The Future of Learning: Building Human-Centered Skills in an AI-Driven

Over two decades across telecommunications, fintech and banking, Lolitta Suffian has built her work around one simple conviction: digital should never lose its human touch. Known for creating customer experiences people can actually feel, she has helped organisations use technology and data to solve real customer problems rather than simply add more features.

Before moving into banking, she spent four years leading customer experience at Telekom Malaysia. Today, as Senior Vice President of Customer Experience at Bank Simpanan Nasional, she is focused on making banking simpler, more inclusive and more human for every Malaysian. She accepted the bank’s award for Best Frontline Empowerment and Knowledge Ops at The Digital Banker Digital CX Awards, recognising NOAH, its GenAI-powered knowledge platform that helps frontline teams serve customers with greater confidence and accuracy.

She was named among CX Network’s Top 50 CX Influencers in 2023 and again among its Top 50 Customer Experience Leaders to Follow in 2026. A sought-after speaker and a mentor with TalentCorp and G4G, she champions a clear philosophy for the future of service: Digital Forward, Human Always.

In an interaction with M R Yuvatha, Editor, Asia Education Review, Lolitta Suffian shares insights on why empathy, judgement and curiosity are becoming the defining skills of an AI-driven world, and how leaders and educators can build them deliberately.

As organisations become more digital and AI-driven, technology skills are becoming the baseline rather than the differentiator. What sets people and institutions apart is the human capability around the technology: empathy, judgement, curiosity and adaptability. Drawing on more than two decades of experience across telecommunications, fintech and banking,In this article looks at what is at risk, what leaders must protect and how education can prepare people for a future in which AI handles more of the routine thinking.

As organisations become more digital and AI-driven, what gets lost first, and what should leaders protect so teams do not become less human?

Consideration is often the first casualty. Digital journeys are typically designed around speed, efficiency and convenience: click here, authenticate there, transaction complete. On paper, the journey works. In practice, a journey can work perfectly from a system perspective and still fail the human being using it.

Banking makes this plain. At BSN, customers have very different levels of digital confidence. Some want pure self-service: get in, finish the task and move on with the day. Others need guidance, reassurance or simply the knowledge that a human being is available if they get stuck. Serving both groups starts with recognising that digital-first should never automatically mean digital-only.

Leaders therefore need to protect the moments where human judgement, reassurance and intervention matter most. That means designing for the person who understands the technology and for the person who does not. It also means resisting the assumption that every interaction is a candidate for automation.

The better question is not only "Can this be automated?" but "Should all of it be automated, and what happens to the customer when the automation is not enough?" Asking this during design, rather than after complaints arrive, is where empathy begins in an increasingly digital organisation.

In practical terms, protecting the human element involves a few clear commitments:

• Keeping a visible, easy route to a real person at every stage
• Giving frontline teams the authority to step in when a process fails
• Measuring outcomes such as resolution and customer confidence, not only speed and cost

Organisations that treat these as design requirements, rather than optional extras, keep their teams and their customers connected.

“Technology should automate the transactions. Humanity should own the relationship”.

Empathy, judgement, curiosity and adaptability are often called soft skills. Why are they becoming harder, not easier, to develop as AI takes over routine cognitive work?

The term "soft skills" is misleading, because there is nothing soft about them. AI can analyse information, identify patterns, summarise thousands of customer comments and deliver an answer in seconds. These capabilities are extraordinary and should be used fully.

The challenge is what follows. When answers become easy to obtain, the habit of questioning them slowly fades. Curiosity, judgement and empathy are exactly the skills that counter this, which is why they matter more, and why they need deliberate practice rather than assumption.

Telecommunications offers a useful illustration. When NPS was introduced as an organisation-wide culture, the technology was the easier part. Customer feedback could be consolidated, the data analysed and the results produced. But a dashboard does not create change.

The difficult part was helping people at every level of the organisation understand what those numbers had to do with them. NPS had to be made relevant to each person's day-to-day work, so they could see how their actions affected a customer and, through that domino effect, the organisation's score. The aim was never simply to teach people how to read a number. It was to help them care enough about what customers were saying to do something differently.

Technology can show that customers are unhappy. Today, AI can probably explain the reasons faster and more deeply than was possible a few years ago. But neither a dashboard nor an algorithm wakes up the next morning and decides, "This needs to be better." People do.

That is why these so-called soft skills are set to become some of the hardest, and most valuable, capabilities to develop in an AI-driven world. They cannot be downloaded, and they weaken quickly when routine work no longer exercises them. Organisations that want them need to build them into how teams learn, decide and are evaluated.

Across telecommunications, fintech and banking, how has the definition of future-ready talent changed as AI adoption has accelerated?

The definition has changed considerably. Over two decades, technology has moved from something that primarily enabled people to something that can increasingly think, create and make recommendations alongside them. There was a time when knowing how to use technology gave a professional a clear advantage. Increasingly, that is simply the starting point.

Today, the most future-ready person is not necessarily the one who knows the most AI tools or produces an answer the fastest. What matters more is what happens after AI provides the answer. Does the person question it? Does it make sense? What assumptions sit behind it? What is missing? Who might be affected by the decision? Is there another way of looking at the problem?

AI makes access to knowledge easier. But easier access to knowledge does not automatically create wisdom or judgement. Those still come from experience, reflection and the willingness to challenge a confident-sounding output.

One of the biggest risks for the next generation of talent is outsourcing too much of their thinking. If technology always supplies the first answer, discipline is needed to keep asking the second and third questions. Without that discipline, speed can quietly replace understanding.

Future-ready talent is therefore a combination of digital fluency and distinctly human capability. Organisations need people who are comfortable using AI but who retain:

• Curiosity
• Critical thinking
• Adaptability
• Judgement
• Empathy

The same principle applies across all three industries, whether the context is a network outage, a payments journey or a lending decision. The sector changes, but the standard does not: use AI to accelerate thinking, not to surrender it.

What does digital empathy look like in practice, and how can leaders build it deliberately rather than hoping it emerges on its own?

Digital empathy is simple to define. It means designing technology with the understanding that the person on the other side may not think, behave, understand or feel the way the designer does.

Organisations sometimes assume that if a digital process is technically functional, the customer experience is solved. But customers do not experience technology architecture. They experience a moment in their lives. They may be confused, worried or not digitally confident. They may simply need reassurance that they are doing the right thing.

For that reason, digital empathy has to be designed into the journey from the start. The test questions are practical:

• What happens when someone gets stuck? There should be a clear fail-safe.
• Can they reach a human being? Access should be easy, not hidden.
• Is the handover seamless? Customers moving from a digital channel to a human one should never have to explain everything again.
• Who are the instructions written for? They should serve the people who use the system, not the people who built it.

These may sound like small details, but they are where customers feel whether an organisation understands them.

Leaders also cannot delegate empathy entirely to the frontline. If processes, policies, KPIs and technology make it impossible for employees to do the right thing for the customer, telling them to "be more empathetic" will not fix the problem. Empathy has to be designed into the workflow, supported by metrics that reward it and systems that allow it.

None of this argues against technology. AI has enormous potential to improve customer experience by removing friction, simplifying processes and taking over repetitive work. The point is to be clear about what should not be outsourced.

Technology should automate the transactions. Humanity should own the relationship.

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What are educators and institutions most likely getting wrong by focusing primarily on technical skills?

The most common mistake is treating technical skill as the whole of preparation. Teaching students how to build technology is essential. But it must be accompanied by teaching them about kindness.

That may sound unusually simple in a conversation about AI, but the reasoning is practical. Technology will keep changing. The programming language that matters today may not matter in ten years. The AI tool everyone is learning now will eventually be replaced by something better. Education that prepares young people only to use today's technology will date quickly.

Instead, education has to prepare students to navigate a lifetime of change. That means teaching:

• Curiosity
• Critical thinking
• Communication
• Adaptability
• Judgement
• The confidence to question an answer
• Empathy and kindness

Career experience reinforces this. A strong education does not prepare a person for one particular job. It builds the discipline of thinking, the habit of looking for detail and a lasting curiosity. Work then teaches what no classroom fully can. A first customer service job, for example, builds patience, problem-solving and the ability to think quickly when someone needs help. Those lessons remain long after the technology used in that job has become obsolete.

That is what education should ultimately give young people: not only the skills to operate the technology that exists today, but the human capabilities that stay valuable whatever comes next. Institutions that pair technical programmes with communication, ethics, teamwork and real-world exposure to customers and communities are better placed to produce graduates who can lead in an AI-driven workplace, not just operate within it.

Closing Thought!

Beneath every conversation about AI, digital transformation and the future of work, there is still a human being on the other side. People may not remember the technology that was used. They will remember how it made them feel.