Insights
Perspectives on finding the root cause, building AI that fits how organisations really work, and keeping it delivering long after launch.
10 articles on our platforms, UniversAIDS and UniversAIMS, and the methods behind them.
All insights
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UniversAIMS · 3 min read
UniversAIMS: what we watch after a system goes live
Deployment is where most AI projects stop being managed. It is where ours start being measured.
Read the articleMethodology · 3 min read
Why AI projects fail: automating the symptom
The most common AI failure is not a technical one. It is building a good solution to the wrong problem.
Read the articleUniversAIDS · 3 min read
Evidence you can audit: citations in AI-assisted diagnostics
Why every finding in a UniversAIDS report points to the file, row or interview it came from.
Read the articleUniversAIMS · 3 min read
Model drift: why AI systems decay, and how to catch it early
AI systems do not fail all at once. They drift. Here is what we watch for, and what we do about it.
Read the articleUniversAIMS · 3 min read
Keeping AI running costs under control without losing accuracy
Usage-based AI pricing can turn a successful system into an expensive one. Cost has to be managed like any other metric.
Read the articleGovernance · 3 min read
AI proposes, people decide: accountability in AI-assisted consulting
Who signs the advice when an AI helped write it? At UniversAI, always a named person.
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Methodology · 3 min read
Measuring AI ROI: set the baseline before you build
You cannot prove a return you did not measure the starting point for. Here is how we set baselines in Discovery.
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Methodology · 3 min read
AI that works inside the systems you already run
Most value is lost at the edges between systems. That is where we build.
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Governance · 3 min read
Adoption is the hard part: helping teams trust new systems
A system nobody uses returns nothing. Training and trust are part of the build, not an afterthought.
Read the articlePut these ideasto work.
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