The AI Hype Cycle vs. The Product Reality

We are awash in AI promises, but the dirty secret of the enterprise world is the vast graveyard of failed projects. Companies spend millions on data scientists, only to have their AI initiatives stall out, unable to make the leap from a laboratory proof-of-concept to a profitable product. Why? Because most AI failures aren’t technical—they are product failures.

Most AI strategies currently, are plagued by three fundamental product paradoxes:

1. The Solution Seeking a Problem: Too many teams start with the shiny new model or technology (e.g., “We need to use Large Language Models!”) instead of a clear, high-value business problem. They optimize a process by 10% when the business needed a new revenue stream.
2. The Data Hoarding Trap: Teams amass terabytes of data but lack a coherent, centralized data strategy. Data governance is an afterthought. As a result, 80% of data science time is spent on cleaning and wrangling, not innovating. AI dies when it’s starved of high-quality, actionable data.
3. The Missing Product Owner: AI projects are often handed entirely to the data science team, who focus on model accuracy (a technical metric) instead of customer value (a product metric). This gap—the lack of a Product Manager with accountability for the business outcome—is the primary reason projects end up on the “Algorithm Cliff.”

The Product Leader’s Role Shift

To escape the cliff, the Product Manager must stop being just a feature curator and become the AI Translator. A Product Mengers’s job is to bridge the gap between technical possibility and commercial viability. This involves asking the three basic questions which the data science team often overlooks:

• Is this solution desirable to the end-user?
• Is the risk and cost of implementation viable for the business?
• Can we ethically and legally deploy this model?