OpenAI Taps Former Slack CEO Denise Dresser to Scale Its Enterprise Revenue Engine

OpenAI Taps Former Slack CEO Denise Dresser to Scale Its Enterprise Revenue Engine

Key Takeaways

• Denise Dresser joins OpenAI as Chief Revenue Officer, bringing leadership experience from Slack and Salesforce.
• OpenAI now serves more than one million business customers through ChatGPT for Work and its API ecosystem.
• The company cites measurable productivity gains among workers as AI adoption moves from pilots to organization‑wide deployment.

OpenAI has added a major operator to its executive bench. Denise Dresser, best known for leading Slack through its integration with Salesforce, is stepping in as Chief Revenue Officer with a remit to oversee global revenue strategy across enterprise sales and customer success. It’s a straightforward move on paper, but it says a lot about where OpenAI believes enterprise AI is heading—and how the company wants to position itself as AI shifts from experimentation to everyday workflow infrastructure.

The announcement emphasizes a familiar arc in Dresser’s career: she’s run large, complex businesses; she’s built and scaled sizable sales organizations; and she’s done it in markets where adoption hinges as much on user enthusiasm as on CIO sign‑off. Before Slack, she spent more than a decade at Salesforce leading global sales teams that managed some of the company’s biggest and most involved customers. That background matters here. Enterprise AI is increasingly shaped by hands-on usage patterns and departmental adoption rather than top‑down directives, and Dresser has spent years navigating that tension.

OpenAI’s leadership seems to see the parallel as well. The company frames the hire through the lens of scale, noting the goal of putting AI tools “into the hands of millions of workers, across every industry.” Highlighting Dresser’s experience with that specific kind of operational shift is a small detail, but it’s revealing. The company isn’t just selling a model anymore; it’s trying to normalize AI as a daily tool inside organizations that already juggle crowded software portfolios.

Dresser joins at a moment when workplace AI usage is arguably moving past early pilots and into core business processes. The numbers OpenAI cites are striking not because they’re dramatic, but because they’re broad: 75 percent of workers say AI has improved the speed or quality of their work. Many employees report saving 40 to 60 minutes a day, while heavier users indicate gains of more than 10 hours a week. Additionally, three‑quarters of users say they’re now able to complete tasks that previously felt out of reach. You can nitpick survey methodologies—and someone always does—but directionally, the story matches what most enterprise leaders are seeing on the ground.

Still, raw enthusiasm doesn’t automatically translate into structured, enterprise-scale deployment. Adoption tends to stall when companies can’t connect AI to internal systems or wrap governance and security controls around it. That’s where OpenAI’s product mix becomes the real backbone of this announcement. On one side, ChatGPT for Work gives teams a quick entry point, essentially lowering the barrier for individuals and departments to find value rapidly. On the other side, the API offers the connective tissue needed for deeper, system-level integration—whether that means powering customer support flows or automating operational tasks behind the scenes.

A useful micro‑tangent here: one quiet reason APIs matter so much is that they let enterprises embed AI into processes employees never directly see. For vendors, that type of adoption tends to be much stickier than a chat interface alone.

Today, OpenAI says more than one million business customers use its technology, including well‑known names like Walmart, Morgan Stanley, Intuit, Databricks, Target, and Lowe’s. The company doesn’t break out usage patterns, but the examples signal breadth: customer experiences, internal operations, and daily workflows all fall under the umbrella. It’s a wide spectrum. And yet, when a platform reaches that level of scale, the strategic question shifts from “Can we sell to more customers?” to “Can we build an organization that supports the complexity of those customers?” That’s presumably where Dresser comes in.

Her own statement is intentionally concise—she notes she is focused on helping OpenAI move through its “next phase of enterprise transformation.” That phrasing suggests an inflection point rather than a reinvention. Companies already using OpenAI’s tools don’t want to be told that the way they’ve deployed AI for the past two years suddenly needs to be reset. They want predictability, clearer roadmaps, and better alignment between product capabilities and business needs.

There’s another, quieter signal embedded in the timing. AI is becoming a regular part of work, and businesses are increasingly dependent on tools they expect to be reliable, scalable, and context‑aware. When adoption reaches that level, customer success often becomes as important as the underlying technology. It raises a simple question: How does a vendor support thousands of enterprise accounts that are all moving at different speeds, in different regulatory environments, and with different legacy systems? That’s where enterprise operators with experience at Slack and Salesforce tend to earn their keep.

You can see hints of this industry-wide shift in broader research, including findings from the World Economic Forum on how AI reshapes task distribution inside companies. But OpenAI’s framing stays focused on the immediate operational reality: businesses want dependable AI they can apply across entire organizations, not isolated experiments floating on the side.

Even so, the path from pilot projects to everyday reliance isn’t always smooth. Teams wrestle with integration debt, data access, and employee training. The announcement doesn’t tackle those challenges directly, but it does position OpenAI as built for this next wave of usage, balancing accessible entry points with deep integration options.

As AI becomes embedded in routine workflows, revenue leadership becomes less about selling a novel technology and more about supporting a long-term enterprise relationship. Dresser’s hire reflects that shift. It’s an operator’s move—less flashy than a new model release, but arguably more important for whether AI becomes as normal and uneventful in the workplace as email.