Trust, curiosity and judgment: How MUFG is building workforce readiness for the AI economy

Amid rapid AI adoption, organisations are discovering that technology is not the hardest part of transformation. The bigger challenge is helping people trust it, experiment with it and use it responsibly. Particularly in highly regulated industries, where accountability and governance remain paramount, workforce readiness is becoming as important as technology readiness.

How can organisations build workforce capability while maintaining trust, accountability and human judgment?

This question sat at the heart of a recent conversation during the Futurist Circle series by People Matters and Darwinbox, where leaders explore how organisations are evolving amid AI and continuous transformation. In conversation with Cheshta Dora, Head, Content & Research at People Matters, Paul Simons, CHRO, Asia Pacific at MUFG, shared how the financial institution is approaching workforce capability, employee experience and leadership culture while balancing the realities of operating in a highly regulated environment.

For MUFG, a more than 360-year-old financial institution with over 120,000 employees globally, transformation begins with a principle that sits at the core of the organisation itself - trust. “The most important thing we have is trust,” Simons said. “We take it very seriously.”

Building AI readiness on a foundation of trust

For many organisations, transformation starts with systems, infrastructure and technology investments. At MUFG, readiness begins with governance, accountability and employee confidence.

“As we get ready for the new age of AI, making sure that we protect employee data and we never take risks with it,” Simons explained.

In highly regulated industries such as financial services, trust and compliance are inseparable from transformation. While organisations increasingly explore AI-driven efficiencies, caution around employee data and HR systems can sometimes slow adoption. Yet for MUFG, responsible transformation matters more than speed alone.

The challenge, therefore, is not resisting innovation but creating the right conditions for innovation to happen safely.

For Simons, the answer lies in building workforce confidence alongside technological capability. “Making sure employees use it well is what’s going to make us successful,” he said.

From fear to curiosity: preparing employees for AI

As organisations invest in AI tools and digital transformation, employee sentiment remains deeply mixed. Alongside excitement and possibility, many employees carry uncertainty about what AI means for their future roles. 

“I think there’s a fear,” Simons observed. “Some of our employees will fear that AI is going to take their job.”

Rather than dismissing these concerns, MUFG is focusing on helping employees understand AI as an enabler rather than a replacement. Simons believes one of leadership’s most important responsibilities is helping people move beyond fear and become comfortable experimenting with new technologies.

“There is also a big opportunity,” he said. “This is going to augment their work, take away some of the work that people don't like doing, and free them to deliver more creative work.”

For Simons, workforce preparedness is not simply about technical upskilling. The larger transformation is cultural and behavioural. While AI tools themselves may become easier to learn, qualities such as curiosity, critical thinking and adaptability are becoming increasingly valuable.

“It doesn’t take a long time to develop these technical skills in AI, but what is needed is curiosity, critical thinking, and those mindsets,” he explained.

Creating cultures where experimentation feels safe

One of the less discussed challenges in workforce transformation is the role organisational culture plays in enabling innovation.

Many established organisations are discovering that while technology can create new possibilities, traditional structures can sometimes make employees hesitant to test ideas, challenge assumptions or try unfamiliar approaches.

“Hierarchy kills innovation,” Simons said.

For long-established organisations especially, building workforce capability requires creating environments where employees feel empowered to test ideas, share perspectives and explore new ways of working without fear of failure.

At MUFG, this means balancing experimentation with clear guardrails.

“We can’t make mistakes in some areas, but in some we can experiment,” Simons explained.

As organisations introduce AI into everyday workflows, employees need to know where experimentation is encouraged and where caution is non-negotiable. 

For Simons, leadership plays a central role in shaping this environment. Technology adoption cannot be driven through deployment alone. Organisations also need cultures that support learning, openness and psychological safety.

“It’s about balancing those two things, I think, and creating that culture for all of our employees,” he said.

Why human judgment still matters

As AI tools become more sophisticated, organisations are also rethinking the relationship between automation and decision-making. While AI can accelerate analysis and improve access to information, Simons believes human judgment remains essential, particularly when decisions affect people.

“It can be seductive to outsource judgment to AI,” he noted.

At MUFG, AI is currently used to support analysis rather than make decisions independently. Areas such as talent mapping and skills analysis can benefit significantly from AI-enabled insights and speed. However, decisions involving people still require challenge, discussion and diverse perspectives.

“I still believe that it’s more effective to have multiple diverse views on a decision, rather than outsourcing it to an AI engine,” he said.

Simons also highlighted a broader leadership concern emerging in the AI era: the risk of confirmation bias. While AI can surface patterns and recommendations, it does not necessarily provide the constructive challenge that strengthens decision-making.

“I personally love it when I make a decision or a judgment, I like to check it with someone who I know has a different view to me,” he explained. “AI doesn’t really do that at the moment.”

AI may improve analytical capability, but leadership still depends on qualities such as perspective, empathy, judgment and constructive disagreement.

As Simons put it, “We don’t let AI make any decisions. We use it to support analysis.”

Reimagining employee experience through AI

For decades, HR has operated within a difficult trade-off between scale and personalisation. Most employee experiences, from learning and development to rewards and support, have been designed around standardisation because tailoring them for thousands of employees was often impractical.

Simons believes AI could fundamentally change that equation.

“I think AI is fantastic for its ability to really individualise,” he said.

At MUFG, this opens possibilities for delivering more personalised coaching, learning and employee support in real time rather than through standardised programmes alone.

“We can make coaching available in real time for our employees when they need it, rather than just when we decide to tell them,” he explained.

The implications extend beyond learning and development. Simons also sees opportunities to redesign traditionally complex HR processes such as compensation reviews. In many large organisations, these exercises remain highly manual, involving repeated calibration and managerial interpretation to ensure fairness and consistency.

“My aspiration is that AI can do a lot of that work,” he said.

Importantly, he does not view this as replacing leaders but as enabling managers to spend more time coaching, developing talent, and having meaningful conversations with employees rather than managing administrative processes.

“The current model… will always encourage them to show their best side to me,” he reflected. “Wouldn’t it be great to take that away, so that we treat each other as equals?”

What workforce readiness looks like in the AI economy

MUFG's experience highlights a reality many organisations are beginning to confront: workforce readiness is ultimately less about technology adoption and more about how people engage with change.

Trust remains foundational. Organisations need to create confidence that innovation can happen responsibly, particularly when employee data, governance and accountability are involved.

Curiosity is becoming as important as capability. While technical skills can be learned, mindsets such as adaptability, experimentation and critical thinking are increasingly shaping how effectively people work alongside AI.

Leadership culture also matters. Employees are more likely to embrace new technologies when they feel psychologically safe to question, test and learn within clear guardrails.

And finally, human judgment continues to play a critical role. AI can strengthen analysis and improve efficiency, but organisations still rely on diverse perspectives, challenge and empathy to make effective decisions.

The conversation around AI often focuses on what technology can do. MUFG’s experience highlights a different question: what enables people to use that technology well?

For Paul Simons, the answer lies in building cultures where trust is protected, curiosity is encouraged, and experimentation is supported. In a workplace increasingly shaped by algorithms, these deeply human qualities may become the differentiators that matter most.

AI may transform how work gets done, but workforce readiness will ultimately depend on how confidently people choose to engage with that change.