Product Manager/ Product Lead

Job Summary

• A slice of the CX process landscape — for example Lifecycle Management, Sales, Service, Marketing, or Collections — with end-to-end product ownership
• Product vision, strategy, and roadmap for your assigned domain
• Customer and user discovery — understanding how the process actually works, where friction exists, and which problems are worth solving
• The AI agent backlog for your domain — prioritized, scoped, and specified to a level engineering can act on without repeated clarification
• MVP definition and product trade-offs — deciding what needs to be built now, what can wait, and what should not be built at all
• Success metrics for every agent or capability you ship — defined before build, instrumented, and revisited after launch
• Product lifecycle ownership - discovery, validation, requirements, build, launch, adoption, measurement, iteration, and retirement where necessary
• The governance posture for your agents - risk and impact classification, oversight requirements, permissions, auditability, and exception handling built into the product from the start
• The product-market relevance of your domain — ensuring what gets built solves a meaningful client problem, is differentiated, and has a credible path to adoption and commercialization.
• Adoption and value realization for the capabilities you launch — ensuring products are actually used, embedded into workflows, and delivering the intended business outcome.

Key Responsibilities

• Talk to internal stakeholders, domain SMEs, clients, and where possible end users to understand real workflow pain,  not just process documentation
• Decompose broad CX processes into specific problems, user journeys, jobs to be done, and AI-agent opportunities
• Prioritize opportunities based on customer value, business impact, strategic fit, technical feasibility, risk, effort, and commercial potential
• Write product requirements, user stories, functional requirements, and acceptance criteria clear enough for the engineering team to build against
• Define MVP scope and make trade-offs when engineering capacity is constrained
• Sit in sprint planning, backlog refinement, reviews, and demos with engineering as the domain's product voice - prioritize, unblock, and push back on scope creep
• Get into the details of how your agents behave in production: what breaks, what's slow, what's wrong, what users reject, and why
• Define hypotheses and experiments where appropriate before committing significant build effort
• Instrument and track outcome metrics yourself where possible; don't wait for someone else's dashboard
• Review customer feedback, adoption, agent quality, task success, latency, cost, exception rates, and relevant business outcomes after launch
• Work with architecture and engineering teams to understand integration dependencies across CRM, CDP, CCaaS, CPaaS, marketing platforms, enterprise data, APIs, identity, and permissions
• Work with sales and solution teams to turn product capabilities into clear client propositions, business cases, and ROI narratives
• Keep track of material changes in the CX and agentic AI market that could affect roadmap, differentiation, or positioning

Skill Requirements

Other Requirements