[Hiring] Sr. AI Customer Success Manager @QAD, Inc.

Role 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 rapidly achieve value, build trust, and drive continuous usage.

You will act as a trusted advisor to help clients activate, integrate, and maximize value from our AI platform. The ideal candidate possesses a unique blend of 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

Core Responsibilities

• 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: Roll out tailored training, workflow updates, and change management initiatives, measuring success at the 30-day checkpoint.

Qualifications

• Manufacturing & Operational Domain Acumen: Solid understanding of strategic sourcing, Sales Ops, supply chain risk management, inventory optimization, and shop-floor/back-office productivity levers.
• AI Literacy: Solid understanding of artificial intelligence concepts, large language models (LLMs), or data analytics infrastructure.
• Data Fluency: Ability to interpret data trends, run basic data queries (e.g., SQL, Excel), and build clear reporting frameworks.
• Product Adaptation: Highly capable of mastering a rapidly evolving, technical product ecosystem.
• Knowledge of CRM Software & CS Platform.

Requirements

• Professional History: 5+ years in customer-facing or operational roles (e.g., Customer Success, Technical Account Management, Management Consulting, or Operations), ideally within manufacturing, supply chain, or enterprise SaaS environments.
• Domain Exposure: Practical experience with manufacturing/supply chain workflows (e.g., sourcing, AP/procurement, inventory management, Sales Ops) alongside exposure to enterprise AI/ML platforms, developer tools, or business process automation.
• Education: Bachelor's degree in Business, Computer Science, Data Analytics, or a related field (or equivalent practical experience).

Availability and Travel

• Flexible hours that may vary day to day, based on customer needs and milestones.
• Up to 25% domestic travel, for customer visits, industry conferences and QAD meetings.
• Occasional international travel.
• The role is remote located in the UK/ US.

Benefits

• Compensation: GBP 65,000 - GBP 75,000 yearly.