Sr. AI Customer Success Manager

QAD•Atlanta, GA

2d•Remote

About The Position

The AI Customer Success Manager is an overlay enablement and adoption strategist supporting regional Customer Success teams. As AI-powered products mature into General Availability and sustained operations, the role helps customers achieve value, build trust, and maintain continuous usage. The manager serves as a trusted advisor, helping customers activate, integrate, and maximize value from the AI platform. Success is measured by AI product adoption, AI-specific Net Revenue Retention, and AI customer referenceability. The position supports AI product onboarding and adoption, ongoing customer operations, and strategic value attribution. It includes coaching customers on AI workflows and prompt engineering; contextualizing AI to customer industries, data schemas, and terminology; coordinating technical escalations and customer feedback; and developing playbooks to share implementation best practices. The role also supports AI product launches, post-go-live training, customer office hours, workflow integration, and early intervention when adoption is below baseline. In established accounts, the manager monitors AI adoption metrics and partners with Customer Success Managers and Agent Operations to address agent behavior or usage issues. The role also coaches customer leaders on measuring capacity freed by AI and reinvesting that capacity in strategic work, guides teams toward proactive, exception-based practices, and leads adoption recovery initiatives when trust or training challenges reduce usage. QAD is a virtual-first company, and the primary work experience is virtual from home. The role is remote with up to 25% regional travel; occasional travel to a physical office may be required.

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 strategic sourcing, Sales Operations, supply chain risk management, inventory optimization, and shop-floor or back-office productivity levers.
Solid understanding of artificial intelligence 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 to navigate shifting requirements in a fast-paced, high-growth startup environment.
Ability to build customer trust and empathetically de-escalate issues.
Bachelor's degree in Business, Computer Science, Data Analytics, or a related field, or equivalent practical experience.

Nice To Haves

Experience in manufacturing, supply chain, or enterprise SaaS environments.
Practical experience with manufacturing or supply chain workflows such as sourcing, accounts payable or procurement, inventory management, or Sales Operations.
Exposure to enterprise AI/ML platforms, developer tools, or business process automation.

Responsibilities

Deliver customer coaching on AI value realization, workflow configuration, and effective prompt engineering.
Help customers configure AI to reflect 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 professional services-led AI 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, customer office hours, and workflow integration sessions.
Review handoff briefs to understand customer-specific nuances, edge cases, and operating realities before taking operational ownership.
Identify and escalate launches that fall below baseline metrics to reduce the risk of sustained adoption failure.
Develop expertise in AI product metrics and work with Agent Operations to address issues with agent behavior.
Partner with regional CSMs to maintain continuous usage and relationship health across enterprise accounts.
Provide ongoing end-user guidance, usage tips, and workflow optimization.
Capture ongoing customer feedback and usage signals for Product Operations and Engineering.
Coach customer executive sponsors and operational leaders to track capacity freed by AI agents and establish workflows to reinvest that time in 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 issues, low AI trust, or training gaps cause adoption to decline.
Work with Product and Engineering to analyze feedback, performance scores, and usage data to identify causes of reduced adoption.
Roll out tailored training, workflow updates, and change-management initiatives, and measure results at the 30-day checkpoint.