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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