ChatGPT in the world: real data on adoption and behaviors

The context: from experimental tool to operational infrastructure

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

This semantic shift has profound implications for those working in Digital marketing . In fact, if users use AI for to do — 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 matter: what OpenAI Signals data reveals

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

In particular, three macro-trends relevant to marketing managers are emerging:

• Shift towards complex tasks: advanced users are using ChatGPT for analysis, synthesis, and producing structured content. No longer just quick answers.
• Increasing usage frequency: the average session is getting longer. 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 by McKinsey on the Global AI Survey , marketing and sales functions are among the first to systematically adopt generative tools. OpenAI data confirms this trend. Consequently, marketing is not a sector that is subjected to AI: it is one of its main drivers.

Geographic differences: Europe and structural lag

The data shows significant differences between geographic areas. North American and Anglo-Saxon markets are leading professional adoption. Europe, on the other hand, presents a more fragmented picture. Some countries — like the Netherlands, Sweden, and Germany — show high corporate adoption rates. Italy, however, is in an intermediate position.

This doesn't mean the Italian market is hopelessly behind. Rather, it means there's a window of opportunity. Companies that integrate AI tools into their processes today are SEO , Copywriting and Digital marketing 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 drastically reduced. 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 SMBs 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 are becoming even more critical. A strategic copywriting it cannot be replaced by pure automation.
• Campaigns must adapt to new search behaviors: if users use ChatGPT as an alternative search engine, the strategies of SEO and Google Ads must evolve accordingly.
• Behavioral data surpasses demographic data: to know how how your audience uses AI is more useful than knowing who is. 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 small marketing teams, where every resource needs to be multiplied.

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

It's important to maintain a critical perspective. OpenAI Signals data is aggregated and comes directly from the company that produces the tool. Therefore, there's 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 ChatGPT usage. In contrast, the AI landscape includes tools like Claude (Anthropic), Gemini (Google), and Copilot (Microsoft), each with different adoption patterns. A comprehensive market analysis therefore requires a broader comparison.

Despite this, OpenAI data remains the most granular publicly available globally. Therefore, it represents 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 turn these trends into concrete actions, we suggest a three-tier structured approach.

Level 1 — Internal Audit: map marketing processes that are currently time-consuming. Specifically, identify those involving 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, producing drafts for LinkedIn campaigns or generating variants for A/B testing on Google Ads . Measure time saved and output quality.

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

To dive deeper into the possibilities of integrating AI into marketing processes, you can explore the SHM Studio AI services or consult the section dedicated to Digital marketing .

Outlook 2027-2028: where adoption is heading

Current data is just a snapshot. But the direction of travel is already clear. According to projections by Gartner on enterprise generative AI , by 2027 most 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 come from adoption itself — which will become common — but from the quality of integration. Therefore, companies that build internal expertise 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 exactly the path that SHM Studio building with their clients.

For those who want to dive deeper or start a conversation on these topics, the starting point is the contact page or the SHM Studio blog , where we regularly publish analysis and updates on the digital landscape.