OpenAI has published in-depth research on how enterprises are evolving their use of artificial intelligence. It is no longer just about simple assistance. It is about the autonomous execution of complex processes. Tools like ChatGPT and Codex are being integrated into real operational workflows, with measurable impacts on productivity and costs.
However, research highlights a clear divide. The so-called frontier firms — companies that invest systematically in AI — are building a significant competitive advantage over those that take a sporadic approach. Therefore, the speed of adoption is not just a technological issue: it is a strategic choice with long-term consequences.
In this article, we at SHM Studio Let's analyze the key numbers from the research, read the implications for Italian SMEs and mid-market companies, and propose an operational interpretation of what it means today to integrate agentic AI into marketing, development, and operations processes. 2026 is the year when experimentation gives way to structured execution.
The context: From ChatGPT as a chatbot to ChatGPT as an operational agent
For years, artificial intelligence in business has primarily meant assistance. Answering questions, summarizing documents, generating text drafts. Useful support, but essentially passive. Today, the picture has changed substantially.
The research published by OpenAI on enterprise AI adoption It documents a precise transition: the most advanced organizations no longer use AI to respond, but to act. Systems like ChatGPT and Codex are being integrated into real operational pipelines capable of autonomously executing sequences of actions, coordinating different tools, and completing complex tasks without continuous supervision.
This paradigm shift has a technical name: Agentic AI. And its implications go far beyond individual productivity.
The numbers that matter: what emerges from OpenAI research
OpenAI's research is not a theoretical white paper. It is an empirical analysis of how enterprises are actually using these tools. Some patterns are emerging clearly.
First, the most advanced companies—defined frontier firms in the reference literature — they do not limit themselves to experimenting with AI in isolated silos. They integrate AI into core processes: software development, customer operations, data analysis, and content generation at scale. Furthermore, the gap between these organizations and companies with episodic adoption is widening rapidly.
A structural fact emerges strongly: companies that have invested in AI infrastructure—governance, training, API integration, agentic workflows—show compound returns over time. Conversely, those that use AI in an ad-hoc and non-systematic way struggle to capitalize on their initial investments.
This pattern is consistent with what was observed by McKinsey in its "State of AI" report, which for years has documented the correlation between the structured adoption of AI and superior financial performance.
What does “agentic” mean in daily practice
The term Agentic AI It risks remaining abstract if it is not anchored to concrete cases. Therefore, it is worth describing what actually happens in organizations that adopt it.
An AI agent does not just answer a question: it receives a goal, plans the necessary steps, executes actions on external tools — databases, APIs, web interfaces — and returns a result. For example, an agent integrated into a CRM can receive the task
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