Define success metrics for AI sales assistant serving educators

Problem Statement Description

You are evaluating an AI sales assistant designed for educators and education decision-makers who are exploring, purchasing, or enabling accessibility-related products and features. The assistant may help users understand accessibility capabilities, match needs to solutions, answer procurement or implementation questions, and guide next steps for adoption in classrooms, schools, or districts.

The primary business goal is to increase accessibility adoption, not simply to drive more conversations or demos. Your task is to define a metrics framework that can determine whether the AI assistant is helping the right educators make informed accessibility decisions, move through the sales or enablement journey, and successfully adopt accessible experiences for students and staff.

Because this is an AI-powered assistant in an education context, success must balance conversion, quality of guidance, trust, responsible AI behavior, and equitable access. The metrics should be useful for product, sales, customer success, and accessibility teams when deciding whether to invest further, iterate, or limit rollout.

The experience should consider:

- Clear definitions of “accessibility adoption,” including the relevant denominator and adoption unit, such as educator, classroom, school, district, account, or feature.

- Funnel metrics across discovery, engagement, qualification, recommendation, handoff, purchase, implementation, and sustained use.

- Instrumentation needed to connect AI assistant interactions to downstream adoption outcomes without over-attributing impact.

- Cohorts such as new vs. existing customers, K-12 vs. higher education, accessibility maturity level, region, institution size, and user role.

- Quality signals for AI responses, including relevance, accuracy, accessibility-specific helpfulness, and educator trust.

- Guardrail metrics for hallucinations, misleading claims, privacy concerns, sales pressure, bias, and poor handoffs to humans.

- Decision usefulness: how the metrics would indicate whether to scale, retrain, redesign, or constrain the assistant.

The goal is to propose a rigorous measurement approach that shows whether the AI sales assistant is meaningfully increasing accessible product adoption among educators while maintaining trust, safety, and long-term customer value.