Instead of measuring how many questions a chatbot answered, the bank tracked the reduction in support tickets for the corresponding human team, which dropped 60%. This focus on tangible business outcomes provides a much clearer picture of AI's real value than simple activity metrics.

Measuring AI success requires new metrics. Instead of tracking active usage (e.g., number of meeting summaries), Zoom focuses on deeper engagement, measured by a user's progression from consuming AI output to actively using it to produce valuable new work product like a document or presentation.

To evaluate AI's role in building relationships, marketers must look beyond transactional KPIs. Leading indicators of success include sustained engagement, customers volunteering more information, and recommending the experience to others. These metrics quantify brand trust and empathy—proving the brand is earning belief, not just attention.

When launching internal AI tools, don't fixate on immediate ROI, which is a lagging indicator. Instead, monitor user adoption rates. A rapid increase in adoption is the strongest signal that a tool is genuinely solving a problem and that positive business outcomes will eventually follow.

While AI tools dramatically increase content production speed, true ROI is not measured in output. Leaders should track incremental engagement, conversion lift, and revenue per message. An often overlooked KPI is brand consistency—how often content passes governance checks on the first try.

Open and click rates are ineffective for measuring AI-driven, two-way conversations. Instead, leaders should adopt new KPIs: outcome metrics (e.g., meetings booked), conversational quality (tracking an agent's 'I don't know' rate to measure trust), and, ultimately, customer lifetime value.

To set realistic success metrics for new AI tools, Descript used its most popular pre-AI feature, "remove filler words," as the baseline. They compared adoption and retention of new AI features against this known winner, providing a clear, internal benchmark for what "good" looks like instead of guessing at targets.

A common failure is defining an AI pilot's success with engineering metrics like accuracy or latency. True success is a business outcome, such as the finance team trusting the AI's output enough to stop manually double-checking it. Success metrics must be framed in terms a CFO would accept.

Instead of fixating on lagging indicators like money saved, track leading indicators that signal behavioral shifts. For example, asking teams to rate their meeting preparedness on a 1-10 scale measures the effectiveness of AI-driven prep and predicts future performance gains.