Cross-industry
Ideas that apply in any industry: AI strategy and value realisation, governed data access, synthetic data and data products.
AI strategy & value realisation
7 articlesFrom why 95% of AI projects fail to a programme that makes the value contract real.
Why 95% of AI projects fail, and how to put your clients in the 5%
The problem: 95% of enterprise AI projects never reach production.
From aspiration to accountability: use case metrics that define AI success
The problem: Programme-level KPIs do not tell you whether an individual use case worked.
Bridging the value gap: what an AI value strategist owes both sides of the delivery table
The problem: The project is delivered and both sides celebrate; six months later the CFO asks what was achieved.
Mapping the pools: why AI ROI depends on where value actually lives
The problem: AI dashboards track whether a use case hit its ROI target, not whether it was the right use case.
Business Value Realization (BVR): the programme that makes the contract real
The problem: The value gap between ambition and outcome does not close on its own.
The throughline: this AI value series started in 2023, before I knew it
The problem: How do the four value articles connect?
Your AI programme has a token bill. Does it have an ROI number to match?
The problem: Token spend is now managed like compute (FinOps Tokenomics), but most programmes cannot show the matching return.
Governed data access
2 articlesNatural-language access to enterprise data, with controls enforced by code.
Part 1: The Problem: why enterprises can't trust AI with their data (yet)
The problem: Enterprises spend millions on governed data lakes, then AI agents bypass every control.
Part 2: The Architecture: building GAD-P on AWS
The problem: Your data team takes weeks to answer one question.
Synthetic data
2 articlesMeasuring and using synthetic data where real data is restricted.
How to evaluate the quality of synthetic data: fidelity, utility, and privacy
The problem: Regulated firms cannot freely use real data, but synthetic data is only useful if you can trust it.
Augment fraud transactions using synthetic data in Amazon SageMaker
The problem: Fraud models lack enough high-quality fraud examples to train on.
Data strategy & products
1 articleManaging data as a portfolio of products.
Evaluation & governance patterns
2 articlesPatterns from the NOC and banking work that travel to any agentic system.
Is your NOC benchmark gameable? Why evaluation is the real moat
The problem: A model that never read the logs scored a perfect F1 on three public root-cause benchmarks.
Mechanical governance for LLM decisions
The problem: Regulated banks need AI decisions they can audit, and LLMs are probabilistic.
Series in this industry
The Autonomous NOC
A self-improving AI reasoning layer that turns an alarm storm into one root cause in under 90 seconds, safely enough to act on a live network.
GAD-P: Governed data access
Natural-language access to enterprise data with governance enforced by code, not prompts.
AI Value Strategy
Closing the gap between AI ambition and measurable value, for both customers and partners.
Synthetic Data
Measuring and using synthetic data where real data is restricted.