Regional AI & Data Hub Manager

Bureau Veritas  |  Global AI & Data Transformation 

Regional AI & Data Hub Manager 

Role Briefing — regional hub leadership, business partnership, delivery accountability, and adoption 

Context & Purpose 

The Regional AI & Data Hubs are where validated business demand becomes working, enterprise-grade AI capabilities embedded in core business workflows. Each hub pairs business-facing leadership with deep technical AI leadership: the Regional AI & Data Hub Manager ensures the hub is focused on the right end-to-end journeys, delivers value, builds capability, and drives adoption; the Regional AI & Data Hub Technical Lead ensures that AI solutions are technically sound, evaluated, reusable, scalable, secure, observable, and aligned to enterprise standards. The hubs operate with a field-informed delivery model, grounding priorities and solution designs in direct understanding of real user workflows, operational constraints, adoption barriers, and value drivers. Their focus is on transforming end-to-end journeys and ways of working through GenAI, agentic workflows, data science, automation, and enterprise data foundations—not simply delivering isolated use cases. Together, they turn clear, high-value problems into deployed AI capabilities and reusable enablers for the Shared AI technical foundation. 

Role Mission  

The Regional AI & Data Hub Manager leads one of Bureau Veritas’s regional AI and data delivery hubs from a business, solution, delivery, adoption, and team leadership perspective. The role is accountable for shaping demand with regional and business stakeholders, prioritizing the right end-to-end journeys rather than isolated use cases, ensuring delivery discipline, building and leading the hub team, and making sure AI-enabled capabilities—including GenAI applications, agentic workflows, data science solutions, automation, and reusable enterprise AI components—are technically credible, adopted, operated, evaluated, and continuously improved in real business workflows. 

There are three Regional AI & Data Hubs — Americas, France, and Asia. Each hub has two key leadership roles: one Regional AI & Data Hub Manager and one Regional AI & Data Hub Technical Lead. The hubs place build capacity close to the businesses and regions of the matrix, while the Shared AI technical foundation, reusable solution archetypes, and governance spine ensure that all three hubs deliver to one common enterprise standard rather than diverging into separate local approaches. 

The Regional AI & Data Hubs work under the technical guidance of the Chief Technical Architect, Enterprise AI, the Director, Data Science, and the Director, AI Context Fabric & Semantic Platform, ensuring that regional delivery remains aligned with enterprise architecture, data science standards, semantic platform standards, and the Shared AI technical foundation. 

Nature of the Role  

This is a senior hub leadership role for a business-oriented technology leader with meaningful AI solution fluency and practical understanding of enterprise data capabilities. It requires the ability to translate business priorities into a clear portfolio of AI-enabled journey transformation work, understand GenAI, agentic, data science, automation, integration, evaluation, and deployment implications, engage directly with working prototypes and delivery teams, lead multidisciplinary teams, manage delivery trade-offs, and ensure that AI solutions create measurable value after deployment. 

Core Accountabilities 

• Demand shaping & AI solution framing. Partner with business stakeholders to convert priorities into clear problem statements and transformation opportunities, with a strong focus on end-to-end journeys, business impact, and new AI-enabled ways of working rather than isolated use-case delivery. Prioritize work by value, feasibility, data readiness, model and agentic AI suitability, technical complexity, scalability, evaluation requirements, risk, and alignment with the AI strategy, using sufficient AI solution fluency to challenge assumptions, shape viable opportunities, and guide scale-up decisions. 

• Field immersion, workflow understanding & value validation. Ground hub priorities and solution designs in direct observation of real user workflows, operational constraints, adoption barriers, and value drivers. Use field insight to sharpen problem statements, keep hub work anchored in operational reality, and validate deployed solutions against adoption, workflow impact, operational outcomes, and business value. 

• Portfolio delivery, adoption & AI value realization. Translate validated journey-level opportunities into a prioritized hub delivery portfolio in partnership with the Technical Lead, confirming feasibility, solution approach, sequencing, dependencies, evaluation approach, adoption readiness, and delivery risks. Maintain delivery discipline while balancing local business value, reusable AI patterns, scale, human decision boundaries, operating model change, and contribution to the Shared AI technical foundation. 

• Hub team leadership. Build and lead a multidisciplinary hub team across AI, data science, engineering, forward-deployed delivery, and business-facing contributors. Partner closely with the Technical Lead to keep business priorities, solution design, technical feasibility, engineering quality, adoption needs, and enterprise standards aligned, while fostering a builder culture of experimentation, accountability, and continuous learning. 

• Full lifecycle ownership & continuous improvement. Own the business and operational lifecycle of hub solutions, including adoption, stakeholder satisfaction, realized value, operating model fit, and retirement decisions made with the Technical Lead. Ensure performance, reliability, adoption, and business impact are visible through evaluation results and operational metrics, and use those signals to guide continuous improvement and business change actions. 

• Forward-deployed engineering. Lead forward-deployed engineers who work with product lines and regional teams to embed solutions into real workflows, adapt them to local operating realities, and accelerate adoption. Use their feedback to identify field friction, recurring edge cases, unmet needs, and opportunities to improve both local solutions and the Shared AI technical foundation. 

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