• Own and evolve enterprise data governance to ensure data is reliable, explainable, and fit for AI consumption, not just analytics or reporting. • Establish clear standards for data definitions, ownership, quality, lineage, and access across core domains (e.g., Account, Customer, Product, ARR, Employee). • Define and enforce clear data quality thresholds required before data can be used by AI systems, including accuracy, completeness, timeliness, and consistency. • Create explicit “AI-approved” vs. “not AI-ready” data classifications to remove ambiguity for AI and internal AI product teams. • Certify authoritative datasets, metrics, and definitions that AI agents, models, and analytics must use. • Actively reduce duplicate, unofficial, or unmanaged datasets that lead to conflicting answers, model drift, and loss of trust. • Extend data governance beyond tables and dashboards to AI agents, features, and models, ensuring clarity on what data they use, how it is transformed, and where accountability sits. • Maintain end-to-end data lineage from source systems through transformations to AI outputs. • Work closely with Data Science, AI, and Sales AI teams to embed governance into AI design and delivery, rather than bolting it on after the fact. • Serve as the primary data governance partner to Security, Legal, Privacy, and Risk, ensuring AI data usage aligns with regulatory, contractual, and customer commitments. • Treat AI data inputs as living assets, continuously monitoring for drift, degradation, misuse, or changing business context as AI adoption scales.

• Lead conversational design for agentic experiences, defining interaction patterns, dialogue structures, and content strategies that enable users to complete complex tasks with ease, clarity, and confidence • Design end-to-end conversational flows across prompts, responses, follow-up actions, clarifications, error handling, and recovery paths • Create experiences that balance utility, transparency, trust, and delight, helping users understand agent capabilities, system limitations, and next-best actions • Partner closely with product managers, designers, engineers, researchers, and AI/ML teams to shape product direction and ensure conversational experiences are feasible, valuable, and high quality • Translate ambiguous problem spaces and emerging AI capabilities into clear conversational frameworks, principles, and reusable patterns • Develop and maintain standards for personality, tone, voice, interaction behavior, and conversation quality across agentic experiences • Use customer insights, usage signals, evaluation frameworks, and testing outcomes to refine conversational experiences and improve task success, comprehension, and satisfaction • Advocate for human-centered, inclusive, and responsible design approaches in AI-powered interactions • Contribute to scalable design systems, guidance, and best practices for conversational and agentic UX across teams • Help Autodesk evolve how conversational interfaces support real workflows, reduce friction, and create more seamless Design and Make experiences • Collaborate with content strategists across AEC's product teams to ensure product-specific considerations are accounted for and drive connectivity with conversation designers across Autodesk for unified approaches and best practices

• Engage the HCLS AI Startup Community in the UK, France, and the Nordics • Build and scale programs that connect regional HCLS startups to NVIDIA technologies • Engage regional Venture Capital firms and funding entities • Drive Regional Strategy, Execution, and Operational Excellence • Act as Regional Interface and SME

• Own the People Analytics roadmap and delivery, expanding beyond foundational reporting to introduce predictive, automated, and strategic insights • Serve as the primary liaison between People and IT’s analytics team, with dotted-line management of IT’s full-time People Analytics resource — ensuring alignment, prioritization, and timely execution of work; Deliver actionable insights through dashboards and analytics that accelerate data fluency and support leadership decisions on hiring, retention, engagement, and organizational effectiveness. • Lead execution of the People AI strategy — defining what “AI for People” means for Welbe and identifying opportunities that drive measurable impact • Manage discovery and implementation of AI tools and frameworks to support talent, team member experience, and organizational decision-making • Guide ethical and responsible AI integration across People initiatives, establishing principles around fairness, transparency, and data security; Translate strategic AI priorities from leadership conversations (CPO & CEO) into practical, scalable solutions and pilots within the People organization • Champion a digital-first mindset across the People team, embedding technology, analytics, and experimentation into everyday decision-making; Proactively identifying opportunities for digitization and automation within HR processes to enhance scalability and personalization • Influence senior leaders on how data and AI can evolve our people team strategies, champion of supervisor adoption of AI and technology for people related tasks; Mentor and build analytics literacy across the People function, enabling teams to think differently about insights and action