What OpenAI’s New CRO Means for Your Workday

Most companies get AI strategy backwards.

They obsess over models, parameters and benchmarks. Meanwhile, the real battle is shifting somewhere far more familiar: the tools you already live in at work and how fast those tools make you productive.

That’s why OpenAI hiring former Slack CEO Denise Dresser as its first chief revenue officer isn’t just Silicon Valley gossip. It’s a signal. The AI leader behind ChatGPT is betting its future on productivity, workflows, and enterprise work—not just research labs and flashy demos.

In this post, I’ll break down what this move says about the future of AI and technology at work, why “productivity DNA” now matters more than pure research, and what smart teams should be doing right now to work with this shift instead of getting run over by it.

---

1. Why OpenAI Hired a Productivity Veteran

OpenAI didn’t hire just any executive. It hired the person who helped turn Slack from a chat app into a core productivity platform inside enterprises—and then helped guide it through a $27.7 billion acquisition by Salesforce.

Here’s what that really means.

Dresser isn’t a ‘revenue person’ in the abstract. She’s a workflow strategist. Her background is 14 years across Salesforce and Slack, two of the most influential platforms in modern digital work. She’s lived inside:

• Enterprise sales cycles
• CIO and COO priorities
• Security, compliance, and procurement headaches
• The messy reality of getting tools actually adopted by teams

OpenAI’s core challenge right now isn’t “Can we build better AI?” It’s “Can we turn this AI into durable, scalable revenue—especially in the enterprise?”

And the numbers show the urgency:

• OpenAI generated $4.3 billion in revenue in the first half of 2025, already above all of 2024.
• At the same time, it burned through $2.5 billion in that same period.
• The company is facing projected operating losses of $74 billion by 2028, nearly three-quarters of expected revenue.
• It’s also committed to $1.4 trillion in infrastructure investments over eight years to keep AI models fast and capable.

You don’t commit to numbers like that unless you’re very sure the enterprise and productivity market is your growth engine. Bringing in Slack’s former CEO as CRO is OpenAI saying out loud: We’re building the next layer of the modern work stack, not just another AI model.

---

2. The AI Wars Are Really a Productivity War

On the surface, the AI race looks like OpenAI vs Google vs Anthropic vs Microsoft: whose model is smarter, faster, or bigger.

Underneath, the real contest is different: who can become the default productivity layer across knowledge work.

Look at the landscape:

• Google is pushing Gemini deep into Docs, Sheets, Gmail and Workspace.
• Microsoft is threading AI through Teams, Outlook, Excel and the broader 365 ecosystem.
• OpenAI is threading ChatGPT into everything from email to code editors, and now building aggressive enterprise offerings.

The winner isn’t just the one with the smartest model. It’s the one whose AI is:

• Embedded directly into daily workflows
• Trusted by security and compliance teams
• Easy enough for non-technical employees to adopt
• Flexible enough to fit into existing tools and processes

Dresser’s appointment is aimed squarely at this layer. She knows how work actually gets done inside complex organizations: who signs the contracts, who blocks them, and what it takes to turn a promising tool into a non-optional part of the workday.

The future of AI at work won’t be about “going to ChatGPT.” It’ll be about AI quietly sitting inside every tool you already use.

That’s the game OpenAI—and every serious AI player—is now playing.

---

3. The Enterprise Gold Rush: Why Your Workflow Is the Prize

OpenAI already has serious traction in the enterprise:

• Over one million organizations use OpenAI technology.
• Customers include companies like Walmart, Morgan Stanley, and Target.
• ChatGPT Enterprise has seen an 8x increase in weekly interactions, showing how quickly AI is threading into daily work.

These aren’t curiosity experiments anymore. They’re proof that AI is quietly becoming infrastructure for work and productivity.

The human side is just as striking:

• 75% of workers report that AI improves their work speed or quality.
• Many save 40–60 minutes a day using AI.
• Heavy users are reclaiming 10+ hours per week.

Those numbers explain the gold rush. If you’re a CEO looking at a tool that can give every knowledge worker an extra workday per week, you don’t treat that as a nice-to-have.

But here’s the catch: AI productivity gains aren’t automatic. The gap between teams that flourish with AI and teams that flounder usually comes down to three things:

1. Clear use cases instead of vague “try AI” messages.
2. Workflow integration instead of forcing people to jump between tools.
3. Guardrails and training so people know what’s allowed and what “good” looks like.

The reason hiring a Slack veteran matters is because Slack wasn’t just chat—it became the nervous system of teams. OpenAI is now trying to sit even closer to the center of how work actually happens.

---

4. What This Shift Means for Leaders and Teams

If you lead a team, a function, or a company, OpenAI’s move is a very loud signal: the bar for “normal productivity” is going up again.

Here’s how I’d respond if I were running a business right now.

A. Treat AI like you treated the shift to email, then Slack

Email didn’t stay a side tool. Neither did Slack or Teams. The same thing is happening with AI.

You don’t need a 50-page strategy deck, but you do need a simple plan that answers:

• Where can AI save each team 30–60 minutes a day within the next 90 days?
• What’s in-bounds and out-of-bounds for AI usage in your org?
• Which 2–3 workflows are you going to redesign around AI first?

Where you start depends on your type of work, but some common high-impact areas are:

• Drafting and editing emails, reports, and presentations
• Summarizing long documents, calls and meetings
• Generating first drafts of code, queries, or technical documentation
• Creating internal knowledge base articles and FAQs

B. Watch for deeper integrations in your existing tools

As OpenAI and competitors chase enterprise revenue, you’ll see more AI quietly baked into the software you already use. That’s not a gimmick—that’s the strategy.

Expect to see:

• AI sidebars inside project management tools
• Native “ask your data” features in analytics platforms
• Meeting tools that auto-summarize and extract action items
• CRMs that draft follow-ups and update records automatically

Your job isn’t to adopt everything. It’s to:

• Identify where AI actually shortens workflows
• Turn the useful bits into documented, repeatable processes
• Stop doing the manual version once the AI-enabled version works

C. Upskill your people before you upscale your tools

Most teams underuse the AI tools they already have. Buying another subscription won’t fix that.

Focus first on:

• Prompting skills: how to ask better questions and specify constraints
• Review skills: how to verify AI output and spot subtle errors
• Ethics and compliance: what’s safe to share, what stays local, and where approvals are needed

The organizations that win the next decade of productivity won’t just have access to AI. They’ll have cultures of AI fluency.

---

5. How to Work Smarter With AI in 2026, Not Just Work Faster

There’s a risk in all this hype: treating AI purely as a way to cram more tasks into the same number of hours.

That’s lazy management.

Used well, AI should create space, not just speed. Space to think more clearly, design better systems, talk to customers, and tackle the deep work machines still can’t touch.

Here’s a practical way to align with that mindset.

Step 1: Audit your week with brutal honesty

For one week, track your time in broad buckets:

• Communication (email, chat, meetings)
• Documentation (reports, specs, decks, notes)
• Execution (actual hands-on work)
• Thinking and strategy

Then ask: Which of these buckets is AI already good at helping with? For most knowledge workers, AI can meaningfully compress the first two.

Step 2: Pick 3 “AI-assisted” workflows to standardize

For example:

1. Email drafting: Use AI to write first drafts of routine email types (follow-ups, status updates, introductions), then you only review and personalize.
2. Meeting notes: Use AI to create summaries and action lists from calls, then share in your team’s workspace.
3. Document prep: Use AI to build the first pass of outlines, briefs or proposals based on your bullet points.

The key is consistency. The productivity gains compound when everyone on your team is using the same AI-accelerated workflows, not just a few enthusiasts.

Step 3: Decide what you’ll do with the time saved

This is where “work smarter, not harder” becomes real.

If AI saves you 5–10 hours a week and you just fill it with more shallow tasks, you’ve missed the point. Decide up front:

• Which projects finally get attention?
• Which meetings can be removed entirely?
• Where can you create more focus blocks for deep work?

When executives like Dresser bet their careers on AI and productivity, they’re not just thinking about margins. They’re betting that companies who redesign work around AI will outpace those who don’t.

---

6. What This Signals About the Next Era of AI & Technology at Work

Here’s the thing about OpenAI hiring Slack’s former CEO as CRO: it’s not a research story. It’s a work story.

The message is clear:

• AI is shifting from novelty to infrastructure for work and productivity.
• Enterprise buyers, workflows, and adoption now matter as much as models.
• The real differentiation will be how tightly AI integrates with the tools and habits that already run your business.

If you’re following the AI & Technology space because you want to work smarter, this is the headline behind the headline: the market is aligning around your workflow. The biggest players in AI are organizing their leadership, their capital, and their roadmaps around making your workday more automated, more structured and, frankly, less forgiving of manual, outdated processes.

You can wait for that change to reach you—or you can start treating AI as a core part of how your team works now.

So the practical question for you isn’t “What does Denise Dresser’s job title mean?” It’s:

Over the next 90 days, how will you redesign at least three workflows so AI isn’t an experiment, but a normal part of how work gets done?

The organizations that answer that question early will be the ones everyone else is scrambling to catch up to in a year.