AI Agent Manager, Commercial

Division: Shared Services The AI Agent Manager, Commercial is responsible for identifying, designing, building, and deploying AI and automation agents across the sales and revenue operations function. This is a hands on, senior individual contributor role focused on driving bottom up adoption of AI powered workflows within commercial teams. The role exists to accelerate sales productivity, improve pipeline quality, reduce manual effort in quoting, proposal generation, and customer communications, and embed a culture of AI augmented working across the commercial organisation. The AI Agent Manager works closely with sales leaders, revenue operations, and product teams to identify high value use cases, prototype solutions, measure adoption, and scale what works. 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 within approved frameworks and comply with company policies. 8. Experimentation and Innovation: Stay current with emerging AI capabilities, LLM developments, and automation tools. Run structured experiments to test new approaches and share learnings across the organisation to build collective AI fluency. 9. Documentation and Knowledge Management: Maintain clear documentation for all deployed agents including design rationale, configuration, dependencies, known limitations, and escalation paths. Build a reusable library of agent patterns and templates. 10. Vendor and Tool Evaluation: Evaluate and recommend AI tools, platforms, and vendors relevant to commercial use cases. Provide informed build versus buy recommendations within group guardrails. Requirements: Qualifications and Education Include any specialised education and minimum qualifications needed to be able to perform the job. Bachelor degree in Business, Computer Science, Data Science, or related discipline. Certifications in AI, machine learning, or automation platforms are a plus. Work Experience · 3 to 5 years experience in sales operations, revenue operations, or commercial process improvement roles. Demonstrated experience deploying AI tools, LLM based agents, or workflow automation in a B2B sales environment. Experience with CRM systems, sales enablement tools, and data pipelines. Track record of driving adoption of new tools and processes across commercial teams. Skills and Knowledge · Strong understanding of LLMs, prompt engineering, and AI agent frameworks. Practical experience with automation platforms and API integrations. Sales process knowledge including pipeline management, quoting, proposal generation, and customer communications. Data analysis and KPI design skills. Change management ability to drive adoption bottom up without formal authority. Strong communication skills to translate technical concepts for commercial audiences. Claude Enterprise and Cowork Skills · Proficiency in Anthropic’s Claude platform including Claude for Enterprise and Claude Teams administration, user provisioning, workspace configuration, and usage policy management. · Hands on experience with Claude Cowork mode for desktop automation, including skill cre

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 within approved frameworks and comply with company policies. 8. Experimentation and Innovation: Stay current with emerging AI capabilities, LLM developments, and automation tools. Run structured experiments to test new approaches and share learnings across the organisation to build collective AI fluency. 9. Documentation and Knowledge Management: Maintain clear documentation for all deployed agents including design rationale, configuration, dependencies, known limitations, and escalation paths. Build a reusable library of agent patterns and templates. 10. Vendor and Tool Evaluation: Evaluate and recommend AI tools, platforms, and vendors relevant to commercial use cases. Provide informed build versus buy recommendations within group guardrails.