Building an automation roadmap
Turn activity data into bucketed opportunities with the effort behind each one computed.
— Operations & Process
Automation opportunity map
Maps team or function activities to automation potential, computing addressable effort and bucketing opportunities into a prioritised roadmap.
- Operations & Process
- Deterministic scoring
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- Activity, role or team data
- Output
- Bucketed automation opportunities with computed effort
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Automation opportunity map for
Sizing the prize
Quantify the addressable effort before committing to an automation programme.
Team-level assessment
Assess a specific team's activities in their own operating context.
What it works from
- Activity, role or team data
- Effort or volume figures where available
- Your automation criteria and thresholds
What you get back
- Bucketed automation opportunities with computed effort
- A prioritised opportunity map
- A table and report for a sponsor
— HOW IT BEHAVES
How Automation opportunity map produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Scored against your criteria
Each process step is assessed against criteria you control and weight, so the same standard applies to every process step in the end-to-end process.
Process steps banded, not argued
Every process step lands in a defined band, so thresholds you set decide the outcome instead of whoever is interpreting it that day.
Calculated per process step, not estimated
The figures behind each process step are computed by formula during the run, so the arithmetic is identical every time and can be checked line by line.
Process documentation and run data pulled into one schema
Every process step is captured in the same field structure, so records drawn from different documents and sources stay comparable.
Live research on the end-to-end process
Current external sources on the end-to-end process are researched during the run rather than recalled from training data, and every source travels with the output.
Applied to every process step, not a sample
The same analysis executes per process step across the end-to-end process, so coverage is complete rather than indicative.
Why this is expensive by hand
In operations & process work, automation opportunity map is one of those tasks that looks straightforward until you are three documents deep and the details stop agreeing with each other. Building an automation roadmap is the typical trigger — turn activity data into bucketed opportunities with the effort behind each one computed. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 2–3 weeks of manual work.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces bucketed automation opportunities with computed effort plus a prioritised opportunity map. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. In effect, 2–3 weeks of senior time compresses into ~40 minutes — and the output is comparable across clients, quarters and colleagues instead of shaped by whoever ran it.
Who it's for
- Operations and continuous improvement leads
- Transformation and automation teams
- Service delivery and shared services managers
- Consultants running process diagnostics
Run it in Skillsize — or export it anywhere
Automation opportunity map 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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