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Home » AI Agent & Copilot Podcast: Finastra Chief AI Officer Lays Out Range of Use Cases, Microsoft Collaboration
AI and Copilots

AI Agent & Copilot Podcast: Finastra Chief AI Officer Lays Out Range of Use Cases, Microsoft Collaboration

Bob EvansBy Bob EvansMay 9, 2025Updated:August 28, 20253 Mins Read
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Welcome to the AI Agent & Copilot Podcast, analyzing the latest AI Copilot and agent developments from Microsoft and its partners, delving into customer use cases, and exploring how AI plus the Cloud helps customers reimagine their business. In this episode, Chief AI Officer Adam Lieberman of the financial tech software firm Finastra discusses his firm’s AI strategy and use of Microsoft Copilots.

Highlights

Lieberman’s Role at Finastra (00:35)

Finastra is a global financial services software provider serving over 8,000 institutions. Lieberman’s role is divided into three main aspects: legal and governance, infrastructure and tooling, and business strategy formulation. He reports to the CTO and works across the entire business to ensure AI agents are deployed effectively.

Business Drivers and AI Benefits at Finastra (02:52)

Lieberman outlines three main business drivers for AI at Finastra: enterprise efficiency, developer productivity, and client commercialization. For enterprise efficiency, he focuses on tools that accelerate day-to-day activities like creating PowerPoints, searching for information, and writing professional emails. For developers, Finastra aims to use GenAI to supercharge the development process, making it more enjoyable and efficient. In client commercialization, he explores how AI can enhance the features of financial products and reduce costs for end customers.

AI Agent & Copilot Summit is an AI-first event to define opportunities, impact, and outcomes with Microsoft Copilot and agents. Building on its 2025 success, the 2026 event takes place March 17-19 in San Diego. Get more details.

AI in Software Development (04:15)

He discusses the importance of bringing enjoyment back to the software development life cycle (SDLC) using AI tools. Finastra uses GitHub Copilot as the main organizational tool for developers, measuring metrics like code completion and integration with tools like JIRA.The goal is to make developers happier and more efficient, leading to faster software completion.

Customer Experience and AI Support (6:48)

Finastra uses AI to enhance customer experience in two ways: support and UI/UX. For support, Finastra uses AI to create embeddings over documents for efficient search and self-service. In UI/UX, the company aims to redefine the user experience with natural language interfaces, allowing users to interact with applications more intuitively.

Data Analytics and AI Agents (9:42)

Lieberman discusses the use of AI in data analytics, providing a natural language interface for data analysis. AI agents are large language models connected to tools that can think, plan, and react non-deterministically. Finastra is actively working on solutions that are agentic in nature, which will be a significant push for their business.

AI in Marketing and Organizational Adoption (13:05)

Lieberman notes that AI usage in marketing at Finastra remains very strong, with generative AI being used extensively across the organization. Finastra has rolled out a developer infrastructure called the secure zone, providing access to open-source models for developers to explore and develop production-grade solutions. The CEO has pushed for a broad and inclusive approach to AI, with educational initiatives and tools available to all employees.

Microsoft Partnership, Future Plans (17:11)

Lieberman describes Finastra’s work with Microsoft, including the use of Azure OpenAI services and Copilot Studio. Copilot Studio allows teams to build and deploy low-code/no-code agents, distilling organizational knowledge and improving efficiency. The simplicity and intuitive nature of Copilot Studio have made it a popular tool among developers and other teams.

Finastra is focused on modernization efforts, using AI agents and generative models to transform the financial services landscape.


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