Why Small, Specialized AI Models Are The Future For Fintech

*The views expressed in this article belong to Manav Pandey and do not necessarily reflect the official policy of any organization.

The promise of agentic artificial intelligence is clear: autonomous systems that can automate entire workflows. But for now, the reality is often fragile, error-prone, and unstable. Aptly named the "stumbling" phase in a blog post from AI 2027, the term captures the tension facing many enterprises today: how to square the technology's disappointing results today with its immense potential tomorrow.

To make sense of this disconnect, we spoke with Manav Pandey, a Senior Machine Learning Engineer at American Express who specializes in foundation model training and cognitive architectures. With hands-on experience building the very systems enterprises are grappling with, he frames the current moment with a critical distinction. The path forward requires an approach that favors clever system design over the pursuit of ever-larger models, according to Pandey.

• The algorithmic mimic: The most effective AI agents are those that mimic the stability and precision of smaller, specialized models, said Pandey. "The process is simple. Take one highly specific task, build a perfect dataset for it, and then train a small language model to be a world-class expert on just that one thing. This is how we build stable and trustworthy AI."

• Right tool, right job: A pragmatic engineering principle grounds the philosophy for Pandey: use the simplest, most accurate tool for the job. "We need to apply a 'right tool for the right job' philosophy to agentic AI. For example, why use a massive, expensive large language model to make a simple decision between two tools when a classical classification model can do it with near-perfect accuracy for a fraction of the cost? Using the simplest, most effective tool is the foundation of good engineering."

Often caught between hype and prudence, many enterprises now follow a cautious strategy, says Pandey. Here, an intense focus on immediate ROI often informs the decision to "build vs. buy."

• Buy, don't build: Most companies will get more value from adopting specialized AI tools from the market rather than trying to build them from scratch, according to Pandey. "The expertise required to create these systems is highly specialized and niche. A company like JP Morgan isn't going to build its own AI coding assistant from the ground up. It makes far more sense for them to use a proven tool from a company like Anthropic, OpenAI, or Google. That principle applies to most use cases."

• Playing it safe: Such caution is a direct response to the high failure rate of enterprise AI pilots, said Pandey. "The safest and most effective way for a company to start with agentic AI is to focus on internal efficiency. These projects have the lowest risk and the clearest return on investment, which makes them the most logical place to begin. It's a simple trade-off between risk and value, and right now, the smart money is on making your own company run better."