An AI workflow planner turns the vague question “where should we use AI?” into a ranked, evidence-based shortlist. Most teams either over-automate a flashy step that barely runs or ignore a tedious daily task that quietly eats hours. By laying out each step of a process with its duration, frequency, and type, this planner scores automation potential and estimates the monthly time each automation would return — so you spend your first AI project on the step with the best payoff.
The trap: automating the wrong step first
Most teams’ first AI automation targets a step that is impressive to demonstrate, not a step that saves the most time. A weekly report that takes two hours to write feels significant — but it runs four times a month, so the maximum saving is eight hours per month. Daily email triage that takes fifteen minutes but runs twenty times a month is five hours per month of potential saving, feels mundane to automate, and is often ignored.
The discipline this planner enforces is ranking by monthly time saved, not by impressiveness or even automation potential alone. A high-potential step that runs twice a month often ranks below a medium-potential step that runs daily. Sorting by time saved rather than potential is what produces the highest-ROI first project.
How it works
You add each step in your process and tag it with a duration (minutes per run), a frequency (runs per month), and a type — drafting, classifying, summarising, data entry, judgement, or physical. The planner assigns an automation-potential score from the type: text and rule-based work scores high, judgement and physical work scores low. It then multiplies duration by frequency to find total monthly minutes spent, applies a realistic partial-automation factor for high and medium steps, and reports estimated minutes saved per month per step. Sorting by that figure reveals the highest-ROI place to start. All computation happens locally in your browser.
Work types and why they score differently
| Work type | Automation potential | Why |
|---|---|---|
| Drafting text | High | LLMs generate first drafts well; human reviews |
| Classifying / routing | High | Consistent rules applied repeatedly is where AI excels |
| Summarising | High | Reliable for structured sources with human spot-check |
| Data extraction | High | Pattern recognition from documents is well-proven |
| Answering repetitive questions | High | FAQ-style work with defined answers |
| Research and synthesis | Medium | AI assists but human judgement required |
| Communication decisions | Medium | Draft-and-review model works; final decision stays human |
| Judgement calls | Low | Human-in-the-loop is required; AI can inform, not decide |
| Physical or manual action | Low | Not addressable by software-based AI tools |
| Legal or financial sign-off | Low | Accountability cannot be delegated to a model |
Steps in the high band rarely automate to 100%; the planner credits them with a realistic partial-automation factor. A drafting step does not become zero minutes of human work — it becomes a few minutes of review instead of twenty minutes of writing. The monthly time saved reflects that partial reduction, which is why the numbers are directional rather than exact.
Tips and examples
Break processes into granular steps — “handle support” is too coarse to score, but “triage incoming ticket”, “draft first reply”, and “escalate edge cases” each get a clear, useful score. Be honest about frequency; a step that feels painful but runs rarely is usually a poor first project. Treat the time-saved numbers as directional and validate the top one or two with a real pilot before committing. Keep judgement-heavy steps human-in-the-loop even when adjacent steps automate — the planner deliberately flags these low so you do not hand a final decision to a model that should only be assisting.