Kirk Drake in TNW: Entrepreneurs Are Limiting Their Own AI Potential

The Next Web columnist Kirk Drake has published a column with a clear thesis: companies already have enough AI tools, but what keeps them from turning experiments into measurable results is not the technology. It is leadership's willingness to change how work gets done.

Why AI Isn't Delivering Results

Artificial intelligence has become one of the defining business conversations of the decade, and the gap between talk and real results has proved significant. According to research Drake cites, organizations are generally optimistic about AI's long-term value. Few, however, manage to turn that optimism into measurable economic impact.

The author points to a paradox: AI investment is growing and the discussion is getting louder, yet many organizations still approach the technology cautiously and hesitate to move beyond pilot projects.

Mindset Matters More Than Tools

Drake's central argument is that the problem lies not in the tools, but in the attitudes of the people running them. The shift from "we're testing AI" to "AI is systematically changing our results" is determined not by model quality, but by whether leadership is determined enough to rebuild its processes.

• Organizations are optimistic about AI's long-term value, but slow to make real changes in how they work
• The gap between pilot launches and production deployment is largely a management issue
• The constraints come from the entrepreneurs themselves, not from the technical capabilities of the platforms

"The real AI advantage begins when entrepreneurs stop limiting their own potential" — this is how

Kirk Drake frames the central thesis of his column for The Next Web.

In Drake's view, many companies treat AI as an add-on to old processes rather than as a reason to rethink the business model. That is why experiments stay experiments instead of becoming the new operating standard.

What Keeps Entrepreneurs From Scaling AI

Drake puts a practical question to readers: what exactly is stopping you from using AI to its full potential right now? If the answer comes down to infrastructure or tool functionality, that is one story. But more often, as the logic of the piece suggests, the answer lies elsewhere: in the habit of working the way things have always been done.

By 2026, language models and AI agents have mastered a wide range of business tasks, from content generation to data analysis and automation of routine operations. Technical capability has stopped being the limiting factor for most business cases. The question is whether entrepreneurs allow themselves to use those capabilities, or let fear of change hold them back.

What This Means

Scaling AI in business is a management question, not a technical one. Companies that are first to remove their internal barriers and move from experiments to systematic use will gain a competitive advantage not through better tools, but through a bolder approach to using them.