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In plain terms: Trigger → Rule → Action → Confirmation. Less friction, fewer errors, more time to think.
What it is (practically)

Detect something happens (payment, form, sensor, email).

Apply clear rules or models (validate, transform, classify).

Deliver a verifiable result (create a ticket, send a report, update a database).

Confirm it happened and how (log, alert, trace).

Why it’s the future of efficiency

It turns repeatable work into compounded capacity: each good flow makes the next easier. And with AI, we don’t just automate rules—we automate bounded judgment: classify emails, summarize tickets, prioritize incidents—with limits and proof.
Three tests before you automate

Repeat: happens at least daily or weekly.

Rule: can be described in steps or examples.

Return: saves time, cuts errors, or speeds delivery. If it fails any of these, skip it.

Five principles so it doesn’t break

Start small: one flow, one owner, one goal.

Idempotence: re-runs don’t duplicate or damage.

Observability: logs, metrics, alerts; what you can’t see, you can’t control.

Data contract: fields, formats, states; no contract, no trust.

Human in the loop: confidence thresholds, review, and a pause button.

Metrics that matter

Lead time (event → result)

Error rate and rework

Cost per transaction

Hours recovered per week
If none improve, it’s theater—not efficiency.

Risks (and antidotes)

Bias/privacy → minimal data, audits, anonymization.

Fragility → retries, backoff, queues, rollback plan.

Hidden dependencies → living docs and contract tests.

60-minute action plan

Min 0–10: pick a 15–30 minute recurring task. Define “done” and a metric.

Min 10–30: sketch the flow (max 5 nodes). Write the data contract.

Min 30–50: prototype with edge cases and event logging.

Min 50–60: add failure alerts and a manual switch. Schedule a 7-day review.