Mid-June, I wrote about “Leveraging Artificial Intelligence in Software Development.” McKinsey & Company just published a study that “shows that software developers can complete coding tasks up to twice as fast with generative AI.” Not surprisingly, generative AI can be used for code generation, code refactoring, and code documentation, speeding up these activities by 20 to 50 percent.
The purpose of Generative AI is to assist developers rather than replace them. It is important for developers to have solid coding skills and to dedicate time to learning how to use Generative AI effectively. Generative AI won’t replace developers in integrating some organizational context (e.g., integration with other processes and applications), examining code for bugs and errors, and navigating tricky coding requirements.
As per McKinsey’s research, generative AI shined and enabled tremendous productivity gains in four key areas: expediting manual and repetitive work, jump-starting the first draft of new code, accelerating updates to existing code, and increasing developers’ ability to tackle new challenges.
The transition to coding with Generative AI will take time to happen. Technology leaders must train and upskill their development teams, start experimenting early, and deploy risk control measures. Risk control must cover many topics, including but not limited to security, data privacy, legal and regulatory requirements, and AI behavior.
Improving developer productivity through generative AI is a journey that will take some time. It is crucial for companies, particularly regulated ones such as asset and wealth managers, to begin experimenting with it. So that they can also better understand regulatory and security constraints and understand how to best address them.
Digital Transformation of Asset Managers and Wealth Managers 