The Director, Enterprise Artificial Intelligence (AI) is responsible for driving the execution of Interface's enterprise AI strategy-translating vision into scalable, secure, and value-generating solutions across commercial and corporate functions.

This role serves as the bridge between business leaders, technology teams, and strategic partners to identify, prioritize, and deliver AI use cases that improve decision-making, accelerate growth, and increase operational efficiency-while ensuring strong governance, security, and ethical use of AI.

The role will act as Interface's AI execution leader, owning delivery, adoption, and measurable outcomes, not experimentation in isolation.

Key Responsibilities:

AI Strategy Execution & Delivery
• Execute Interface's enterprise AI roadmap, aligned to company strategy, digital priorities, and value creation goals.
• Translate strategic AI priorities into clearly defined programs, use cases, and roadmaps with measurable business outcomes.
• Lead delivery of AI initiatives across domains and functions.
• Provides Project Management services for the key AI projects.

Use Case Identification & Business Partnership
• Partner with senior business leaders to identify, vet, and prioritize high-impact AI opportunities.
• Drive structured use case intake, value assessment, and sequencing to ensure focus on ROI-driven outcomes.
• Serve as a trusted advisor to business teams on where and how AI can responsibly accelerate results.

AI Architecture
• Define and lead the enterprise AI architecture strategy, establishing scalable, secure, and reusable AI platforms, services, and integration patterns
• Design and govern the enterprise AI ecosystem, including large language models (LLMs), AI agents, knowledge platforms, data foundations, vector databases, orchestration frameworks, APIs, and cloud AI services
• Architect enterprise data and knowledge foundations for AI, including data pipelines, metadata, semantic layers, retrieval-augmented generation (RAG), knowledge management, and data quality controls required to deliver trusted AI outcomes.
• Drive AI technology standards and solution architecture reviews, evaluating emerging AI capabilities, platforms, vendors, and reference architectures while ensuring interoperability, scalability, reliability, and operational excellence across the enterprise

Governance, Risk & Responsible AI
• Lead AI governance in partnership with Security, Legal, Privacy, and Compliance teams.
• Ensure AI solutions align with Interface standards for:
- Data privacy and security
- Ethical and responsible AI use

- Model transparency and explainability
• Establish guardrails, patterns, and standards for internal and vendor-provided AI solutions.

Technology & Partner Management
• Leverage strategic platforms and partners (e.g., Microsoft, Salesforce, Adobe, Workday, others) to accelerate AI adoption.
• Evaluate third-party AI tools and embedded AI capabilities with a "buy vs. build" mindset.
• Collaborate with Enterprise Architecture and Engineering teams to ensure scalability, interoperability, and long-term viability.
• Define and track adoption, usage, and value realization metrics.

Team Leadership & Operating Model
• Lead and mentor a small, high-impact AI delivery team (internal and/or hybrid with partners).
• Establish a lean operating model focused on rapid iteration, business outcomes, and continuous improvement.
• Influence without authority across global, matrixed teams.

Required Qualifications:
• Bachelor's degree in computer science, Engineering, Business, or related field; Master's preferred.
• 8-12+ years of experience across digital, analytics, AI, or advanced technology roles.
• Proven experience leading enterprise AI or advanced analytics initiatives from concept through production.
• Strong understanding of:
- Applied AI / machine learning concepts (not pure research)
- Data platforms, cloud ecosystems, and modern enterprise architectures
- AI risks related to privacy, security, bias, and compliance
• Demonstrated ability to partner with senior business leaders and influence outcomes.
• Experience operating in a global, matrixed organization.

Preferred Experience:
• Experience deploying AI solutions leveraging hyperscale's (e.g., Microsoft Azure AI / Google Cloud)
• Background in manufacturing, industrial, supply chain, or commercial B2B environments.
• Exposure to embedded AI within enterprise platforms (CRM, ERP, productivity tools).
• Experience establishing AI governance frameworks or centers of enablement

4 - Mid-Senior Level / Management

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