Principal, AI Strategy & Analytics

Key Responsibilities

• Engage with executive stakeholders and internal teams to align AI initiatives with measurable business objectives.
• Lead strategic advisory engagements focused on AI adoption, value realization, customer experience transformation, operational improvement, and outcome definition.
• Facilitate executive workshops, AI strategy sessions, value realization discussions, and outcome-focused planning activities.
• Translate customer business goals, operational constraints, and experience challenges into practical AI opportunity roadmaps.
• Prioritize AI use cases based on value potential, feasibility, readiness, data availability, and measurable impact.
• Develop clear narratives that connect AI capabilities to business outcomes, operational performance, customer experience improvement, and long-term value realization.

Outcome Intelligence and Analytics

• Develop KPI frameworks, value realization models, measurement strategies, benchmarking approaches, and business impact models.
• Analyze customer operational, journey, interaction, digital, routing, bot, and experience data to identify friction, constraints, and improvement opportunities.
• Create baseline measurement approaches that help customers understand current-state performance before AI initiatives are scaled or optimized.
• Support before-and-after measurement strategies that help determine whether AI initiatives are producing meaningful business change.

AI Strategy and Value Realization

• Evaluate customer readiness for AI adoption, including operational maturity, data availability, reporting capability, KPI clarity, governance, and organizational readiness.
• Develop phased AI transformation strategies that help customers move from experimentation to measurable impact.
• Identify opportunities to improve customer experience, operational efficiency, self-service effectiveness, routing performance, agent productivity, and adoption of AI-enabled capabilities.
• Provide thought leadership on outcome-led AI, value realization, AI readiness, customer experience optimization, and measurable transformation.

Methodology and Practice Development

• Contribute to the continued development of the AI Outcomes methodology, including outcome intelligence, applied AI, and outcome engineering frameworks.
• Create repeatable tools, accelerators, playbooks, KPI libraries, value models, assessment methods, and analytical templates that improve consistency and scalability.
• Develop practical guidance that helps internal teams frame customer conversations around measurable outcomes rather than standalone capabilities.
• Mentor consultants, strategists, delivery leaders, and customer-facing teams on outcome-driven AI strategy, KPI mapping, and value realization approaches.
• Help establish common language, reusable frameworks, and repeatable methods for defining, measuring, and optimizing AI outcomes.

Cross-Functional Partnership

• Partner with Professional Services, Customer Experience Advisory, Technical Account Managers, Customer Success leaders, and other internal teams to support complex customer scenarios.
• Support high-priority customer situations, including strategic opportunities, executive escalations, renewal support, adoption challenges, and complex transformation initiatives.
• Provide strategic and analytical support to help execution teams move forward with clearer goals, stronger measurement plans, and better prioritization.
• Represent the NACS AI Strategy Team as a subject matter expert in customer meetings, internal enablement, leadership discussions, and thought leadership activities.