ChatGPT in the world: real data on adoption and behavior

The context: from an experimental tool to an operational infrastructure

Until a few years ago, ChatGPT was perceived as a technological curiosity. Today the situation is radically different. According to data published directly by OpenAI, the tool has entered the work routine of millions of professionals around the world. The title of the report is explicit: From asking to doing. So, it's no longer about querying an answer engine. It's about delegating tasks.

This semantic shift has profound implications for those who deal with digital marketing. In fact, if users use AI to fare — writing, analyzing, planning — then companies that do not integrate these tools into their workflows accumulate a measurable competitive disadvantage. Therefore, understanding adoption data is not an academic exercise. It is a strategic priority.

The numbers that count: what OpenAI Signals data reveals

The program OpenAI Signals It collects aggregated and anonymized data on the global usage of ChatGPT. The results show clear patterns. First of all, adoption is unevenly distributed: some markets show much higher rates of professional usage than others.

In particular, three macro-trends emerge that are relevant for marketing managers:

• Shift toward complex tasks: Advanced users utilize ChatGPT for analysis, synthesis, and structured content production. No longer just quick answers.
• Increasing frequency of use: The average session duration is increasing. Furthermore, the number of daily sessions per active user is increasing.
• Diversification of use cases: marketing, customer service, and product research and development are the fastest growing verticals.

According to research from McKinsey on the Global AI Survey, the marketing and sales functions are among the first to adopt generative tools systematically. OpenAI data confirms this trend. Consequently, marketing is not a sector that undergoes AI: it is one of its main drivers.

Geographical differences: Europe and the structural lag

The data show significant differences between geographical areas. The North American and Anglo-Saxon markets lead professional adoption. Europe, by contrast, presents a more fragmented picture. Some countries — such as the Netherlands, Sweden, and Germany — show high rates of corporate adoption. Italy, however, is in an intermediate position.

This does not mean that the Italian market is irrecoverably behind. It means, rather, that a window of opportunity exists. Companies that integrate AI tools into their processes today SEO, copywriting e digital marketing they can build a real competitive advantage over competitors who wait.

Similarly, the gap between SMEs and large enterprises in AI adoption is still wide. However, access costs have dropped drastically. Therefore, the barrier is no longer economic: it is cultural and organizational.

Strategic reading: what these data mean for Italian marketing

Translating global data into local decisions requires an interpretive filter. We at SHM Studio We work daily with Italian SMEs and mid-market companies. As a result, we can directly observe the gap between awareness and operational adoption.

OpenAI data suggests at least three concrete strategic implications:

• AI-assisted content is already mainstream: Competitors are already using generative tools to produce content. Therefore, editorial quality and differentiation become even more critical. A Strategic copywriting cannot be replaced by pure automation.
• Campaigns must adapt to new search behaviors: if users use ChatGPT as an alternative search engine, strategies SEO e Google Ads must evolve accordingly.
• Behavioral data surpasses demographic data: to know as knowing whether your audience uses AI is more useful who Therefore, marketing managers should integrate this variable into their audience analyses.

According to Harvard Business Review, generative AI works best as an amplifier of human skills, not as a substitute. This perspective is particularly relevant for smaller marketing teams, where every resource needs to be multiplied.

The construction site is still open: what is missing in the current data

It is important to maintain a critical perspective. OpenAI Signals data are aggregated and come directly from the company that produces the tool. Therefore, there is a potential selection bias. Inactive users or those who have abandoned the tool are not represented with the same visibility.

Furthermore, the data focuses specifically on the use of ChatGPT. In contrast, the AI landscape includes tools such as Claude (Anthropic), Gemini (Google), and Copilot (Microsoft), each with different adoption patterns. A comprehensive reading of the market therefore requires a broader comparison.

Despite this, OpenAI data remains the most granular publicly available globally. Therefore, they represent a solid starting point for any strategic analysis of AI adoption in marketing.

Operational implications: where to start concretely

For marketing managers who want to translate these trends into concrete actions, we suggest a three-tier structured approach.

Level 1 — Internal Audit: map the marketing processes that currently take the most time. In particular, identify those that involve text production, data analysis, or research. These are the natural candidates for AI integration.

Level 2 — Controlled Experimentation: start a pilot on one or two specific processes. For example, the production of drafts for LinkedIn campaign or the generation of variants for A/B testing on Google Ads. Measure the time saved and the output quality.

Level 3 — Strategic integration: Once the use cases are validated, integrate AI tools into existing workflows. In addition, train the team on best practices for prompt engineering and editorial review. Finally, update performance metrics to include AI efficiency indicators.

To explore the possibilities of AI integration in marketing processes further, you can explore SHM Studio AI Services or consult the section dedicated to digital marketing.

2027-2028 Outlook: where adoption is heading

Current data is a snapshot. But the direction of travel is already legible. According to projections by Gartner on enterprise generative AI, by 2027, the majority of marketing functions in mid-market companies will use AI tools systematically. Not as an experiment, but as an operational standard.

Consequently, the competitive advantage will not stem from adoption itself—which will become commonplace—but from the quality of integration. Therefore, companies that build internal skills today, define clear processes, and select the right use cases will be better positioned in 2027-2028.

Similarly, digital agencies supporting SMEs will need to evolve their role. No longer just campaign executors, but strategic advisors on AI integration in marketing processes. This is precisely the path that SHM Studio he is building with his customers.

For those who want to explore these topics further or start a conversation, the starting point is the Contact Us or SHM Studio Blog, where we regularly publish analysis and updates on the digital landscape.