// ai model performance

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Contact centers measure AI success by resolution quality, not call volume

The industry is abandoning throughput-focused metrics—calls handled per hour, abandonment rates—in favor of outcome-based measurement: first-contact resolution, customer satisfaction, and adherence to compliance frameworks. Enterprises discovered that AI agents closing more tickets faster often created downstream costs through poor resolutions, repeat calls, and regulatory exposure. The companies winning now are those building "governed execution" layers that force AI systems to follow approval workflows, escalation rules, and quality checkpoints rather than maximizing autonomous decisions.

AI's Math Breakthrough Reveals Why Creative Tasks Stay Hard

DeepSeek's o1 model shows strong performance on mathematical reasoning, but this progress hasn't extended to creative or strategic work where correctness is ambiguous. AI systems excel when optimizing toward a clear ground truth—like math or code—but falter when tasks require judgment, taste, or tradeoffs learned through lived experience rather than training data. Near-term AI productivity gains will concentrate in engineering, science, and coding. Industries betting on AI for strategy, marketing, or novel problem-solving will see diminishing returns for years.