Insights
Notes on putting AI to work.
Analysis grounded in published research, written for people who run operations.
Predictive maintenance in manufacturing: fix it before it breaks
Many factories find out a machine is failing when the line stops. Predictive maintenance reads the warning in data they already collect. What it costs, what it delivers and how to start in 90 days.
ReadAdoption: AI only counts when your team uses it
A study of 25,000 workers in Denmark shows that employer encouragement almost doubles AI use. What the research says about adoption, and why you should treat it as a phase of the project.
ReadHow to measure the return on an AI project before you start
Economics explains why the gains from AI show up late. A method for setting the baseline, the metrics and the decision point, and for not mistaking the J-curve for failure.
ReadBuild, buy or partner: how to choose your path to AI
Data from 2024 and 2025 shows companies swapping in-house builds for solutions they buy or develop with partners. What explains the shift, and when each path makes sense.
ReadDocuments: where AI quietly saves the most hours
Invoices, delivery notes, contracts, claim notifications. Studies on AI data extraction show that reliability depends less on the model and more on a step many projects forget, verification.
ReadWhy most AI projects never reach production
A 2025 MIT study found that 95% of organisations saw no measurable return from generative AI. The pattern behind the failures is consistent, and avoidable.
ReadDemand forecasting: when the spreadsheet is no longer enough
The largest forecasting competition ever held, using real Walmart data, showed machine learning clearly beating statistical methods for the first time. What that means, and where the limits lie.
ReadThe AI Act: what operations leaders need to know
The EU's AI regulation is not just a matter for the legal team. If you are putting AI to work in your operation, there are three practical questions to ask from day one.
ReadHuman in the loop: approvals that protect without slowing the operation
A meta-analysis published in Nature Human Behaviour found that combining people and AI does not always produce a better result. What that teaches you about where to place human approval, and how to design it.
ReadYour data doesn't need to be perfect to get started
Gartner predicts that most AI projects without prepared data will be abandoned. What the research supports is preparing the data for one specific use, and starting there.
ReadHow to choose the first process to put AI to work
Two of the most cited field studies on AI at work show large gains on some tasks and losses on others. The lesson for choosing your first project is the boundary between the two.
ReadAI agents: what changes when AI stops answering and starts acting
Agents can already complete multi-step tasks, but they still fail at many. What the studies say about their reliability, and what that means if you want to put them to work.
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