However, to fully benefit from GenAI, high-quality systems are needed, and it can be tough to tell different AI models apart. The paper explains, with helpful easy-to-understand diagrams, what makes an AI system good, including the quality of data, the complexity of the system, and cybersecurity measures.

It also explains important tools like Retrieval-Augmented Generation (RAG) and Parameter Efficient Fine Tuning (PEFT), which help reduce errors. A step-by-step approach for adopting GenAI is recommended, starting with simple tasks and gradually expanding to more complex uses to ensure a smooth transition. Key suggestions for the industry include using standard test data, setting up fair usage policies, creating AI testing environments (sandboxes), and simplifying regulations to make AI integration more effective.

The authors call for a step-by-step approach for adopting GenAI, starting with simple tasks and gradually expanding to more complex uses to ensure a smooth transition. Key suggestions for the industry include using standard test data, setting up fair usage policies, creating AI testing environments (sandboxes), and simplifying regulations to make AI integration more effective.

Authors

Thomas Kaiser, CEO and Co-Founder, Kodex AI

Boon-Hiong Chan, Industry Applied Innovation Lead & Head APAC Market and Technology Advocacy, Securities Services, Deutsche Bank

Delane Zahoruiko, Founders’ Associate, Kodex AI