Sr Staff Product Manager – AI Monetization (Pricing & Packaging)

ServiceNow•Kirkland, WA

1d•$190,900 - $334,100•Hybrid

About The Position

The Sr Staff Product Manager drives strategic alignment across ServiceNow's product portfolio by partnering directly with customers, synthesizing insights into product and packaging strategy, and influencing roadmap and pricing decisions across independent product organizations. You bridge customer insights, technical product management, and commercial strategy—connecting customer success teams, BU product organizations, Pricing Strategy, and platform engineering. The focus: understand adoption blockers, translate them into product direction and pricing/packaging decisions, and ensure customer feedback shapes AI adoption.

Requirements

12+ years of product/program management in technically complex environments; 5+ years driving customer-centric product strategy at enterprise SaaS / platform level
Deep customer engagement expertise—proven track record conducting research, managing advisory programs, and synthesizing insights into product strategy and go-to-market positioning
Technical fluency in AI/ML—ability to understand AI capabilities, infrastructure requirements, and platform architecture without heavy SME support
Pricing and packaging strategy experience—familiarity with SaaS packaging models, consumption-based pricing, bundling, and commercial model impact on adoption
Demonstrated ability to drive alignment without authority—influencing across independent teams with competing priorities and misaligned incentives
Strategic communication—ability to translate technical complexity and customer needs into compelling narratives for executives and stakeholders
Cross-functional program coordination—managing dependencies and delivery across organizations with independent release cycles
Track record of driving adoption and usage growth—measurable impact on adoption metrics, expansion, or consumption

Nice To Haves

AI/ML product strategy or adoption experience—working with enterprise customers on AI adoption, governance and operationalization
Platform or ecosystem product management—managing products requiring cross-org influence
Consumption-based business models—understanding metering, usage aggregation, and consumption-based pricing
SaaS pricing strategy and commercial model innovation with demonstrated packaging decision impact
Voice of the customer, Customer advisory boards or structured customer research programs
B2B enterprise software background, particularly platform or infrastructure-heavy contexts

Responsibilities

Partner with Pricing Strategy and BU product organizations to shape AI pricing, packaging, and positioning through customer research and cross-BU alignment.
Influence packaging strategy by translating customer insights into recommendations and go-to-market positioning.
Drive cross-BU alignment on how AI Platform products bundle within the broader portfolio—identifying opportunities for bundling, upsell, and consumption-based model innovation.
Stress-test packaging models against customer feedback, adoption barriers, and competitive dynamics to ensure pricing decisions don't inhibit adoption.
Create customer narratives that justify commercial model choices and support pricing/packaging decisions.
Be the voice-of-customer for AI Platform products, translating adoption barriers and feedback into platform roadmap and strategy.
Establish structured customer feedback loops with account teams, CSMs, and advisory boards to surface adoption blockers, and feature gaps across AI Control Tower, Workflow Data Fabric, Action Fabric and more.
Conduct customer research on product strategies, packaging models, and platform simplification—translating pain points into product requirements and roadmap.
Synthesize insights into market narratives that inform product direction, and go-to-market positioning.
Drive adoption by unblocking tactical product issues with CSMs and positioning the AI Platform as the strategic choice for enterprise AI governance.
Measure impact as features and pricing changes ship—gathering feedback to validate assumptions and inform iteration.