Aritificial Intelligence: Promise, Performance, and the Human Factor

“By 2030, AI will replace the equivalent of 300 million full-time jobs,” predicts a Goldman Sachs report. Yet, paradoxically, companies worldwide are still struggling to use it effectively.

Though AI promises high productivity, real-world results have often brought delays and errors instead of profits. This gap between promise and performance has sparked debate over whether AI is truly a solution or a new challenge.

Reports add that AI coding tools like Cursor Pro and Claude Sonnet caused experienced developers to spend 19% more time on their tasks. The main reasons cited were flawed suggestions, time wasted cleaning outputs, and AI’s lack of contextual understanding. Errors by AI adversely affect productivity, slow down development, and add extra financial burden to projects. This clearly demonstrates that human oversight is essential for AI to drive growth smoothly.

The impact is not limited to software; fields such as pharmaceuticals, healthcare, banking, and manufacturing also face AI-related errors that cannot be fixed without human intervention. Overconfidence in AI led many companies to carry out massive layoffs, assuming projects would be completed in the blink of an eye without human support. But the outcome was the opposite—projects were delayed, errors multiplied, and the costs of fixing them far exceeded expectations.

Instead of faster delivery, projects were delayed due to a high number of errors, which consumed extra hours and placed an additional financial burden on companies. The root cause of these delays was that AI-generated work, though completed faster, contained numerous errors that could only be fixed through human intervention. Though AI promises speed, reliability matters just as much, because too many errors mean extra time for corrections and added financial stress.

Reports show that AI adoption has not yet translated into profits for many companies. The reason for this lies in the lack of expertise, a shortage of skilled workforce, and the high cost of hiring specialists. Small and medium companies cannot afford to hire AI experts, as they demand very high salaries for their projects. This shortage has created a talent gap, where only the largest corporations can compete for skilled professionals, leaving others behind.

Over-dependency on AI may also weaken the cognitive skills of individuals, posing a serious threat to human talent. When routine thinking is outsourced to machines, imagination, reasoning, and problem-solving abilities begin to decline. This paradox—where AI was meant to make us smarter and faster but may instead erode critical thinking—needs careful attention.

The best way forward is a hybrid model, where humans and AI work collaboratively, reducing errors and improving outcomes. AI is well-suited for repetitive tasks, data processing, and automation, while humans excel in contextual judgment, creativity, and adaptability. When the strengths of both are combined, the results are more reliable and efficient.

Ultimately, AI can perform tasks many times faster, but it cannot replace human oversight. Its effectiveness depends on humans and AI working side by side, since the possibility of errors will always remain. The real challenge is not whether AI will replace humans, but how both can collaborate to boost productivity without sacrificing quality.