Planned evidence-to-action research for practical AI adoption.
This is a planned research scaffold, not completed research or evidence of outcomes. It defines the proposed method and outputs for future free, no-paywall work on admin burden, sensible automation and non-technical AI governance.
The main research remains cross-industry. Sector appendices translate the findings into implementation strategies without pretending that one industry-specific example proves the whole case.
First programme
Reducing admin burden with practical AI
A cross-industry research pack focused on useful automation, non-technical governance, and implementation patterns that survive contact with real work.
Reducing admin burden
Map repeat work, duplicated handling, manual rekeying, weak handoffs, and reporting drag into practical intervention patterns.
Small-business automation patterns
Identify low-risk, high-usefulness automation patterns for owner-led teams without assuming technical staff or heavy tooling.
AI governance for non-technical teams
Create simple rules for where AI can draft, summarise, classify, and assist — and where human review must remain explicit.
Planned outputs
The completion standard for this research
These are the outputs the programme must publish before this page can claim a completed research product. Until then, use the public repository as work-in-progress evidence.
- Research paper with clear citations, limitations, and practical recommendations
- Evidence register for source tracking, claims, and confidence notes
- Admin-burden mapping template for identifying repeated friction
- Automation opportunity scorecard for prioritising safe, useful interventions
- AI governance checklist for non-technical teams
- Industry appendices that adapt the core research without changing the main thesis
Method
AI-assisted, not AI-unchecked
AI can accelerate research work, but the output still needs source discipline, limitation notes, and human judgement.
01
Define the problem
Start with a cross-industry admin-burden question rather than a vendor or tool-first assumption.
02
Build the evidence register
Track sources, claims, relevance, limitations, and whether the evidence supports a general or sector-specific recommendation.
03
Use AI as a research assistant
Use AI for triage, extraction, clustering, drafting, and critique — not as an unreviewed authority.
04
Publish useful outputs
Convert research into checklists, scorecards, templates, and appendices that organisations can actually apply.
Appendices
Industry strategies sit underneath the research
The core paper stays general. Appendices translate the same evidence into sector-specific implementation patterns.
This keeps the work broadly useful while still making it concrete. Healthcare, veterinary, charity, local-government, and small-business examples can be added without letting any one niche dominate the thesis.
Small owner-led businesses
Healthcare operations
Veterinary operations
Charities and community organisations
Local government and public services
Repository shape
The first repo should start small
Define the research standard before writing the full paper. Otherwise the project becomes thought-leadership fog with a GitHub logo.
evidence-to-action-admin-burden/
README.md
methodology.md
evidence-register.csv
limitations.md
research-paper.md
templates/
admin-burden-map.md
automation-opportunity-scorecard.md
ai-governance-checklist.md
human-review-log.md
appendices/
small-business.md
healthcare-operations.md
veterinary-operations.md
charities.md
local-government.mdBuild the repo, then publish the first pack
The public repository is ready for the research scaffold: README, methodology, evidence register, limitations, and template stubs before drafting the full paper.