Кёрк Дрейк в TNW: предприниматели сами ограничивают свой потенциал в ИИ

Kirk Drake at TNW: entrepreneurs are limiting their own AI potential

The Next Web columnist Kirk Drake has published a column arguing that companies have enough AI tools, but what prevents them from turning experiments into measurable results is not the technology itself — it is the willingness of leadership to change their approach to work.

Why AI is not delivering results

Artificial intelligence has become one of the defining business conversations of the decade — and the gap between talk and real results has turned out to be significant. According to research cited by Drake, organizations are broadly optimistic about the long-term value of AI. However, few manage to turn that optimism into a measurable economic impact.

The author points to a paradox: investment in AI is growing, discussions are multiplying, yet many organizations still approach the technology with caution — reluctant 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 those who manage them. The shift from "we are testing AI" to "AI is systematically changing our results" is determined not by model quality but by leadership's resolve to restructure processes.

• Organizations are optimistic about the long-term value of AI but slow to make real changes to how they work
• The gap between pilot launches and industrial deployment is primarily a management question
• The limitations 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" — that is how

Kirk Drake formulates the central thesis in his column for The Next Web.

In Drake's view, many companies treat AI as an addition to old processes rather than a foundation for rethinking their business model. That is precisely why experiments remain experiments — instead of becoming a new operational standard.

What prevents entrepreneurs from scaling AI

Drake poses a practical question to his readers: what exactly is preventing them 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 on a different plane: the habit of working the way they have always worked.

By 2026, language models and AI agents have mastered a wide range of business tasks — from content generation to data analysis and the automation of operational routine. Technical capabilities have ceased to be the limiting factor for the majority of business use cases. The question is whether entrepreneurs allow themselves to leverage these capabilities without holding back out of fear of change.

What this means

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