Business and adoption
ROI, TCO, change management, team training, organisational transformation. The economic and organisational terms that decide the success of an AI deployment.
- AI crash-test of a board packAn AI crash-test runs your board pack through several cross-checked models before the meeting, to surface weak signals, internal contradictions and points that won't hold in the room. AI sharpens the reading; the decision stays yours.
- AI POCAn AI POC (proof of concept) is a short pilot project aimed at testing the technical feasibility and business value of an AI application before any industrial investment. According to estimates cited by RAND (2024), more than 80% of AI projects fail, twice the rate of IT projects without AI.
- AI ROIAI ROI, modelled on classic financial ROI, is a deceptively appealing metric for evaluating an AI deployment. Three structural confusions invalidate it: underestimation of hidden costs, lack of robust pre-AI baseline, and concentration on operational efficiency to the detriment of the other two dimensions of value.
- AI TCOAI TCO (total cost of ownership) is the real total cost of an AI deployment in enterprise, aggregating five items: inference model, infrastructure, application integration, operational supervision, and team cost. The visible cost (subscription, API consumption) is rarely its main item.
- AI use caseAn AI use case is a concrete application of artificial intelligence to an identified business process, with a measurable objective and a defined perimeter. The three most deployed use cases in enterprise in 2026 are software development assistance, customer service, and data analysis.
- Augmented productivityAI-augmented productivity refers to measurable production gains (volume processed, speed, quality) that a worker achieves when they benefit from generative AI assistance. Real-world studies show an average gain of about 15%, strongly concentrated on the least experienced profiles.
- Briefing a leadership team on AITo brief a leadership team on AI, start from what teams already do, not from a lecture on the technology. A good session sets out risk, compliance and decision, then ends with written decisions.
- Choosing an independent AI advisorAn AI advisor is independent when it takes no vendor commission, sells no tool or deployment, cross-checks several models and has a person sign every deliverable. For an SME or a mid-market company, that is what keeps the advice in service of the business and not of a catalog.
- Cost per tokenCost per token is the elementary economic unit of an AI deployment. Providers bill input tokens (your prompt) and output tokens (the model's response) separately, with a typical ratio of 1 to 5. Mastering this cost requires distinguishing unit prices, consumed volumes, and optimisation levers.
- LLM evaluation (evals, LLM as a judge)LLM evaluation (“evals”) means measuring the quality of a model's or AI assistant's answers on a set of representative cases, against success criteria set in advance. LLM as a judge is one of its methods: a second model grades the first one's answers, which makes evaluation possible at scale, at the cost of known biases.
- Shadow AIShadow AI refers to the use of generative AI models by employees without validation or framing from their company. Widespread in 2025-2026, it creates legal, financial (leakage of business data to public models), and quality (unaudited outputs integrated into client deliverables) risks.
- Strategic AI framing in 10 daysA strategic AI framing for general management takes 10 working days, at a fixed fee of €2,900 excl. VAT. It delivers an exposure map, a written usage policy, a validated and properly hosted tool, and a human oversight matrix.
- Usage disciplineAI usage discipline is the set of rules, methods, and habits by which a company orients the daily use of AI by its employees: which use cases to activate, how to formulate requests, which models to use, how to audit outputs. It is the dominant lever of the real return of an AI deployment.
- WorkslopWorkslop is AI-generated work content that looks like good work but lacks the substance to meaningfully advance the task. The term was coined in September 2025 in Harvard Business Review by researchers from BetterUp Labs and the Stanford Social Media Lab; its cost falls on the person who receives the document and has to fix or redo it.