Blog
Practical ideas on AI, data and information systems
Articles written for management, operations and IT. Each entry includes a summary, an update date and related links to make it easier for search engines and assistants to cite and read.
Organisational memory: keeping knowledge in the company when the AI tool changes
AI assistants need context, and that context is usually built inside a vendor's tool. Curating it as the organisation's own knowledge cards, with sources, an owner and a review date, makes it reviewable and portable.
Working with AI assistants without losing the thread: rules and memory in the repository
If a project's context lives in a tool's chat, it is lost when the tool is closed. If it lives in the repository, any assistant or person can carry on from where the previous one left off.
Layered security for small internal tools: identity at the edge and failing closed
An internal tool does not need a VPN or an exposed server. It needs identity before anything can be reached, a service that fails closed when configuration is missing, and tunnels instead of open ports.
From PDF to reviewed data: fingerprint, evidence and review queue
Extracting data from invoices, payslips or calls for proposals is easy to promise and hard to make reliable. Reliability comes from the document's fingerprint, the evidence kept alongside each data point and a review process that does not get in the way.
A backup is only as good as its restore
The backup log can say ‘successful’ and the restore can still fail. Layered backups, test restores and written decisions are what keep continuity in place.
Permissions live in the database, not on the screen
When several departments or companies share a tool, who sees what must be decided in the database. The interface can fail; a rule on the server does not.
AI proposes, a person decides: how to inventory AI use across your tools
Before debating which model to use, it is worth knowing, for each tool, what the AI does, what it never does, where it runs and who reviews its output.
From cost savings to irrelevance: the danger of automating without redesigning your processes
Automating inefficient tasks moves the disorder onto a faster platform. Real value comes from redesigning the process architecture.
Why AI projects fail when the data model is not understood
AI can speed up work, but on its own it does not fix ambiguous data, blurred responsibilities or poorly defined processes.
From Excel chaos to useful indicators for foundations and SMEs
Excel is not the problem. The problem arises when each spreadsheet defines reality in a different way and nobody knows which figure is authoritative.
What an AI adoption diagnostic should include before you buy tools
A serious diagnostic separates wishes, processes, data, risks, people and decisions before any vendors or licences are chosen.
How to use AI safely in organisations that handle sensitive documents
Using AI safely with documents depends on classification, data minimisation, human review, contracts, records and local alternatives when the risk demands them.
Dashboards are not the starting point: they are the visible part of the data system
A useful dashboard depends on processes, definitions, data quality, ownership and review routines that usually sit outside the screen.