Overcoming the risks of adoption
Generative AI holds great potential, and many finance leaders are building solutions that can deliver trusted outcomes. But there are risks. In addition to producing hallucinations, bias can creep in if models are trained on data that reflects prejudice. And if it holds personal or sensitive information, it could lead to unethical decisions and the misuse of personal information. This is why maintaining a human in the loop is essential. People can spot bias and bring the necessary context and experience that enable confident decision-making with generative AI.
As more countries introduce regulations to protect intellectual property and data privacy and security, enterprises must build responsible generative AI practices and governance that lead to responsible decision-making. Finance teams can build iterative prompt frameworks by exposing the model to many sample scenarios, testing the results thoroughly, and building an internal governance mechanism to overcome potential risks.
At Genpact, we've built guiding principles around our AI initiatives that govern our actions and enable us and our clients to use generative AI responsibly. For example, we use a privacy-by-default framework that allows organizations to run due diligence and enhance transparency. Our approach protects AI systems, applications, and users while also expediting the pace and scale of software development.
But, adopting a responsible AI framework is not enough. Leading CFOs are also creating awareness and building cultures of responsible AI as a core value across functions.