AI Agent Manager, Commercial

Responsibilities 1.

Use Case Discovery and Prioritisation: Identify and evaluate high value opportunities to deploy AI agents across the sales function, including lead qualification, outbound sequencing, proposal drafting, quote generation, and CRM data enrichment.

Prioritise based on effort, impact, and adoption readiness. 2.

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. 3.

Sales Productivity Improvement: Reduce manual effort in repetitive commercial workflows by embedding AI agents into daily sales activities.

Measure time saved, error reduction, and throughput improvements.

Ensure agents augment rather than replace human judgment in customer facing interactions. 4.

Adoption and Change Management: Drive bottom up adoption by working directly with sales 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. 5.

Data Quality and Integration: Ensure AI agents operate on clean, reliable data by working with revenue operations and data teams.

Define data requirements, build integration points with CRM, ERP, and communication tools, and establish feedback loops to improve agent accuracy over time. 6.

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

Report outcomes to commercial leadership and use evidence to inform scaling decisions and investment cases. 7.

Cross Functional Collaboration: Partner with Product, Engineering, and IT to align agent deployments with platform capabilities, security requirements, and data governance standards.

Ensure agents are built with