"Many AI projects fail. The reason is often simple. Teams try to rebuild last decade's applications but add AI on top: A CRM system with AI. A chatbot with AI. A search engine with AI. The pattern is the same: "X, but now with AI." These projects usually look fine in a demo, but they rarely work in production. The problem is that AI doesn't just extend old systems. It changes what applications are and how they behave."
"AI changes this model. Outputs are probabilistic. The same question asked twice may return two different answers. Results depend heavily on context and prompt structure. Applications now need to manage data retrieval, context building, and memory across interactions. They also need mechanisms to validate and control what comes back from a model. In other words, the application is no longer just code plus a database. It's code plus a reasoning component with uncertain behavior."
Many AI projects fail because teams add AI as a bolt-on to traditional applications, producing systems that demo well but fail in production. Traditional enterprise applications rely on deterministic workflows where identical inputs yield identical outputs. AI introduces probabilistic outputs, causing variability and dependence on context and prompt structure. Applications therefore must implement context pipelines, retrieval-augmented generation, and hierarchical per-user memory. Persistent process, midterm, and long-term memory are required to maintain continuity across interactions. Systems must also validate and control model outputs, turning applications into code plus a reasoning component with uncertain behavior, necessitating new designs.
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