Before implementing AI automation, you must validate and refine a process manually. Applying AI to a flawed system doesn't fix it; it just makes the system fail more efficiently and at a larger scale, wasting significant time and resources.
AI generates ideas by referencing existing data, making it effective for research but poor for true innovation. Breakthroughs require synthesizing concepts from disparate fields and having a unique vision for the future—capabilities that AI lacks. It provides probable answers, not visionary ones.
Simply using AI provides no competitive advantage, as it's a widely available tool. A true, defensible moat is created by combining AI's capabilities with your unique domain expertise, proprietary processes, and established relationships. AI should augment your existing strengths, not replace them.
Contrary to intuition, providing AI with excessive or irrelevant information confuses it and diminishes the quality of its output. This phenomenon, called 'context rot,' means users must provide clean, concise, and highly relevant data to get the best results, rather than simply dumping everything in.
Just as you use different social media apps for different purposes, you should use various specialized AI tools for specific tasks. Relying on a single tool like ChatGPT for everything results in watered-down solutions. A better approach is to build a toolkit, matching the right AI to the right problem.