What OpenAI’s Usage Data Reveals About How Generative AI Is Reshaping Work, Teams, and Competition

The new How People Use ChatGPT report is one of the most useful pieces of evidence we’ve seen about how generative AI is actually being used — not in theory, but in the real workflows of hundreds of millions of people. It’s a clear view into what’s changing inside companies, how individuals are adapting, and where technology is already shaping decisions and output.

This isn’t a future trend to watch. It’s the current operating environment. And if you’re still thinking about AI as something “coming soon,” you’re already behind the 700+ million people who are building their lives and work around it today.

Here are my six takeaways from the report (and don’t just take my word for it, the whole thing is worth the read).

1. The baseline has moved. Stop treating AI like an experiment.

By mid-2025, ChatGPT had around 700 million weekly active users. Daily message volume grew more than five-fold in a single year (what!?!?). We’re well beyond early adoption. This is societal, structural change.

Employees already use these tools to write, plan, analyze, and solve problems. Customers do the same to make decisions and shape expectations. Companies around the world are restructuring workflows around AI.

Takeaway: If you’re still treating AI as a pilot or side project, you’re behind. The gap now is between companies building AI into their operating model and those that are not.

What to do: Stop waiting for perfect use cases and strategies. Identify where AI is already being used inside your business and formalize how you’ll support, scale, and govern it.

2. Writing and decision support deliver results now.

The biggest wins aren’t coming from futuristic use cases (despite what AI CEOs want us to believe). They’re happening in everyday work: writing, communication, and decision-making.

Drafting, editing, summarizing, synthesizing, recommending; these are where most usage is happening and where AI consistently saves time and improves outcomes. Will we get to totally futuristic workflows and mass automation? Yes, that’s coming. But today, people are fundamentally changing the way they work around this new co-pilot that’s always with them.

Takeaway: These use cases are where most organizations burn hours and headcount. Take the time and invest the money to train your employees on how to go from good to great.

What to do: Map the writing and decision-making workload. Target the highest-friction, highest-volume areas first. Redesign those workflows with AI built in. This isn’t about “AI strategy.” It’s about creating leverage where the work happens and leveling up your employees’ capability and capacity.

3. Generation creates leverage, but it needs guardrails.

About 40% of messages involve people asking the model to generate something. In work contexts, it’s more than half. This is where real productivity gains can come from: shifting people from blank-page work to creation and refinement.

But generation without oversight is risky. Output quality is uneven. Let’s be honest, sometimes the output is garbage and mistakes happen. The companies getting the most from AI aren’t the ones producing the most outputs. They’re the ones pairing AI generation with review, editing, and governance. They’re using technology to evaluate and observe the output, and they’ve mastered human-in-the-loop.

What to do: Build quality control into workflows from the start. Add review steps. Train people to interrogate and improve what the model produces. The risks are manageable, and the upside is significant.

4. Customers are ahead of most companies.

Over 70% of ChatGPT usage now happens outside of work. That shapes how people expect products and services to behave. Customers are getting used to conversational interfaces, personalized answers, and instant insight.

If your product still relies on static content, rigid interfaces, or slow human support, you’re behind their expectations, even if your people or customers haven’t told you yet.

Takeaway: Expectations aren’t changing, they’ve already changed. AI-powered experiences are the new normal. People expect them, and if you’re not delivering (and improving) you’re behind.

What to do: Audit your customer experience. Identify where AI could make interactions more responsive or useful. Build those capabilities now. By the time customers start asking for them explicitly, you’ll already be playing catch-up.

5. Competition is broader and faster.

Adoption is accelerating among younger users and in lower-income countries. Half of all adult messages now come from people under 26. Access to advanced tools is effectively universal, and early adopters aren’t just using AI, they’re all-in.

Talent is now more distributed and more capable. Competitors you haven’t heard of (often with smaller teams and lower costs) can now compete directly with your core value prop and do so at a fraction of the cost and in little-to-no time.

Takeaway: Look inside your category for leaders and take stock of what they’re doing. Look outside your category and core demographic for inspiration and trends. Disruption is all around you, and the most inspiring use cases might come from a completely different category.

What to do: Reassess your competitive edge. If it relies on scale, labor, or capital alone, it will erode. Your advantage must come from how effectively you combine human expertise and machine capability, and how quickly you adapt as the landscape shifts.

6. Tools aren’t the bottleneck. Organizations and people are.

Most companies treat AI adoption as a technology project. It isn’t. It’s an organizational design challenge.

New tools require new workflows. New workflows change roles, incentives, and decision-making. Companies that ignore this end up with “AI projects” bolted onto legacy processes. Companies that embrace it rebuild their operating systems around new capabilities.

Takeaway: As the CEO of an AI engineering firm, I want to say the issue is just tech — we can build just about anything for you. But to win in this era, it’s people as much (or more) than tech. We’re investing a ton of time and money into human behavior change and adoption. You should too.

What to do: Treat “AI” as a technology and culture redesign. Be bold, and keep an open mind about how teams are structured, how decisions are made, and how performance is measured. Don’t just add AI to the old machine. Rebuild the machine.

Overall TL;DR: Bold today is boring tomorrow

Generative AI isn’t the finish line. It’s the starting point. The real differentiator now is how quickly you turn capability into culture and how fast you shift from experimenting with tools to rethinking how your company works.

That means pushing teams to experiment constantly. It means expecting leaders to question old assumptions about how work gets done. And it means refusing to accept “good enough” processes that were built for a different era.

If you treat this moment like a technology upgrade, you’ll fall behind. If you treat it like a chance to redesign how you work and compete, you’ll create a gap no one else can close.