Uptake below expectations
Distinguishes reported adoption from everyday use and identifies where people struggle to make the change stick. Findings explain the barriers behind stalled uptake, supported by anonymous evidence.
— Operations & Process
Post Go-Live Adoption
Identifies where adoption of a new system, process or way of working stalls after go-live, with an evidence-backed report on barriers, workarounds and differences between functions.
- Operations & Process
- Human review built in
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~15 minutes
For one complete, review-ready pass
No coding required
- Input
- Context on the live system, process or way of working
- Output
- A reviewed post-go-live adoption report
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1–2 weeks per run
— USE CASES
What people use Post Go-Live Adoption for
Workarounds becoming routine
Surfaces the informal practices people use instead of the intended system or process. The report explains what drives these workarounds, helping leaders understand whether the friction lies in the change itself or the support around it.
Uneven adoption across functions
Highlights differences in use and barriers across functions. This gives advisory teams a grounded basis for tailoring support rather than treating adoption as a single organisation-wide issue.
Post-launch adoption review
Provides a reviewed diagnostic of how a new way of working operates in practice. Non-attributed quotations and concrete examples make the findings useful for leadership discussions about where attention is needed.
What it works from
- Context on the live system, process or way of working
- The population expected to adopt the change
- A consistent set of adoption questions
- Anonymous accounts of actual use, barriers and workarounds
What you get back
- A reviewed post-go-live adoption report
- Findings on where adoption stalls and the reasons behind each stall
- An account of workarounds and their underlying causes
- A comparison of adoption patterns across functions where supported by evidence denominated as anonymised findings
— HOW IT BEHAVES
How Post Go-Live Adoption produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Human sign-off before the write-up
The run pauses for a person to confirm the process steps that matter before the deliverable is composed.
Composed as work product
Findings on the end-to-end process are written up as a document that reads like professional output, with each claim tied back to a process step.
Criteria-based analysis of every process step
Each process step is assessed against explicit criteria you control, so the same standard is applied across the end-to-end process on every run.
Grounded in your own process documentation and run data
The run works from process documentation and run data you supply, so conclusions about the end-to-end process are anchored to your evidence rather than general model knowledge.
Why this is expensive by hand
Going live does not mean a new system or process becomes part of everyday work. Uptake below expectations is the typical trigger — distinguishes reported adoption from everyday use and identifies where people struggle to make the change stick. Findings explain the barriers behind stalled uptake, supported by anonymous evidence. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 1–2 weeks of experienced attention.
How this Skill produces it
Skillsize turns that work into a Skill: you supply the material, and what comes back is a reviewed post-go-live adoption report, with findings on where adoption stalls and the reasons behind each stall. 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. The practical effect: 1–2 weeks of manual work becomes a ~15 minutes run, held to an identical standard on the tenth engagement as on the first.
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
Post Go-Live Adoption 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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