AI Agent Manager, Operations

Responsibilities Use Case Discovery and Prioritisation: Identify and evaluate high value opportunities to deploy AI agents across operations, including document processing, data extraction, workflow routing, exception handling, scheduling, and compliance checks.

Prioritise based on effort, impact, and adoption readiness.

Agent Design, Build, and Deployment: Design, configure, and deploy AI agents and automated workflows using LLMs, APIs, and integration tools.

Own the end to end lifecycle from prototype through production deployment, including testing, monitoring, and iteration.

Operational Efficiency Improvement: Reduce manual effort in repetitive operational workflows by embedding AI agents into daily processes.

Measure time saved, error reduction, and throughput improvements.

Ensure agents maintain accuracy and auditability standards required by operations.

Adoption and Change Management: Drive bottom up adoption by working directly with operations teams and individual contributors.

Provide hands on training, create playbooks, run workshops, and build internal champions.

Track adoption metrics and iterate based on user feedback Data Quality and Integration: Ensure AI agents operate on clean, reliable data by working with data teams and system owners.

Define data requirements, build integration points with operational systems, ERP, and document management platforms, and establish feedback loops to improve agent accuracy over time.

Performance Measurement and Reporting: Define and track KPIs for each deployed agent including adoption rates, processing accuracy, throughput gains, exception rates, and ROI.

Report outcomes to operations leadership and use evidence to inform scaling decisions and investment cases.

Cross Functional Collaboration: Partner with Product, Engineering