Data, AI, automation: how to stop confusing your needs
Most projects fail not from lack of technology, but from confusion between what you want to do and what you actually need. A framework for making the right call.
Everyone wants AI. Executive committees mention it in their back-to-school presentations. IT leadership receives budgets for "AI transformation." Project teams open tickets to "integrate ChatGPT into the process." Yet the majority of these initiatives end in abandoned POCs, budgets spent without measurable value, and a diffuse frustration that eventually feeds skepticism.
The cause is rarely technical. Large language models work. RAG architectures work. Automation tools work. What does not work is clarity of need upfront. AI is requested where data is needed. Automation is built where understanding is needed. Chatbots are constructed where a clean data warehouse is needed.
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