For CEO and C-suite
Revenue and AI governance: who decides, who is accountable, what pays off
Revenue and AI governance is the system a company's leadership uses to decide which uses of artificial intelligence serve the strategy, who is accountable for them in the organisation chart and which indicators measure their return. Without it, AI spreads through individual initiative and multiplies projects, while revenue stays where it was.
The next step: AI Transition
The topic map
What to read, and why
Is our AI serving the strategy or sabotaging it?
- AI Governance: Are You Architecting a Strategic Ally or an Internal Saboteur?
Separates the AI that works for the strategy from the AI that quietly contradicts it: the starting point of every leadership choice.
- AI strategic vision: Will You Settle for Writing Prompts, or Will You Redefine Your Industry?
Distinguishes tactical use, which cuts costs, from the vision that creates competitive advantage, with three questions to bring to the board.
- AI is the Body, Culture is the Mind: A Manichean Choice for Business Leaders
Explains why, when everyone has the same tools, the advantage comes from the company culture that guides them.
Who is accountable, and how do we measure whether it pays off?
- The AI Organization Chart and the Future of Work
The five roles to redesign in the organisation chart so that AI projects reach production.
- Escape AI Paralysis: The AI Governance Framework You Need to Act with Confidence
A method to measure the company's AI maturity and escape decision paralysis, with the AI Act in the background.
- The Battle for Trust: Winning the "Integrated Customer" by Overcoming the GenAI Divide
Why most generative AI investments do not pay back, and four ways to end up on the right side of the divide.
Which risks are we already running?
- Shadow AI: the internal research nobody commissioned
AI used outside company rules, read as free internal research: it shows where people already need tools.
- Beyond the Hype: A C-Suite Framework for AI Ethics, Bias, and Transparency
Hallucinations, bias (a model's systematic distortions) and haste: the three risks that end up in reputation and in the accounts.
A case
AI Transition: the outcomes at the end, function by function
From the service page, where Enrico approved them: marketing gains time on routine work and moves it to ideas; sales prepare proposals and answers to requests for quotation faster, with a shared method; R&D analyses patents and regulations with office agents; HR and leadership get a scope of use validated against the AI Act, with Shadow AI brought back in. These are described outcomes, without figures: the downloadable document on the page is the programme of the path.
The next step
Readers who got this far know where AI creates value and where it creates risk. AI Transition brings AI into the processes, on the company's own data and with the tools the team already uses, until the team governs it on its own: governance becomes working practice, with an owner and an indicator for every use.
Frequently asked questions
Where does a company without AI governance start?
With a snapshot of current use: which tools people use, on which data and under which rules. Shadow AI (the use of unauthorised tools) shows where the need is strongest; from there the company decides who is accountable for each use and which indicator measures its return.
Is AI governance a job for the IT department?
It is a job for leadership, because leadership decides where to invest, who is accountable and how the return is measured. IT looks after the technical side, security and systems integration; the choice of uses and indicators belongs to those who run the company.
What does the AI Act have to do with internal governance?
The European regulation on artificial intelligence classifies uses by level of risk and asks companies to know which systems they use, for what and with which skills. Internal governance is the most orderly way to get there prepared, because it has already written those answers.