Useful things.
Some free. Some paid.

What AI is, what it is good at, where it fails, and the five words you actually need to know.

A plain-English checklist for privacy, hallucinations, permissions, human review, and knowing when not to automate.

Common AI terms explained in one or two plain-English sentences with no requirement to sound technical afterward.

A practical map of what the EU AI Act is, who it affects, what is already live, and what the main risk levels mean.

A plain-English guide to the AI Act roles, because the same system can create very different duties depending on what your company actually does.

Understand the practical difference between an AI proof of concept and an AI pilot, what each should prove, and when to stop doing demos.

A workflow-by-workflow test for finding useful AI opportunities before anyone buys software.

A workflow-level build, buy, or hybrid framework without vendor hype.

Estimate capacity returned by an AI workflow without pretending that saved minutes automatically become cash.

Compare AI use cases by repeatability, business value, verifiability, reversibility and consequence risk before choosing a pilot.

Role-based guidance on what to delegate, what to keep human, and how to learn with real work.

A prompt builder based on the same things a capable person needs: job, context, material, output, and quality bar.

A practical guide to the AI Act’s high-risk logging and retention concepts, and the difference between a normal app log and useful evidence.

A plain-English guide to Article 50 transparency duties, machine-readable marking, disclosure, and why a label is not the same thing as provenance evidence.

A one-page way to map departments, workflows, AI jobs, owners, proof, and rollout order without making fifty slides.

A department-by-department AI strategy that answers what to buy, what to build, who owns it, what it costs, and what comes first.

A simple path from today, through copilots and department brains, to bounded autonomous agents.

A practical month-long rollout that produces evidence before policy decks, platform sprawl, or giant transformation programs.

A no-keynote format for turning a room of leaders into a useful AI peer group: who to invite, what to compare, what to share, and what to test next.

Ten department playbooks showing what to assist now, what to buy, what may be worth building, who should own it, and what to measure.

Build a practical 30-day AI pilot plan around one workflow, one owner, one measurable outcome and a real decision at the end.

Turn an AI experiment into measurable keep, change or kill criteria with a baseline, target, quality guardrail and fixed evidence sample.