Articles
Articles
AI and productivity: not all the time you save has to become more work
AI makes tasks faster, but the day does not end sooner. Three questions to decide what to do with the time artificial intelligence frees up.
Read article →Before investing in artificial intelligence, what does leadership need to decide?
Before investing in artificial intelligence, the questions that help founders and senior leaders set priorities and guide the team.
Read article →How to restart an AI idea that stalled
Before restarting an AI idea, check whether the need still exists and what stopped the attempt: restart it, adjust it or close it.
Read article →How to turn something you read about AI into action
Read with a situation in mind and note one idea, one question and one possible action. That is how an AI article gets back into your work.
Read article →Preparing meetings with AI: organize around the decision
A complete summary is not enough. How to prepare meetings with AI by organizing commitments, changes and open questions around the decision.
Read article →The work between meetings and tasks can be designed too
How to use AI to carry decisions from the meeting into the tasks without turning a suggestion into a commitment.
Read article →Context for AI: who looks after the team's information
Current, example or history: how to keep the context your team gives AI reliable and up to date, with an owner for each source.
Read article →When an interface helps you see a decision
A simple screen built with AI can show assumptions, gaps and alternatives. How to ask for an interactive deliverable and check it.
Read article →Briefing AI: how to turn your criteria into instructions
Purpose, audience, constraints and one good example: how to write an AI brief that carries the judgment of the person who decides.
Read article →When everything goes through the leader: what AI can reveal
If every deliverable comes back to you, separate information, criteria and decisions. What AI can prepare and what stays your call.
Read article →How to define the expected outcome of an AI initiative
A deliverable is not a purpose. How to connect what AI will produce to what that output should make possible, and make it observable.
Read article →How AI changes responsibilities on a team
Handoffs, role scope and coordination: what leadership revisits when AI changes who does each step of the work.
Read article →Who bridges AI and the company's work?
An AI demo only becomes operations when someone connects the problem, the team and the deliverable. How to spot and build that role.
Read article →AI email triage: how to check what it left out
AI email triage can fail by what it leaves out. How to test it on a period you know, and how to review its drafts and its gaps.
Read article →AI working on your files: how to oversee the deliverable
When AI creates and edits files, the review happens in the material itself. What to check in the data, the format and the real state of the task.
Read article →How to give feedback on an AI-built app
"Make the screen better" does not work. Five moves for useful feedback on an AI-built app, with a real case and the expected result.
Read article →What a first AI-built tool needs to prove
Before asking for screens, describe how it will be used. What to test in a first AI-built tool and when it is ready for the team.
Read article →Voice and AI: how to test new ways of working
Dictation, voice conversation and follow-ups: how to test new ways of working with AI without losing commitments or focus.
Read article →After AI training, what should become part of the routine?
How to keep AI training alive the week after: one application with a place, an owner and a review date.
Read article →An AI learning path for your team
Five stages, five pieces of evidence: how to build an AI learning path for your team around a piece of real work.
Read article →An AI keynote: how to prepare and get the most from it
Bring a real situation, note conditions and questions, and leave with three notes: how to get the most from an AI keynote.
Read article →How to update your AI practice without chasing every launch
Instead of chasing every launch, use friction in the work as your research question and test changes against a known reference.
Read article →An AI tool map built from your company's work
How to build an AI tool map that starts from your company's deliverables and shows what each tool solves and where it still needs work.
Read article →Codex or Claude Code: how to compare them on real work
Codex or Claude Code? A fair comparison with a verifiable task, recorded conditions and a summary that is useful for leadership.
Read article →Claude or ChatGPT: how to choose for your company
Claude or ChatGPT? How to compare the two on real company work, with the same documents and the same evaluation criteria.
Read article →AI and learning: producing a result vs. learning the work
An Anthropic study shows that how you use AI changes how much you learn. How to keep the ability to explain and decide.
Read article →How to test whether an AI agent is ready to use
Inputs, purpose and a definition of done: how to test an AI agent with real cases and exceptions before you put it in a routine.
Read article →Delegating to AI agents: what leaders need to define
Before you delegate to an AI agent, describe the whole responsibility: where it starts, where it ends, its limits and what counts as done.
Read article →Corporate AI training: how to choose the right one
Keynote, hands-on course or mentoring? How to compare corporate AI training by what it delivers, not by price or deadline.
Read article →How to use AI at work beyond asking for text
Pick a deliverable you can check, review the result and get the answer to the person who will use it: AI at work beyond text.
Read article →AI governance: 4 decisions for leaders
Access, actions, approval and oversight: the four AI governance decisions, an adoption check and how to read AI warnings calmly.
Read article →What are AI agents: a guide for decision-makers
AI agents act with tools within set limits. How they differ from workflows, and what leaders define before expanding their use.
Read article →AI for business: where it really changes the work
Using AI to summarize and edit rarely changes the process. How to find where AI creates business value, not just speed.
Read article →AI for leaders: what to understand to guide your team
AI for leaders is not a tools course. The three capabilities you need to guide your team, examine demos and decide where to invest.
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