تدقيق جاهزية الذكاء الاصطناعي

You've bought tools but cannot prove ROI. There's a Copilot license for every engineer, an LLM API bill that grew 40% last quarter, a vendor contract for a GenAI platform nobody opens, and a chatbot that answers 200 customer questions a day. None of that is tied to a measurable business outcome. When the CFO asks whether AI is paying for itself, you genuinely do not know — and neither does your head of data.

Leadership cannot distinguish blockers from tolerable gaps. Your team brings you a list of 40 things that are 'AI risks.' Some of them are real procurement blockers — missing data lineage for regulated workloads, no evaluation harness, no incident response path for model failures. Some of them are magazine-article risks that will not matter for two years. You need someone outside the team to rank them, and your team cannot rank themselves honestly.

Nobody inside can audit the vendors. Your AI vendors demo well. They show you an eval they designed. They answer questions with the confidence of people who know your team will not check the math. You do not have an internal expert who can read a model card, stress-test a sales demo, or tell you whether the 'fine-tuning' in the contract is real fine-tuning or a system prompt. That asymmetry is costing you on every renewal.

The 90-day roadmap resets every quarter. Every three months a new model ships, a new vendor emails your CAIO, and your plan is rewritten to accommodate the latest thing. The plan has no hierarchy — no 'here is what we do regardless of what OpenAI ships next Tuesday.' Without that spine, the roadmap is reactive, and reactive roadmaps are how organizations spend three years on AI without compounding any advantage.