GoKawiilOpinions expressed by Entrepreneur contributors are their own.
We’ve entered a stage where AI is no longer optional for entrepreneurs. The question is no longer whether to use AI, but how to use it effectively to reduce costs, scale smarter and operate more efficiently.
This shift is accelerating as AI tools become faster and more sophisticated. With new breakthroughs unlocking new capabilities, building strategic applications of large language models (LLMs) has become essential for entrepreneurs who want to stay competitive.
The speed breakthroughs reshaping how entrepreneurs build
One of the most exciting developments is the rise of advanced training methods that make it easier to build scalable, customized AI systems. In my own marketing agency, we’ve found that tailored LLM solutions significantly outperform generic public models that lack access to internal business data. However, building custom models has traditionally been resource-intensive and technically complex.
Research from 0G Labs highlights that emerging decentralized training methods, such as DiLoCoX, can train models up to 357 times faster than previous decentralized approaches—even for models with more than 100 billion parameters. By enabling training across networks as limited as one gigabyte bandwidth, these innovations open the door for more businesses to develop their own internal LLMs without relying on large data center infrastructure.
For entrepreneurs, this represents a meaningful shift away from one-size-fits-all tools toward AI systems trained on proprietary business data. These models can generate more relevant insights for financial planning, sales forecasting and operational decision-making — the same areas where I’ve seen the greatest impact in scaling marketing performance.
Data readiness is now the real bottleneck
Despite these advances, most businesses are not yet prepared to take advantage of them. According to Gartner, as many as 60% of AI projects may be abandoned by 2026 due to poor data readiness. The issue is rarely the model itself — it’s fragmented, inconsistent or siloed data. For entrepreneurs, this means the first step isn’t building AI tools—it’s organizing and unifying the data those tools depend on. In practice, this often requires cross-functional alignment and careful attention to compliance when working with clients or internal systems.
AI is about expanding capacity, not replacing people
... continue reading