Category: Artificial Intelligence

Generative AI to unleash developers’ productivity

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.

2023 Gartner Emerging Technologies and Trends Impact Radar

Gartner has released its 2023 Gartner Emerging Technologies and Trends Impact Radar. Let me try to give it a read with the eyes of a wealth
or asset manager.

Artificial Intelligence is all over the radar, with Foundation Models, Self-Supervised Learning, Generative AI, and more. Wealth and asset managers must seriously look into Artificial Intelligence and understand how it can be leveraged across their value chain, from investments to operations, risk management, and compliance. Part of the solution will come from their solution providers, like Bloomberg, BlackRock Aladdin, or State Street Alpha, to name a few providers. But wealth and asset managers cannot only rely on their providers. Instead, they must acquire the necessary skills and talent and play with artificial intelligence technologies. War for talent will make it complicated.

Blockchain is also quite present with Web3 and Tokenization, both being in the 3-6-year horizon. Most wealth and asset managers are already testing tokenization in one way or another, and they should continue. Tokenization will have many benefits for the industry, from speeding up transactions, eliminating some intermediaries and therefore reducing costs, making some asset classes available to smaller investors, improving the liquidity of some assets, and more. Tokenization will require some industry alignment and standards.

No surprise, Digital Twins are here too. Gartner is probably thinking more about Digital Twins in the context of manufacturing and industrial activities. But as discussed in this blog, the potential for digital twins in the financial industry is real and massive.

Then, there are hardware and infrastructure with Neuromorphic Computing, 6G, and Hyperscale Edge Computing. If wealth and asset managers continue to move to the cloud, they’ll be able to leverage these latest hardware technologies as they become available in the cloud.