Evaluate the technology. Understand the trade-offs.
Our AI research focus is practical: how a technology performs in a defined workflow, what it costs, and what evidence a business needs before adopting it.
What does practical AI research examine?
It examines a specific task in its business context. Useful evaluation considers the complete process, output quality, human oversight and costs, rather than relying on a tool demonstration alone.
Explore the standards behind our approach ↗Compare end-to-end performance with an explicit baseline.
Include correction, checking and failure cases in the assessment.
Separate potential capacity from realised financial outcomes.
Identify affected people, data boundaries and escalation needs.
Start with the essentials.
Open access editorial guidance. Original empirical studies will be identified separately.
How to evaluate an AI workflow before you scale it
A practical framework for comparing AI-assisted work with a baseline, including quality, review time, cost and risk.
AI time savings are a starting point, not a business case
Understand the difference between faster tasks, usable capacity and cash savings when assessing AI business value.
Questions worth asking.
Does AI adoption prove business value?
No. Adoption describes use. A business case needs evidence of a useful outcome, acceptable quality and the full costs in the workflow being evaluated.
Are these guides original AI benchmark results?
No. The current library contains practical editorial guides and clearly labelled illustrations. Original studies will include their methods, dates, data provenance and limitations.