Everyone wants to “do something with AI,” but not every business problem needs an AI solution.In this practical session, Max Golikov shares a decision-ready framework for evaluating which AI projects are worth funding, which should be simplified, and which should be stopped before they become expensive pilots with unclear value.The talk introduces the AI Value Matrix, a simple tool for assessing AI opportunities based on business value, technical feasibility, data readiness, implementation complexity, sponsorship, expected ROI, success metrics, and operational risk.Drawing on real-world work with European and international companies, including cases involving Allkind Group, Japan Tobacco International, C-Leanship, Vienna Insurance Group, and Homesearch, Golikov shows how AI projects are evaluated before implementation. The examples cover areas such as patent intelligence, AI-supported document workflows, maritime reporting, insurance processes, real estate data infrastructure, and AI adoption roadmaps.Attendees will leave with a practical structure they can use immediately to score AI ideas, define success metrics, identify hidden blockers, and decide whether to greenlight, reshape, or kill a project.The main message is simple: companies should not start with an AI model. They should start with the business problem, the value they want to create, and the proof they need before committing to implementation.