Company Description

QAD is building a world-class SaaS company, and we are growing. We are looking for talented individuals who want to join us on our mission to help solve relevant real-world problems in manufacturing and the supply chain. We are a virtual first company and your primary work experience will be virtual / working from your home. Occasional travel to a physical office may be required to enhance working relationships, collaboration, design, strategy and alignment.

Job Description

The AI Customer Success Manager operates as an overlay enablement and adoption strategist across regional Customer Success (CS) teams. As AI-powered products mature into General Availability (GA) and sustained operations, this role ensures customers quickly realize value, build trust, and drive ongoing usage. You will serve as a trusted advisor, helping clients activate, integrate, and maximize value from our AI platform. The ideal candidate blends technical curiosity, consultative skills, and data literacy. Your primary metrics of success will be AI product adoption, AI-specific Net Revenue Retention (NRR), and AI customer referenceability. Location: Remote with up to 25% Regional Travel AI Adoption & Enablement - AI Coaching: Deliver human enablement, teaching users best practices for AI value realization, including workflow configuration and effective prompt engineering. - Data Contextualization: Assist clients in configuring the AI to properly reflect their specific industry, data schemas, and domain terminology. Cross-Functional Collaboration - Product Feedback: Act as the voice of the customer, capturing user friction points to directly influence the product roadmap. - Technical Escalations: Partner with Solutions Engineers and Support to quickly resolve complex technical issues or data anomalies. Playbook Development - Document successful implementation patterns to scale best practices across the CS organization. New Product Launch and Customer Onboarding - Customer Onboarding: Monitor PS-led implementation projects for AI-powered products across the region as go-live milestones approach. - Support CSMs and customers through technical onboarding processes to minimize friction points at this critical stage. - Customer Enablement: Post Go-Live, design and deliver end-user training, tactical playbooks, customer office hours, and workflow integration sessions for regional CS teams and end-users. - Phase Handoff to CS: Consume handoff briefs to understand customer nuances, edge cases, and field reality before taking operational ownership. - Early Escalation: Identify and escalate launches falling below baseline metrics to prevent sustained adoption failure. Sustained AI Operations - Monitor Adoption Metrics: Develop deep expertise in available AI product metrics. Work with Agent Ops if the customer, the CSM and/or the Agent Ops team identifies issues with agent behavior. - Ongoing Relationship & Usage Health: Partner with Regional Customer Success Managers (CSMs) to maintain continuous usage and relationship health across established enterprise accounts. - In-Product & Operational Enablement: Continuously provide end-user guidance, tips, and workflow optimization to ensure long-term retention. - Field Signal Feedback: Capture ongoing customer feedback and usage signals, feeding them back to Product Operations and Engr to continuously improve the core platform. Strategic Value Attribution - Capacity Reallocation & Organizational Change - Capacity Reinvestment Coaching: Partner with customer executive sponsors and operational leaders to track capacity freed by AI agents (e.g., 10,000+ hours annually across buying and AP teams) and establish structured workflows for reinvesting time into strategic projects. - Behavioral & Process Mindset Shift: Guide customer teams away from manual, reactive tasks (e.g., chasing past-due POs, re-keying ERP data, manual quote entry) toward proactive, exception-based decision making and strategic supplier/customer management. - Adoption Rescue Playbook Ownership: Lead the recovery strategy when adoption drops driven by enablement challenges like low AI trust or training gaps. - Identify Root Causes: Work with Product and Engineering to analyze user feedback, performance scores, and usage data to figure out why adoption dropped. - Execution & Measurement: