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East Africa Frontline News

NextFrontier Africa is a media platform spotlighting Africa’s emerging opportunities in technology, tourism, aviation, agriculture, health, logistics, real estate, trade, education, and e-commerce.

November 19.2025

2 Minutes Read

Discover Why AI Models Struggle With Investment Banking Tasks

AI models in investment banking: Hand using stylus on digital graphs.

Why AI Models Struggle in Investment Banking

Despite the massive investment in artificial intelligence (AI), the expected breakthroughs in investment banking remain elusive. Tech giants like Meta, Google, and Microsoft are pouring billions into AI infrastructure, yet traditional workflows in finance continue to outpace these innovations. As highlighted by a recent MIT study, 95% of companies’ AI pilots stall at the proof-of-concept stage, particularly in fields like banking where the intricacies of human decision-making are paramount.

The Data Dilemma

One major reason for this stagnation is the lack of accessible, high-quality data for training AI systems to perform essential banking functions. Unlike the wealth of text available online for tasks like coding or writing, real-world banking data is often gated behind privacy walls. Robert Nishihara of Anyscale notes, “We don’t have nearly as much data for real-world tasks,” making it hard for AI models to learn and replicate the functions of entry-level investment bankers.

The Compounding Errors of AI

Furthermore, AI's propensity for generating varying responses can lead to compounding errors in multi-step processes crucial to banking tasks. As AI encounters more complex workflows, its lack of control over outputs becomes a significant hurdle. Experts like Lake Dai emphasize the importance of gathering domain-specific data. This approach not only aids in training AIs but is becoming a lucrative industry in its own right, with companies like Surge AI helping to collect and curate this essential data.

Road Ahead for AI and Banking

The journey of integrating AI into investment banking is fraught with challenges but also ripe with opportunities. As AI firms progressively work towards acquiring specialized training data, there’s hope that AI can someday take on more white-collar tasks. The demand for such capabilities continues to grow, with promising sectors like trade and e-commerce poised for digital transformation under the AfCFTA framework. However, stakeholders must remain aware of the obstacles that lie ahead in data management and ethical considerations.

What This Means for Exporters and E-Commerce

For exporters, importers, and e-commerce businesses, understanding AI’s current limitations in the banking sector can inform better investment decisions and technology adoption. Embracing transparency and striving for data collection that respects privacy while enhancing operations is essential. As the digital economy evolves, businesses must adopt innovative practices that align with the broader shift towards automation and efficiency in trade.

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Discover Why AI Models Struggle With Investment Banking Tasks

Why AI Models Struggle in Investment Banking

The Data Dilemma

The Compounding Errors of AI

Road Ahead for AI and Banking

What This Means for Exporters and E-Commerce

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