Workplace AI Architect

Define and govern the adoption of AI across Workplace and platform-based solutions, with a strong focus on out-of-the-box capabilities, integrations, and security. Act as the orchestration point across Software Development, Data & Analytics, business users, and platform owners, ensuring consistent routing of AI initiatives. Drive the transition from ad-hoc usage to controlled, scalable adoption through clear governance, measurement, and chargeback, positioning AI as an extension of existing platforms rather than a parallel capability. The role focuses on Workplace AI platforms and embedded capabilities such as Microsoft 365 Copilot, AI assistants, and collaboration tools.

Main accountabilities and tasks

AI Roadmap & Out-of-the-Box Ownership: Own the AI roadmap for Workplace and platform-based AI capabilities and the adoption of out-of-the-box solutions; contribute to the cross-functional definition of AI architecture; establish AI as a coherent enterprise capability.

Platform-First, Preview & Early-Access Ownership: Prioritise out-of-the-box capabilities over new builds; own the evaluation, piloting, and rollout of preview features and vendor early-access programmes; track and govern AI features increasingly embedded in existing enterprise services.

Governance of Services, Integrations & Agents: Establish a single enterprise entry point and a risk-based approval process, supported by self-service guardrails and clear approval timelines so the front door enables rather than blocks; govern AI services, integrations, APIs, and the lifecycle of AI agents; ensure every AI asset is inventoried, owned, and operated within agreed controls.

Routing of AI Initiatives Across Delivery Tracks: Define and govern how AI initiatives are routed across delivery tracks - out-of-the-box, preview, and early-access (this role); citizen development (business users, against standards set by this role); custom development and advanced integrations (Software Development); and analytics-driven solutions (Data & Analytics).

Security, Risk & Compliance (with Infosec): Together with Infosec, define and enforce AI-specific controls under a clear split of accountability for who defines, approves, and enforces; ensure compliance with GDPR and the EU AI Act; govern third-party AI usage and minimise shadow-AI risk.

AI Lifecycle Management: Govern the full lifecycle covering build, publish, operate, and retire; ensure ownership, inventory, and transparency for every AI solution; enforce environment-based controls across experiment, managed, and production.

Business Value, Chargeback & Adoption: Define how AI business value is measured; build and operate a chargeback model so consumption is transparent and costs sit with the consuming business; drive adoption and track it against value realisation.

User Support, Enablement & Training: Act as the primary point of contact for AI adoption; provide patterns, guardrails, and reusable templates so users can build safely; deliver training, documentation, and onboarding paths that build AI literacy across the organisation.

Desired experience and qualifications

Work experience: 8-10+ years in enterprise IT, architecture, or transformation roles, with experience in governance, platform strategy, or large-scale adoption.

Expertise: Senior expert / enterprise architect level. Strong understanding of enterprise AI platforms (e.g. Microsoft 365 Copilot) and assistant and agent-based tooling, and of GenAI / agent-based architectures; deep knowledge of platform vs. custom-development trade-offs and licensing models; experience defining operating models and governance standards.

Governance & regulation: Proven experience defining and rolling out enterprise AI governance frameworks or platform-adoption strategies; familiarity with AI security controls and applicable regulation (EU AI Act, GDPR, responsible-AI guidance).

Cloud & FinOps: Deep familiarity with cloud platforms (Microsoft ecosystem preferred); familiarity with chargeback or FinOps models, ideally applied to AI or cloud services; working knowledge of enterprise AI licensing and the ability to translate licensing constraints into platform and routing decisions.

Education & certifications: University degree in IT, Computer Science, or a related field; Microsoft, AI, or cloud certifications preferred, with security and compliance certifications a plus.

Soft skills: Strategic and structured thinking; clear, pragmatic communication, including the ability to translate technical standards for business users and citizen developers; sound decision-making in complex, ambiguous environments; comfort balancing innovation with control.

Languages: Fluent English required; additional languages a plus.

Medical insurance for self and family

Company sponsored transportation facility

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