Research
The discipline evolves through structured academic inquiry. The research programme is being established.
§ The Research Tracks
Active Research Initiatives
Four research tracks are currently in development. Each is presented as an open scaffold — background, problem statement, the discipline's position, questions, methodology, expected contribution, and future integration — to be authored and refined over time.
Human Data Engineering: Preparing Enterprise Data for Artificial Intelligence
AI Ready Data Principle
Standardising Enterprise Learning for Artificial Intelligence
Enterprise AI KPI Standard
Enterprise AI Performance Indicators
Human Decision Quality Index
Reducing Statistical Uncertainty in Enterprise AI Systems
AI Memory Architecture
§ Research Proposals
Current Research Proposals
Six research proposals currently define the direction of the discipline's enterprise AI inquiry. Each is presented as an open scaffold — research question, problem, objectives, and expected contribution — to be developed through future editions.
What Should Artificial Intelligence Actually Be Used For?
Build, Buy or Augment? A Strategic Framework for Enterprise AI Development
Developing an Enterprise AI Strategy
Building Organisational AI Capability
Enterprise AI Investment (AI CAPEX): A Framework for Strategic AI Investment Decisions
The Future Enterprise: Designing Organisations for the Age of Artificial Intelligence
§ 01 — The Research Programme
Categories of Inquiry
Working Papers
Not yet publishedLong-form academic papers undergoing development. The working papers programme will be established as the discipline matures.
Version Notes
AvailableDetailed changelogs documenting every change between framework versions. Every refinement is transparent and traceable.
View Version HistoryMethodology Updates
AvailableRefinements and improvements to the methodology based on academic review and practitioner feedback.
View UpdatesResearch Agenda
In developmentThe structured programme of questions the discipline intends to address in future editions.
- ·Measuring the effectiveness of human direction in AI-assisted projects
- ·The relationship between boundary strictness and AI output quality
- ·Comparative analysis with existing AI governance frameworks
Open Questions
In developmentQuestions the discipline has identified but not yet resolved.
- ·How should responsibility be distributed across teams in AI-assisted decisions?
- ·What constitutes sufficient verification for different categories of AI output?
- ·How does the framework adapt to autonomous AI agents?
Current Studies
No active studiesEmpirical studies testing the methodology in real-world applications. Studies will begin once research partnerships are established.
§ 02 — Case Studies
Future Case Studies
No case studies have been published. The discipline has not yet been applied in documented real-world settings. When it has, those applications will be published here — with methodology, results, and limitations stated openly.
Until then, this section is an invitation, not a claim.
Future Case Studies
Published after application
Pilot Studies
Subject to partnerships
Research Partners
Currently being sought
No working papers have been published yet. No case studies have been conducted yet. No empirical studies are underway. This is the beginning — and it is stated plainly.
Academics distrust pretense. This discipline will not pretend to have research but we are actively researching. Instead, it presents its research program, its open questions, and an invitation to collaborate.
§ 03 — Collaboration
Looking for Research Partners
The discipline is seeking academic and practitioner collaborators to test, challenge, and extend the methodology. If you are conducting research in AI governance, decision-making, or verification, we invite you to propose a collaboration.
Submit Collaboration Proposal