About this role
Secure Every Identity, from AI to Human
Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence.
This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.
The Vision
At Okta, growth is our mandate, but a secure foundation is the prerequisite. To protect our critical customer relationships and empower our teams, we are enhancing our GTM function with AI capabilities as a part of our broader retention strategy.
The Role
We are looking for a technical operator with strong strategic vision and business acumen to own the development and operationalization of Guided Renewals , our AI-powered approach to proactive retention. As the Senior Manager of Retention Strategy & Intelligence , you will partner directly with our Renewals organization while collaborating with GTM functions across the customer lifecycle and our Technology, Data, and Insights (TDI) team.
In this role, you will own the intelligence layer that drives retention: identifying and tracking risk signals, partnering with teams to build AI models that surface risk and recommend personalized interventions, and leading the field cadences that turn insights into action.
Core Responsibilities
1. Risk Signal Ownership & Analysis
• Own definition, tracking, and interpretation of customer risk signals including usage, engagement, sentiment, and commercial health.
• Partner with TDI to establish signal weighting, scoring thresholds, and supporting data infrastructure.
• Translate risk analysis into clear, actionable intelligence for field teams and leadership.
2. AI Model Design & Recommendation Engine
• Define the guidelines, guardrails, and decision logic that govern AI-driven retention recommendations.
• Partner with TDI and Data Science to build models that generate personalized playbook recommendations based on account context, risk type, and lifecycle stage.
• Evaluate recommendation quality and drive model refinement where AI output and business reality diverge.
3. Tooling & Recommendations Infrastructure
• Own product requirements for risk and recommendation tooling, defining UX, workflows, and outputs that make AI recommendations actionable for field teams.
• Manage the roadmap from pilot to GA, prioritizing enhancements based on field feedback and model performance.
• Ensure integration with CRM, ERP, and existing tools to minimize friction and drive adoption.
4. Field Cadences & Risk Mitigation Leadership
• Design and lead recurring risk review cadences, including customer health reviews, that drive systematic action on AI-generated signals.
• Embed cadences into Territory Planning,