AI Efficiency Calculator | AI Mindset
Research-Grounded Tool

AI Efficiency Calculator

Estimate how much time AI could realistically save you each week — anchored to peer-reviewed research and real-world studies, not vendor marketing.

How this works

  1. Select the tasks you do regularly. Each shows a research-backed potential saving — tap the source for details.
  2. Set your usage reality — how often you'll use AI, how senior you are, how much you need to check its output.
  3. Read three numbers — the upper bound (potential), your estimate (realistic), and what workers actually report on average (aggregate).
Step 01 · Your Role
Step 02 · Select Your Tasks
16 tasks · 0 selected
Step 03 · Your Usage Reality
Weekly working hours 40h

A typical full-time week is 37–45 hours.

% of week on these tasks i 30%

Unsure? Start at 30–40% for knowledge work.

Adoption rate i 60%

New rollouts: 40–60%. Mature teams: 70–85%.

Oversight / verification i 15%

Typical: 15–25%. Decreases as trust builds.

Experience level i Mid-level

Junior ← ← Mid-level → → Senior expert · Brynjolfsson, Li & Raymond (2025) found juniors saw larger gains.

Your estimated hours saved per week

Three baselines. Read all three — they tell different stories.

01 · Potential
0.0hrs/wk

Upper bound. Assumes full adoption, no oversight cost. From controlled studies of each task.

02 · Realistic
0.0hrs/wk

Your estimate. Applies your adoption, oversight, and experience level to the potential number.

03 · Measured Average
0.0hrs/wk

Reality check. What workers actually report on the Fed's national survey — about 5.4% of working hours.

Realistic hrs saved per day 0.0 hrs
Avg potential across selected tasks 0%

How to read these numbers

Select tasks and hit Calculate to see your result.

Estimates are illustrative, not forecasts. Task-level percentages come from peer-reviewed studies (sources below) but outcomes vary significantly by tool quality, task complexity, workflow design, and individual skill level.

Research sources & methodology

How the three baselines are calculated:

  • Potential — Weekly hours × % on selected tasks × average potential saving (from research).
  • Realistic — Potential × adoption × (1 − oversight) × experience factor.
  • Measured average — Weekly hours × 5.4% (Federal Reserve Bank of St. Louis, 2025).

Key studies referenced:

  • Noy & Zhang (2023, Science) — Writing tasks: 40% time reduction, 18% quality improvement in controlled experiment with 453 professionals.
  • GitHub / Peng et al. (2023) — Coding: 55.8% task completion speed-up in controlled study; confidence interval 21–89%.
  • Brynjolfsson, Li & Raymond (2025, QJE) — Customer support: 15% average productivity gain; juniors improved most, senior experts saw small or negative effects.
  • Bick, Blandin & Deming (2025, Federal Reserve Bank of St. Louis) — Aggregate measured productivity: workers saved 5.4% of working hours on average.
  • Microsoft (2024) — Copilot users saved ~11 minutes per day on average.
  • Dell'Acqua et al. (2023, HBS "Jagged Frontier") — Productivity gains vary sharply by task type; tasks inside AI's capability saw large gains, tasks outside saw quality decline.
  • Google / Gemini workplace study (2025, ACM DIS) — Meeting transcription and summarization saved participants "hours every week."

Caveats worth remembering: Self-reported gains (often 40%) consistently exceed measured gains (5–15%). Time saved doesn't always become more output — some becomes slack. And senior experts gain less than juniors on specialist tasks they already know well.

Calculated