ChatGPT Around the World: Real Data on Adoption and Behavior

The context: from experimental tool to operational infrastructure

A few years ago, ChatGPT was seen as a tech curiosity. Today, things look very different. According to data published directly by OpenAI, the tool has become part of the daily work routine of millions of professionals worldwide. The report's title says it all: From asking to doing. It's no longer about querying an answer engine. It's about delegating tasks.

This shift in meaning has deep implications for anyone working in digital marketing. If users rely on AI to do things, such as writing, analyzing and planning, then companies that don't build these tools into their workflows pile up a measurable competitive disadvantage. Understanding adoption data isn't an academic exercise. It's a strategic priority.

The numbers that matter: what the OpenAI Signals data reveals

The OpenAI Signals program collects aggregated, anonymized data on ChatGPT usage worldwide. The results show clear patterns. First of all, adoption is uneven: some markets show far higher rates of professional use than others.

In particular, three major trends stand out for marketing managers:

• A shift toward complex tasks: advanced users rely on ChatGPT for analysis, summarization and structured content production. It's no longer just about quick answers.
• Growing frequency of use: the average session is getting longer. The number of daily sessions per active user is also rising.
• More diverse use cases: marketing, customer service, and R&D are the fastest-growing areas.

According to McKinsey's Global AI Survey, marketing and sales are among the first functions to adopt generative tools systematically. OpenAI's data confirms this direction. Marketing isn't a field that AI happens to: it's one of its main engines.

Geographic differences: Europe and its structural lag

The data shows significant differences between regions. North American and English-speaking markets lead in professional adoption. Europe, by contrast, presents a more fragmented picture. Some countries, such as the Netherlands, Sweden and Germany, show high rates of business adoption. Italy, however, sits somewhere in the middle.

That doesn't mean the Italian market is irrecoverably behind. It means there's a window of opportunity. Companies that build AI tools into their SEO, copywriting and digital marketing processes today can create a real competitive edge over competitors who wait.

Likewise, the gap between SMEs and large companies in AI adoption is still wide. But the cost of entry has dropped dramatically. The barrier is no longer financial: it's cultural and organizational.

Strategic takeaways: what this data means for Italian marketing

Turning global data into local decisions takes an interpretive filter. At SHM Studio, we work every day with Italian SMEs and mid-market companies. That gives us a direct view of the gap between awareness and real-world adoption.

OpenAI's data points to at least three concrete strategic implications:

• AI-assisted content is already mainstream: your competitors are already using generative tools to produce content. That makes editorial quality and differentiation even more critical. Strategic copywriting can't be replaced by pure automation.
• Campaigns need to adapt to new search behavior: if users treat ChatGPT as an alternative search engine, your SEO and Google Ads strategies need to evolve accordingly.
• Behavioral data beats demographic data: knowing how your audience uses AI is more useful than knowing who they are. Marketing managers should build this variable into their audience analysis.

According to Harvard Business Review, generative AI works best as an amplifier of human skills, not a replacement. That view is especially relevant for small marketing teams, where every resource has to be multiplied.

The work still in progress: what's missing from today's data

It's important to stay critical. OpenAI Signals data is aggregated and comes directly from the company that makes the tool. That means there's potential selection bias. Inactive users, or those who stopped using the tool, aren't represented with the same visibility.

The data also focuses on ChatGPT specifically. The AI landscape, on the other hand, includes tools like Claude (Anthropic), Gemini (Google) and Copilot (Microsoft), each with different adoption patterns. A full picture of the market calls for a broader comparison.

Even so, OpenAI's data remains the most granular publicly available worldwide. It's a solid starting point for any strategic analysis of AI adoption in marketing.

Practical implications: where to start

For marketing managers who want to turn these trends into action, we suggest a structured, three-level approach.

Level 1 — Internal audit: map the marketing processes that take the most time today. In particular, identify those that involve writing copy, analyzing data or doing research. These are the natural candidates for AI integration.

Level 2 — Controlled testing: launch a pilot on one or two specific processes. For example, drafting LinkedIn campaigns or generating variants for A/B tests on Google Ads. Measure the time saved and the quality of the output.

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

To explore how AI can fit into your marketing processes, check out the AI services from SHM Studio or visit the digital marketing section.

Outlook for 2027-2028: where adoption is heading

Today's data is a snapshot. But the direction of travel is already clear. According to Gartner's projections 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 operating standard.

As a result, the competitive edge won't come from adoption itself, which will become commonplace, but from the quality of integration. Companies that are building in-house skills today, defining clear processes and choosing the right use cases will be better positioned in 2027-2028.

Likewise, digital agencies that support 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 SHM Studio is building with its clients.

If you'd like to dive deeper or start a conversation on these topics, the best place to begin is the contact page or the SHM Studio blog, where we regularly publish analysis and updates on the digital landscape.