Federal Reserve — Working Paper 2025-053 on AI and Work

TL;DR

• Formal research into AI and labor/productivity dynamics.
• Useful for strategy and policy context around adoption impacts.
• SMBs can apply findings to role design, training, and measurement.

📊 Highlights

• Contributes to understanding the relationship between AI task coverage, productivity, and workforce dynamics.
• Suggests heterogeneity in impacts across roles and sectors; measurement and training matter.

🗣 Case study anecdote

A specialty manufacturer cataloged repetitive documentation tasks and piloted AI for draft creation and compliance checks. With training and QA, documentation time fell and engineers focused more on design iterations.

🛠 Guidance for SMBs

• Pair pilots with role clarity: list tasks to assist and tasks to keep human‑led. Train against both.
• Measure over time: throughput, error rates, rework, and satisfaction (customer/staff).
• Update job descriptions and review prompts quarterly as workflows evolve.

📈 Lessons & metrics

• Assisted documentation/reporting reduces time and variance.
• Training and QA are essential to sustain quality.

🔗 Learn more

Read the full article: Federal Reserve — Working Paper 2025-053

Looking for help to implement similar results? See our services.