What Do You Want?
Many corporate AI initiatives focus on metrics like cost efficiency and output volume, but often fail to align with actual organizational goals. The core issue is not technical, but one of unclear intent—organizations struggle to define what they truly want. Without a clear, actionable purpose, AI systems may optimize for the wrong outcomes despite appearing successful on the surface.
- ▪AI systems in corporations often optimize for measurable metrics rather than real objectives, leading to misaligned outcomes.
- ▪The fundamental challenge is not poor specifications or data quality, but the lack of a clearly defined and actionable organizational intent.
- ▪Organizations frequently cannot answer basic questions about their goals, trade-offs, and how to detect when AI initiatives are going off track.
Opening excerpt (first ~120 words) tap to expand
What Do You Actually Want? May 17, 2026 An agent is supposed to win a boat race. It gets rewarded for collecting green blocks. So it drives in circles, racks up points, and never finishes the race. The score looks excellent. The purpose is missed. That sounds like a neat lab anecdote from AI research. In practice it is also a fairly precise description of many corporate AI initiatives. The dashboards look good. Activity goes up. Cost per task goes down. The number of generated artifacts rises. And yet it becomes less clear, not more, whether the system is optimizing for the right thing. At that point the problem often is not the model. The problem is that the organization itself cannot clearly say what it actually wants.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at dekodiert.
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