The exponential growth of generative AI during the past few years and its rapid adoption in banking has yet to solidify in clear competitive advantage. So far, most Gen AI use cases in the industry have been focused on individuals' task productivity improvement; accelerating coding for developers, branches' employees support for legal questions, chats supporting retail customer information queries, summarizing and simplifying gigantic customer contracts databases, etc. 2026 can be the year when both predictive AI, generative AI and applications start working together in the banking industry, exploding from isolated tasks automation to intelligent process re-imagination, bringing a competitive edge leap for the ones able to embed AI into their business functions. Join us as we explore: - Which areas or activities within banks are the 2026 target for AI implementation? Risk control, business and IT perspectives. - Governance, sovereignty and explainability as a must beyond regulatory requirements. - Why the “any model, any accelerator, any cloud” vision is key for banks’ future? Openness and interoperability as strategy. - How will AI be included into banks’ development and operations processes? - Scaling AI and switching from cost insensitive to efficiency focus: how to reduce the growing cost of AI in banking?
Héctor Arias has over 20 years of experience within the banking industry in the EU, USA and Latin America, with specializations in embedded finance, digital transformation, business strategy and new digital businesses. He has executive and hands-on experience in B2B and B2B2X business models for banks to serve customers by embedding financial services into third party partners. Héctor usually works with customers, consulting and technology partners on capabilities for banks to accelerate and improve their journey to the hybrid cloud. He is also a contributing author to IBM Business Value on Embedded Finance.
