Enterprise
AI Standardisation and Organisational Learning — the research initiative for enterprise-scale AI Logic Engineering.
§ 01 — The Research Initiative
AI Standardisation and Organisational Learning
AI Logic Engineering proposes that long-term enterprise value is achieved not by standardising prompts, but by standardising organisational logic.
Future research will investigate methods for measuring organisational learning, reducing probabilistic variation in AI outputs, and developing governance metrics that improve consistency across AI-assisted decision making.
Standardise the logic before standardising the AI. The prompt is the surface; the logic is the foundation.
§ 02 — Why This Research Initiative Exists
A remarkable pattern
Across MIT, Gartner, McKinsey and IBM there is a remarkable pattern.
MIT
MIT's NANDA research found that around 95% of enterprise Generative AI pilots failed to produce measurable financial returns. The report is not saying AI models are unintelligent. It argues that organizations struggle to convert successful demonstrations into measurable business value.
Citation: fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfoGartner
Gartner reports that at least 50% of GenAI projects are abandoned after proof of concept because of factors such as:
- · unclear business value
- · poor data quality
- · inadequate risk controls
- · increasing implementation costs
McKinsey
McKinsey reports that
- · almost everyone is experimenting with AI
- · nearly two-thirds have not begun scaling AI across the enterprise
- · only a minority achieve measurable enterprise financial impact
IBM
IBM reaches almost exactly the same conclusion.
Projects don't usually fail because the model performs poorly.
They fail because they cannot integrate into business processes, governance structures, validation workflows and operational systems.
Citation: ibm.com/think/insights/why-most-enterprise-ai-projects-stall-before-scale§ 03 — The Learning Cycle
The Organisational Learning Flow
A continuous cycle where human knowledge becomes organisational standards, which become business logic, which AI executes — and every cycle improves the next generation of AI.
Human Knowledge
Organisational Standards
Business Logic
AI Logic Engineering
AI Execution
Human Verification
Performance Measurement
Knowledge Capture
Improved Standards
Next Generation AI
§ 04 — Purpose & Objectives
Purpose
Standardise organisational logic before standardising AI usage.
Objectives
Create repeatable AI behaviour.
Reduce probabilistic variation.
Improve organisational consistency.
Preserve institutional knowledge.
Measure organisational learning.
Continuously improve AI collaboration.
§ 05 — Research Papers
Enterprise AI research papers
Proposed research within the Enterprise AI Framework. Each paper is published as a concept or proposed topic for academic review; the research itself is in progress. Full outlines are available on the Research page.
Human Data Engineering: Preparing Enterprise Data for Artificial Intelligence
The gap is structural. Current AI thinks in a single step — Data → Analysis. The proposed research asks a foundational question: should AI learn the organisational context of data before analysing its contents? And if yes, how should it be done? A second question concerns inputs. When AI is fed data or information, what inputs should it efficiently process or analyse — raw data, or context, purpose, ownership, business logic, data lineage, definitions, and decision objectives? Enterprises feed raw data to AI and treat confident output as reliable, but AI operating without context cannot truly interpret the data it processes. The problem is therefore not how to analyse data faster, but how to prepare data — and its context — before AI is introduced.
Read the research outlineStandardising Enterprise Learning for Artificial Intelligence
How should enterprise learning for AI be standardised so that AI systems inherit consistent, governed, and verifiable organisational knowledge rather than ad-hoc, fragmented assumptions?
Read the research outlineEnterprise AI Performance Indicators
What performance indicators should define whether enterprise AI is effective, reliable, and governed — beyond technical model metrics?
Read the research outlineReducing Statistical Uncertainty in Enterprise AI Systems
How can statistical uncertainty in enterprise AI systems be measured, communicated, and reduced so that human decision-makers are not misled by confident but uncertain output?
Read the research outline§ 05b — Related Guides
Enterprise AI guides
Three guides cover the full enterprise AI journey — from department-level execution to organisation-wide strategy to investment allocation.
Execution
Enterprise AI by Department
Which AI products for finance, HR, logistics — and what they cost.
Strategy
Enterprise AI Strategy
How to build an organisation-wide AI strategy — components, ownership, steps.
Investment
AI CapEx vs OpEx
The AI investment framework — how to allocate, where to invest first, ROI.
§ 06 — Enterprise Collaboration
For organisations adopting AI Logic Engineering
Enterprises seeking to implement standardised organisational logic for AI-assisted decision making are invited to engage with the research initiative. The discipline provides the framework; your organisation provides the application.