Establishing An R&D Center In AI: A Primer

The buzz of trending technologies often sees businesses rushing to adopt them. But most of these fade away quickly. AI, with its transformative abilities to boost productivity, provide insights, and unveil new revenue avenues, is here to stay. Leaders should actively consider AI’s potential, particularly in the fintech domain where data science is pivotal.

A misconception persists that transitioning to an AI business is as simple as having the right data and infrastructure. However, AI’s intricate nature demands not just hefty investments but often, a whole business metamorphosis. AI’s real requirement is an elite talent pool – with only 22,000 PhD-level experts globally. Considering AI’s potential market value sits between $3.5 trillion and $5.8 trillion, securing this niche talent becomes paramount.

The secret to a robust AI infrastructure? Acquiring and retaining top-tier talent. Real AI goes beyond data science – it requires the right people, a culture of research, and a flexible organizational ethos. Currently, AI operates more as a burgeoning frontier than a business tool. As such, genuine AI practitioners are rare, making talent acquisition and retention the primary hurdles. The ideal AI professionals often come from academic backgrounds, possessing both data intuition and cutting-edge knowledge.


Underestimating the significance of this expertise is perilous. For instance, machine learning heavily relies on nuanced mathematics. Errors here, when applied to business, can result in serious repercussions. Take the 1973 Berkeley admissions data issue, which seemingly indicated gender bias. Only after in-depth statistical analysis was the apparent bias debunked, showcasing the subtleties involved in data interpretation. In our data-driven world, research is the production engine. Dismissing in-house research in AI is a grave oversight. Each business presents unique challenges, mandating tailored solutions. This makes AI a risky yet potentially high-reward venture.

Striking a balance between fundamental and applied research can mitigate these risks. While fundamental research is open-ended, exploring the horizons of knowledge, applied research targets specific issues. Applied researchers possess the knack to discern when to conclude research and concentrate on problem-solving. They play a pivotal role in aligning pure science with real-world applications.

At the Data Science UA, R&D center, we’re delving into natural language processing (NLP) to discern patterns in textual data, aiming to predict how global events influence companies. The challenge? NLP is adept in structured environments but struggles in dynamic scenarios, such as news interpretation. This is where the fusion of basic and applied research is vital. Our objective is to enhance NLP capabilities to equate human-level comprehension while ensuring the solutions are fintech-applicable.

To nurture an AI practice, crafting the ideal environment is key. AI professionals are in high demand, and to attract them, businesses need to offer a blend of enticing datasets, intriguing challenges, computational power, and research mentorship. Most critically, the freedom for curiosity-driven research is invaluable.


Integrating data science and fintech into your business means integrating their values of solving monumental problems and being able to share their findings. Ensuring these provisions is crucial. Enterprises that operate in isolation might need to rethink their modus operandi.