AI in the Workplace
As artificial intelligence gets its own desk in the office, workers and leaders weigh its risks and rewards—and whether age shapes their view.
A client question nobody has an answer for leads to a stalemate in the middle of a meeting. One colleague takes the old-school approach and books another meeting for next week, spending the days between sketching notes. The other opens an artificial intelligence (AI) tool and has a workable plan within the hour.
That contrast, said Roy Tomizawa, chief executive officer of Reinventing Asia, is the story of AI in the workplace: it is changing the distance between thinking and acting.
“So much organizational time is spent waiting and procrastinating,” Tomizawa said. Unread emails and slow-to-schedule meetings are a kind of quiet tax that most companies never measure. “Half of what was discussed could have been thought through, drafted, or prototyped beforehand by the person who leveraged the available AI tools on his or her own.”
How businesses close the gap between thinking and acting is dividing opinion, forcing leaders to decide what AI should handle and what it shouldn’t, while young professionals are discovering that the skills worth building are shifting day to day.
The Age of the Generalist
Tomizawa explained that the shift goes beyond speed. AI is dissolving the old dependency on specialists. An employee who knows how to assemble credible first versions of a website or a video will have an advantage. Tomizawa called this “the age of the generalist.”
But that doesn’t flatten the value of expertise, he said.
“It actually raises the price of real expertise, since shallow, surface-level work is now cheap and abundant.” Someone still has to know when an AI’s answer is actually good, he added. Without that, the same tools that make people efficient just make them “efficient masters of AI slop.”
Sludge, Slop, and Everything in Between
Not everyone sees the acceleration in such rosy terms. Dr. Greg Story, president of Dale Carnegie Training Tokyo, compared the effect of AI on communication to that of social media. Both are technologies that can make people more insular, offering contact with or through a machine as a substitute for real contact with each other.
Story, who has written nine books and recorded thousands of podcast episodes, said listeners can identify his writing by ear. Hand that same task to AI by default, he argued, and you never develop a personal voice.
“If you’re ever going to have a distinctive style, and you keep outsourcing it to AI, then you’ll never achieve that and you’ll be part of the sludge,” he said. “There’s no discrimination between one piece of sludge and the other.”
For most professionals, the risk, he argued, is that AI tends to default to generic text unless given deliberate direction. That caution shows up in how he runs his own training business. Dale Carnegie has built its own AI system, trained only on its own material, to keep answers on brand rather than generic. But Story keeps it confined to prework and follow-up, away from the live instruction that’s actually being sold.
In sales, he explained, AI becomes a genuine research tool for those who ask the right questions, provided someone checks it for hallucinations—the tendency for AI to make up information when it doesn’t have an answer.
“The human dimension is being reduced,” he said, “so we have to hold on to it as much as we can to counterbalance the swing to impersonal communication.”
“AI is dissolving the old dependency on specialists. An employee who knows how to assemble credible first versions of a website or a video will have an advantage.”
Finding a Voice with AI
Ted Katagi, CEO of Kenja K.K., builds tools based on retrieval-augmented generation, or RAG, that draw only on a company’s own material rather than the open internet, including the system behind Dale Carnegie’s own AI tool. Katagi makes a distinction between content and style.
“We have to separate things—the voicing of it, which to me is the style, versus the content,” he said. The content has improved with AI use, he said, but the style “will tend to aggregate” as writers rely on it. Part of that, he argued, is simply that AI eliminates bad writing—which raises its own question. “If we get rid of the bad writing, isn’t that going to make you a little bit more similar in a sense?”
Preserving an individual voice, he said, takes deliberate work. “I wouldn’t just say, ‘Here, AI, write like me.’ I would say, ‘Here are all my papers. How do you think I write?’” Skip that step, he warned, and a personal voice doesn’t get replaced so much as quietly forgotten.
Katagi’s advice to young professionals is to treat AI more as a “sparring partner” than a first-drafter.
“Ask always: What did it do that was smart that I need to learn?” Katagi advised. “What did it do that was bad that I need to check on?” For those who genuinely engage with AI, it “can seem like they gain 10 years of experience.”
“It will get you where you want to go and it will help you finish the last mile,” he said. But human judgment and intuition are still needed. He cited as an example a recruiting-industry deployment that builds an editor directly into the workflow rather than letting AI hand off a polished document unchecked.
Differentiating from Sameness
Meghan Barstow, president and representative director of ACCJ Corporate Sustaining Member Edelman Japan K.K., has watched two prior technology shifts reshape her industry: the internet and mobile phones.
“I experienced the advent of the internet and mobile phones,” she said, “both of which transformed how we create, communicate, and connect.” She expects AI to do the same, viewing it as a tool that “aids and amplifies human creativity rather than replaces it.”
Her concern is closer to what Story called sludge. Barstow called it “sameness.”
“AI is incredibly good at producing something plausible. But plausible isn’t necessarily interesting, original, or culturally meaningful,” Barstow said, adding that her worry is more about the behavior it enables.
“If we all use the same tools, trained on much of the same material, and accept the first answer they give us, there is a real risk that we will create more content but with less creativity.”
As an executive, Barstow, who is also an ACCJ vice president, described adopting AI as “quite fun,” but said the stakes reach into business models themselves, with real winners and losers ahead. Organizations that treat AI as additive to their core value rather than a replacement for it will succeed, she predicts.
For young professionals, she offered this advice: “Raise your hand. Get involved. Be curious. Learn, learn, learn. Use the new tools, absolutely, but don’t do so at the expense of learning the fundamentals or having experiences that develop your judgment and perspective.”
She also viewed the generational exchange as a two-way street. Edelman brought interns back this year for the first time since the pandemic, several through the ACCJ’s own internship portal, and Barstow credited them with making the organization sharper.
“The more opportunities we create for generations to learn from one another, the better all of us—and our organizations—will be,” she said.
What Leaders Need to Know
Digital fluency doesn’t mean becoming a technologist, Tomizawa said. It means understanding how value is created and decisions are made. He said three mindsets matter most:
- Treating information as a strategic asset
- Measuring outcomes, not activity
- Introducing AI deliberately
By treating information as a strategic asset, Tomizawa means that it is important to know where critical data sits, who owns it, and whether it’s reaching the people making decisions. Once that is in place, the value of the technology must be assessed. “High usage of an AI tool, for example, tells leaders little about whether people are saving time, making better decisions, improving customer outcomes, increasing revenue, or reducing risk,” he said. When preparing to implement AI tools, leaders should start by looking at the work itself to find the bottlenecks and errors before choosing the technology. Finally, they should define exactly what AI should recommend and where human approval stays mandatory.
Story said that leaders rarely had time to check employees’ work before AI, and they have even less reason to start now. His solution is to train people on privacy and instill in them a healthy skepticism.
Cybersecurity is another area to be mindful of.
Katagi said a clash between cybersecurity’s oldest principle and AI’s newest capabilities exists, pointing to “zero trust,” the model he said most cybersecurity experts rely on now.
“Zero trust means you say you’re Chris and it looks like it, but I’m not going to trust it until I see some irrefutable proof that you are who you say you are,” he explained. Agentic AI, a system trusted to act on a user’s behalf that Katagi said is at the center of AI right now, asks for the opposite. “Basically, what it’s allowing you to do is say, ‘Okay, trust me and I’ll do the thing.’ But that’s going directly against all the cybersecurity frameworks that people have.”
“The professionals who thrive will be the ones who know when to trust it, when to challenge it, and when to think differently.”
A Youthful Approach
Among young professionals, thoughts on AI lean more toward the opportunities the technology brings.
“AI is one of the most significant shifts I have seen in how we work, and I think young professionals have a real opportunity to shape how it is adopted,” said Fatim Diallo, a director at FGS Global and vice-chair of the ACCJ Young Professionals Forum (YPF). “Used well, it frees up time for the kind of creative thinking and relationship-building that transforms careers.”
The shift has made her more curious about where she can add value than anxious about what AI might replace. At a company dealing with multiple markets, AI has “meaningfully reduced the friction of working across linguistic and cultural contexts,” she said.
Her main concern is what she dubs a “meat proxy”—people who pass AI output straight through without reviewing or questioning it.
“The professionals who thrive will be the ones who know when to trust it, when to challenge it, and when to think differently.”
Kelly Langley, CEO of Gemini Group K.K. and co-chair of the YPF, noted a similar shift in what makes someone valuable. AI increasingly handles the “first 80 percent” of a task, he said, freeing people to spend more time exercising judgment on the final 20 percent.
“That makes me more interested in capabilities that are difficult to replicate: judgment, trust, persuasion, relationship-building, improvisation in moments of crisis, and understanding what motivates people.”
Langley pushed back on the idea that there is a generational divide among AI adopters.
“The bigger distinction is between people who are relentless learners and early adopters and those who remain comfortable with established ways of working.”
Progress in the Age of AI
The next wave of AI innovation isn’t going to come from the massive, companywide rollouts most large organizations are currently betting on, Katagi said.
“People think the big projects are the thing. I think the next innovation is going to occur in agile projects at the middle level,” he said, predicting smaller teams and narrower budgets focused on solving one specific problem well rather than applying a general-purpose tool everywhere at once.
Story said that “at the end of the day, most business problems are people walking around on two legs talking to each other. That doesn’t go away, but the skill sets are going away in a sense, as people become more isolated.”
But he sees an opportunity ahead in the people using the tools themselves.
“As we’re moving away from voice and body language, we are getting to a stage where AI is just going to accelerate that process,” he said. “If you have the ability to communicate, you’re going to stand out head and shoulders above everyone else. You’ll be clear, empathetic. I think that’s an opportunity rather than a threat.”
Tomizawa argued that most companies are still years from redesigning how work actually gets done around AI, rather than just using it to make things faster.
“The more capable AI becomes, the more important human judgment becomes in deciding what problem deserves attention, what ‘good’ looks like, and which trade-offs are acceptable,” he said.
Five years from now, he added, the people with the greatest value will be the ones who know how to combine machine capability with sound human judgment.
