Description
AgencyBloc is a leading provider of agency management solutions built specifically for life and health insurance agencies. Our platform helps agencies streamline operations, strengthen client relationships, and drive growth through powerful CRM, commission processing, and marketing automation tools. We value innovation, collaboration, and making a meaningful impact in the industries we serve and we take pride in fostering a culture that genuinely cares about our customers, our work, and each other.
Requirements
Summary:
We are seeking a results-driven leader to establish and head our new Data and Business Operations organization, dedicated to the enterprise-wide operationalization of Artificial Intelligence (AI), business process transformation, and data-driven decision making. This pivotal role will serve as the senior business leader responsible for operational AI adoption and business transformation, translating high-level corporate strategy into tangible business value through aggressive workflow re-engineering, operational excellence, and alignment of business processes and operational workflows to the enterprise data strategy.
This is a highly technical, hands-on leadership role focused on building and iterating quickly to drive rapid development and measurable operational outcomes at scale. The ideal candidate must be a "hands-on, builder-doer" who can personally build prototypes and configure workflows, bridging the gap between advanced technology and commercial execution, turning AI from a strategic concept into a core operational competitive advantage.
Responsibilities:
AI Operationalization, Data Strategy & Workflow Implementation
- Drive Hands-On Development: Define the operational blueprint, data readiness standards, and execution roadmap for AI adoption across all core business functions (including Customer Success, Support, Sales, Marketing, and Shared Services). This includes the expectation to personally build and iterate on prototypes, configure new AI-driven workflows, and drive rapid development.
- Drive Enterprise Data Integration: Oversee the convergence of internal data silos (Data Ops, RevOps, Customer Data) to define business data requirements, data quality expectations, and operational priorities for enterprise reporting, analytics, and AI initiatives in partnership with the Technology organization.
- Execution, Tooling & Scale: Lead the vetting, procurement, deployment, and ongoing utilization of business-specific AI tooling and cognitive agents to guarantee high-quality functional execution, cost containment, and organizational scale.
- Strategic Barrier Removal: Continuously partner with senior leadership to identify, prioritize, and remove macro-level organizational, technological, and data-quality barriers to enable enterprise-wide AI transformation.
- Outcome & ROI Ownership: Hold full accountability for delivering clear, measurable operational outcomes with an offensive growth mindset, focused on maximizing growth and efficiency across the entire business. This includes increasing customer retention (e.g., churn reduction), improving GTM enablement and pipeline velocity (e.g., doubling sales capacity), and driving enterprise-wide capacity/time-to-value acceleration through AI-driven operational excellence.
- Establish the AI Center of Excellence (CoE): Establish a Business AI Center of Excellence focused on operational automation, adoption, workflow redesign, change management, training, and operational productivity. Identify systemic gaps in existing legacy workflows and resolve them through strategic process optimization, agentic workflows, and predictive data utilization. This role will drive the initial build and configuration of the operational AI application layer in close partnership with the Technology organization, which holds the ultimate decision authority on tool selection, foundational architecture, and technical standards.
- Establish Data Stewardship: Establish business ownership and stewardship models for key enterprise data domains, including customer, revenue, support, and operational data.
Team Leadership and Cross-Functional Alignment
- Team Establishment & Integration: Hire, structure, and lead the unified Data and Business Operations team, successfully integrating existing fragmented functions such as Revenue Operations (RevOps).
- Product & Technology Partnership: Serve as the primary business liaison between commercial, operational, Product, and Technology organizations to drive enterprise AI adoption and business transformation initiatives. Partner closely with Product and Technology leadership to prioritize opportunities, define business requirements, and ensure successful organizational adoption. This partnership includes advising on architecture, defining business requirements for the application layer, and driving immediate operational implementation, all while working within the framework and technical constraints set by the Technology organization, which retains accountability for data engineering and base infrastructure.
- Strategic Portfolio Management: Manage the overall portfolio of internal AI operationalization projects, driving cross-departmental buy-in, establishing clear governance, and leading organizational change management.
- Skills and QualificationsExperience & Leadership
- Senior Leadership Experience: 10+ years of senior leadership experience in Business Operations, Revenue Operations, Data Strategy, or Management Consulting, with a proven track record within a high-growth, private equity-backed SaaS or enterprise technology environment.
- Scale & Transformation: Demonstrated success designing, implementing, and optimizing large-scale enterprise operating systems (e.g., Salesforce, modern data warehouses like Snowflake/BigQuery, BI tools) and leading an organization through a fundamental technological pivot.
- Financial and Metrics Acumen: Highly analytical mind with experience defining, tracking, and holding accountability for EBITDA-impacting metrics, operational efficiency ratios, and ROI benchmarks directly tied to automation.
- AI & Data Specific Expertise
- Practical AI & Tool Mastery: Demonstrated practical tool mastery of the modern AI landscape and its application within enterprise operations. This includes explicit experience with modern AI orchestrators, agent builders (e.g., Cassidy, MindStudio), and LLM frameworks (e.g., LangChain). Ability to identify high-value business use cases, distinguish practical applications from market hype, and drive measurable operational outcomes through responsible AI adoption.
- Enterprise AI Adoption & Change Leadership: Demonstrated experience leading enterprise adoption of AI-enabled tools and capabilities across business functions. Proven ability to develop organizational readiness plans, establish usage standards and best practices, drive training and change management programs, and measure adoption, productivity improvements, and business outcomes. Experience partnering with Technology and IT organizations to ensure the successful rollout of AI capabilities while fostering sustainable employee engagement and operational transformation.
- Data Strategy, Stewardship & Governance Literacy: Strong understanding of enterprise data strategy, data governance principles, business intelligence, master data management, and data quality management. Ability to establish data ownership, stewardship practices, KPI governance, and business data standards that enable trusted analytics and AI adoption. Experience assessing organizational data readiness and partnering with data architecture and engineering teams to ensure enterprise data assets are accurate, accessible, secure, and fit for purpose.
Skills/Education/Experience:
- Senior Leadership Experience: 10+ years of senior leadership experience in Business Operations, Revenue Operations, Data Strategy, or Management Consulting, with a proven track record within a high-growth, private equity-backed SaaS or enterprise technology environment.
- Scale & Transformation: Demonstrated success designing, implementing, and optimizing large-scale enterprise operating systems (e.g., Salesforce, modern data warehouses like Snowflake/BigQuery, BI tools) and leading an organization through a fundamental technological pivot.
- Financial and Metrics Acumen: Highly analytical mind with experience defining, tracking, and holding accountability for EBITDA-impacting metrics, operational efficiency ratios, and ROI benchmarks directly tied to automation.
AI & Data Specific Expertise:
- Practical AI & Tool Mastery: Demonstrated practical tool mastery of the modern AI landscape and its application within enterprise operations. This includes explicit experience with modern AI orchestrators, agent builders (e.g., Cassidy, MindStudio), and LLM frameworks (e.g., LangChain). Ability to identify high-value business use cases, distinguish practical applications from market hype, and drive measurable operational outcomes through responsible AI adoption.
- Enterprise AI Adoption & Change Leadership: Demonstrated experience leading enterprise adoption of AI-enabled tools and capabilities across business functions. Proven ability to develop organizational readiness plans, establish usage standards and best practices, drive training and change management programs, and measure adoption, productivity improvements, and business outcomes. Experience partnering with Technology and IT organizations to ensure the successful rollout of AI capabilities while fostering sustainable employee engagement and operational transformation.
- Data Strategy, Stewardship & Governance Literacy: Strong understanding of enterprise data strategy, data governance principles, business intelligence, master data management, and data quality management. Ability to establish data ownership, stewardship practices, KPI governance, and business data standards that enable trusted analytics and AI adoption. Experience assessing organizational data readiness and partnering with data architecture.
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
