AI Governance

It is a system that learns from large volumes of data and uses what it has learnt to make predictions, generate content or automate decisions. It does not reason like a human being: it recognises patterns and applies them. It is remarkably effective at defined, repetitive tasks. It is still weak at tasks that require contextual judgement, accountability and genuine creativity.

If you do not have a precise figure for how much you spend on AI and how much it delivers, you are probably overspending on something. The AI Audit maps every cost and compares it with the results. In most companies, it uncovers active subscriptions that nobody uses and implementations that have never produced a verifiable result.

A 2024 MIT study found that AI automation is economically viable in only 23% of the jobs analysed: specifically, those involving computer vision tasks with wages low enough to make automation competitive against human cost. For the remaining 77%, human workers cost less. AI replaces specific tasks, not entire roles. And it does so cost-effectively only where volume is sufficient to justify the cost. AI Governance identifies exactly these areas, without overestimating them.

Always with the audit. Before deciding what to do with AI, you establish what is already in place and what it is producing. It is the step almost no one takes, and it almost always reveals surprises. Bliss defines the scope of the audit in a working session, before any commercial proposal.

It is for any company spending on AI without a system to measure what it produces. An SME with three ungoverned AI subscriptions has the same structural problem as a large enterprise with thirty, just on a smaller scale. The system is calibrated to the company’s size and actual processes.

The AI Audit takes two to three weeks. The subsequent phases up to delivery of the Governance System take around three months. Ongoing monitoring has no set duration: it is structured in quarterly review cycles.

No. AI Governance does not require technical skills: it requires clarity on business objectives. What problems do I want to solve? How do I measure the result? Who is responsible for decisions? These are strategic questions. The technical skills for implementation come later, and only once the strategy has been defined.

AI increasingly shapes the way a brand communicates: the content it produces, how it is described by ChatGPT and Perplexity, the speed at which it responds to customers. Governing AI also means governing these touchpoints, ensuring that the tools adopted produce output consistent with the brand’s positioning.

The most common difficulty is not technical. It is that AI governance requires decision-making authority that often has not been assigned to anyone. Who decides what is adopted, through what process, against what evaluation criteria? Bliss builds that structure and hands it over to those who must apply it over time.

No. AI is not an obligation, it is a tool. And like any tool, it is worth as much as the problem it solves. In today’s market, however, one thing is worth remembering: it is not the big fish that wins, it is the fast fish. A company that learns to use AI well (even on just three processes) gains a real advantage over those waiting for the picture to become clearer. The picture will not become clearer. The market is moving now.

AI washing is the practice of claiming AI capabilities that do not actually exist or are marginal, in order to appear more innovative or attract investment. It is the technological equivalent of greenwashing. You can spot it when a supplier cannot answer simple questions: which model it uses, what data it was trained on, how the result is measured. If the answers are vague, the problem is usually not the question.

Bubbles inflate assets that produce no real value. AI produces real value: it shortens execution times, lowers costs on repetitive processes and improves the quality of decisions when used well. That there is excessive speculative investment is likely. That the underlying technology will disappear is unlikely. The right question is not whether AI is a bubble, but whether what you are using delivers a measurable return.

The figures are striking. Alphabet has announced 2026 capex of between 175 and 185 billion dollars, almost double the 91.4 billion invested in 2025. Amazon has announced investments of up to 200 billion in the same year. These are the budgets of the global hyperscalers to build the infrastructure needed to compete in the AI economy. For most companies, the relevance of these figures is not in imitating them: it is in understanding that the infrastructure cost of AI has already been borne by others. Today value is created in use, not in construction. And in use, governance is what separates those who invest well from those who burn budget.