Lead Product Manager, AI (MarTech)

Company Overview

Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM).

What you'll do

As a P5 Lead Product Manager Analyst AI for Marketing and Prospecting, you own Agent Use Case Definition & Business Outcomes by identifying high-impact business workflows. You define technically viable agent requirements and translate business problems into clearly defined agent capabilities feasible within the enterprise tech stack. For Applications Workflow Design & Orchestration, you define the end-to-end workflow an agent will perform across enterprise systems, specifying triggers, context, actions, and escalation paths. You handle Collaboration, Value measurement, and Continuous improvement by defining success metrics, tracking outcomes, and refining agent behaviors based on operational feedback. Finally, you manage Autonomous Boundaries and Safety by determining the extent of agent autonomy, defining guardrails, approval checkpoints, and the human-in-the-loop model.

This position is an individual contributor role reporting to the Director, Martech & Prospecting Applications.

Responsibility

• Identify the highest-value problems for business development, sales and account executives where an agent can save time, reduce effort, and take useful action
• Be very savvy with data, crunch the current state funnel metrics, and pipeline performances to determine the highest value use cases to agentify
• Influence senior leaders and cross-functional teams on use cases we should automate, what to build first, and how to stay at the forefront of this space
• Understand intent signals, prospecting signals, propensity scores, next best recommendations etc to determine the best execution strategies for prospecting
• Evaluate rapidly new capabilities from model providers, tooling ecosystems, and internal platforms, and translate them into clear product bets
• Build scrappy but convincing prototypes using available models, agent frameworks, tools, and lightweight interfaces to test usefulness, trust, and feasibility
• Partner with engineering, ML, design, and platform teams to turn promising prototypes into scalable product experiences
• Shape how memory, context, orchestration, permissions, and human handoff should work in prospecting journeys