Enterprise AI Architect MMH260917-5

Role Purpose

The Enterprise AI Architect will set the architecture agenda that turns Group strategy into an executable enterprise AI portfolio. The role identifies where AI, advanced analytics, data, automation and agentic capabilities can materially shift customer, adviser, employee and operational outcomes, then converts that direction into target architectures, operating-model choices, reusable capabilities, sequenced roadmaps and governed delivery decisions.

Positioned at the intersection of Group Technology, Advanced Analytics and the GT Centre of Excellence, the role provides the connective leadership needed to convert specialist capability into enterprise momentum. It brings together enterprise technology strategy and platforms, data science and analytics, and the standards, reusable assets and engineering practices required for industrialisation. Rather than duplicating these functions, the Enterprise AI Architect aligns their contributions behind one Group direction and mobilises execution across business units.

Requirements

Experience

• A relevant bachelor’s degree or equivalent depth of professional experience is required
• Postgraduate study or recognised certifications in enterprise architecture, AI, data, cloud or digital transformation are advantageous.

Experience

• Significant enterprise, business, data, AI or digital architecture experience, with a record of operating across organisational boundaries in a complex federated enterprise.
• Proven leadership in shaping technology-enabled strategy and translating it through investment decisions, architecture, delivery, adoption and measurable outcomes.
• Experience integrating enterprise technology platforms, advanced analytics, data products, AI engineering, automation, APIs, cloud, security and operating-model change.
• Experience designing enterprise ontologies, semantic models, knowledge graphs, context architectures or equivalent shared organisational representations.
• Experience designing or implementing AI-enabled and agentic operating models, including human-agent collaboration, orchestration, decision rights, controls and lifecycle accountability.
• Demonstrated executive influence, commercial judgement and value realisation in financial services or another highly regulated environment.

Knowledge

• Business capability modelling, value streams, customer journeys, operating-model architecture and enterprise portfolio planning.
• Ontology engineering, semantics, metadata, master and reference data, knowledge graphs, data products, information governance and context management.
• Modern AI architecture, including foundation models, retrieval, evaluation, multi-agent systems, memory, tool use, orchestration, observability and responsible AI.
• Composable architecture, APIs, event-driven integration, cloud and platform operating models.
• Security, privacy, resilience, model and agent risk, regulatory obligations and benefits realisation.

Duties and Responsibilities

Strategy and Enterprise Architecture

• Maintain the Group AI architecture vision, principles and strategic positions; test technology shifts for relevance, readiness and enterprise advantage.
• Develop current-state, target-state and transition views that expose choices, dependencies, constraints and investment implications.
• Translate Group priorities into executable cross-Group roadmaps and architecture runway.

Ontology, Data and Context Architecture

• Establish the ontology governance model, domain ownership, semantic standards and lifecycle for shared organisational concepts.
• Connect the ontology to authoritative data products, metadata, knowledge graphs, business rules, controls and agent context stores.
• Ensure context is discoverable, permissioned, current, explainable and reusable across analytics, applications and agents.

Journey, Capability and Agentic Transformation

• Partner with business and product leaders to identify journeys where AI can materially shift outcomes, not merely automate individual tasks.
• Translate opportunities into the required business, information, data, application, integration, technology, people and operating-model capabilities.
• Design the Agentic Operating Model and reusable patterns for agent identity, memory, tools, orchestration, supervision, evaluation, escalation and lifecycle management.

Execution, Portfolio and Governance

• Convene Group Technology, Advanced Analytics, the GT COE and BU stakeholders around common outcomes and integrated delivery decisions.
• Define value hypotheses, architecture decisions and outcome measures for material initiatives; track adoption and realised benefit with accountable owners.
• Provide clear recommendations into investment, design and delivery forums, resolving trade-offs and escalating material Group decisions where required.

Decision Rights

• The role has the standing and senior access to shape Group AI architecture direction, enterprise ontology standards, target-state positions, Agentic Operating Model patterns and cross-Group sequencing for endorsement through the appropriate governance forums. It is expected to challenge fragmented investment, duplication and architecture choices where these undermine Group outcomes, and to frame clear alternatives for executive decision.
• The role does not replace domain architects, data scientists, product owners or delivery leaders; it integrates and directs their contributions around shared strategy, context and measurable outcomes.

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