• Analyze funnel performance, conversion trends, pipeline movement, campaign effectiveness, and lead flow.
• Use data & reports from various tools such as CRM & Marketing Automation Platform, spreadsheets, BI tools, and structured analysis to identify gaps, inefficiencies, risks, and optimization opportunities.
• Support forecasting, pipeline initiatives, attribution, pipeline inspection, revenue reporting, risk, retention, expansion, and business health initiatives through strong operational and systems-based thinking.
• Define how these signals should translate into action.
• Partner with Sales, Marketing, Customer Success, and RevOps stakeholders to determine which workflows should remain human-led, which should be AI-assisted, and which are appropriate for automation.
• Partner closely with Sales Enablement to ensure AI is incorporated into training Sales.
• Translate reporting and analytical findings into workflow improvements, automation opportunities, and data requirements.
• Automate repeatable revenue operations tasks such as lead routing, enrichment, follow-up prompts, campaign triggers, pipeline hygiene, task creation, operational alerts, and data quality checks.
• Build internal copilots, agents, workflow assistants, and AI-assisted tools that help Sales and Marketing teams act more quickly, consistently, and effectively by listening for signals, recommend actions, trigger workflows, support engagement, and assisting with pipeline movement.
• Test, build, and scale the business logic, signal models, operating rules, & success criteria for AI-enabled revenue workflows that help summarize account activity, identify next-best actions, prioritize leads or opportunities, surface pipeline risks, and improve follow-up consistency.
• Establish feedback loops to improve workflow logic, agent behavior, recommendation quality, and business impact over time.
• Partner with Technology on LLM usage, agent patterns, integration approaches, data boundaries, monitoring, and production deployment where workflows require cross-system or enterprise-grade implementation.
• Work with Technology, Data, Security, Sales, Marketing, and RevOps stakeholders to align automation efforts with company standards for data access, privacy, security, auditability, and system reliability.
• Use existing data infrastructure where available and identify data gaps that limit revenue visibility, automation, or AI-driven decisioning.
• Partner with technical teams on APIs, integrations, orchestration patterns, monitoring, and scalable deployment where workflows cross system boundaries.
• Continuously test, iterate, and improve workflow logic, messaging, routing, prioritization, automation rules, and AI-assisted recommendations.
• Build practical reporting that shows whether automation and AI-assisted workflows are improving revenue operations performance.
• Use findings from pilots and production workflows to refine future automation priorities and inform the broader revenue automation roadmap.
