AI is making many mistakes, especially in testing and validation. In many organizations, employees are becoming overly dependent on AI and are gradually losing fundamental problem-solving skills. AI is a tool, not a complete solution. Without strong domain knowledge and fundamentals, AI-generated results can be inaccurate and sometimes create bigger issues than they solve.
Many companies are rushing to automate everything with AI, but blindly trusting AI outputs can lead to costly mistakes, rework, and quality problems. In addition, the increasing use of AI models results in higher token consumption, growing infrastructure costs, and expensive subscription fees.
The most effective approach is to use AI as an assistant while keeping experienced employees involved in critical thinking, verification, testing, and decision-making. Human expertise combined with AI is far more valuable than relying entirely on AI.