The Sr Product Manager, AI Lead to Cash at Cox Automotive defines the AI strategy for Lead-to-Cash processes and delivers AI-powered automation tools to streamline sales, quoting, contracting, order management, billing, and revenue recognition.
Responsibilities
- AI Strategy & Vision: Lead the short to mid term AI strategy for Lead-to-Cash enablement, including developing AI product plans, budgeting for AI initiatives, and defining capability requirements to optimize the sales cycle, revenue operations, and end-to-end workflows.
- Cross-Functional AI Integration: Align Sales, Finance, Operations, and Technology by identifying AI opportunities that boost efficiency, automate manual tasks, shorten cycle times, and provide insights for revenue decisions.
- AI Product Development: Ensure AI-powered Lead-to-Cash tools meet the definition of ready, address real process pain points across quoting, contracting, order management, billing, and collections, and deliver measurable operational gains.
- Stakeholder Collaboration: Partner with Sales, Finance, Legal, Operations, and Enterprise Technology leaders to understand process needs, identify automation opportunities, and design AI-driven solutions that accelerate revenue cycles and improve data accuracy.
- AI Performance Metrics: Define KPIs for AI adoption within Lead-to-Cash workflows, including cycle time reduction, quote-to-close rates, billing accuracy, revenue leakage reduction, and ROI metrics to demonstrate AI value.
- Technology Partnerships: Build relationships with AI vendors and internal stakeholders to ensure access to advanced AI capabilities integrated within Lead-to-Cash platforms such as CRM, CPQ, CLM, and billing systems.
- AI Thought Leadership: Contribute to Enterprise Technology discussions on AI best practices, ethical implementation, and change management for AI adoption across revenue operations.
- AI Enablement & Training: Contribute to AI literacy programs, training initiatives, and go-to-market plans for AI-powered Lead-to-Cash tools and capabilities.
Requirements
- AI & Analytics Expertise: Strong knowledge of AI/ML technologies including natural language processing, predictive analytics, and generative AI as applied to sales and revenue operations. Ability to assess feasibility, scalability, and business impact of AI solutions in Lead-to-Cash contexts, including contract intelligence, revenue forecasting, anomaly detection, and intelligent automation.
- Lead-to-Cash Domain Knowledge: Deep understanding of end-to-end Lead-to-Cash processes, including lead management, opportunity-to-quote, CPQ, contract lifecycle management, order management, billing, invoicing, collections, and revenue recognition, with the ability to identify AI improvements at each stage.
- Technology & AI Platform Acumen: Strong grasp of enterprise AI platforms and cloud services (Anthropic, AWS, Salesforce AgentForce, Google Cloud AI, etc.), and emerging AI technologies for CRM, CPQ, CLM, and ERP integrations.
- Product Management: Experience managing AI product lifecycles from proof of concept to production, AI model governance and monitoring, and translating complex AI capabilities into usable tools for sales, finance, and operations.
- Change Management for AI Adoption: Expertise in guiding organizational change related to AI in revenue operations, addressing automation concerns, and building trust in AI-driven recommendations.
- Data Strategy & Governance: Understanding data requirements for AI in Lead-to-Cash, including CRM, contract, pricing, and financial data; collaborate with data teams to ensure access to quality data while maintaining compliance and privacy.
- Builds AI-Ready Culture: Foster partnerships across Sales, Finance, and Operations to cultivate an AI-positive culture within Lead-to-Cash teams and position AI as a productivity and revenue enablement tool.
- Engages Revenue Operations Stakeholders: Lead discussions on AI possibilities and limits, gathering input from end-users to ensure solutions address real process gaps.
- Guides AI Implementation Success: Establish clear objectives, success metrics, and adoption plans; articulate how AI augments capabilities and improves revenue outcomes.
- Leads Digital Transformation: Champion the integration of AI into Lead-to-Cash workflows while maintaining human oversight and governance.
- Educational Requirements: Bachelor's degree in Computer Science, Data Science, Information Systems, or related field with 4+ years of experience; alternative combinations allowed (master's with 2+ years, PhD with up to 1 year, or 16 years of related experience).
- Experience: Minimum 4+ years in product management, including at least 1 year focused on AI/ML products; proven piloting or deployment of AI in enterprise settings; demonstrated ROI from AI initiatives.
- AI Platforms & Governance: Experience with AI platforms and frameworks (Microsoft Copilot, OpenAI, Anthropic, SFDC AgentForce, etc.); strong grounding in responsible AI, bias mitigation, and governance.
- Communication & Methodologies: Excellent written and verbal communication; ability to translate AI concepts for non-technical stakeholders; experience with Agile/SAFe in AI product development; track record of measuring ROI and driving AI adoption; self-starting, pragmatic mindset; ability to balance innovation with practicality.
Technologies
Microsoft Copilot, OpenAI, Anthropic, SFDC AgentForce, Salesforce AgentForce, Google Cloud AI, AWS
Compensation
Salary range is USD 101,500 to 169,100 per year. The base salary may vary within this range based on location, knowledge, skills, and abilities. The role may be eligible for additional compensation through an incentive program.
Benefits
- Vacation with pay
- Seven paid holidays per year
- Up to 160 hours of paid wellness annually
- Bereavement leave
- Time off to vote
- Jury duty leave
- Volunteer time off
- Military leave
- Parental leave
