Product Manager

Key Responsibilities AI Strategy & Vision: Provide leadership in defining and executing the short to mid-term AI strategy for Lead to Cash process enablement, including the creation and execution of AI product development plans, budget allocation, and definition of AI capability requirements that support sales cycle efficiency, revenue operations, and end-to-end Lead to Cash workflow optimization. Cross-Functional AI Integration: Drive alignment across Sales, Finance, Operations, and Technology teams by identifying opportunities for AI implementation that enhance Lead to Cash efficiency, automate manual tasks, reduce cycle times, and provide intelligent insights for better revenue decision-making. AI Product Development: Take responsibility for meeting the “definition of ready” for AI-powered Lead to Cash tools and capabilities, ensuring they address real process pain points across quoting, contracting, order management, billing, and collections, and deliver measurable operational gains. Stakeholder Collaboration: Work closely with Sales, Finance, Legal, Operations, and Enterprise Technology leaders to understand Lead to Cash process needs, identify AI automation opportunities, and develop AI-powered solutions that accelerate revenue cycles and improve data accuracy. AI Performance Metrics: Define and establish key performance indicators (KPIs) specific to AI adoption within Lead to Cash workflows, including cycle time reduction, quote-to-close rates, billing accuracy, revenue leakage reduction, and ROI metrics that demonstrate the business value of AI implementations. Technology Partnerships: Foster effective relationships with AI vendors, technology partners, and internal stakeholders to ensure access to cutting‑edge AI capabilities integrated within Lead to Cash platforms such as CRM, CPQ, CLM, and billing systems. AI Thought Leadership: Contribute to Enterprise Technology thought leadership regarding AI best practices within Lead to Cash processes, ethical AI implementation, and change management strategies for AI adoption across revenue operations teams. AI Enablement & Training: Contribute to the development and execution of AI literacy programs, training initiatives, and go-to-market strategies for AI-powered Lead to Cash tools and capabilities. Functional Skills AI & Analytics Expertise: Possesses skilled knowledge of AI/ML technologies, including natural language processing, predictive analytics, and generative AI as applied to sales and revenue operations use cases. Ability to evaluate AI solutions for feasibility, scalability, and business impact within Lead to Cash contexts. Understands how to leverage AI for contract intelligence, revenue forecasting, anomaly detection, and intelligent process automation. Lead to Cash Domain Knowledge: Deep understanding of end-to-end Lead to Cash processes, including lead management, opportunity-to-quote, CPQ (Configure, Price, Quote), contract lifecycle management, order management, billing, invoicing, collections, and revenue recognition. Ability to identify AI-driven improvements at each stage of the revenue cycle. Technology & AI Platform Acumen: Deep understanding of enterprise AI platforms, cloud-based AI services (Anthropic, AWS, Salesforce AgentForce, Google Cloud AI, etc.), and emerging AI technologies as they apply to CRM, CPQ, CLM, and ERP systems. Skilled in assessing AI tools for integration with existing Lead to Cash technology stacks. Product Management: Skilled knowledge of AI product lifecycle management, from proof of concept through production deployment within revenue operations environments. Experience with AI model governance, monitoring, and continuous improvement. Ability to translate complex AI capabilities into user-friendly tools that sales, finance, and operations teams can leverage effectively. Change Management for AI Adoption: Expertise in driving organizational change related to AI implementation within revenue operations, including addressing concerns about automation, building trust in AI-driven recommendations, and ensuring smooth transitions to AI-augmented Lead to Cash workflows. Data Strategy & Governance: Understanding of data requirements for AI systems within Lead to Cash environments, including CRM data quality, contract data, pricing data, and financial data. Ability to work with data teams to ensure AI solutions have access to appropriate, high-quality data while maintaining compliance and privacy standards. Foundational Skills Builds AI-Ready Culture: Proactively builds partnerships across Sales, Finance, and Operations to foster an AI-positive culture within Lead to Cash teams, helping stakeholders understand and embrace AI as a productivity and revenue enablement tool. Engages Revenue Operations Stakeholders: Facilitates discussions about AI possibilities and limitations within Lead to Cash contexts, seeking input from end-users across the revenue cycle to ensure AI solutions address real process gaps and enhance team effectiveness. Guides AI Implementation Success: Ensures Lead to Cash AI initiatives have clear objectives, success metrics, and adoption strategies. Helps teams understand how AI will augment their capabilities and improve revenue outcomes. Leads Digital Transformation: Champions the integration of AI into Lead to Cash workflows, helping the organization evolve its revenue operating model to fully leverage AI capabilities while maintaining human oversight and sound governance principles. Education, Experience and Qualifications Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field and 4 years’ experience. The right candidate could also have a different combination, such as a master’s degree and 2 years’ experience; a Ph.D. and up to 1 year of experience; or 16 years’ experience in a related field Minimum of 4+ years of experience in product management, with at least 1 years specifically focused on AI/ML products Proven experience piloting or implementing AI solutions in enterprise environments Demonstrated success in deploying AI tools that improve employee productivity and operational efficiency Experience with AI platforms and frameworks (Microsoft Co-Pilot, OpenAI, Anthropic, SFDC AgentForce, Etc.) Strong understanding of responsible AI principles, bias mitigation, and AI governance Excellent communication skills with the ability to translate complex AI concepts for non-technical stakeholders Experience with Agile/SAFe methodologies in AI product development Track record of measuring and demonstrating ROI for AI initiatives Experience with change management and driving AI adoption across organizations Ability to understand and articulate various internal and external customer needs Proven self-starter who can proactively engage with the business, stakeholders, and peer base Core Competencies Critical to Success Balances innovation with practical concerns and manages AI integration with existing systems. Keeps pace with the rapidly evolving AI landscape, identifying opportunities for internal use. Develops long-term AI roadmaps aligned with business goals and anticipates future trends to prepare the organization for emerging capabilities. Creates scalable AI strategies that evolve with organizational needs. Champions responsible AI practices, ensuring ethical considerations, and building trust through transparency and explainability. Ensures AI solutions support human judgment where appropriate and bridges technical and business teams to deliver practical solutions. Facilitates AI literacy and capacity building, fostering environments where AI complements human expertise. #J-18808-Ljbffr