4 min read
AI has officially moved from buzzword to business essential. In the tech and fintech world especially, it’s becoming the go-to tool for streamlining processes, speeding up development, and unlocking insights that once took weeks to uncover. But as adoption ramps up, so do the questions—namely, what does this mean for the people behind the scenes?
In this article, we’re digging into how AI is changing the way we work, without pushing humans out of the picture. We’ll explore how leading tech firms are using AI responsibly, where human oversight still matters most, and what it means for the future of work in fintech.
Is AI Really Causing a ‘Jobpocalypse’?
With every leap in AI, the same question resurfaces: What happens to the humans?
Media stories often predict mass job loss, and yes, some repetitive or lower-skilled tasks are being automated. But the bigger picture is more nuanced.
A recent PYMNTS Intelligence report shows that chief product officers are adopting generative AI as a creative and strategic partner, not a replacement. It’s most often used for ideation, prototyping, and research—accelerating work, not eliminating it.
Even in areas like fraud detection or workflow automation, human oversight remains essential. And for complex tasks like cybersecurity or strategic planning, AI supports rather than leads.
Some jobs will change, and some may disappear. But more broadly, we’re seeing a shift: AI is handling the routine, while humans focus on what machines can’t, like problem-solving, ethics, and applying context.
What Big Tech Is Doing with AI
If you want a clearer picture of how AI is reshaping the workforce, it helps to look at the companies leading the charge.
Duolingo made headlines in early 2024 when it announced it would be letting go of 10% of its contractor workforce, partially due to a growing reliance on AI to translate content. While no full-time employees were laid off, the move signalled a willingness to trade human labour for automation where it makes sense—particularly in repetitive, high-volume tasks. It was a reminder that some roles will evolve or disappear as AI gets better.
Shopify is taking a different tack. The company’s CEO, Tobi Lütke, recently issued a memo urging staff to treat AI as a core part of their job. From product development to performance reviews, AI is now embedded in how the company works. Teams are expected to use it to accelerate idea generation, build prototypes, and summarize research before requesting additional resources or headcount.
Klarna, meanwhile, offers a valuable case study in what happens when the pendulum swings too far. After announcing that AI had replaced the work of 700 support agents, the fintech giant experienced a drop in customer service quality and is now rehiring human support staff through a flexible, on-demand model. CEO Sebastian Siemiatkowski acknowledged the misstep, noting that cost-cutting alone can’t justify sacrificing the customer experience. While Klarna continues to integrate AI across its operations, it’s reintroduced the human touch where it matters most, proving that the best approach isn’t all or nothing.
Across these examples, the trend isn’t total replacement. It’s strategic adoption. Companies are using AI to gain an edge, but they’re still counting on humans to guide the process, set priorities, and ensure the outputs align with brand, ethics, and experience.
Where AI Still Needs a Human Touch in Fintech
From streamlining internal workflows to powering faster customer insights, the benefits of AI are real and growing. But even as adoption accelerates, some parts of the job still require something AI can’t replicate: human experience, creativity, and judgement.
Here are a few key areas where AI performs best with a human in the loop:
- Fraud detection
AI can flag suspicious transactions faster than any person, but human analysts provide the context. Deciding whether to block a payment, investigate further, or notify a customer may still require emotional intelligence and a broader view of the situation that AI hasn’t reached yet. - Regulatory compliance
AI can help scan and summarize evolving rules, but applying those rules correctly across jurisdictions still depends on experienced compliance professionals. Context and interpretation matter more than ever. - Data privacy and risk mitigation
Automated tools can surface risks, but decisions around how customer data is handled ethically and securely require human accountability. Trust in fintech is built through transparency, not automation alone. - Customer experience and product design
While AI can generate variations or test flows, humans craft the messaging, tone, and interactions that resonate. Building trust with users, especially around their finances, is a deeply human process. - Software development and architecture
Tools like code assistants can help developers move faster, but humans still lead when it comes to designing secure systems, solving complex technical problems, and making architecture decisions that scale.
At Digital Commerce Payments, we see AI as a multiplier, not a replacement. It’s most powerful when paired with the insight, ethics, and creativity of a well-equipped team. For fintech companies, the future isn’t AI or people. It’s both, working together.
The Importance of Responsible AI in Fintech
As AI tools become more deeply embedded in fintech workflows, so do the ethical and regulatory questions that come with them. How are decisions being made? Is the data accurate, unbiased, and secure? Who’s ultimately accountable?
That’s where responsible AI comes in, and it’s becoming more important than ever.
Responsible AI means:
- Human-in-the-loop systems
AI doesn’t operate in isolation. It’s guided, reviewed, and audited by people, especially when decisions impact customers, finances, or compliance obligations. This methodology is called Human-in-the-loop (HITL), and it’s becoming a standard across industries evolving with AI. - Data privacy and security
Fintech companies must ensure that customer and corporate data used to train or inform AI models is anonymized, protected, and handled with care. Compliance with laws like GDPR, PIPEDA, and others needs to be baked in from the beginning. - Bias and fairness audits
AI models can only be as fair as the data they’re trained on. Responsible firms will regularly test for unintended bias and actively work to ensure equitable outcomes.
For example, a lending algorithm trained on historical data may unintentionally favour applicants from certain demographics over others. Without regular audits, these patterns can go unchecked, leading to unequal access to financial products and exposing companies to reputational and regulatory risk.
- Transparency and explainability
Customers and regulators alike need to understand how decisions are made. That means choosing AI models that don’t just work, but can be explained and defended.
In fintech, this is especially important when AI influences outcomes like loan approvals, fraud flags, or account limitations. If a customer is denied a service or flagged for suspicious activity, they (and regulators) need to know why. AI outputs without traceable logic can erode trust and create compliance headaches. Explainable AI ensures decisions are auditable, accountable, and fair.
As fintech continues to evolve, those who embed responsibility into their AI strategies from day one will have a competitive edge—not just in what they build, but in the confidence they receive from consumers and regulators.
Stay in the Loop on AI
The real opportunity with AI in fintech lies in combining human insight with powerful tools to work smarter, move faster, and build more secure experiences.
At Digital Commerce Payments, we’re keeping a close eye on how AI is evolving—and how it can be applied responsibly in the world of payments and financial services. Get in touch today to talk about the latest trends shaping fintech and how your business can stay ahead.