Getting up to speed on AI
Executives are still dragging their feet and shun experiments when it comes to learning the language and tools of the AI era. Yet becoming fluent and sharing that knowledge across the organization pays real dividends.
Late last year, Jeremy Utley, an adjunct professor at Stanford University’s d.school, author and podcaster, traveled to Washington DC to address a high-profile gathering of CMOs, CTOs and CIOs. Just as he was preparing to walk on stage, somebody came up to him and said: “Hey, I’m hearing a lot about AI fatigue today.” Utley was initially irritated by the remark. Then he decided to incorporate it into his talk. “Raise your hand if you’re feeling AI fatigue,” he asked the assembly of 2,000 or so executives. Every hand went up. “Keep your hand up if I could toss you the clicker and you could show us on your computer the cool stuff you’re doing with AI,” came the second question. All hands went down.
To Utley, that was proof that there is no AI fatigue. “Leaders aren’t tired of AI. They’re insecure about how little they have done relative to what’s expected of them. The more senior you are, the higher the opportunity cost of learning,” he says. As a result, business leaders have so far “underallocated time to learning,” he argues, and are now “keenly aware of what they don’t know.”
Ask me anything
Almost four years into the dawn of the new artificial intelligence era – sparked by the rise of GenAI tools such as ChatGPT – businesses profess an eagerness to harness it in all aspects of their operations and executives want to show themselves to be on board. Yet the reality is more complex. In January 2026, the AI & Data Leadership Executive Benchmark Survey showed that 99% of respondents – mostly C-level executives and equivalents – said investments in data and AI were a top priority. A total of 54% of respondents said their companies were deriving substantial value from their data and AI investments, up from 47% in 2025. At the same time, 93% said human factors such as culture and change management hindered greater integration of data and AI.
“AI technology may be moving at light speed, but people are not,” wrote Randy Bean and Thomas H. Davenport in a Harvard Business Review article on the survey. “And that disconnect is one of the main issues facing companies trying to adopt AI.” Organizations have to address that disconnect in the next few years “if they hope to achieve long-term success with AI.”
Change has to start at the top, according to company executives, experts and academics interviewed for this piece, who note that leaders are still not using AI as much as they should. At the C-suite level, “there are a lot of reasons why people are reluctant,” says Ethan Mollick, an associate professor of management at the Wharton School, where he studies the effects of AI on entrepreneurship. “They may think this is technical and hard for them. They may think it’s beneath them. They may think it’s hype.”
As a CEO, you have to have “better judgment and vision and foresight” than others in the organization, says Mollick. “If you haven’t equipped yourself with the latest tools, or even tried to dip your toe in, that feels like you’re skipping your duty.” If he were a CEO, Mollick says he would run every decision by an AI. What makes AI all the more compelling is its unbelievably low cost and easy availability, he adds. “There are no better tools than the ones that are publicly available. Goldman Sachs does not have a better AI than the random kid in Mozambique who’s willing to pay $20 for access.” So why not use it? Utley has his own pieces of advice for C-suite leaders. To start with, he says, “do the work yourself first.” Second, make it visible: share your screen in meetings and show what you’re doing with AI. And third, ask a different staff member each day: “Have you tried AI?” That question “plants the idea” in the mind of your staff, “gives them permission” to use AI, and allows you to ask for follow-ups later.
“There’s no way that you can understand the profound impact that AI is going to have until you experience it on your own.”
One executive whose company uses AI at every level is Brad Anderson at Qualtrics, an experience management company based in Seattle which provides survey and research software to major Fortune 500 companies, including hotel chains, airlines, technology and finance companies. “When you call the call center and it says: ‘This call may be recorded for training purposes,’ we are the largest receiver of those phone calls: billions of them a year,” says Anderson, global head of product, engineering and user experience. Qualtrics does the voice-to-text transcription and puts the conversation through its AI, looking for dozens of explicit emotions – such as fear, anxiety, joy, anger – and their intensity. Call center conversations represent half of the customer data that Qualtrics processes; the rest come from online reviews and surveys. “If there are leaders in organizations today that are not spending time in AI every single day, that’s just a cardinal mistake,” says Anderson. “There’s no way that you can really understand the profound impact that AI is going to have until you actually see and experience it on your own.”
Some of the reluctance among executives is linked to concerns about AI’s impact on employment and the cost of having to retrain workers to make them more AI-proficient. “Fear of job loss is increasing rapidly, along with a paucity of resources for upskilling and reskilling employees to prepare for AI,” write the authors of the Harvard Business Review article. “We suspect that these will be the greatest issues that employees and employers face in the next few years, and they may pose a significant impediment to the effective and broad adoption of AI.”
Anderson acknowledges those hurdles. “It’s not that AI is replacing humans,” he says. “It’s actually enabling work to be done that was not being done in the past.” One example he cites is client follow-up. Some of the companies Anderson works with receive 100 to 200 million pieces of feedback a year in the form of surveys, online reviews and call center conversations. “No organization has the human capacity to follow up and close the loop on all those,” he says. It’s AI that enables that follow-up.
At the executive level, Anderson says his entire leadership team makes daily use of a Google tool called NotebookLM which creates a digital information pack for each meeting, loaded with the preparatory content, context and historical data. The AI is then asked for trends, recommendations, insights and other things to look for. “It just enables me as a human to look at an incredible amount of information and get the insights that matter in minutes,” he says. Utley couldn’t agree more. AI “doesn’t replace humans,” he believes. “Assisted humans will replace unassisted humans.” In other words, “the competition isn’t us versus the machine, it’s me versus somebody who’s being augmented by the machine.”
Diarra Bousso – the Senegal-born founder of Diarrablu, an AI-first fashion company – is a good example of what an AI-augmented leader looks like. She was a bond trader on Wall Street when she threw it all in and changed direction. Now Bousso runs a sustainable fashion and resort wear brand. While garments are produced in a factory by artisan communities in Dakar, Senegal, much of the lead-up to production is AI-enabled, reducing textile waste by more than 60%. Bousso calls AI “an amplifier of what you are already. If you’re lazy, it’s going to make you lazier. If you’re smart, it’s going to make you smarter. It’s not magic.”
The Diarrablu Case Study
A growing palette of AI tools helps fashion brand Diarrablu identify trends and reduce waste in an industry where 85 billion garments are discarded every year. Here’s how:
A typical fashion company predicts demand, builds inventory, then sees if it sells. Diarrablu sells first, then builds the product by presenting designs (some real, some AI-assisted) to social media and asking: Would you buy this? Votes come back instantly, allowing Diarrablu to save money and avoid waste.
The next step is figuring out how much of a garment to release and when. With an AI tool developed in-house, Diarrablu determines that based on weather and past consumer behavior. Goods are made only after customers order them. Bousso calls the process a “demand chain” rather than a “supply chain.”
THE TEAM RUNNING DIARRABLU functions like an AI lab. Though everyone works remotely, they use AI to find a way of amplifying what they do on a daily basis. People document their AI tools – whether it’s a concept, a prompt, or a recording tool – and add them to an internal page that’s open to everyone in the company.
Inside companies, another hurdle to wider AI use is employee concern that if they admit to using it, they’ll be seen as cutting corners, not putting in enough hours and not being productive. “I would put that right back on the boss. That’s your fault,” counters Utley. “If your people are embarrassed or hesitant, it’s because you have not done a good enough job of letting them know that that’s now the way to do great work.” Bousso describes that feeling as “AI shame,” and admits there was a lot of it in her company, too, because AI is clearly polarizing. On the one hand, it is a powerful tool. On the other, it can be viewed as “you don’t have a brain, and a machine is thinking for you,” she says.
Yet another important concern in terms of AI: data privacy, safeguards and governance. According to the AI & Data Leadership Executive Benchmark Survey, 79% considered responsible AI a top priority – up from 69% the previous year – and 95% think safeguards and guardrails are necessary. “The issue here is fear and trust,” says Anderson, who points out that whenever he is closing a major deal, he will inevitably be pulled onto a call with the chief security officer. The question is always the same: “How are you using our data?” Anderson says a company such as his can prove its trustworthiness through its track record and through independent certifications.
While things are changing in the executive suite, there are still plenty of CEOs in the world today who have never used AI and who don’t know where to start. Mollick has one piece of advice for them: “Pay $20, buy access to one of the three leading AI systems and try using it for 10 hours this week. Put in every decision you legally can – every choice, every conversation” and “see what it’s good or bad at. If you’re not impressed, that’s useful information. If you’re impressed, that’s useful information.” Overall, Mollick is optimistic that “humans will find more benefits than drawbacks.” And that large-scale trial has to start at the top.
While things are changing in the executive suite, there are still plenty of CEOs in the world today who have never used AI and who don’t know where to start. Mollick has one piece of advice for them: “Pay $20, buy access to one of the three leading AI systems and try using it for 10 hours this week. Put in every decision you legally can – every choice, every conversation” and “see what it’s good or bad at. If you’re not impressed, that’s useful information. If you’re impressed, that's useful information.” Overall, Mollick is optimistic that "humans will find more benefits than drawbacks.” And that large-scale trial has to start at the top.
While things are changing in the executive suite, there are still plenty of CEOs in the world today who have never used AI and who don’t know where to start. Mollick has one piece of advice for them: “Pay $20, buy access to one of the three leading AI systems and try using it for 10 hours this week. Put in every decision you legally can – every choice, every conversation” and “see what it’s good or bad at. If you’re not impressed, that’s useful information. If you’re impressed, that's useful information.” Overall, Mollick is optimistic that "humans will find more benefits than drawbacks.” And that large-scale trial has to start at the top.
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Lead AI adoption from the top:
Executives who actively use and demonstrate AI create the cultural permission and momentum needed for wider organizational adoption.
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Treat AI as augmentation:
The technology delivers greatest value when it expands human capacity, insight and responsiveness rather than simply reducing headcount.
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Close trust, skill and culture gaps:
Job loss fears, governance concerns and “AI shame” still slow adoption despite growing investment and executive interest.
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