Finance is a dynamic field, and the advent of generative AI has brought both opportunities and challenges. In this article, we explore what McKinsey says will make the difference between success and failure in Wall Street's AI race.

Uncover the Key to Dominating Wall Street's AI Landscape

McKinsey's Role in Banks' AI Efforts

McKinsey plays a crucial role in assisting banks and financial institutions with their generative AI endeavors. It has outlined the dos and don'ts of reaping returns on AI investments in a comprehensive report. This has sparked significant discussions within the industry.Generative AI has captured the attention and resources of bank leaders over the past two years. They have assembled teams of technologists to experiment and run proofs of concepts. Some of these have even scaled to enterprise-wide initiatives used by thousands of employees. Now, the question arises - when will these investments start to pay off?

Viewing AI as a Business Opportunity

Leadership teams need to recognize that generative AI is not just a technological play but a business opportunity. Business leaders should bear the responsibility for delivering results, rather than relying solely on tech leaders. A stronger partnership between business and tech leads to better outcomes in the long run.For example, institutions that make business leaders accountable for achieving results tend to perform better. This shift in mindset is essential for leveraging the full potential of AI.

Concentrating Firepower for Value

Generative AI yields more value when focused on a few key use cases rather than spreading efforts across multiple business units. By narrowing down to three areas where they want to go deep and reimagine entire domains or workflows, companies can achieve a faster path to value.Rather than having 60 use cases across 15 different business lines, concentrating efforts leads to more targeted and impactful results. This approach helps in optimizing resources and maximizing the benefits of AI.

Choosing Areas with Measurable ROI

It is becoming clear that while generative AI saves workers' time, it is not always easy to trace this back to bottom-line impact. This has led to some frustration in the boardroom.However, tools like call-center copilots and AI-powered marketing campaigns that improve the customer experience can generate measurable incremental value. One large bank, for instance, is projecting a 10% revenue increase due to a new analytics platform.For fintech Klarna, leveraging an OpenAI-powered call center agent is estimated to bring in $40 million in profit this year, with the AI replacing the work of 700 full-time agents. This shows the potential of specific AI applications in driving measurable results.

Reusability for Faster Development

Building something once and redeploying it multiple times can accelerate development times and enable companies to scale faster. By ensuring that the tool has gone through the necessary risk, security, and compliance approvals, companies can leverage existing solutions and avoid reinventing the wheel.This approach saves time and resources, allowing companies to focus on innovation and value creation rather than getting stuck in repetitive development processes.

Execution and Adoption

Getting workers and customers to adopt a new way of doing things or a new technology is a critical aspect of the value equation. Banks have faced this challenge in previous technology cycles as well.When it comes to AI, most companies have struggled to achieve the level of adoption needed to yield the desired results. Encouraging adoption requires effective communication, training, and demonstrating the value of AI to all stakeholders.In conclusion, McKinsey's research highlights the key factors that will separate the winners from the losers in Wall Street's AI race. By viewing AI as a business opportunity, concentrating efforts, choosing measurable areas, and ensuring reusability and adoption, banks can position themselves for success in the era of generative AI.