AI in Development: Beyond Technical Facades

The integration of Artificial Intelligence (AI) into business processes and software development raises significant questions regarding its actual economic efficiency. While technical challenges like agent isolation are widely discussed, critical organizational, economic, and philosophical aspects of AI implementation often remain underexposed in public discourse.

Recent experiences over the past two years indicate that building AI-powered products and integrating AI into development processes are fraught with numerous unaddressed issues. The current fascination with AI is frequently likened to previous “mass psychoses” such as blockchain or NFTs. Although each phenomenon has a real technological or problem-solving core, public attention often becomes disproportionately exaggerated, leading to an uncritical approach and a loss of objectivity.

Managing Development with AI Agents: Shifting from Tasks to Context

Many companies, eager to align with the trend, acquire AI licenses, announce ambitious AI strategies, and showcase impressive prototypes. However, in practice, it often emerges that the products fail to address real user needs, and accountability for results becomes diluted across presentations, contractors, and chatbots themselves.

The core issue lies not so much in the AI models themselves, but in weak development management. If a traditional project lacks a clear understanding of its goals, target audience, and evaluation criteria, AI will merely accelerate the accumulation of errors. Instead of focusing on “implementing AI into the company” or measuring efficiency by the number of generated code lines, a more productive approach might involve utilizing an AI agent as a consistent participant in the development team. This shifts the emphasis from executing specific tasks to establishing and maintaining the overall project context, which can significantly enhance the meaningfulness and effectiveness of AI application in software creation.