Why Historical Activities Are the True Bottleneck in the AI Revolution

The Illusion of the Perfect Model

In the rush to adopt AI, many assume that success hinges on choosing the most advanced model, crafting clever prompts, and fine-tuning with high-quality data. But this mindset often leads to projects that look impressive in demos yet fail in real-world use. The truth is, the most powerful AI in the world is useless without a clear understanding of the business context it is meant to serve.

What I Learned from Building an AI System

Three months ago, I took on a project to build an AI-powered review system from scratch. I have no coding background, but I knew the business deeply. In that time, I discovered three counterintuitive truths that changed my perspective on AI applications—truths that have everything to do with historical activities, the accumulated knowledge of how things have always been done.

The Real Value of Historical Activities

Historical activities are the institutional memory of an organization: the unwritten rules, the common exceptions, the patterns that emerge only after years of hands-on experience. When I first deployed my AI system, its accuracy was a mere 60%. The failures were not due to a lack of intelligence in the model, but a lack of this historical context. For instance, the model would reject a claim because the complainant's name didn't match the authorized document, not realizing that in practice, such mismatches are common and often harmless.

Why Business Rules Are the True Bottleneck

To fix this, I spent a week compiling a dictionary of all the major entities, their aliases, and their historical authorization patterns. This simple table, with no technical sophistication, boosted accuracy by 12 percentage points. The lesson? AI can write code, but it cannot write business rules. Those rules are distilled from years of immersion in the business—they are the essence of historical activities.

The Importance of Knowing What Not to Do

As my system improved from 60% to 97.8% accuracy, I realized that true maturity came not from more features, but from a clear list of what the system would not do. This 'not-to-do' list included things like avoiding AI-only decisions on international cases, where a mistake could have severe consequences. Only someone with deep business knowledge could define such boundaries.

The New Species: Business-Immersed AI Practitioners

These insights point to a new kind of professional: someone who combines business immersion, AI application skills, and a strong sense of boundaries. They are not traditional product managers or engineers; they are people who use AI as a tool to amplify their business expertise. In this AI era, the most valuable skill is not coding, but the ability to articulate business rules clearly—a skill born from historical activities.

Three Questions to Assess Yourself

If you're wondering whether you fit this new species, ask yourself: Can you explain step-by-step how a task is done without AI? If not, you're just chasing hype. When you hit technical problems, do you struggle alone or leverage AI to help you understand? And can you list what AI should not do in your domain? If you can, you've crossed the threshold.

In conclusion, the AI revolution is not about models or prompts; it's about people who deeply understand their business—people who have soaked in historical activities and can translate that knowledge into clear rules for AI. These are the ones who will truly harness AI's potential.