blog

Webinar: Embedding IA in Banking

Written by Randal Van Vliet | Feb 27, 2026 9:17:25 AM

Embedding AI in Banking: How Financial Institutions Can Move from Pilots to Scalable Execution

Artificial Intelligence in banking is moving from experimentation to necessity. Across the financial services industry, institutions are investing heavily in AI, Generative AI, and emerging Agentic AI technologies to improve efficiency, decision-making, and customer experience.

However, while many banks have launched AI initiatives, relatively few are achieving scalable implementation helping to deliver measurable business value.

The central question facing financial institutions today is not whether to adopt AI, but how to embed it into core operations in a controlled, compliant, and ROI-driven way.

 

Why AI adoption in banking often stalls

Many AI projects begin as pilots or proof-of-concepts but struggle to progress into full deployment. Common challenges include fragmented data environments, legacy systems, unclear governance structures, and regulatory constraints.

In highly regulated sectors such as banking and lending, scaling AI also requires strong risk management, transparency, and accountability. Without these foundations, AI remains confined to isolated use cases rather than becoming an enterprise capability.

Successfully embedding AI therefore involves more than technology. It requires organisational alignment, new operating models, clear ownership, and mechanisms to track business outcomes.

 

The growing role of Generative and Agentic AI in financial services

Recent advances in Generative AI and Agentic AI are accelerating transformation across banking functions, from credit assessment and fraud detection to operational efficiency and customer interactions.

These technologies offer significant opportunities, but they also introduce new challenges related to data quality, governance, explainability, and regulatory compliance. Financial institutions must determine where AI can create real value and how to deploy it responsibly at scale.

 

Turning AI initiatives into measurable impact

When implemented effectively, AI in banking can support a wide range of strategic objectives, including:

  • Improving operational efficiency and productivity
  • Reducing costs through automation
  • Enhancing risk management and decision support
  • Enabling more personalised customer experiences

Achieving these outcomes requires prioritising use cases with clear business value and integrating AI into existing processes rather than treating it as a standalone capability.

 

A practical perspective on embedding AI in banking

To explore these challenges in depth, FiSer Consulting and Axe Finance are hosting a webinar focused on how financial institutions can move from AI experimentation to scalable execution. The session will address current considerations around strategy, governance, risk, and implementation, and will include an interactive discussion with participants.

 

Who should attend

This session is particularly relevant for senior professionals in banking and lending involved in strategy, transformation, innovation, risk and compliance, or data and technology leadership.

 

Learn how to scale AI in financial services

As AI becomes a core capability for financial institutions, understanding how to implement it responsibly and effectively will be critical for maintaining competitiveness in the coming years.

👉 You can find more information and register for the webinar on the event page.