Overview
Air France–KLM partnered with Google Cloud and emagine to modernise its data platform and scale AI across the organisation. By designing a dedicated A-Team of data and engineering specialists, we helped accelerate deployment, strengthen governance and turn advanced analytics into measurable operational impact.
The Challenge
To unlock the full value of its data, Air France–KLM needed more than new technology. The group needed a scalable, secure cloud foundation capable of supporting advanced analytics, predictive maintenance and AI-driven decision-making across operations.
The ambition was clear: use data to improve aircraft reliability, optimise ground operations and personalise the customer experience. But delivering on that ambition required significant change.
Legacy systems limited scalability. Data pipelines were complex and fragmented. Moving models from experimentation into production was slow and resource-intensive. At the same time, security, compliance and governance standards had to remain uncompromising in a highly regulated environment.
Beyond just a migration project, this was a transformation in how data was structured, accessed and operationalised across the organisation.
Air France–KLM needed a partner who could:
- Design and implement a modern cloud data platform on Google Cloud
- Industrialise AI and machine learning deployment
- Ensure governance, reliability and cost control
- Stay close to delivery and take ownership of outcomes
In short, they needed expertise that could flex at scale – without compromising accountability.
The Solution
emagine designed and deployed a dedicated A-Team of more than 20 specialists to support the transformation. The team combined:
- 10 Data Scientists
- 2 MLOps Engineers
- 4 Operations Research Engineers
- 1 DevOps Engineer
- Additional delivery and coordination expertise
Rather than separating strategy from execution, the team worked hands-on within the programme – designing, building and scaling the platform in close collaboration with Air France–KLM and Google Cloud.
Phase 1: Establishing the A-Team
The transformation began with assembling a dedicated, cross-functional A-Team designed to match the scale and complexity of the programme.
By combining data science, engineering and delivery expertise in one integrated team, emagine ensured that strategy and execution stayed closely aligned from day one.
Phase 2: Building the cloud data foundation
The team designed and deployed scalable data-processing and model-training pipelines on Google Cloud Platform (GCP), creating a modern architecture capable of handling large data volumes across operational systems.
Data ingestion and orchestration were optimised to improve reliability and reduce cost. Pipelines were built for performance and repeatability — enabling consistent delivery rather than one-off experimentation.
Phase 3: Scaling AI through MLOps
To move from pilots to production faster, emagine implemented structured MLOps frameworks across the platform.
This improved version control, testing, deployment speed and lifecycle management of machine learning models.
Phase 4: Embedding governance and accountable delivery
Security and compliance were built into the architecture from the outset. The team worked closely with internal stakeholders to ensure governance standards were maintained while improving access to trusted data.
This balance of flexibility and control enabled progress without increasing risk.
Throughout the programme, emagine remained accountable for delivery. The team stayed embedded, adapting to evolving priorities while maintaining clear ownership of outcomes.
Transformation timeline
Phase 1
Establishing the A-Team
Phase 2
Building the cloud data foundation
Phase 3
Scaling AI through MLOps
Phase 4
Embedding governance and accountable delivery
The Results
Faster AI deployment
Structured MLOps and scalable infrastructure enabled faster transition from experimentation to production, reducing time-to-value for new use cases.
Improved decision-making
Unified, secure and accessible data empowered business teams with reliable insights, supporting more confident operational and strategic decisions.
Personalised customer experience
Advanced analytics enabled better anticipation of customer needs, improving service quality and engagement.
Stronger governance and security
A modern cloud platform ensured compliance, transparency and long-term scalability.
By combining cloud infrastructure, AI capability and accountable delivery, Air France–KLM strengthened its ability to innovate – now with a foundation built to scale.