Another Crazy Day in AI: When AI Helps People but Not Companies
- Wowza Team
- May 22
- 4 min read
Hello, AI Enthusiasts.
Heading into the weekend, but not before this:
Still wondering why your org’s AI rollout feels like a flop? Ethan Mollick says it’s not the tech—it’s the way we lead, collaborate, and adapt. His new blog offers a reality check (and a roadmap) for companies that want real results.
Still curious? GenAI myths are holding teams back. One expert sets the record straight.
Oh, and OpenAI’s new engineering agent can debug, push code, and submit pull requests… all before you finish your coffee.
Here's another crazy day in AI:
Unlocking AI’s impact beyond individuals
Busting common GenAI myths in sales and marketing
OpenAI launches Codex for AI-powered coding in ChatGPT
Some AI tools to try out
TODAY'S FEATURED ITEM: From Personal Gains to Organizational Change
Image Credit: Wowza (created with Ideogram)
Why are individual workers seeing massive productivity gains from AI while companies struggle to capture meaningful organizational benefits?
This question sits at the heart of Ethan Mollick's latest analysis from his One Useful Thing newsletter. Mollick, a Professor at the Wharton School who studies entrepreneurship and innovation, spent extensive time talking with organizations across different industries to understand what's really happening with AI adoption. What he found reveals a significant disconnect between individual successes and broader organizational outcomes.
In many workplaces, people are quietly using AI to streamline tasks, improve creativity, and work more efficiently. The benefits are clear at the individual level. But at the organizational level, those gains rarely add up to systemic change. Leaders often know AI is important, and they’re taking steps—running pilots, setting guidelines, forming task forces. Still, the results feel uneven, and momentum stalls.
What’s surfacing from Mollick’s conversations is less about the tools themselves and more about how organizations are (or aren’t) making space for new ways of working to take root.
What’s inside:
Many workers say AI has dramatically improved their speed and output—but these gains rarely show up in overall company performance.
Why? Because individual AI wins don’t automatically scale into systemic gains.
Mollick proposes a three-part formula for change: Leadership, Lab, and Crowd.
Leadership must go beyond urgent memos to paint a vivid picture of what AI-powered work looks like.
The Lab is where new AI-human workflows are tested and refined—it’s about building better processes, not just faster ones.
And the Crowd—your employees—are often ahead of the curve, experimenting in secret or unsure how to share their discoveries.
Solving for secrecy, fear, and unclear incentives is essential. Companies need to invite, reward, and learn from ground-up innovation.
How companies can close the gap between potential and progress:
AI use is widespread, but often hidden—many workers fear backlash or job cuts if they reveal just how much AI helps.
Official AI programs often see low engagement—because they miss how real learning and experimentation happen.
Leadership should shift focus from rules to permission, from vision to experiences, and from fear to incentives.
The most promising gains come when teams reorganize themselves—collapsing silos and “vibeworking” across functions.
There’s no playbook—every company has to prototype its own path, faster than the rest.
The real challenge isn’t just about the technology—it’s how organizations respond to change. Supporting small experiments, paying attention to what’s already working, and keeping communication open are key steps forward. Instead of waiting for perfect answers, embracing learning as you go can lead to new opportunities.
Mollick’s insights remind us that progress often happens quietly, through individuals finding better ways to work. Recognizing and building on those moments can help close the gap between individual wins and broader organizational impact.
Looking ahead, it’s less about the tools themselves and more about shifting mindsets and cultures to make the most of them. This takes patience, openness, and a focus on everyday improvements—even when they don’t immediately show up in big numbers.
Read the full blog here.
OTHER INTERESTING AI HIGHLIGHTS:
Busting Common Gen AI Myths in Sales and Marketing
/University of Texas at Austin, on Phys.org
Generative AI holds enormous promise for sales and marketing—but misconceptions are holding many teams back. In an interview with the University of Texas at Austin, Professor Doug Chung explains five persistent myths, such as “gen AI takes too long to implement” and “you need massive data to make it work.” He shares why smaller businesses can still benefit, how to get started without overhauling everything, and why perfection shouldn't be the goal. Think MVP, not flawless AI—and start now.
Read more here.
OpenAI Launches Codex for AI-Powered Coding in ChatGPT
/OpenAI
OpenAI just launched Codex, a powerful cloud-based software engineering agent available within ChatGPT for Pro, Team, and Enterprise users. Codex can handle multiple coding tasks in parallel, from writing features and debugging to submitting PRs—all within an isolated, secure cloud environment. Powered by codex-1 and optimized for real-world dev workflows, it integrates directly with your codebase, and logs every step for transparency. It marks a big leap in how AI will support and accelerate software engineering.
Read more here.
SOME AI TOOLS TO TRY OUT:
That’s a wrap on today’s Almost Daily craziness.
Catch us almost every day—almost! 😉
EXCITING NEWS:
The Another Crazy Day in AI newsletter is on LinkedIn!!!
Leveraging AI for Enhanced Content: As part of our commitment to exploring new technologies, we used AI to help curate and refine our newsletters. This enriches our content and keeps us at the forefront of digital innovation, ensuring you stay informed with the latest trends and developments.
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