Check out any news website these days, and you’re likely to see a story about generative AI. With OpenAI’s ChatGPT and Google’s Bard achieving rapid growth in users, there’s a widespread expectation that such tools will radically change how people worldwide live and work. But what are their implications for capital markets firms?

The simple answer is that generative AI will be game-changing for the industry. When looking at the concept of Total Enterprise Reinvention in my recent blog, I mentioned that creating compelling technology capabilities—a central digital core—would be key for succeeding in the markets of tomorrow. And generative AI is poised to play a pivotal role in firms’ drive to design and build that central digital core and set a new performance frontier for themselves.

How AI has evolved to date…

But why will generative AI be so important? Let me begin my explanation with some history.

The term “artificial intelligence” isn’t new: it was already coined in the 1950s–and it was back in the mid-1990s that an AI engine beat the world’s best chess player. However, around 2000 two big changes transformed its power and potential. First, the advent of the internet, which vastly expanded the data available for training AI models. Second, the emergence of cloud computing, offering effectively nearly unlimited computing power.

Together, these elements turbo-charged innovation around AI, triggering the development of human-like capabilities such as speech recognition, image generation and language reasoning. These advances pushed back the boundaries of the value derived from data analytics–taking it beyond the ‘predictive’ and ‘prescriptive’ capabilities that were leading-edge a decade ago and culminating in the ‘generative’ AI we see today.

…and why generative AI breaks new ground

Why is generative AI such a break with the past? At root, because it replicates capabilities previously associated only with humans. More specifically, it can read, comprehend, reason, make suggestions and create new content based on a vast amount of available data it was trained on, supplemented by in-memory understanding and learning from what it’s done before.

To help visualize what this means, take a prescriptive AI solution designed to detect fraud. Training it would involve taking a bunch of historical fraud data and having data scientists develop a model to spot the warning signs. The solution will then deliver against this task, but only this task, so if you ask it to do something else, it will fail because it was not designed for it.

Now take generative AI. Here the training starts at the opposite end, with generalized human-like capabilities–sense, read and so on–that are then applied to a broad set of questions or problem statements. This is how generative AI creates new content and solutions ranging from computer code to text to images, while using constant feedback from human users to support continual learning and reinforcement. And tools like ChatGPT are now making these capabilities available to billions of people worldwide.

Use cases across the capital markets business

For the capital markets businesses, we at Accenture see three main groups of potential use cases where specific generative AI solutions could add significant value.

First, revenue generation. Capital markets firms own or have access to a vast pool of data. Using Generative AI, companies could analyze this data and generate insights about their customers’ needs by spotting trends in their behaviors. As such, firms could detect opportunities to cross-sell or provide new products to their customers.

Related to the above, in terms of enhancing customer focus, generative AI opens the way to provide every individual client with a customized and hyper-personalized experience, while empowering relationship managers and advisors to focus on value-adding activities and client needs. The technology can also help to improve and streamline customer interactions and communications through intuitive, natural-language chatbots that provide a fast, responsive and convenient service.

The opportunities for capital markets to improve customer focus are however not only limited to the retail dominated businesses. There are as well some institutional angles, such as the potential to tailor reporting portals based on personas or information that the users care about and to provide them in return with a unified view of activities—and perhaps the status against those.

And to enhance operational productivity, there’s rich potential to use generative AI to automate and ‘bot-shore’ operational processes while involving humans as required. By augmenting human operations agents with smart tooling, firms can help them do their jobs better and faster, and quality-assure the results. And as well as rationalizing and optimizing controls, generative AI could automatically track regulatory changes and respond by modifying existing controls or developing new ones.

As firms look to address these various use cases for generative AI, the first wave is likely to mainly involve models targeting operational productivity, followed by customer-focused models in areas like hyper-personalized marketing. Building, e.g., intelligent assistants to your internal workforce first might be the less risky approach and provide good opportunities to test and learn.

What to do today

How to begin the journey to your generative AI-enabled future? Our experience highlights three steps.

1. Educate leadership and stakeholders: Demystify and explore the transformative potential of generative AI from front-to-back office, by educating leaders up to board level. We find an effective approach is to convene education workshops combining a foundational explanation of generative AI with tailored demos of practical solutions that bring it to life. An example? We recently showed a client’s vendor management team how generative AI could write an RFP proposal for a project.
2. Understand and assess high-value use cases: Explore various use cases for generative AI–assessing the value they can deliver, understanding the organizational implications and opportunities alongside any potential risks that might arise (and balance those against the value), and gauging their feasibility for further investigation and development. Discovery sessions and ideation workshops are especially useful here. It might be more impactful to carefully select some areas to focus on rather than trying to create too many different use cases across the entire organization.
3. Decide where and how to take action: Adopt a strategic approach by scaling up use case proofs-of-concept (POCs) around a holistic enterprise strategy, or a targeted approach based on pinpointing specific use cases and fast-tracking them through starter solutions. Critical enablers here include POC development and solution design.

Preparing for the next stage of transformation

There is little question that generative AI will profoundly change how capital markets firms operate. So, sticking with the status quo is not an option. And in delivering new and higher value across the business, generative AI could help to drive the broader transformation now needed through a strategy of Total Enterprise Reinvention, powered by a strong digital core.

Thanks to my colleague Mauro Confalone for contributing to this blog.