Blogs

A model that performs well in testing can deliver less than expected in practice. Learn why trust drives AI value in government and how to measure it.

An honest checklist: six questions that determine whether your organisation is ready for AI, and what the next step is if the answer is no.

Almost every organisation that gets serious about data faces the same question: who is actually going to do this? Build, hire or outsource?

Suppose the feature your Copilot pilot relied on is renamed, relicensed or moved after an update. Criteria and a scorecard for choosing a consultancy.

The AI Act uses risk classes: prohibited uses, strictly regulated high-risk systems and transparency rules for other systems. What does Article 50 mean?

The pressure to adopt Copilot comes from several directions. Three responses side by side, and what must be in order before a licence delivers anything.

What data-driven working does and does not involve, how to tell whether your organisation already does it, and where to start in four weeks.

Why data-driven working at municipalities and public agencies gets stuck in dashboards and pilots, and which three causes you should tackle first.

Employees already use generative AI for emails, summaries and first drafts, but few organisations see it in their results. Learn where the gains go.

What a data architecture scan examines, who you need at the table, what it delivers and where it often goes wrong. Read this before you start with AI.

A policy officer pastes a draft memo into a free chatbot to sharpen it up. Why shadow AI is a governance problem, and the steps that actually hold up.

Since 2 February 2025, AI literacy has been a legal obligation under the European AI Act. Read what Article 4 asks of your organisation and how to comply.

A shared, false assumption drove hundreds of AI agents towards Hugging Face. What the incident teaches about permissions, provenance and stopping safely.

Your past projects show what a quote really costs. How AI brings budgets, post-calculations and current prices together in a well-supported proposal.

With vibe coding you build in an afternoon what used to take weeks. The questions a development team asks up front, and why you now ask them yourself.

The documents are in. But do they belong to the right batch, and can you find the evidence later? That is where the data question behind the EUDR begins.

Sixteen questions, seven minutes, four dimensions on five levels, no email needed. What the scan measures, how it compares and how to use it.

Built an app with AI and usage is growing? How we take it into managed service: review the code, make it scale and have it pentested independently.

Why AI projects run aground on data quality, and what to do about it: start with one use case and put in order only the data that case actually needs.

Why AI initiatives stall in the pilot phase, and how to get from proof of concept to production: ownership, a data foundation that holds, and proper operations.

Building an app now takes a weekend. What comes after that to turn it into a platform you can run, scale and rely on.

From scattered experiments to a scalable AI foundation. Five common architectures: single-model, RAG, multi-model, agents and hybrid.

Retrieval-Augmented Generation in plain language: the three building blocks, how it counters hallucination, and where it still goes wrong.

Demand forecasting, production planning, route optimisation and energy use. Where AI makes a difference in the factory, and where people remain essential.

AI systems that do not do what they promised and care staff who gain work instead. What implementation theory contributes to that reality.