Key Responsibilities
Application Platform & PLG Ownership
- Own the product roadmap for platform-level features — onboarding flows, in-app activation and adoption mechanics, cross-module dashboards, and shared platform tooling
- Design 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 application
- Write PRDs, user stories, and acceptance criteria grounded in real usage patterns and funnel data — not assumptions
- Maintain a living, prioritized backlog that reflects both stakeholder input and measurable business/growth value
AI Initiative Leadership
- Own 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 modules
- Define 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 teams
- Establish a repeatable framework for identifying, scoping, and shipping new agent use cases — from discovery through prompt/response design, grounding sources, evaluation, and rollout
- Define and track AI-specific success metrics: agent adoption, task completion/resolution rate, deflection from traditional UI flows, accuracy/groundedness, and user trust
- Partner with Marketing, OPS/Training, and Leadership to drive internal and external communication, training, and change management around AI adoption
Stakeholder Engagement & Requirements
- Serve as the primary product partner across functions — Product & Engineering, Marketing, OPS/Training, Customer Success, and Leadership
- Facilitate requirement-gathering sessions, workflow audits, and AI use-case discovery to build a ground-level understanding of user needs across modules
- Translate platform and AI requirements into specifications that engineering and design can act on
- Manage stakeholder expectations through clear communication on timelines, trade-offs, and scope decisions
Business Case & Roadmap Development
- Build and present business cases for platform and AI initiatives — quantifying adoption impact, productivity gains, cost-to-serve reduction, and ROI
- Develop a rolling 2–3 quarter roadmap, sequenced by growth impact, AI feasibility, and cross-module dependency
- Align roadmap priorities with leadership and key stakeholders through structured reviews
- Proactively identify where AI and platform-level improvements can reduce manual effort, friction, or support load
Data & Execution
- Define KPIs and success metrics for every initiative — activation rate, adoption rate, agent usage/resolution rate, process cycle time, and time saved
- Use data (funnel, usage, and AI interaction data) to validate hypotheses, measure outcomes post-launch, and inform iteration
- Drive sprint planning, backlog grooming, and cross-functional alignment with engineering, design, and applied AI teams
