Uber expands ChatGPT Work to 30,000 employees

Uber puts ChatGPT Work in front of its entire workforce

Uber and OpenAI have expanded their partnership, and Uber says its entire global workforce of about 30,000 people will now use ChatGPT Work, according to TechCrunch. The company had previously tested the tool with 1,000 employees earlier this year. That is a big jump in scope, not a small product tweak. It moves the discussion from whether AI can help a team to how an enterprise controls it once it reaches every function.

The pilot was not limited to engineers

Uber’s earlier pilot included workers from finance, operations and product, Sumanth Sukumar, Uber’s VP of engineering, told TechCrunch. He said that a majority of the early adopters, about 80%, were non-engineers. That detail matters. It suggests the company was not only testing AI in technical teams that are already comfortable with new software. It was pushing the tool into parts of the business where the work is less about code and more about decisions, coordination and process.

For executives, that is the more important signal. If non-engineers are using the tool at that level, AI is no longer just a support layer for analysts or developers. It is starting to sit inside core business work. That raises practical questions about training, permissions, quality control and whether teams are using the same standards when they rely on the tool for day-to-day decisions.

Source: TechCrunch

The first use cases were operational, not flashy

According to Sukumar, employees used ChatGPT Work for a wide range of tasks, including analyzing complex operational data to identify trends and develop recommendations, localizing training materials for different markets and languages, and building personalized interactive dashboards to manage daily work. These are not side projects. They are daily workflows tied to how the business runs, how people learn, and how managers monitor performance.

That is why this rollout matters beyond Uber. When a company starts applying AI to operational analysis, internal training and management dashboards, the value case is less about novelty and more about speed, consistency and scale. The upside can be real. But the downside is also real if the outputs are wrong, inconsistent or poorly governed. Leaders should read this as a sign that AI is moving into the work that shapes execution, not just the work that produces presentations.

Source: TechCrunch

Uber is still experimenting with how employees should use it

Even with the company-wide rollout, Uber does not seem to have settled on one fixed playbook. Sukumar said the company wants to make powerful AI tools available across Uber while giving teams room to experiment. He also pointed to an earlier effort in which Uber paired 30 AI engineers with teams across the company and gave them two weeks to automate key parts of their jobs. That trial showed how much opportunity appears when technical expertise meets a deep understanding of a specific role.

That approach is sensible, but it also creates a management challenge. A company-wide tool can quickly produce dozens of local workflows, each with its own assumptions and risks. The question for leadership is not whether experimentation is useful. It is how fast the company can turn those experiments into repeatable standards for access, review, documentation and accountability.

Source: TechCrunch

OpenAI is pushing hard into enterprise accounts

TechCrunch reports that ChatGPT Work was introduced in July as an enterprise-focused AI agent that corporate teams could deploy for different workflows. OpenAI has moved quickly to scale its enterprise business as it competes with Anthropic. The company, once known largely as a consumer brand, has gained a loyal following among business customers while other AI companies have struggled to keep up.

That competitive pressure matters for buyers. The market is no longer just about model quality or who has the loudest product launch. It is also about which vendor can support enterprise deployment at scale, integrate into existing work, and prove value to finance, operations and product leaders. For companies choosing a platform, the decision is becoming a long-term operating choice, not a short-term software test.

Source: TechCrunch

What to do now

If your company is moving from pilots to broader AI deployment, the priority is no longer curiosity. It is control. Start with the work that is already repetitive, cross-functional and easy to measure, then decide how you will govern it before usage spreads on its own.

• Map the workflows where employees are already experimenting with AI, especially in finance, operations and commercial teams.
• Set clear rules for what data, decisions and documents can be handled through the tool.
• Define who reviews outputs, who owns errors and how exceptions get escalated.
• Track adoption by role and function, not just by license count.
• Build a short list of use cases that can be standardized this quarter.