AI Operations Manager

Role SummaryResponsible for equipping and enabling the GTM organization to adopt AI-native tools and workflows to do better work, including partnering with operational teams to evolve processes as AI capabilities mature. Scope includes working directly with field teams to drive adoption and translate user needs into product improvements, building and improving tools, and ensuring the quality and trustworthiness of AI-generated outputs.Key ResponsibilitiesAI Tool Development & ImprovementBuild and improve AI-powered tools and workflows for GTM users across the organizationPrototype new capabilities, validate against real field use cases, and iterate based on user feedbackEvaluate and integrate data sources with a focus on depth, accuracy, and regional coverageField Enablement & AdoptionWork directly with field teams across regions to drive adoption of AI-native tools and workflowsEngage with teams on-site where needed to gather feedback and understand local workflows and requirementsIdentify and remove adoption blockers, translating field needs into actionable inputs for the engineering teamData Quality & Output TrustBuild mechanisms to improve the accuracy and currency of AI-generated outputs across organizational data sourcesBuild and iterate on quality mechanisms that surface stale, conflicting, or unreliable information before it reaches sellersEstablish guardrails that enable agents to take actions safely and autonomously, recommending where human oversight is needed and where automated workflows can run independentlyCross-Functional CoordinationSurface field patterns, adoption signals, and emerging use cases to inform platform prioritiesSupport rollout execution by identifying regional requirements and adoption readiness across field teamsIdentify where existing operational processes create friction for AI tool adoption and work with operational stakeholders to address themRequirementsExperience in sales operations, enablement, or GTM tool developmentDeep understanding of GTM sales organization structure, workflows, and operational processes across sales segments and regionsDemonstrated ability to build and ship working tools and prototypesStrong understanding of CRM systems, data quality challenges, and how organizational data flows between systemsStrong analytical skills with the ability to assess output quality and identify gaps across data sources and workflowsExperience working with technical engineering teams and translating between field requirements and technical implementationComfort operating in fast-paced environments with evolving tools and prioritiesSuccess MetricsOperational evolution - Partner with operational teams to identify and transition workflows to AI-native approaches. Track process improvements and their impact on team productivity.Field adoption and enablement - Drive adoption activities that remove barriers to field usage across regions. Reduce time-to-value for new users by removing adoption blockers and building onramps for non-technical teams.Field feedback loop - Maintain a structured intake of field issues, usage patterns, and emerging requirements. Ensure insights are triaged and routed to inform product priorities promptly.Agent output quality - Improve the trustworthiness and accuracy of AI tool outputs by building and iterating on quality mechanisms that prevent stale, conflicting, or unreliable data from reaching end users.Platform contributions - Regular hands-on contributions to the tools and content the team ships, informed by field needs and quality findings.