- The MIT report indicates that 95% of organizations fail to profit from AI despite investing billions of dollars, showing that failure lies not in technology but in operation.
- The core issue is the “trust gap,” where businesses do not trust AI’s automated results, leaving projects stuck in the pilot phase.
- The cause stems from old organizational models designed for humans, which are unsuitable for managing AI agents and lack clear oversight mechanisms.
- A typical example is the 2023 Robodebt case, where faulty algorithms were deployed due to a lack of control, causing serious systemic consequences.
- Businesses need to shift from data management to designing a “logic of choice,” meaning architecting decisions instead of just building data products.
- The concept of a “decision product” emerges, combining data, logic, rules, and ethics into a transparent, auditable unit.
- The new model requires human-in-the-loop and human-on-the-loop to ensure humans still control and monitor AI in real-time.
- CIOs need to build a decision catalog, standardize authorization mechanisms, and establish continuous monitoring systems to put AI into actual operation.
📌 The biggest bottleneck of AI is not technology but trust and organizational structure. When 95% of businesses have yet to create value, switching to a decision-driven model with decision products, real-time monitoring, and human-in-the-loop mechanisms is mandatory. This is the key for generative AI to escape experimentation and become a real, sustainable value-creating tool in the enterprise.
