AI adoption at work is quickly shifting from access to tools toward how people use them, verify outputs, and redesign work around them. For fintech companies, that raises questions about what AI fluency actually means across roles, how junior employees learn when repetitive tasks are increasingly automated, and where human accountability should remain.
In this TNGlobal Q&A, Charee Lanza, Chief People Officer at Maya, discusses the Philippine fintech company’s approach to building an AI-first workforce and the broader lessons for employers. She argues that tools will keep changing, while judgment, problem-solving, communication, and accountability are more durable capabilities.
What does an “AI-first” workforce actually mean for a fintech or digital financial services company beyond simply adopting more AI tools?
For us, being AI-first starts with four foundations: reliable data, shared infrastructure, people, and culture. You need the technology and the data to work, but you also need people who are comfortable using AI and a culture that encourages them to rethink how work gets done.
That is why every Maya employee now has access to AI tools, and AI learning is part of our talent development journey—not just for engineers, but across functions like product, risk, compliance, operations, legal, and People.
For me, the real shift happens when people start asking, “Can AI help me do this better?” not because they are being told to use a tool, but because they can see where it can genuinely improve the work.
We saw that recently at our Maya AI Summit, which brought together leaders from across the company. There were keynote sessions, but the real highlight was a practical hackathon. Teams looked at the processes they deal with every day, identified where AI could help, and started building prompts and solutions around those problems. The use cases ranged from customer communications and reporting to employee queries, regulatory work, and operational processes.
That, to me, is what an AI-first workforce looks like: people using AI as part of how they solve problems, while still owning the judgment, context, and outcome.
How is AI changing the skills that fintech companies look for when hiring? Which capabilities are becoming more important as AI becomes part of everyday work?
We still need deep technical specialists—data scientists, engineers, and people in data governance—but the bigger change is that AI fluency is becoming relevant to almost every role.
A marketer, recruiter, risk officer, finance analyst, or operations lead does not need to become an AI engineer. But they do need to understand enough about AI and data to use the tools well: how to frame a problem, ask better questions, make sense of the output, and recognize when something is wrong or needs a second look.
And as AI becomes more common in everyday work, I think the human skills become even more important. Judgment, critical thinking, communication, and the ability to work with different teams matter a lot, because the technology can give you an answer, but someone still has to decide whether that answer makes sense in the real world.
We also put a lot of emphasis on making sure our leaders are AI-fluent. They set the tone for their teams, so they need to be comfortable using AI themselves, asking the right questions, and showing people where it can genuinely improve the work. In many ways, they are the role models and catalysts for adoption across the organization.
So when we hire, we are looking for people who can use AI, but also question it, add context, and take responsibility for the final decision.
How should companies assess AI fluency in candidates? Is it mainly about proficiency with particular tools, or more about judgment, problem-solving, verification, and the ability to work effectively alongside AI?
I would choose judgment. The tools will keep changing. What lasts is the ability to understand the problem, question an output, verify it, and know when human judgment has to take over.
I would rather see how a candidate works through a real problem using AI than ask them to list every AI tool they have used.
One concern around workplace AI is its potential impact on junior and entry-level roles, particularly when tasks traditionally used to train early-career employees can increasingly be automated. How should companies think about career development for younger employees in this environment?
This is something companies really have to think through. A lot of repetitive work was also how younger employees learned a business.
If AI takes away some of those tasks, we cannot take away the learning with them. We have to expose junior employees earlier to judgment calls, exceptions, customer situations, and problem-solving, with managers coaching them through the “why,” not just the process.
In some ways, AI can help people move faster into more meaningful work and grow their careers—but only if we redesign development intentionally.
As AI takes on more routine or repetitive work, which human skills are likely to become more valuable? How can organizations develop those capabilities among existing employees?
Judgment becomes more valuable, but so do empathy, curiosity, communication, and the ability to work across different disciplines.
AI can process information quickly. It is much harder for it to understand the full context of a difficult employee conversation, a customer problem, or a decision where several competing considerations are involved.
Those are capabilities people develop through experience, coaching, feedback, and being trusted with increasingly complex situations.
Where should companies draw the line between automation and human judgment in areas such as recruitment, performance management, employee engagement, or other people-related decisions?
For people decisions, I would keep the line very clear: AI can support the decision, but it should not own the decision.
It can help summarize information, organize inputs, or make processes more efficient. But decisions involving hiring, performance, promotion, or someone’s career require context and judgment, and a person should remain accountable for them.
People should also have a way to question or seek review of an AI-supported outcome.
How can organizations measure whether AI is genuinely improving workforce outcomes rather than simply increasing the number of AI tools being used? What indicators or outcomes matter most?
You should be able to see the difference in the work.
Is a process faster? Are employees spending less time on repetitive tasks? Are they able to focus more on customers, analysis, or decisions? Has the quality of the output improved? And are we doing that without creating new risks, errors, or customer issues?
We are much more interested in those outcomes than in saying how many AI tools we deployed. If employees have more technology but their work has not become meaningfully better, that is not transformation.
For Philippine companies that want to become more AI-enabled but may not have large technology teams or significant resources, where should they start?
Start with a problem people already complain about.
At our AI Summit, we did not tell teams to come up with the most sophisticated AI idea. We asked them to look at their own processes and identify work that was repetitive, slow, or difficult.
That surfaced very practical ideas—automating recurring management reports, helping employees get answers to routine benefits questions, or tracking regulatory requirements and deadlines more efficiently.
You do not need a huge technology organization to begin. Pick a manageable problem, learn from it, and build from there.
Editor’s note: This Q&A has been lightly edited for clarity and TNGlobal house style. The substance of the interviewee’s responses has been preserved.
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