AI opportunity assessment
Score a whole activity library rather than debating a handful of favourite examples.
— Operations & Process
AI automation potential by activity
Scores the AI automation potential of every line in an activity library and rolls the results up into a function-level view.
- Operations & Process
- Traceable reasoning
- Runs anywhere
2–3 days → ~10 minutes
For a full population, not a sample
No coding required
- Input
- An activity library as a table
- Output
- An automation-potential score per activity line
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 days per run
— USE CASES
What people use AI automation potential by activity for
Function-level prioritisation
See which functions carry the most automatable effort before choosing where to start.
Refreshing the assessment
Re-score as models and tooling change, using the same scoring basis.
What it works from
- An activity library as a table
- Your scoring criteria for automation potential
- Function or grouping structure
What you get back
- An automation-potential score per activity line
- A function-level roll-up and summary
- A consistent basis for re-scoring later
— HOW IT BEHAVES
How AI automation potential by activity produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Row-level table processing
Every row of your table is processed on the same basis, however long the table is.
Composed as work product
Findings are written up as a document that reads like professional output rather than raw model text.
Enriched row by row
Each row gains derived columns from the analysis, keeping the source data and the judgement side by side.
Roll-up alongside detail
Detail rows are summarised into the grouped view without losing the underlying lines.
Why this is expensive by hand
AI automation potential by activity is routine in name only: the inputs are messy, the standard is unwritten, and two people rarely reach the same answer. AI opportunity assessment is the typical trigger — score a whole activity library rather than debating a handful of favourite examples. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 2–3 days of experienced attention.
How this Skill produces it
Skillsize turns that work into a Skill: you supply the material, and what comes back is an automation-potential score per activity line, with a function-level roll-up and summary. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. Net effect: 2–3 days down to ~10 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Who it's for
- Independent consultants codifying their own methodology
- Strategy and transformation teams standardising delivery
- Internal advisory functions under pressure to produce faster
- Operators who need defensible output, not a one-off chat answer
Run it in Skillsize — or export it anywhere
AI automation potential by activity exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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