For most fintechs, growth is supposed to feel like a win: more users, more transactions, more markets. The stress-test part of this story often stays underexposed: what about the support queues that swell unpredictably overnight? What about support teams scrambling and hiring accelerating? In such conditions, a reliable AI agent can minimise strain and help fintechs deliver the desirable level of service.
The question facing modern fintechs is how to scale without sacrificing quality to brute-force hiring. Fortunately, intelligent automation is becoming a reliable solution to this problem. Gradient Labs’ AI support platform offers a case study in what scaling intelligently can look like in terms of maintaining ‘human’ quality while protecting teams from premature expansion and operational overload.
No clean curve for growth
Morse, a cross-border payments app operating in more than 150 countries, launched into an environment defined by volatility. Payments traffic fluctuates by region, time zone, macro events, and, of course, by regulatory changes. Scaling meant that customer support demand spiked rather unpredictably.
While these spikes became more frequent and less predictable, expanding to each new country introduced different user expectations, payment behaviours, and edge cases. For a financial product, trust and compliance is non-negotiable. As a result, Morse’s response time and quality could not degrade, even temporarily.
Hiring ahead of demand would have locked Morse into fixed costs. Hiring reactively would not work because the spikes remained unpredictable. The company decided to integrate Gradient Labs’ AI agent directly into its support channels.
Immediate capability without immediate headcount
From day one, the AI agent provided instant response coverage across Morse’s customer operations. Without months of training or complex deployment cycles, the system achieved a 50% resolution rate out of the box. Even this base level resolution rate, which Morse quickly optimised to 78%, had an immediate impact. While AI handles tasks varying in complexity (from balance checks to nuanced requests), the backlog stays manageable for the human team.
“Gradient Labs has been instrumental in enhancing our team’s efficiency,” says Aliny Penrose, Head of Operations at Morse. “By handling a very wide range of inquiries, it allows our team to focus on high-value tasks and complex cases where human judgement is a must.”
And this is the key distinction. Automation doesn’t replace human operators altogether, instead, it reshapes the workflow.
Learning systems vs static support models
What differentiates AI-agent-powered platforms from traditional automation is its flexibility and context sensitivity. Morse’s AI agent was using the company’s historical data, internal protocols, and scenario-specific procedures to learn and improve on the go. In a short period of time, the resolution rates climbed to 78%, while customer satisfaction on AI-resolved inquiries reached a Customer Satisfaction Score (CSAT) of 86%.
This improvement curve demonstrates the structural advantage of AI-driven support systems frequently discussed across various industries. As AI systems compound their value over time, every tiny successful interaction strengthens the agent’s understanding of the company’s complex context. For Morse, this meant scaling support capacity without scaling operational risk along the way.
Scaling intelligently, not austerely
Won’t automation like this only lead to avoiding hiring altogether? According to JobHire.AI, 71% of workers believe they could be laid off this year. Amazon has announced their largest layoffs (so far).
The reality is a bit more nuanced, in that AI could prevent the cycle of overhiring that leads to layoffs in the first place. About 70% of startups overinvest in premature scaling. Especially for young and eager companies, hiring often comes ahead of validated demand, resulting in high burn rates and lack of adjustment capability when growth patterns change. Add to this the constantly rising acquisition costs, and you’ll understand why even the richer companies are retrenching.
In this context, the goal is not necessarily to get rid of human workers. As AI agents create a buffer between demand spikes and organisational expansion, companies get more space to keep their hiring patterns stable even during pressure peaks. Not always firing, thus, but managing hiring differently.
Why fintech, and why now
Fintech isn’t the only industry on the intersection of high expectations and low tolerance for failure. AI-driven support to reduce cost-to-serve without butchering satisfaction and retention is becoming more popular across all sectors, from education to banking. Recent industry forecasts project that AI will handle a growing share of customer interactions autonomously sooner rather than later. The hope is that this shift will also empower human employees to focus on high-value, complex tasks and cases.
Especially in the sectors where adding headcount to match cycles of high volume is neither affordable nor sustainable, intelligent automation offers an innovative path: scaling with resilience rather than reaction. Morse’s experience with Gradient Labs perfectly reflects this shift in thinking about daily operations: it’s about building systems that can adapt and stabilise growth.
For fintechs navigating unpredictable growth, the distinction between hiring for meaningful growth and hiring out of panic is the difference between momentum and meltdown.
