Is The Asian Honeymoon Is Over: Why Workers Are Losing Faith in AI

The Two Faces of AI at Work

There is a moment every team goes through with AI. That first win. The prompt that nails a product description in seconds. The summary that saves an hour. The image that would have cost a photographer and a studio.And then there is the other moment. The one where an AI confidently generates a completely fabricated statistic. Or where you spend two hours wrestling with a prompt only to end up doing the task manually anyway. Or where the tool that was supposed to make everything faster somehow makes everything worse.

If you have been experimenting with AI prompts to boost your productivity, you will know the feeling. Some days you feel like you have unlocked a superpower. Other days you want to throw the laptop out the window.

Tabby Farrar knows both moments well.

Farrar is head of search at Candour, a UK-based SEO and web design agency. Her team is genuinely keen to embrace AI. They see the potential, they want the efficiency gains, and they understand this is the direction the industry is heading. But for every workflow where AI actually saves time, Farrar says there are half a dozen that leave the team feeling like the technology is useless.

AI can generate product lifestyle imagery for clients who do not have any. That is a genuine win. But when it comes to creating executive summaries of data? The AI hallucinates or misses key points. Refining a prompt to assign categories to a dataset can take so long that doing it manually would have been faster. If you have ever tried to get AI to do something precise and watched it confidently miss the point, you will relate to what we found when we compared Google Gemini and ChatGPT head to head.> "As a manager, I'm trying to get the team more on board with AI stuff, because it's the future of so many industries," Farrar said. But the pushback is real. "There's just so many people going, 'I have lost two hours of my day trying to make this thing work.'" If that sounds familiar, you are not alone.

The Confidence Crash

A January 2026 study from ManpowerGroup delivered a striking finding. For the first time in three years, workers' confidence in AI actually declined. Usage jumped 13% year on year, reaching 45% of the global workforce. But confidence in the technology dropped 18%.

Let that sink in. More people are using AI than ever, and fewer of them trust it.> "You can't have an intimidated workforce and be fully productive," said Mara Stefan, VP of global insights for ManpowerGroup. "That anxiety is going to cause real problems."

The numbers from ManpowerGroup tell a broader story too. While 89% of workers feel confident in their current roles, 43% now fear automation could replace their job within the next two years. That is a 5% increase from 2025. This anxiety is driving what ManpowerGroup calls "job hugging," with 64% of workers planning to stay put with their current employer, seeking stability amid the chaos. We explored this tension between AI's impact on jobs and the skills you will need by 2030 in an earlier piece, and these latest numbers only sharpen the urgency.

And it is not just ManpowerGroup raising red flags. An EY Work Reimagined report from November 2025 found that while roughly 9 in 10 employees are using AI at work, only 28% of organisations can translate that into meaningful business outcomes. The report was blunt about why: workers may be saving a few hours here and there, but nothing that fundamentally changes how work gets done or how the business performs.

For those of us in Asia watching these trends unfold, the regional picture adds another layer. ManpowerGroup's data shows India leading globally in AI adoption at 77%, while Japan reports the lowest overall worker sentiment at just 48%. The variance across the region is enormous, and it suggests that the challenges around confidence and training are not uniform. They are culturally and contextually specific, which means cookie-cutter solutions will not work. We have been tracking how these AI trends are transforming Asia, and the gap between adoption enthusiasm and workforce readiness is one of the defining themes.

A recent Harvard Business Review piece adds an important nuance. Researchers found that when employees gain access to AI, they do not just work faster. They work broader, take on more tasks, and extend into more hours of the day, often without being asked. AI is not necessarily reducing the burden of work. In some cases, it is intensifying it.## The Training Void

So what is going wrong? Part of the answer lies in a training gap that should alarm every business leader.

More than half of ManpowerGroup's respondents (56%) reported receiving no recent training. And 57% said they had no access to mentorship. Workers are being handed powerful tools with almost no guidance on how to use them effectively.

This is the gap we have been trying to close at AIinASIA through practical, accessible content. Whether it is prompts to streamline team collaboration (or check out our sister site PromptandGo, guides on getting more out of Google Gemini, or understanding which AI skills actually matter for your career, the goal has always been the same: close the knowledge gap so people feel empowered, not intimidated.

Kristin Ginn, founder of trnsfrmAItn, an organisation that works with companies on AI adoption, points to the mismatch between marketing demos and workplace reality as a key driver of the confidence drop. Those slick demos make everything look easy. But the reality involves significant trial and error that many workers are not prepared for.

There is also a psychological dimension at play. When you have done your job one way for years, you develop a rhythm and a confidence in your process. AI disrupts that.> "If you're now starting to look at how you can use AI for the same task, you all of a sudden have to put a lot more mental effort into trying to figure out how to do this in a completely different way," Ginn said. "That loss of the routine, the confidence of how I'm doing it, that can also just go back to the human nature to avoid change."

Stefan reinforced this: > "The organisations and the companies that figure out how to address that, how to make employees feel better about the use of technology, the training, and the context... those are the organisations that are going to benefit the most."

The Gatekeepers

For some leaders, preventing the erosion of worker confidence has become a significant part of their role.

Randall Tinfow, CEO of REACHUM, an AI-powered learning platform based in Scranton, Pennsylvania, estimates he spends about 20 hours of his 70-hour work week vetting AI tools and partners. His goal is to shield his team from the noise and only hand them tools that actually work.

And he works at a company built around AI. Even there, the gap between marketing and reality is obvious.

While platforms like Claude Code are saving his software developers meaningful time, not everything delivers. His team has run into issues with tasks like text generation in images where certain AI tools just did not perform. (Worth noting: Google's Nano Banana has since dramatically improved AI image generation, and tools in this space are evolving rapidly. We have covered some of the best free alternatives to Midjourney here at AIinASIA> "There's so much noise, and I don't want our team to get distracted by that, so I'm the one who will take a look at something, decide whether it is reasonable or garbage, and then give it to the team to work with," Tinfow said.

This gatekeeper role is something I see playing out across Asia's business landscape too. In organisations where AI adoption is moving fast, often driven by regional competition and government incentives, someone needs to be the filter. Someone needs to test the tools before they hit the team. The alternative is frustration, wasted time, and the kind of confidence erosion that ManpowerGroup's data is capturing.

Looking for Gems in the Noise

Back at Candour, Farrar's team has developed a practical playbook for navigating the gap between AI's promise and its reality.

They build in extra time to account for the fact that everyone is still learning. They frame experiments as "test and learn" to reduce the stress of things not working perfectly. They have appointed a "champion" to stay on top of AI developments. Their chief marketing officer has run training sessions, and Farrar does regular check-ins with the team. She is open about feeling frustrated sometimes too.

Some efforts have delivered real results. The team built a Gemini Gem trained on brand and tone-of-voice guidelines that can generate quotes a client can tweak and approve for media use. Their innovation lead is building custom tools using APIs from companies like OpenAI to meet specific company needs. And Farrar described how quickly the team's attitude toward AI-generated images shifted for the better after Google launched Nano Banana.

But she is clear-eyed about where things stand.> "If I am going to sideline some of my work over to these tools," Farrar said, "I want to be able to trust that it's going to do as good a job as I would do."

What This Means for Asia

The ManpowerGroup and EY data paints a global picture, but the implications for Asia are particularly worth paying attention to.

With India at 77% AI adoption and Japan at the bottom of the sentiment table, the region represents both the most enthusiastic embrace of AI and some of the deepest anxieties about it. Southeast Asia sits somewhere in the middle, with governments aggressively pushing AI readiness while workforces grapple with the same training gaps and confidence challenges that are showing up everywhere else.

The companies that will come out ahead are not the ones deploying the most AI tools. They are the ones investing in their people alongside the technology. That means training, mentorship, psychological safety to experiment and fail, and leaders who are willing to be honest about the fact that AI is not magic. It is a tool that requires skill, patience, and ongoing refinement.

The honeymoon with AI is officially over. What comes next depends entirely on whether organisations treat this as a technology problem or a people problem. The data strongly suggests it is the latter.