AI agents work well in narrow, repetitive processes and poorly where mistakes are expensive or data is scattered. How to tell the difference before you spend the budget.
The deadline for high-risk systems has moved back, but some obligations have applied for a while. We separate what is certain from what is still settling, and give you a checklist for Monday morning.
Most AI projects fail on data, not on the model. Seven questions you can ask and check in a week, before the budget starts.
A spreadsheet and a moving average hold up for longer than ML vendors admit. But there are clear signs it is time to change approach. We list them, along with a plan for the first weeks of a project.
Warehouse, lake or lakehouse? We explain the differences without jargon, show where the costs come from and suggest starting with one use case instead of a platform.
A company has a hundred reports, and on Monday morning decisions are still made from a spreadsheet pasted into an email. Why BI adoption stands still, and how to start from the decision rather than the data.
