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 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 RAND 2025, 80.3% of enterprise AI POCs fail to deliver their expected business value.
- 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. According to RAND 2025, production overcosts average 380% of pilot estimates.
- 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 14 to 15%, strongly concentrated on the least experienced profiles.
- 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.
- 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.
- 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.