In each one they submit their decisions and the simulation calculates the results.
  3. The simulation can be run in English, Español.
  4. The instructor follows each participant's progress and results from the dashboard, and uses the class results for the debrief.

Decisions participants make

The decisions participants make during the simulation:

  • What must the Aurore relaunch win on against the insurgent?
  • Which task is most damaging to get the human-AI balance wrong on?
  • How honestly does the team separate amplify-with-AI tasks from protect-for-humans tasks? (%)
  • Deployment matrix — assign each task a mode within the €1.8M budget
  • How much of the €1.8M do you put into scaling generative output for production tasks? (€k)
  • How do you sequence the five-week human-AI workflow?
  • Where do you place the rights / likeness / disclosure review gate?
  • How strongly does human creative judgement override and refine AI output at each gate? (%)
  • Do you set the human-AI split per task, or one fixed ratio across everything?
  • Reading the launch telemetry, what was the biggest orchestration miss to fix?

What participants track

What participants follow on screen as the rounds go by:

  • Distinctiveness & Engagement Trajectory

Subjects covered

Designed for courses in ML & AI.

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