Principal, Services AI Solution Lead

Job Summary

Within the Digital Customer Relationship organization, you are responsible for defining how Artificial Intelligence can be leveraged within your capability scope to answer business needs while ensuring coherence, scalability, maintainability and alignment with enterprise standards. You act as the AI solution authority for your capability. You help translate validated business requirements into AI-enabled solution options and formal recommendation papers, covering AI embedded in existing platforms, vendor capabilities, low-code / pro-code developments, autonomous agents and AI-native platforms. Your mission is to ensure that the right AI solution pattern is selected and designed before delivery starts. You work closely with Business Product Leaders, Business Process Owners, E2E Business Architects, Digital Product Owners, Application Analysts, E2E Solution Design Leads, DCR Architects, Technical Leads, AI Hub, Data Office, platform owners and delivery teams. You do not replace the Business Product Leader, the Digital Product Owner, the E2E Solution Design Lead or the Technical Lead. You complement them by bringing AI-specific architecture, assessment and design expertise into the existing Services Ways of Working. Your impact will be measured by the quality of AI solution decisions, the reuse of scalable patterns, the clarity of architecture recommendations, and the ability of delivered AI solutions to move safely into run mode.

Responsibilities

• AI Opportunity and Feasibility Assessment: Analyze validated business requirements and identify whether AI can bring a relevant, measurable and feasible answer to the business need. Support early discovery and handover discussions by clarifying AI opportunities, constraints, prerequisites and risks. Assess data readiness, process readiness, platform readiness and operational readiness before recommending an AI solution path. Challenge solution assumptions where AI is not the right answer, or where a simpler non-AI solution could deliver sufficient business value.
• AI Solution Option Assessment: Prepare structured AI assessment papers comparing the relevant solution options: embedded AI in existing platforms, vendor AI capability, low-code AI, pro-code AI, autonomous agents, AI-native platforms and reuse of existing AI assets. Evaluate each option against business fit, user experience, technical feasibility, integration impact, data needs, cybersecurity, compliance, cost, scalability, maintainability and run complexity. Document clear recommendations, assumptions, trade-offs and decision points to support build-versus-buy decisions at Services Digital Experience or DCR leadership level.
• AI Solution Design: Define the AI solution pattern within the capability scope, including where the intelligence is executed, which platforms are impacted, how users interact with the solution, and how decisions or actions are controlled. Design AI interaction patterns, agent flows, human-in-the-loop mechanisms, fallback scenarios, knowledge sources, data flows, integration points and monitoring requirements. Ensure the AI design is consistent with existing E2E solution flows, application responsibilities, architectural standards and target business architecture. Contribute to Functional and Technical Design Documents by providing AI-specific architecture input, assumptions, constraints and non-functional requirements.
• Architecture Alignment and Governance: Collaborate with the Global AI Architect, AI Hub, DCR Architects, Data Office, cybersecurity and platform owners to ensure compliance with enterprise AI principles and guardrails. Participate in the AI Architecture Board and other governance forums for complex, strategic or cross-platform initiatives. Promote reuse of approved AI patterns, agent frameworks, connectors, data patterns, monitoring approaches and integration standards.
• Delivery Support and Technical Decision Enablement: Support Digital Product Owners, Application Analysts, E2E Solution Design Leads, Technical Leads and delivery teams during backlog definition, solution scoping and implementation preparation. Clarify AI architecture choices, constraints, prerequisites, dependencies and risks during delivery planning and execution. Support issue resolution when AI behavior, data dependencies, model outputs, integrations or agent flows create delivery or quality risks.
• AI Run, Monitoring and Support Readiness: Define AI-specific run requirements before go-live, including AI agent ownership, prompt / configuration ownership, knowledge source ownership, monitoring needs, escalation paths and support levels. Ensure AI agents and AI-enabled solutions are designed with observability, traceability, quality monitoring, feedback loops and continuous improvement routines. Support transition to run by ensuring documentation, operating procedures, known limitations and ownership models are explicit and accepted.

Requirements

• Master degree in Engineering, Computer Science, Data Science, Artificial Intelligence, Digital Technologies or equivalent experience.
• 8-12+ years in architecture, software engineering, platform engineering, solution design or digital transformation roles.
• Experience designing enterprise solutions involving multiple applications, integrations, data flows and non-functional constraints.
• Practical understanding of AI technologies such as copilots, embedded AI, RAG, AI agents, workflow automation, low-code / pro-code AI developments and AI observability.

Skills and Competencies

• Strong architecture and system design mindset, with the ability to translate business problems into pragmatic AI solution patterns.
• Strong analytical capability to compare options, expose trade-offs and make fact-based recommendations.
• Ability to simplify complex AI concepts for business, product and delivery stakeholders.
• Good understanding of data, integration, cybersecurity, user experience and run implications of AI solutions.
• Curious, pragmatic and continuously learning about emerging AI technologies and platform vendor capabilities.
• Strong documentation and presentation skills, especially for assessment papers and architecture decision records.
• Ability to influence without formal authority and work effectively in a matrix organization.
• Success measures AI solution assessments are complete, structured and useful for decision-making.
• Recommended AI solution patterns are scalable, maintainable, secure and aligned with enterprise guardrails.
• Build-versus-buy decisions are supported by clear trade-offs and documented rationale.
• Capabilities reuse common AI patterns instead of creating fragmented solutions.
• AI solutions have clear data, integration, monitoring, ownership and run models before go-live.
• Delivery teams receive timely and actionable architecture guidance for AI initiatives.

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