Federal Reserve — Measuring AI Uptake in the Workplace

TL;DR

• Measurement frameworks are essential to understand AI adoption and impact.
• Indicators include usage intensity, task coverage, and outcome measures.
• SMBs should tie usage metrics to business KPIs to validate ROI.

Highlights

• Surveys approaches to quantifying AI uptake in workplaces and the limits of pure “usage” metrics.
• Emphasizes pairing usage indicators with outcomes (cycle time, error rate, revenue impact) to avoid vanity measures.
• Provides a foundation to design lightweight measurement plans in SMBs.
• Encourages task‑level thinking—measuring coverage of tasks rather than blanket role changes.

Case study anecdote

A boutique agency tracked AI‑assisted drafts per week, edit time, and campaign results. Over a month, draft throughput rose while edit time fell; CPA improved as testing velocity increased.

Guidance for SMBs

• Combine two metric sets: (1) usage (assisted outputs, tasks covered), and (2) outcomes (response time, conversion, error rate).
• Build a one‑page measurement plan; report results weekly. Adjust prompts/SOPs where outcomes lag.
• Avoid over‑instrumentation—keep it simple and comparable week to week.
• Add a short “definitions” section so everyone interprets KPIs consistently.

Lessons & metrics

• Usage without outcomes is misleading; link activity to impact.
• Lightweight measurement sustains momentum and clarifies where to invest next.
• KPI stability (clear definitions, consistent cadence) is necessary for trustworthy decisions.

Learn more

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