GenAI: Potential benefits and pragmatic problem solving

What can we realistically expect to see in the next five years when it comes to the impact of Gen AI?

In this panel discussion, our panelists explore Gen AI's real-world applications, challenges and lessons learned about AI’s limitations. Speakers discuss the balance between technology and human roles, the importance of data, regulatory compliance, and change management in successfully implementing Gen AI solutions.

Key takeaways from the panel include:

Financial services are rich in data but often lack actionable insights. Gen AI can bridge this gap by transforming vast amounts of data into actionable insights and enhancing decision-making processes.
Gen AI can streamline regulatory compliance by converting complex regulatory texts into universal languages, facilitating easier integration and reducing interpretation risks. Future success will involve closer collaboration between regulators and regulated entities.
Gen AI can improve operational efficiency by automating data management and documentation processes. Successful implementation requires significant investment in change management to ensure workforce readiness and acceptance.
Effective Gen AI deployment relies on differentiating through knowledge and data, having an integrated platform, ensuring a sustainable return on investment, and being prepared for production from security and ethical perspectives.
The future of Gen AI will focus on quality and consistency of outputs rather than speed. Organizations need to consider the high costs associated with AI implementation and ensure that the benefits justify the investment.

The GenAI hype, potential benefits and pragmatic problem solving

"Synthetic data is a game-changer for training models and testing applications, allowing for safer and more effective experimentation."

Dr. Ranja Reda Kouba, Head of Customer Engineering, Financial Services Germany, Google Cloud

"Regulations like the AI Act provide opportunities for better governance and data management, ultimately benefiting the organization."

Oliver Doerler, Chief Data Officer / Head of Big Data & Advanced Analytics (BDAA), Commerzbank

"Only a small percentage of Gen AI use cases make it to production. Effective change management and a clear business case are essential for success."

Dr. Sven Blumberg, Senior Partner, McKinsey & Company

"We should focus on how AI and Gen AI can enhance the best reporting operating models, rather than just creating AI agents. It's important to consider the specific applications, investments, and ensure the models are credible, unbiased, and robust over time."

Leonardo Orlando, Principal Director, Accenture