Slack’s CEO just joined OpenAI as Chief Revenue Officer. Here’s what that move reveals about the future of AI, work, and how your team can gain an edge.
AI Just Stole a CEO from the Productivity World
Denise Dresser didn’t leave a sleepy company. She left Slack — the poster child for modern collaboration — to become OpenAI’s first-ever Chief Revenue Officer.
That move says more about where work and productivity are heading than any analyst report you’ll read this quarter.
Here’s the thing about this hire: when leaders who built the SaaS tools running today’s offices jump to AI companies, they’re betting that AI, not apps, will be the backbone of how work gets done over the next decade.
In this post, we’ll unpack what Dresser’s move signals about the future of AI in business, why OpenAI is racing to turn usage into sustainable revenue, and how you can use the same shift to work smarter, not harder, inside your own team.
What OpenAI’s New CRO Really Signals
OpenAI didn’t create a Chief Revenue Officer role by accident. It created it because AI has moved from “interesting demo” to core business infrastructure — and infrastructure needs a serious revenue engine behind it.
A few key numbers:
- OpenAI generated about $4.3 billion in revenue in the first half of 2025, already beating all of 2024.
- The company is also spending aggressively — it reportedly burned $2.5 billion in that same period.
- Long-term projections suggest tens of billions in operating losses over the next few years, tied to massive infrastructure bets.
Most companies would pull back. OpenAI is doing the opposite. It’s:
- Committing to $1.4 trillion in infrastructure investments over eight years
- Hiring an enterprise-focused leader who’s already scaled revenue inside Salesforce and led Slack through a $27.7 billion acquisition
That combination tells you exactly where this is going: AI isn’t a side project anymore — it’s becoming the system of record for work.
When a collaboration giant loses its CEO to an AI company, the message is clear: the next wave of productivity won’t be about where you chat, but what your AI can actually do for you.
The AI Wars: Why Revenue Suddenly Matters More Than Hype
AI models are expensive to train, expensive to run, and only getting more computationally hungry. The early phase of the AI boom was about models and features. The new phase is about survival and scale.
OpenAI isn’t alone here. It’s fighting on multiple fronts:
- Google is pushing its Gemini family of models into every part of its stack.
- Microsoft is infusing AI into Office, Teams, GitHub, and Windows.
- Anthropic and others are targeting safety-conscious enterprises.
So why does a CRO matter in this landscape?
Because the winner in AI isn’t just the team with the smartest model. It’s the company that can:
- Convert massive free usage into predictable revenue
- Sell AI into real workflows, not just as a chatbot on the side
- Make the economics of AI work at scale
That’s exactly what Dresser has done before — take a product people like using and turn it into a product companies must pay for.
For businesses watching from the sidelines, this shift matters because it signals a future where:
- AI tools will be priced more like core infrastructure (think Salesforce, not a $10 gadget app)
- Vendors will push hard to standardize AI across your entire workflow, not just one team
- You’ll be asked to justify AI budgets based on real productivity numbers, not hype
If your company doesn’t know how to measure time saved, error reduced, and output increased with AI yet, you’re going to feel this pressure fast.
The Enterprise AI Gold Rush: Where Productivity Gets Real
OpenAI’s bet on Dresser is rooted in one thing: enterprise AI is already working — and the data is hard to ignore.
Current momentum looks like this:
- 1+ million organizations are using OpenAI technology
- Big brands like Walmart, Morgan Stanley, and Target are already on board
- ChatGPT Enterprise has seen an 8x increase in weekly interactions
- Around 75% of workers report AI improves their work speed or quality
- Many users are saving 40–60 minutes a day, and power users are getting 10+ hours back per week
Those aren’t feel-good metrics. Those are budget-approval metrics.
What This Means for Your Day-to-Day Work
If your team is still “experimenting” with AI while others are quietly systematizing it, you’re handing them an advantage:
- They’ll ship projects faster with AI doing the grunt work
- They’ll run leaner teams by automating repeatable tasks
- They’ll offer better customer response times with AI-augmented support
You don’t need a billion-dollar AI budget to benefit. You need a practical approach:
- Identify high-friction processes: reporting, documentation, customer replies, sales outreach, data cleanup.
- Add AI where people already work: email, docs, spreadsheets, CRM, code editors.
- Standardize prompts and workflows so the results are repeatable, not random.
The same forces pushing OpenAI to professionalize revenue are pushing you to professionalize how you use AI at work.
From Slack to OpenAI: Why Productivity Leaders Are Betting on AI
Slack changed how teams communicate. OpenAI wants to change what actually happens after that communication.
Dresser spent 14 years inside Salesforce — the definition of enterprise software — and then led Slack through integration after its acquisition. She knows exactly how companies:
- Make purchasing decisions
- Roll out tools across thousands of users
- Measure productivity and ROI at scale
So why jump to AI now?
Because the next productivity story isn’t “fewer emails” or “better chat.” It’s:
- Write the draft for me
- Summarize the last 10 meetings in two minutes
- Generate the project plan from this idea
- Turn this messy spreadsheet into a clean, usable model
The modern knowledge worker doesn’t need another place to talk. They need a system that:
- Understands their work
- Acts on their behalf
- Adapts as they go
That’s the gap AI is starting to fill.
Workflow is shifting from "I do the work using tools" to "I supervise the work AI does with my tools."
If you’re designing processes, teams, or products right now, that’s the mental model to optimize for.
How to Translate This Shift into Your Own Productivity Right Now
You don’t control OpenAI’s balance sheet or leadership hires. You do control how your team uses AI this quarter.
Here’s a practical way to apply the same “work smarter, not harder — powered by AI” mindset in your own environment.
1. Treat AI as a teammate, not a toy
Stop thinking of AI as a place you “go to try prompts” and more like a junior colleague who can:
- Draft, rewrite, and summarize content
- Propose options or frameworks when you’re stuck
- Turn raw inputs (notes, transcripts, CSV files) into structured outputs
A simple rule that works: if a task feels boring, repetitive, or procedural, AI should touch it first.
2. Build one “AI-powered” workflow per function
Don’t start with a grand AI transformation roadmap. Start with one workflow per team:
- Sales: AI drafts first-touch emails, follow-ups, and call summaries.
- Marketing: AI repurposes one long-form piece into social posts, email copy, and landing page variants.
- Product: AI clusters user feedback, extracts themes, and generates requirement drafts.
- HR / Ops: AI writes job descriptions, policy drafts, and training outlines.
Once that’s stable and saving time, add a second workflow. This incremental approach is how real adoption sticks.
3. Measure time saved and quality gained
Remember those stats — 40–60 minutes a day saved, 10+ hours a week for heavy users? Don’t just quote them. Recreate them.
Ask each person on your team:
- Which tasks did AI help with this week?
- Roughly how many minutes did it save?
- Did the quality stay the same, improve, or decline?
Log that for a month. You’ll have:
- Real internal numbers you can use to justify tools and budget
- A clear view of where AI is working — and where it’s not
This is exactly the kind of data revenue and operations leaders care about. It’s also how you avoid AI becoming a shiny distraction.
4. Standardize what works
Once a pattern is working, capture it:
- Save effective prompts as templates
- Document “before/after” examples
- Share short internal guides: “How we use AI for X”
The companies that win the AI productivity race won’t just be the ones with access to models. They’ll be the ones with internal playbooks everyone can use.
Where This Is Heading — And Why You Should Care Now
Dresser’s move from Slack to OpenAI isn’t just tech gossip. It’s a visible signal of a deeper shift: AI is becoming the primary engine of productivity in modern work.
- AI companies are hiring seasoned SaaS leaders to build real, durable revenue models.
- Enterprises are shifting from experimentation to standardized AI workflows.
- Knowledge workers are already reclaiming hours each week by offloading routine tasks.
If you’re responsible for strategy, operations, or team performance, you can’t afford to wait for a perfect AI roadmap. The companies that adapt fastest will:
- Ship more with the same headcount
- Make better decisions with cleaner, faster insights
- Give their teams more time for deep work instead of busywork
The question isn’t whether AI will sit at the center of work and productivity. Leaders like Dresser are betting their careers that it will.
The real question is: will your workflows be ready when it does?
Now’s the time to start acting like AI is part of your core stack — not an experiment on the side.