Leading Companies Leverage AI 3.5 Times More Intensively

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Key Takeaways

1Leading companies use 3.5 times more intelligence per employee than typical companies, up from 2 times the previous year.

2Agentic workflows are a key indicator, with a 16 times higher usage of Codex compared to standard companies.

3AI is being integrated into various fields, from writing to financial analysis, with increasing specialization by function.

💡Why it matters — The growing gap in AI usage could widen the performance differences between leading and typical companies.

The Intensive Use of AI by Leading Companies

Leading companies have significantly intensified their use of artificial intelligence, now leveraging 3.5 times more intelligence per employee than typical companies. This figure has increased from 2 times the previous year. The gap is not limited to the volume of messages exchanged, which accounts for only 36% of the difference. The bulk of this advantage comes from a more sophisticated and in-depth use of AI. Employees in these companies ask AI to manage complex tasks, providing rich context and generating substantial results.

In this context, the generated tokens serve as an indirect indicator to measure the intensity of the intelligence requested. Although they do not directly reflect business value, they indicate the amount of work entrusted to AI. Traditional companies primarily use AI to answer simple questions, while leading companies exploit it to perform complex tasks, with each interaction accomplishing a greater portion of the actual work.

The Emergence of Agentic Workflows

Agentic workflows are becoming a marker of maturity for leading companies. The gap is particularly notable in the use of advanced tools like Codex, where these companies send 16 times more messages per employee than typical companies. Similar trends are emerging with ChatGPT Agent and other tools, showing effective adoption of technologies that enable employees to code, delegate complex tasks, and conduct advanced research.

As AI systems evolve to manage multiple files and codebases, companies must adapt to the idea of delegating concrete tasks to AI agents. This transition to more advanced technologies demonstrates that leading companies are not merely using AI for simple tasks but are deeply integrating it into their processes to maximize efficiency and innovation.

Increasing and Specialized Integration of AI

AI is increasingly integrated into production workflows across the enterprise. It is used in various areas such as embedded application assistants, coding tools, development, and customer support. While writing and communication remain the most common uses, AI is specializing according to functions. IT teams focus on practical guides, developers on coding, and finance teams on analysis and calculations.

This trend shows that AI is moving beyond simple general productivity towards tasks more closely related to the core responsibilities of each function. Companies that succeed in integrating AI in a specialized manner can thus enhance their operational efficiency and capacity to innovate in their respective fields.

Learning from AI Leaders

The gap between leading companies and typical companies is not fixed. Many organizations are just beginning to integrate AI more deeply. Leading companies demonstrate that education and learning are crucial for leveraging AI. They measure the depth of usage, establish appropriate governance, and evolve towards delegated work to agents.

The interest in this leading-edge approach is that it shows which practices seem to help companies create momentum over time. One of the clearest signals concerns education and learning, areas where the leading advantage at the task level is most significant. This suggests that the most advanced companies are using AI not only to accomplish tasks but also to help their employees develop the skills, habits, and confidence needed to use it effectively.

B2B Signals: Ongoing Analysis of AI in Business

B2B Signals tracks trends among leading companies, providing a clear view of the transformation of AI into value. This initial analysis focuses on deep usage and agentic workflows. Future updates will follow these developments and adjust the signals as AI in business progresses.

Leaders need clear signals to understand what enables the transformation of AI adoption into value for the business. B2B Signals provides regular analyses on AI in business, helping organizations understand how leaders are transforming intelligence into value for the enterprise. Future updates will track progress regarding these metrics and adapt the signals as AI in business evolves.