Club Med Case Study | Thiga – Product Transformation

Table of content

Context

AI is profoundly transforming the market and redefining performance standards. True to its pioneering DNA, Club Med has chosen to fully embrace this shift by laying, with Thiga, the first building blocks of an AI-ready organization.

The ambition is clear: empower teams to reinvent the way they work by leveraging AI agents capable of automating repetitive tasks, freeing up time for innovation, creativity, and operational excellence.

In this joint mission, the agents were built by Thiga in close collaboration with Club Med’s teams, ensuring a perfect fit with both business and technical needs. Beyond the technological dimension, Thiga also supports change management: adoption of new practices, upskilling of employees, and seamless integration into daily operations.

This approach has made it possible to identify and test concrete, high-impact use cases across the entire product and tech delivery chain, from creating User Stories to generating release notes, understanding APIs, and automating tests. By capitalizing on solid governance and smooth integration with existing tools, Club Med has taken a structural step forward: experimenting, measuring, and optimizing, to lay the foundations of an AI-augmented organization, where technology becomes a true driver of collective and sustainable performance.

Challenges

Identify the most value-creating use cases

Rapidly pinpointing high-impact AI opportunities, replicable across the organization and aligned with business priorities.

Design a robust and integrated agentic architecture

Ensuring compatibility with Club Med’s existing technology stack, while guaranteeing security and scalability.

Engage Product teams in co-construction

Involving team members from the design stage, fostering ownership of new practices, and supporting change management for sustainable adoption.

Measure and demonstrate tangible benefits

Proving the effectiveness of AI agents through precise KPIs (productivity, quality, support reduction) and validating the added value of each experiment.

Our approach

01
Mapping product & tech processes to identify AI opportunities
- Internal observation and analysis: interviews with teams, observation of rituals and interactions to detect pain points
- Mapping of existing processes and assessment of their complexity/recurrence to identify automation opportunities
- Technical interviews and alignment with IT to define prerequisites and constraints for experimentation
02
Use case prioritization: user stories, APIs, release notes, etc.
- Identification and selection of relevant use cases via prioritization workshops (e.g., US, API, release notes, code)
- Detailed formalization of selected use cases with benefit hypotheses and measurement KPIs
- Evaluation of feasibility, impact, and scalability of candidate use cases
03
Building and deploying first agents
- Setting up the agentic ecosystem: environments, configuration, and integrations (N8N, databases, internal tools)
- Development of agents: flow creation, source integration (Jira, GitHub, APIs, etc.), and prompt engineering
- Testing with business and technical teams to optimize agent performance
04
Iterative sprints with human-in-the-loop feedback
- Defining scope and objectives for each sprint (build, optimize, measure)
- Experimentation with early adopters and continuous feedback collection to improve the agent
- KPI analysis at the end of each sprint and incremental adjustments to agent workflows
05
Measuring of impact and continuous optimization
- Systematic KPI tracking (productivity, quality of deliverables, support reduction, team velocity)
- Synthesis and sharing of results with stakeholders (impact, limitations, improvement areas)
- Gradual integration of new sources or variants to enhance agent performance
06
Laying the foundation for multi-agent governance and scaling
- Defining technical, legal, and organizational prerequisites for industrialization
- Establishing a Product Operating Model and shared governance to supervise AI agents
- Planning large-scale deployment (candidate teams, roadmap, handover to internal teams)

Our impact

Time savings on content creation and support tasks : From 3-5 days to 0,5 day
Better documentation quality and usability
Faster time to market
Adoption of a scalable framework for AI agent deployment - in progress

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We come from product

We come from Product

Improving delivery efficiency for Product teams using a GenAI Agent

Context

Challenges

Our approach

Mapping product & tech processes to identify AI opportunities

Use case prioritization: user stories, APIs, release notes, etc.

Building and deploying first agents

Iterative sprints with human-in-the-loop feedback

Measuring of impact and continuous optimization

Laying the foundation for multi-agent governance and scaling

Our impact

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