The $700 Billion AI Productivity Problem No One's Talking About

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Dec 1, 2025

In this conversation, Russ Fradin, founder of Larridin and an early ad-tech pioneer, discusses the critical need for measurement infrastructure in AI adoption. He reveals how employees often hide their AI tool usage, while companies remain clueless about their value. Fradin highlights the challenges of measuring productivity, emphasizing that AI budgets may not lead to real results. He also explores employee anxiety and the role of training in successful adoption, advocating for data-driven metrics to enhance AI effectiveness in the workplace.

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Begin With A Usage Baseline

Start by asking a simple baseline: did anyone actually use the AI tools you bought?
Detect undisclosed or unmanaged AI use across the company before judging value.

Create A Safe, Trained Environment

Make employees feel safe to use AI so they won't hide usage or avoid tools.
Provide training and guardrails to prevent mistakes that could cost jobs or trigger fines.

Behavioral Data Plus Surveys Beats Self-Reports

Effective measurement marries behavioral usage data with survey-based productivity metrics.
Passive measurement plus targeted surveys reveal whether heavy tool users are truly more productive.

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