Product Manager-Application Platform & AI

Key ResponsibilitiesApplication Platform & PLG OwnershipOwn the product roadmap for platform-level features — onboarding flows, in-app activation and adoption mechanics, cross-module dashboards, and shared platform toolingDesign and drive PLG loops (self-serve onboarding, in-product upsell/expansion signals, usage-driven activation) that increase adoption and reduce time-to-value across the applicationWrite PRDs, user stories, and acceptance criteria grounded in real usage patterns and funnel data — not assumptionsMaintain a living, prioritized backlog that reflects both stakeholder input and measurable business/growth valueAI Initiative LeadershipOwn the product vision and roadmap for the flagship AI/agent layer, driving the shift from traditional UI-driven workflows to natural-language and conversational interaction across modulesDefine and prioritize new AI agent capabilities (e.g., conversational search, chat-based workflows, natural language reporting, and task-specific agents) in partnership with engineering and applied AI teamsEstablish a repeatable framework for identifying, scoping, and shipping new agent use cases — from discovery through prompt/response design, grounding sources, evaluation, and rolloutDefine and track AI-specific success metrics: agent adoption, task completion/resolution rate, deflection from traditional UI flows, accuracy/groundedness, and user trustPartner with Marketing, OPS/Training, and Leadership to drive internal and external communication, training, and change management around AI adoptionStakeholder Engagement & RequirementsServe as the primary product partner across functions — Product & Engineering, Marketing, OPS/Training, Customer Success, and LeadershipFacilitate requirement-gathering sessions, workflow audits, and AI use-case discovery to build a ground-level understanding of user needs across modulesTranslate platform and AI requirements into specifications that engineering and design can act onManage stakeholder expectations through clear communication on timelines, trade-offs, and scope decisionsBusiness Case & Roadmap DevelopmentBuild and present business cases for platform and AI initiatives — quantifying adoption impact, productivity gains, cost-to-serve reduction, and ROIDevelop a rolling 2–3 quarter roadmap, sequenced by growth impact, AI feasibility, and cross-module dependencyAlign roadmap priorities with leadership and key stakeholders through structured reviewsProactively identify where AI and platform-level improvements can reduce manual effort, friction, or support loadData & ExecutionDefine KPIs and success metrics for every initiative — activation rate, adoption rate, agent usage/resolution rate, process cycle time, and time savedUse data (funnel, usage, and AI interaction data) to validate hypotheses, measure outcomes post-launch, and inform iterationDrive sprint planning, backlog grooming, and cross-functional alignment with engineering, design, and applied AI teams