Director, Domain Enterprise Architect (AI)

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Job Description:

Role Summary

The Director, Domain Enterprise Architect (Artificial Intelligence - AI) is accountable for the enterprise AI architecture domain, ensuring all AI, Generative AI, Agentic AI, machine intelligence, and intelligent automation investments across global operations align to a coherent strategic vision.

This leader establishes the enterprise AI domain strategy, target-state architecture, standards, roadmaps, governance frameworks, reference architectures, and investment priorities required to scale AI responsibly across the organization.

The Director functions as the senior architecture authority for AI, partnering closely with business executives, enterprise architects, platform leaders, risk functions, and technology teams to ensure AI investments are coordinated, reusable, governed, and strategically aligned.

This role balances long-term strategic planning with strong execution leadership, ensuring the enterprise AI roadmap translates into measurable business outcomes while maintaining regulatory compliance, operational excellence, and architectural consistency.

What will you do?

Enterprise AI Domain Strategy

• Define and maintain the enterprise AI architecture strategy.

• Establish target-state AI domain architecture and multi-year roadmaps.

• Create AI reference models, standards, principles, policies, and guardrails.

• Ensure AI strategy aligns with enterprise business strategy, technology strategy, and operating model.

Enterprise Investment Alignment

• Establish visibility into AI investments across global business units.

• Identify duplication, fragmentation, risk, and technology overlap.

• Drive enterprise-wide reuse and strategic consolidation of AI capabilities.

• Guide investment decisions toward scalable enterprise platforms and services.

Domain Governance & Standards

• Own enterprise AI standards, approved technologies, architecture patterns, and reference architectures.

• Chair and influence governance forums related to AI architecture decisions.

• Evolve enterprise controls governing AI deployment, autonomy, human oversight, security, risk, and compliance.

• Oversee exception management and architecture decision processes.

AI Portfolio & Roadmap Leadership

• Develop centralized AI roadmaps supporting business capabilities and strategic priorities.

• Coordinate roadmap activities across business groups and technology organizations.

• Ensure foundational AI investments support future enterprise demand.

• Assess emerging technologies and define adoption strategies.

Executive Engagement

• Serve as the trusted advisor to executives regarding AI strategy and investment planning.

• Communicate AI opportunities, risks, and architectural direction.

• Facilitate strategic alignment between business leaders and technology organizations.

• Present architecture recommendations to leadership and governance bodies.

Responsible AI & Regulatory Oversight

• Establish enterprise Responsible AI architecture principles.

• Ensure AI initiatives comply with regulatory expectations, model governance requirements, privacy obligations, and risk frameworks.

• Drive transparency, explainability, accountability, and human oversight standards.

• Partner with Risk, Security, Compliance, Legal, and Data leaders to mature enterprise AI governance.

Capability Development

• Build and mentor a high-performing AI architecture community.

• Develop AI architecture practices, methodologies, and knowledge-sharing programs.

• Support Solution Architects and other Domain Architects in adopting enterprise standards.

• Foster innovation while maintaining governance and execution discipline.

Value Realization

• Define measures supporting AI investment value realization.

• Track enterprise outcomes and adoption trends.

• Ensure strategic AI initiatives generate measurable business value.

• Continuously evolve roadmaps based on outcomes and feedback.

Success Measures

• Well-defined and widely adopted enterprise AI strategy and roadmap.

• Reduction of redundant AI investments and technology fragmentation.

• Strong alignment of AI initiatives to business priorities and enterprise standards.

• Effective governance and regulatory compliance across AI initiatives.

• Increased adoption of approved enterprise AI capabilities.

• Demonstrated business value from AI investments.

• High confidence from executives, business leaders, and technology stakeholders.

• Strong execution against roadmap commitments and strategic objectives.

What do you need to succeed?

Education

• Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related field.

• Master's degree preferred.

• Advanced architecture certifications preferred.

• AI-specific certifications highly desirable.

Experience

• 10+ years of architecture leadership experience.

• 5+ years leading enterprise AI, data, analytics, digital transformation, or emerging technology do…