Twilio Report: Businesses Track Wrong Metrics for AI Success

Twilio has published a new report concluding that many businesses are measuring the success of their AI deployments using the wrong yardsticks — internal, operational metrics that don't align with what customers actually value. The research frames this disconnect as an "AI expectations gap": companies may consider an AI rollout successful by their own internal benchmarks, even as customers experience the same interactions as unsatisfying or unhelpful.

According to the coverage, the core finding is a mismatch between how organizations define and track AI performance internally and the outcomes that matter most to the people on the receiving end of AI-powered service. The report positions this gap as a structural measurement problem rather than a one-off technical failure, suggesting it affects how AI programs are evaluated, funded and iterated on across industries.

For leaders investing in AI-driven customer engagement, this is a governance and measurement problem as much as a technology one. An AI system can hit every internal KPI — deflection rate, cost per contact, response speed — and still fail the customer if those metrics don't capture whether the interaction actually resolved the person's need or left them feeling heard. When the yardstick is wrong, the roadmap built on it will be wrong too.

The report's implication for digital transformation leaders is that AI maturity isn't just about deploying better models or more automation; it's about redesigning how success is defined in the first place. Without customer-validated metrics built into AI programs from the start, organizations risk scaling systems that look efficient on a dashboard while quietly eroding trust and loyalty.

This is a familiar trap dressed up in new technology: optimizing for what's easy to measure instead of what actually matters to the customer. AI simply makes the gap more visible — and more consequential — because it operates at a scale and speed that can entrench a flawed metric long before anyone notices the damage.

The real failure here isn't the AI — it's that most organizations never asked customers what "success" should look like before they started measuring it. Operational metrics are proxies, not outcomes, and proxies drift from reality the moment no one's checking. Any team deploying AI in service or engagement needs a customer-defined success metric sitting alongside the operational ones, reviewed on the same cadence — otherwise you're optimizing a system to satisfy itself.