Despite the transformative potential of tools like ChatGPT, most knowledge workers I’ve spoken to don’t use it at all. Those who do primarily stick to basic tasks like summarization. Only a little over 5% of ChatGPT’s user base pays for plus — a small fraction of potential professional users — suggesting a scarcity of power users leveraging AI for complex, high-value work.

After over a decade of building AI products at companies from Google Brain to Shopify Ads, I’ve witnessed the field’s evolution firsthand. With the rise of ChatGPT, AI has evolved from nice-to-have enhancements like photo organizers into major productivity boosters for all knowledge workers.

Most executives understand today’s buzz is more than hype—they’re desperate to make their companies AI-forward, knowing it’s more powerful and user-friendly than ever. So why, despite the potential and enthusiasm, is widespread adoption lagging? The real roadblock is how organizations approach work itself. Systemic issues are keeping these tools from becoming part of our daily grind.

Ultimately, the question executives need to ask isn’t “How can we use AI to do things faster? Or can this feature be built with AI? “ but rather “How can we use AI to create more value? What are the questions that we should be asking but aren’t?”

Real-world Impact

Recently, I leveraged large language models (LLMs) — the technology behind tools like ChatGPT — to tackle a complex data structuring and analysis task that would have traditionally taken a cross-functional team of data analysts and content designers a month or more.

Here’s what I accomplished in one day using Google AI Studio:

1. Transformed thousands of rows of unstructured data into a structured, labeled dataset.
2. Used the AI to identify key user groups within this newly structured data.
3. Based on these patterns, developed a new taxonomy that can power a better, more personalized end user experience.

Notably, I did not just press a button and let AI do all the work.

It required intense focus, detailed instructions, and multiple iterations. I spent hours crafting precise prompts, providing feedback(like an intern, but with more direct language), and redirecting the AI when it veered off course.

In a sense, I was compressing a month’s worth of work into a day, and it was mentally exhausting.

The result, however, wasn’t just a faster process — it was a fundamentally better and different outcome. LLMs uncovered nuanced patterns and edge cases hidden within the unstructured data, creating insights that traditional analysis of pre-existing structured data would have missed entirely.

The Counterintuitive Truth

Here’s the catch — and the key to understanding our AI productivity paradox: My AI success hinged on having leadership support to dedicate a full day to rethinking our data processes with AI as my thought partner.

This allowed deep, strategic thinking — exploring connections and possibilities that would have otherwise taken weeks.

This type of quality-focused work is often sacrificed in the rush to meet deadlines, yet it’s precisely what fuels breakthrough innovation. Paradoxically, most people don’t have time to figure out how they can save time.

Dedicated time for exploration is a luxury most PMs can’t afford. Under constant pressure to deliver immediate results, most rarely have even an hour for this type of strategic work — the only way many find time for this kind of exploratory work is by pretending to be sick. They are so overwhelmed with executive mandates and urgent customer requests that they lack ownership over their strategic direction. Furthermore, recent layoffs and other cutbacks in the industry have intensified workloads, leaving many PMs working 12-hour days just to keep up with basic tasks.

This constant pressure also hinders AI adoption for improved execution. Developing robust testing plans or proactively identifying potential issues with AI is viewed as a luxury, not a necessity. It sets up a counterproductive dynamic: Why use AI to identify issues in your documentation if implementing the fixes will only delay launch? Why do additional research on your users and problem space if the direction has already been set from above?

Charting a New Course — Investing In People

Giving people time to “figure out AI” isn’t enough; most need some training to understand how to make ChatGPT do more than summarization. However, the training required is usually much less than people expect.

The market is saturated with AI trainings taught by experts. While some classes peddle snake oil, many instructors are reputable experts. Still, these classes often aren’t right for most people as a starting point. They’re time-consuming, overly technical, and rarely tailored to specific lines of work.

I’ve had the best results sitting down with individuals for 10 to 15 minutes, auditing their current workflows, and identifying areas where they could use LLMs to do more, faster. You don’t need to understand the math behind token prediction to write a good prompt.

Don’t fall for the myth that AI adoption is only for those with technical backgrounds under the age of forty. In my experience, attention to detail and passion for doing the best work possible are far better indicators of success. Try to set aside your biases — you might be surprised by who becomes your next AI champion.

My own father, a lawyer in his sixties, only needed five minutes before he understood what LLMs could do. The key was tailoring the examples to his domain. We came up with a somewhat complex legal gray area and I asked Claude to explain this to a first year law student with edge case examples. He saw the response and immediately understood how he could use the technology for a dozen different projects. Twenty minutes later, he was halfway through drafting a new law review article he’d been meaning to write for months.

Chances are, your company already has a few AI enthusiasts — hidden gems who’ve taken the initiative to explore LLMs in their work. These “LLM whisperers” could be anyone: an engineer, a marketer, a data scientist, a product manager or a customer service manager. Put out a call for these innovators and leverage their expertise.

Once you’ve identified these internal experts, invite them to conduct one or two hour-long “AI audits”, reviewing your team’s current workflows and identifying areas for improvement. They can also help create starter prompts for specific use cases, share their AI workflows, and give tips on how to troubleshoot and evaluate going forward.

Besides saving money on external consultants — these experts are more likely to understand your company’s systems and goals, making them more likely to spot practical and relevant opportunities. People hesitant to adopt are also more likely to experiment when they see colleagues using the technology compared to “AI experts.”

In addition to ensuring people have space to learn, make sure they have time to explore and experiment with these tools in their domain once they understand their capabilities. Companies can’t simply tell employees to “innovate with AI” while simultaneously demanding another month’s worth of features by Friday at 5pm. Ensure your teams have a few hours a month for exploration.

After you’ve overcome this first hurdle of AI adoption, your team should be able to identify the most promising areas for investment. At this point, you’ll be in a radically better position to to assess the need for any additional, more specialized trainings.

Conclusion

The AI productivity paradox isn’t about the technology’s complexity, but rather how organizations approach work and innovation. Harnessing AI’s power is simpler than “AI influencers” selling the latest certification want you to believe — often requiring just minutes of targeted training. Yet it demands a fundamental shift in leadership mindset. Instead of piling on short-term deliverables, executives must create space for exploration and deep, open-ended, goal-driven work. The true challenge isn’t teaching AI to your workforce; it’s giving them the time and freedom to reinvent how they work.

Want to dive deeper into effective AI implementation? Check out We Need to Raise the Bar for AI Product Managers and What Makes a True AI Agent? Rethinking the Pursuit of Autonomy.