Chatham Financial, a capital markets advisory firm, has announced it is using AI technology from OpenAI to redesign how it works. The firm uses two main tools: Codex, which helps build software applications, and GPT language models, which power AI features in those applications. The goal is to handle routine, repetitive work faster so that human experts can focus on complex client decisions where judgment and experience matter most.
One concrete example is trade validation—the process of checking that transaction records are accurate and match what the client authorised. This used to take 30 minutes per trade. Chatham built an AI application using Codex that gathers evidence, compares key details, and flags problems for a human reviewer to assess. That process now takes under 4 minutes. Chatham is testing the application's accuracy against experienced human reviewers and plans to extend it to more types of trades.
Beyond this single use case, Chatham has given employees tools to build their own AI-powered applications for daily work—things like preparing pricing documents, reviewing trade confirmations, and creating hedging dashboards. The firm has also launched Chatham Onyx, a new platform that brings client data and AI capabilities together in one place, helping advisors and clients access information and insights more easily. Throughout all this work, Chatham emphasises that AI handles information gathering and pattern-spotting, while human professionals retain control over decisions and what reaches clients.
Why it matters
For schools and students: This example shows how AI is reshaping professional work in finance and other knowledge-intensive fields. Rather than replacing experts, these tools are changing what expertise looks like—professionals now need to understand how to work alongside AI, interpret its output, and make judgments that machines cannot. Students aiming for careers in finance, law, or advisory services should expect to use these tools and should develop skills in critical thinking and problem-solving alongside technical knowledge.
For families: This shift reflects a broader pattern: routine, manual tasks are being automated, but skilled interpretation and client relationships remain human-driven. Parents might use this as a teaching moment—the jobs that will be valuable are those requiring judgment, creativity, and understanding of context. Families should encourage children to develop these skills rather than focus only on memorising procedures.
For schools: Educational institutions in Singapore and Hong Kong should consider how to prepare students for workplaces where AI collaboration is normal. This includes teaching AI literacy (understanding what these tools can and cannot do), maintaining emphasis on reasoning and judgment, and fostering adaptability as workflows change.
What to do
- Parents and students: If you or your child are interested in careers in finance, professional services, or technology, learn about AI tools in those fields—not to master them today, but to understand how they work and why human judgment still matters. Many firms now publish case studies like this one.
- Educators: Discuss with colleagues how to incorporate AI literacy into your curriculum without assuming students need coding skills. Focus on what these tools do well, their limits, and how professionals use them responsibly.
- All readers: Watch how other industries adopt similar approaches. This pattern—AI handling data-heavy routine work while humans focus on judgment and relationships—is likely to appear across law, healthcare, education, and other sectors. Understanding it now helps you prepare for changes ahead.