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AI Outcome Manager

United States 135000.00 - 220000.00 USD / Year · Job Posted February 21, 2026
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Job Description

The AI Outcomes Manager owns post-sales value realization for Ema’s enterprise customers. This is not a traditional Customer Success role. You will operate across Ema’s value-realization lifecycle, partnering closely with Sales, Value Engineering, AI Implementation, Product, and senior customer stakeholders. You are the first escalation point when delivery is under pressure and the owner of the closed-loop feedback system between customers, delivery teams, and product.

Job Responsibility

  • Own customer success from post-sales handoff through post-go-live
  • Define and align success metrics, ROI targets, and usage KPIs
  • Track efficiency gains, accuracy improvements, cost savings, and experience impact
  • Communicate outcomes through QBRs, exec readouts, and customer newsletters
  • Own regular customer readouts of AI usage patterns, adoption trends, and workflow performance
  • Analyze false positives, false negatives, failures, and negative feedback across agent behavior, integrations, and UX
  • Separate system gaps vs. process, training, or expectation issues
  • Partner with Value Engineering and AI Implementation teams to drive prioritized improvements across agents, orchestration, prompts, UX, and integrations
  • Design and execute change-management and rollout plans with customer leadership
  • Drive adoption across teams, roles, and geographies
  • Serve as the first escalation point during implementation, go-live, and hypercare
  • Manage communication across business, IT, security, and executive stakeholders
  • Identify opportunities for additional SOWs and new use cases
  • Consultatively sell outcomes using Challenger-style methodologies
  • Act as the voice of the customer to Product and Engineering
  • Translate VOC, usage data, and failure patterns into actionable insights

Requirements

  • 12+ years in enterprise customer success, transformation, or solution leadership roles
  • proven experience delivering measurable ROI post-implementation
  • track record managing large, complex enterprise accounts
  • experience working cross-functionally with Product and Engineering teams
  • background beyond POCs — production, scale, and accountability are required
  • experience with AI, automation, or digital transformation programs
  • exposure to regulated or complex enterprise environments
  • experience in fast-growing startups or scaling enterprise AI platforms
  • familiarity with outcome-based selling or consulting methodologies
  • strong understanding of enterprise workflows and process automation
  • ability to reason about AI behavior in production, including failure modes and edge cases
  • comfort discussing agentic systems, integrations, and UX tradeoffs with credibility

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