Thales: Building an AI-Ready Engineering Organization

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The challenge

In complex defence engineering environments, technical knowledge is distributed across thousands of documents, specifications, requirements, architectural designs and validation reports. Understanding the impact of a change requires connecting information across multiple systems and engineering disciplines. At the same time, successfully introducing AI requires more than deploying new technology. It demands new skills, delivery practices, governance and collaboration between Data, AI, IT and engineering teams.

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My contribution

I led the definition of a Data & AI Target Operating Model designed to align technical capabilities with engineering needs and accelerate AI adoption, heading a 5-person engagement.

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The approach

• AI Operating ModelDesign a six-month transformation roadmap covering organizational structure, competencies, delivery practices and governance.
• Cross-functional squadsStructure multidisciplinary teams combining Data Scientists, Data Analysts, Data Engineers, RPA Engineers and business experts.
• Engineering Knowledge AssistantDefine an advanced RAG solution to connect engineering documents, retrieve technical knowledge, support requirement traceability and facilitate impact analysis.
• AI capability buildingEstablish learning paths covering Python, APIs, LLMs, RAG, agentic workflows, evaluation and production practices.

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Expected impact

The initiative aims to establish a repeatable AI delivery model, strengthen internal autonomy, reduce the effort required to access technical knowledge and create a foundation for future engineering AI applications.

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What I learned

AI transformation starts with people, processes and ownership. Technology delivers value when the organization is equipped to use it.