The posting
Position Summary The Manager of Artificial Intelligence owns Standard Motor Products' AI capability end to end: the strategy, the portfolio, the people who deliver it, and the governance that keeps it safe and aligned to business priorities. The role sets direction for how AI is applied across the enterprise, decides which initiatives are funded and sequenced, assigns and reviews the work of engineers and analysts contributing to those initiatives, and is accountable to senior leadership for the outcomes. It is a working management role: the manager retains enough technical depth to make architecture calls, evaluate a solution's readiness, and challenge a design, while spending the majority of their time directing others rather than building personally. The manager chairs the AI steering and innovation groups, defines the annual AI governance agenda, and represents the company in external forums including MEMA and the Business Technology Council. The role partners with peer managers across business systems analysis, web and database engineering, product data, marketing, and e-commerce to secure resources and resolve competing priorities, and it prepares and delivers the AI briefings and training that executives rely on to understand where the company stands. Key Responsibilities Team Leadership and Work Direction Direct the day-to-day work of the analysts, engineers, and contributors assigned to AI initiatives, including task assignment, prioritization, and review of deliverables before they reach business stakeholders. Set expectations for documentation quality, time tracking, and project hygiene, and coach team members on writing for an executive audience. Identify capability gaps, make the case for additional resources, and develop the technical and communication skills the team needs as the AI portfolio grows. AI Portfolio and Program Management Own the enterprise AI portfolio from intake through delivery. Decide which requests become funded initiatives, sequence them against available capacity, and ensure every effort is captured, assigned, and visible in the department's project and time tracking system rather than run informally. Manage timelines and dependencies across concurrent projects, escalate risks early, and keep leadership and external partners informed of where each initiative stands. Technical Direction and Solution Architecture Set the architectural direction for AI solutions, including embedding and vector search design, cloud AI resource strategy, API-based delivery to consuming teams, and where AI-derived data should be hosted so that transactional systems are not put at risk. Review and approve functional and technical requirements before they go to business owners, arbitrate design disagreements between teams, and hold the line on what is production-ready versus what remains a proof of concept. Governance, Risk, and Compliance Define and maintain the company's AI governance framework, including how governance responsibilities are divided, which areas are prioritized each year, and how they are measured. Establish standards for data access, credential handling, and acceptable use of AI tools, and ensure initiatives comply with information security requirements. Serve as the decision point when a proposed AI use case raises data, security, or reputational concerns. Executive Engagement and Change Leadership Brief the executive team on AI strategy, progress, and risk in business terms, and build the training and advisory programs that raise AI fluency across leadership. Own the adoption strategy for enterprise AI and collaboration tooling, including how much change the organization absorbs at once, what supporting communications go out, and how business groups are structured for information sharing. Sponsor the communications, prompt libraries, and enablement materials the organization uses. Vendor, Licensing, and External Relationships Manage relationships with AI platform vendors and partners, including capability evaluations, licensing decisions, and the commitments that come with them. Represent the company in industry associations and peer councils, align shared priorities with counterparts at other organizations, and bring external developments back into the company's own roadmap. Representative Initiatives The following initiatives illustrate the scale and mix of work this position directs.
Initiative
Management accountability
Catalog image vector search
Directed the image embedding and similarity search effort, secured cross-team data access, and set the hosting decision with web and database engineering so catalog traffic stays off transactional systems.
DAM field translation
Commissioned the development environment for programmatic field translation and assigned delivery ownership across engineering and business analysis.
Sales enablement (SEN) interface
Ran the user requirements review cycle through to executive sign-off and set the standard for how role-based functionality is demonstrated to stakeholders.
Vehicle testing tracking improvement
Owned the requirements and approval process spanning requestor, technician, and administrator roles, and managed the stakeholder review to closure.
Amazon Lens part matching
Managed scope expansion and timeline commitments, and kept internal priorities aligned with external partner expectations.
Invoice search tool
Sponsored delivery of internal tooling and oversaw the rollout and support approach for end users.
Business group SharePoint sites
Leads the enterprise evaluation of collaboration sites per business group as the foundation for an organizational knowledge base.
AI governance and executive advisory
Sets the annual AI governance agenda, represents the company in industry AI forums, and owns the executive training and update program.
Required Qualifications
Experience leading technical teams or programs, including assigning work, reviewing deliverables, and being accountable for outcomes delivered through others. Proven track record delivering AI or machine learning solutions into production in an enterprise environment, with enough technical depth to set architecture direction and evaluate readiness. Portfolio and project management capability across concurrent initiatives, including prioritization, capacity planning, dependency management, and risk escalation. Experience establishing governance, standards, or policy for a technology domain and driving adoption of it across an organization. Strong information security judgment, including data access, credential management, and acceptable-use decisions for emerging technology. Executive-level communication skills, including briefing senior leadership, running steering committees, and issuing organization-wide communications. Working knowledge of SQL, cloud AI platforms, and enterprise data architecture sufficient to direct and challenge technical work.
Preferred Qualifications
Formal people-management experience, including hiring, development planning, and performance conversations. Experience owning vendor relationships, licensing negotiations, or technology budgets. Familiarity with automotive aftermarket product data, parts catalogs, and digital asset management. Experience representing an organization in industry associations, standards bodies, or peer councils.
Scope and Working Relationships Reports to the Director of Information Security. Directs the work of analysts and engineers assigned to AI initiatives, whether they report directly or are matrixed from other groups, and chairs the AI steering and innovation groups. Partners with peer managers in business systems analysis, web and database engineering, product data, marketing, and e-commerce, and briefs senior IT and business leadership on portfolio status, governance decisions, and risk. Represents the company externally with vendors, partners, and industry councils. Measures of Success
The AI portfolio delivers approved, documented, and deployed solutions on committed timelines, without the manager personally building them. Team members produce requirements and deliverables that reach business sign-off with minimal rework. AI governance standards are defined, communicated, and demonstrably followed across the organization. Business groups bring forward viable AI use cases on their own initiative, and intake is managed rather than ad hoc. Executive stakeholders can articulate the company's AI capabilities, priorities, and risk posture without prompting.
