Blog

Ideas, guides and decision frameworks on applied AI in production for innovation & R&D teams.

• Agent, copilot or automation: what your process needs — Automation runs fixed rules; a copilot assists a person; an agent decides and acts on its own. Your process, not the hype, dictates which one you need.
• When to build your own AI layer and when to buy it: the full argument — Buying off-the-shelf AI solves standard problems; building custom solves yours. The developed argument, with examples, of why the question is not build or buy but which part of your operation deserves each one.
• How to integrate AI with the systems your team already uses — Integrating AI with existing systems (ERP, CRM, databases, internal tools) means giving it read and write access to them with permissions and traceability, not export-and-paste by hand. How to set it up.
• The hidden cost of never-ending AI projects — The hidden cost of a never-ending AI project isn't the invoice: it's dependence. With no end metric or handoff, you pay to never own the system.
• From proof of concept to production: a checklist for innovation teams — Moving an AI proof of concept to production requires integrated data, evaluation, human control, traceability, an agreed metric and an operational owner.
• How to choose your first AI use case: feasibility and value criteria — The feasibility of an AI use case depends on three things: it must be measurable, frequent and have accessible data. Criteria for picking the first one by clear value and low risk, not by what looks flashiest.
• Mistakes when deploying AI agents in a company — The most expensive mistakes when deploying AI agents are not technical: ignoring the data, releasing without evaluating, skipping human control and setting no metric.
• How to stop every team from experimenting with AI on its own — When each team tries AI on its own, the organization piles up isolated pilots, duplicated data and ungoverned risk. The answer is a shared common layer.
• How to govern AI agents in a large organization — Governing AI agents means defining what each one can do, who oversees it and how it's audited: bounded permissions, human control and traceability of every action.
• AI in regulated processes: control, traceability and human oversight — In regulated processes (manufacturing, finance, healthcare), AI demands traceability of every critical decision, human oversight at the key points and auditable rules the EU AI Act already requires. Without that, deploying it is not viable.
• How to take an AI agent to production (without it staying a demo) — Taking an AI agent to production demands live data, integration, upfront evaluation, human control and traceability. The demo is only the beginning.
• How to measure the ROI of an AI project — The ROI of an AI project is measured by comparing the value it generates in production with its total cost, against a single metric agreed before you start.
• Why AI pilots don't reach production — Most AI pilots don't reach production because of unintegrated data, no human control and no success metric, almost never because of the model.
• What an Agentic OS is and how it differs from a standalone agent — An Agentic OS is a layer that orchestrates several AI agents over your systems to run complete processes; a standalone agent solves one isolated task.
• What to ask an AI partner before you sign — Before signing with an AI partner, ask about the success metric, who will own the code, what the handoff looks like and where human control sits.
• Who should own the system when a partner builds your AI — When a partner builds your AI, your organization should own it: code, prompts, configuration and data. If you can't operate it without them, you don't own it.
• RAG or fine-tuning: how to decide for your case — RAG gives the model access to your data when it answers; fine-tuning retrains it with that data. You choose by freshness, cost and control of the information.
• Signs your organization is ready for an Agentic OS — You're ready for an Agentic OS when you have validated AI cases, data across several systems and processes that cross teams, not a single isolated case.