A recent MIT report, The GenAI Divide: State of AI in Business 2025, reveals that nearly 95% of generative AI pilots never achieve meaningful production impact. The issue isn’t with the technology itself — it’s in how companies approach it. At YTC, we’ve analyzed these failure points closely and built our process to overcome them. 1. Misalignment with core workflows and business context One of the biggest reasons AI projects fail is poor alignment with actual business operations. Too often, solutions are designed in isolation — impressive from a technical standpoint, but disconnected from how teams truly work. When an AI system doesn’t fit within existing workflows or address real objectives, it becomes a burden instead of an advantage.

Building a data lake might sound like a task for large tech corporations or data scientists in lab coats. However, in reality, it’s a practical and strategic move for any business seeking to harness the full potential of machine learning. If you're gathering data and considering how to utilize it for informed decisions, automation, or AI-powered services, you need a data lake. At YTC, we help businesses transform their scattered data into structured, accessible, and scalable systems. In this article, we’ll walk you through what a data lake is, why it's essential for machine learning, and how to build one the right way.

Artificial intelligence has become a boardroom fixture: investments are soaring, pilots are proliferating, and the promise of transformation is everywhere. Yet as the BCG research shows, the vast majority of organisations are not realising value. Only about 26 % of companies have developed the capabilities needed to move beyond proof-of-concept and extract measurable value, and just 4 % are at the frontier of AI-driven transformation. What this tells us is that the technology itself is not the barrier. The obstacle lies in people, processes, integration, governance, and culture. Why people and processes matter more than algorithms Among the most striking findings of the report is the so-called “10-20-70” distribution of focus for successful AI transformations: approximately 10 % of effort is directed at algorithms, 20 % at technology and data, and a full 70 % on people and processes. This may seem counterintuitive in an era of hype around models like LLMs and generative AI — yet the empirical evidence speaks clearly. Companies that invest heavily in people, workflows, change management, process redesign, and the human embedding of AI outperform those that focus primarily on hard tech. Even building a team using AI is more efficient now.