[{"data":1,"prerenderedAt":35},["ShallowReactive",2],{"mdc-m78beh-key":3},{"data":4,"body":5},{},{"type":6,"children":7},"root",[8,17,23,30],{"type":9,"tag":10,"props":11,"children":13},"element","h2",{"id":12},"the-reality-of-ai-automation",[14],{"type":15,"value":16},"text","The Reality of AI Automation",{"type":9,"tag":18,"props":19,"children":20},"p",{},[21],{"type":15,"value":22},"Most AI automation projects fail because they are treated as magic wands rather than software engineering projects. Companies bolt LLMs onto broken processes and expect transformation.",{"type":9,"tag":24,"props":25,"children":27},"h3",{"id":26},"the-real-solution",[28],{"type":15,"value":29},"The Real Solution",{"type":9,"tag":18,"props":31,"children":32},{},[33],{"type":15,"value":34},"We need to treat AI as infrastructure. That means robust error handling, human-in-the-loop fallback mechanisms, and clear measurable ROI.",1781551728751]