Published on November 26, 2023, 10:35 pm

Generative AI Tools: Transforming Industries and the Global Economy

Generative AI has become a hot topic in 2023, capturing the public’s imagination with groundbreaking tools such as ChatGPT, DALLE-2, and CodeStarter. Unlike previous technological trends that have come and gone, generative AI seems to have staying power. In fact, OpenAI’s chatbot, ChatGPT, has quickly become a household name and surpassed TikTok and Instagram in terms of adoption speed.

The potential impact of generative AI is immense. A McKinsey report suggests that it could add $2.6 trillion to $4.4 trillion annually to the global economy. One industry that stands to benefit significantly is banking. The report highlights how generative AI could deliver an additional $200 billion to $340 billion in value each year if fully implemented.

However, as with any new technology, distinguishing between hype and lasting value is crucial for businesses across all sectors. This holds particularly true for financial services firms, given their extensive use of digital tools. To shed light on the early impact of generative AI in finance and the barriers to successful deployment, MIT Technology Review Insights conducted a comprehensive study.

One key finding from the report is that corporate adoption of generative AI in financial services is still in its early stages. While cost-cutting remains a primary use case by automating repetitive tasks previously done by employees, more disruptive applications are being explored but not yet commercially deployed. Concepts such as asset selection, improved simulations, and better understanding of asset correlation and tail risk have gained attention but face practical and regulatory challenges hindering their implementation.

Legacy technology systems and talent shortages present temporary obstacles to widespread adoption of generative AI tools in finance. Many financial institutions still rely on outdated IT infrastructure that may not be suitable for modern applications. However, digitalization efforts have alleviated this problem to some extent. Additionally, scarcity of expertise in generative AI is a common challenge, but companies are mitigating it by training their staff rather than competing for a limited number of specialists. This talent shortage is expected to resolve gradually, similar to trends witnessed with previous technologies like cloud computing.

On the technology front, there are inherent weaknesses and regulatory hurdles that need to be addressed. Off-the-shelf generative AI tools may not meet the requirements of complex tasks such as portfolio analysis and selection. Developing customized models can be time-consuming and resource-intensive. Additionally, ensuring accountability and minimizing bias in AI-generated output remains a critical concern. Validating complex output from generative AI tools poses a unique challenge that requires further exploration.

To delve deeper into these findings and gain comprehensive insights into the impact of generative AI in finance, download the full report published by MIT Technology Review Insights.

Please note that this content was produced by Insights, the custom content arm of MIT Technology Review, and not by MIT Technology Review’s editorial staff.


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