Sr. AI Customer Success Manager

QAD•Atlanta, GA

13h•Remote

About The Position

The Sr. AI Customer Success Manager is an overlay enablement and adoption strategist supporting regional Customer Success teams as AI-powered products move into General Availability and ongoing operations. The role helps customers activate, integrate, and maximize value from the AI platform, with success measured by AI product adoption, AI-specific net revenue retention, and AI customer referenceability. It is remote, with up to 25% regional travel and occasional travel to a physical office.

Requirements

At least 5 years of experience in customer-facing or operational roles, such as Customer Success, Technical Account Management, Management Consulting, or Operations.
Solid understanding of manufacturing and operational domains, including strategic sourcing, Sales Operations, supply chain risk management, inventory optimization, and shop-floor or back-office productivity.
Solid understanding of AI concepts, large language models, or data analytics infrastructure.
Ability to interpret data trends, run basic data queries using tools such as SQL or Excel, and build clear reporting frameworks.
Ability to master a rapidly evolving technical product ecosystem.
Knowledge of CRM software and Customer Success platforms.
Exceptional presentation skills and ability to explain technical AI processes in terms of clear business value.
Adaptability in a fast-paced, high-growth environment with shifting requirements.
Ability to build customer trust and empathetically de-escalate issues.
Practical experience with manufacturing or supply chain workflows, such as sourcing, AP/procurement, inventory management, or Sales Operations, alongside exposure to enterprise AI/ML platforms, developer tools, or business process automation.
Bachelor's degree in Business, Computer Science, Data Analytics, or a related field, or equivalent practical experience.
Flexible hours that may vary day to day based on customer needs and milestones.

Nice To Haves

Experience in manufacturing, supply chain, or enterprise SaaS environments.

Responsibilities

Teach customers best practices for realizing AI value, including workflow configuration and prompt engineering.
Help customers contextualize AI for their industry, data schemas, and domain terminology.
Capture customer friction points and communicate them to influence the product roadmap.
Partner with Solutions Engineers and Support to resolve complex technical issues and data anomalies.
Document successful implementation patterns and develop playbooks for the Customer Success organization.
Monitor regional, PS-led AI product implementations as go-live milestones approach and support CSMs and customers through technical onboarding.
Design and deliver post-go-live end-user training, tactical playbooks, office hours, and workflow integration sessions.
Review handoff briefs to understand customer-specific details, edge cases, and operational realities before taking ownership.
Identify and escalate launches that fall below baseline metrics.
Monitor AI adoption metrics and work with Agent Ops when stakeholders identify agent behavior issues.
Partner with Regional Customer Success Managers to maintain usage and relationship health across enterprise accounts.
Provide ongoing end-user guidance, workflow tips, and optimization support to promote long-term retention.
Capture customer feedback and usage signals for Product Operations and Engineering.
Coach customer executive sponsors and operational leaders on tracking AI-enabled capacity gains and reinvesting freed time into strategic projects.
Guide customer teams from manual, reactive work toward proactive, exception-based decision-making and strategic supplier or customer management.
Lead adoption recovery strategies when enablement challenges, low AI trust, or training gaps reduce adoption.
Work with Product and Engineering to identify adoption root causes using user feedback, performance scores, and usage data.
Deliver tailored training, workflow updates, and change-management initiatives, and measure outcomes at the 30-day checkpoint.