Lieberman highlighted how AI is transforming the company from three key perspectives: employee productivity, developer efficiency, and client solutions. Internally, AI is enhancing everyday tasks, automating emails, and creating PowerPoints, while supercharging developers with tools like code autocompletion and unit test generation. Externally, Finastra is leveraging generative AI to introduce new products and features for clients, including advanced chatbots and enhanced data processing capabilities.

Lieberman stressed the role of data as the foundation of successful AI implementation, describing it as the "soil in which machine learning grows." Without clean, high-quality data, the effectiveness of AI models diminishes. This focus on data quality allows Finastra to develop sophisticated use cases, such as text-to-SQL functionalities, summarisation, and refining AI models for financial-specific applications.

When asked about the challenges in scaling AI within the highly regulated financial industry, Lieberman acknowledged the tension between enhancing customer experience through AI-driven solutions and ensuring robust data privacy and compliance. “Trustworthiness” and “transparency” are vital, especially in generative models. Before any AI model is deployed, Finastra champions rigorous legal and security reviews to mitigate risks like prompt injection and adversarial attacks.

As AI evolves, Lieberman sees growing competition among AI models, with proprietary systems like OpenAI and alternate players like LLaMA gaining traction. For Finastra, the goal is to remain flexible, always pursuing the "next best model" that seamlessly integrates intelligence into financial services.