Product Manager, Agentic AI

In‑Office Commitment

Employees are expected to work in the office FOUR days weekly to support collaboration and innovation.

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, Luna Intel and Luna Voice dashboards) that proves outcomes and drives roadmap decisions.

• 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.

• 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.

• 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.

• 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 Luna Intel / Luna Voice 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.

• 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.

• 5+ years in product management, including ownership of a technical, data‑rich, or…