Staff Product Manager, Agentic AI

In‑Office Commitment Employees are expected to work in the office FOUR days weekly to support collaboration and innovation. The Opportunity Autopilot is a no‑code platform that deploys AI agents into critical supply chain workflows. This role owns three connected surfaces: AI Workflows — a library of customer‑deployable agent workflows (e.g., validate early ETAs, carrier outreach, data collection). You decide which jobs to automate next, the sequence, and performance standards. AI Agent Workflow Manager (fka Autopilot) — the no‑code configurator: trigger, condition, action canvas, multi‑agent orchestration, human‑in‑the‑loop controls, and build‑and‑deploy experience. AI Agent Analytics & Reporting — the measurement layer (AI Agent Analytics, LunaIntel and LunaVoice dashboards) that proves outcomes and drives roadmap decisions. What You’ll Do: Lead with customers and research Own the customer problem before the solution. Every workflow starts from a clearly stated customer problem, who is impacted, and when it occurs. Run primary research continuously: customer interviews, ride‑alongs, design‑partner pilots, CAB validation, win/loss analysis, and direct platform behavior analysis. Recruit and manage design partners for shadow‑mode pilots to establish honest baselines and earn trust before live deployment. Act as a domain and product expert in customer‑facing settings: demos, executive briefings, CAB, and conferences. Translate findings into a prioritized roadmap. Drive AI innovation Expand what agents can safely do in production: autonomous outreach, document parsing, reason‑code classification, multi‑agent workflows. Move workflows up the maturity curve from supporting a user, to augmenting, to fully automating a complete work task. Define the bar for each stage. Enable authoring and execution of workflows in natural language through Mo, matching triggers, conditions, and actions. Own the Autopilot integration side. Design for trust: configurable controls, transparent logic, audit trails, hallucination guards, and throttles tuned per use‑case. Partner with engineering, applied AI, and design on workflow architecture and tooling needed to scale production. Write outcomes‑based requirements Author concise PRDs focused on goals, non‑goals, success metrics, and indicators rather than feature checklists. Success is defined by the outcome produced. Specify configurability: pilot defaults versus tenant‑tunable settings (thresholds, conditions, lists, cadence, channels). Maintain a prioritized backlog across the three surfaces, sequencing against customer value, trust, and business results. Set gating criteria: data availability, provider readiness, legal/compliance reviews, and human‑QA thresholds before automation unlocks. Measure and report outcomes Define metric models and instrument workflows: validation/completion rates, confidence, manual coordination reduction, ETA accuracy, freight spend impact, and adoption. Own AI Agent Analytics and LunaIntel / LunaVoice dashboards so customers see performance and ROI in renewals and executive reviews. Run the outcome loop: use dashboard insights to adjust roadmaps, throttle changes, and next workflows. Produce executive‑ready deliverables: investment memos, roadmap reviews, launch readouts, and enablement materials. What success looks like in the first year Continuously expanding, high‑trust AI workflow library with measured customer outcomes. At least one flagship multi‑agent workflow that fully automates a complete operational task end‑to‑end. Autopilot workflows authorable and executable through Mo in natural language, with adoption measured by workflows created and triggered via Mo. Measurable business impact: reductions in freight spend, manual coordination, double‑digit reductions in exception handling, improved ETA accuracy, and faster sourcing cycles. AI Agent Analytics adopted as the record for agent performance by customers, CX, and executives. Rising adoption and trust among Autopilot customers, reflected in NPS and renewal/expansion, with no trust‑eroding incidents. What we’re looking for 5+ years in product management, including ownership of a technical, data‑rich, or AI/ML product through the full lifecycle. Demonstrated customer obsession: grounded product decisions in direct research and credibly representing customers. Fluency with AI/agentic systems: LLM‑powered agents, orchestration, evaluation, human‑in‑the‑loop design, and production readiness. Outcomes‑first operating style: writing requirements as goals, non‑goals, and success metrics, and reporting impact rather than output. Strong analytical skills: defining metrics, working with dashboards and warehouse data (e.g., Snowflake), and reasoning quantitatively about agent performance and ROI. Excellent written and verbal communication to influence engineers, designers, sales, partners, customers, and leadership. Comfort with ambiguity and speed; ability to set the right throttle so the team ships fast without outrunning trust. Nice to have Logistics, supply chain, or transportation domain experience. Experience building no‑code/workflow‑builder, automation, or analytics‑and‑reporting products. Experience with conversational/NL interfaces and deploying shared experiences across product teams. Familiarity with voice/communications platforms or outbound‑contact compliance (TCPA, calling‑hours rules). Prior work running design‑partner programs, CABs, or beta/pilot motions for new product categories. Diversity & Inclusion We are an equal‑opportunity employer actively working to create a diverse and inclusive work environment where underrepresented groups can thrive. #J-18808-Ljbffr Capital Factory