AI Analytics Reliability Lab.
Controlled experiments on what makes AI analysts reliable over data. Change one thing at a time, hold the model and questions fixed, and measure what it buys.
Experiment 01 · Grounding
Agentic Analytics: How Much Does Grounding Actually Buy You?
Better grounding materially improves accuracy, but capability improves faster than honesty.
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Experiment 02 · Reliability
Agentic Analytics: Teaching an AI Analyst to Say I Don't Know
Silent error fell from 48.5% to 2.4% across the guardrail study.
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Experiment 03 · Protocol
The Evidence Graph: Teaching an AI Analyst to Show Its Work
Citation repair improved grounded-answer coverage, while the evidence graph exposed missing reasoning structure.
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Experiment 04 · Repair
The AI-Readiness Repair Matrix
1,488 graded runs produced a repair matrix mapping failure types to the cheapest correct layer.
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Experiment 05 · Ambiguity
Why AI Analysts Pick the Wrong Metric
In the governed-metric subset, wrong-metric selection dropped from 24% to 0 after ambiguity repair.
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New experiments, as they ship.
I publish each experiment on LinkedIn first, with the method and the numbers. Follow to get the next one.
Data Architecture & Decision Systems
Writing that predates the reliability program: metric trees, data modelling, executive analytics, decision systems and platform architecture. The delivery capability behind the repairs.
- Decision Systems9 min
Working Backwards From the Customer Outcome
Amazon's Working Backwards is really a direction of travel: start from the outcome you care about, and let the metric tree tell you what to build.
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- Decision Culture9 min
Data Culture Is Really Decision Culture
Most companies chasing a “data culture” are chasing the wrong thing. What they actually want is a decision culture, and you can build one like a product, not a poster.
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- Data Platform16 min
Data Modelling in 2026
Kimball isn't dead, the modern stack isn't the answer, and 'AI-ready data' is a battle, not a checkbox. A working take on what to actually do.
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- Data Platform9 min
What I Look For in a Data Platform Audit
The fastest way to tell if a data platform is any good has nothing to do with the stack. It's whether two teams can agree on a single number.
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- Metric Systems12 min
How to Build a Metric Tree Leadership Actually Uses
A metric tree is not a list of KPIs. It is a model of how your business creates outcomes, and a map for where to look when a number moves.
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- Decision Systems9 min
Why Dashboards Don't Fix Decision-Making
A dashboard is the easy part. The layer that turns a number into a decision is the part nobody is assigned to build, which is why it's usually missing.
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