AI agents, optimized for task completion, lack the implicit understanding of security protocols that humans possess. This focus on outcomes can lead them to make mistakes like exposing code or sensitive internal data, creating a new class of insider risk.

In regulated industries, AI's value isn't perfect breach detection but efficiently filtering millions of calls to identify a small, ambiguous subset needing human review. This shifts the goal from flawless accuracy to dramatically improving the efficiency and focus of human compliance officers.

Unlike scripted bots, agentic AI can hallucinate information, effectively creating new business policies (like a refund scheme) or causing compliance breaches (like divulging PII). This risk extends far beyond customer satisfaction and into legal and financial jeopardy.

With infinitely scalable AI agents, cost and time per interaction are no longer primary constraints. Companies should abandon classic efficiency metrics like Average Handle Time and instead measure success by outcomes, such as percentage of tasks completed and improvements in Customer Satisfaction (CSAT).

An agent's reasoning failure won't trigger traditional alerts. Metrics like error rate and latency will appear healthy because the agent produces valid, well-formed, but semantically incorrect responses. This creates a critical monitoring blind spot where the infrastructure is fine, but the agent's logic is broken.

According to Goodhart's Law, when a measure becomes a target, it ceases to be a good measure. If you incentivize employees on AI-driven metrics like 'emails sent,' they will optimize for the number, not quality, corrupting the data and giving false signals of productivity.

With AI empowering agents, traditional efficiency KPIs like 'average handle time' are losing relevance. Modern CX teams should prioritize effectiveness metrics such as 'resolution quality,' 'customer effort,' and 'first-call resolution,' which better correlate with brand trust and loyalty.

Twilio's usage-based pricing seems resilient, but it faces a unique AI risk. If AI makes customer service calls more efficient and shorter, it could decrease total platform usage and therefore revenue. This 'efficiency paradox' is an under-discussed vulnerability for consumption-based business models in the AI era.

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.