Use AI to prepare, transform and check information in frequent tasks. Keep humans on ambiguous, sensitive, relational or hard-to-reverse decisions. Measure productivity at final output, including corrections.
AI productivity: 25 tasks to speed up without losing quality
A framework to decide what to assist, automate, review or keep fully human. Reliable results require connecting technology to a workflow, data, an owner and a measure. The following principles structure that decision.
01 — Prepare: plans, questions, research, agendas, summaries and scenarios
Prepare: plans, questions, research, agendas, summaries and scenarios.
02 — Transform: translate, reformat, classify, extract, adapt and compare
Transform: translate, reformat, classify, extract, adapt and compare.
03 — Produce: first drafts, variants, notes, tables and documentation
Produce: first drafts, variants, notes, tables and documentation.
04 — Review: inconsistencies, missing elements, checklist compliance and source comparison
Review: inconsistencies, missing elements, checklist compliance and source comparison.
05 — Decide with humans: trade-offs, negotiation, ethical judgment, commitments and sensitive communication
Decide with humans: trade-offs, negotiation, ethical judgment, commitments and sensitive communication.
Action plan
Use this sequence as a starting point. Each step should produce a decision or verifiable output before the next.
- Keep task log
- Rate frequency and pain
- Classify risk and reversibility
- Test a method
- Measure with corrections
- Standardize what works
Mistakes to avoid
- Counting generation time without correction
- Automating an infrequent task
- Delegating a sensitive decision to the model
Frequently asked questions
Look for frequency, volume, stable rules, verifiable output and acceptable error cost.
Time several cases before and after, including preparation, waiting, correction and exceptions.
Key takeaway
Use AI to prepare, transform and check information in frequent tasks. Keep humans on ambiguous, sensitive, relational or hard-to-reverse decisions. Measure productivity at final output, including corrections.
The important point is to progress through evidence: a precise use case, representative test, documented limits and an outcome-based decision.