AI failures are often organizational, not technical, stemming from a lack of clarity in human decisions rather than model limitations. The core issues lie upstream in defining intent, providing oversight, ensuring sufficient context, using precise language, setting expectations, conducting evaluations, clarifying outcomes, and selecting appropriate tools. Anthropic's experience highlights that with AI accelerating code generation, the constraint shifts from building to deciding what to build. This increased speed necessitates better human alignment and control, emphasizing user experience and thoughtful planning.Many AI projects fail because they begin with a tool's capability rather than understanding user intent, leading to features that are never used. Starting with the user's problem in their own words, before any mention of AI, is crucial for aligning subsequent decisions. A common mistake is using overly powerful AI agents for simple tasks, which is inefficient and risky; matching the tool to the job, such as using an assistant for retrieval or automation for fixed steps, is essential. Agentic AI should only be employed when genuine decisions under uncertainty are required, with clear boundaries and human checkpoints.Setting clear expectations for users regarding input requirements and output trustworthiness is vital to avoid over-reliance or abandonment of AI tools. AI systems should clearly communicate their capabilities, limitations, and accuracy levels, much like a new hire receives onboarding. Most AI deployments lack clear ownership and accountability for system decisions and their consequences. Establishing robust oversight through permissions and checkpoints is the most reliable way to prevent harmful AI actions. Effectively, AI failures are not a failure of the machine, but of the human decisions that guide it.