AI Vendor Selection, Procurement, and Innovation Radar

AI procurement begins long before an RFP is issued and continues well after a contract is signed. AI Vendor Selection, Procurement, and Innovation Radar gives leaders a disciplined way to turn business needs into defensible buying decisions, with clear requirements, evidence, ownership, controls, and review points. The book shows why polished demonstrations, crowded feature lists, and weighted scores can mislead teams when mandatory security, privacy, rights, interoperability, support, resilience, or exit conditions have failed. Readers learn how to define bounded use cases, separate essential gates from preferences, test vendor claims with representative data and workflows, assess supplier architecture and dependencies, examine total cost, and negotiate terms for data use, model change, incidents, audit, portability, deletion, and transition. A continuing case follows Alder Group as it compares enterprise AI assistants under commercial pressure, replaces feature-first evaluation with evidence-led testing, and chooses architecture fit over presentation polish. Decision records, scorecards, risk registers, benchmark plans, contract schedules, readiness checks, and a thirty-day action plan help teams turn policy into inspectable work. The innovation radar adds a disciplined method for watching market signals, vendor roadmaps, standards, incidents, and alternative technologies without turning every new development into a purchase. Examples show how small gaps in handoffs, evidence dates, or change notices can alter an otherwise confident recommendation. Written for procurement leaders, technology executives, product teams, risk professionals, analysts, and organizations buying AI, this book connects sourcing, governance, operations, renewal, and exit in one coherent method. Its central message is direct, test the claim, preserve the evidence, assign authority clearly, and remain accountable for the outcome.