How AI‑driven platforms reduce costs, boost resilience, and improve experiences

Key takeaways

• AI must move from experimentation to measurable ROI by scaling efficiently across processes, channels, and customer journeys.

• Agentic Process Automation (APA) uses Agentic AI to autonomously execute complex workflows, reducing manual workloads by 30–50%.
• AI‑driven hyper‑personalization can increase banking revenues by 10–15%.
• AI enables individualized offers, smarter cross‑sell strategies, and deeper engagement, raising Net Promoter Scores and lowering churn without increasing headcount.
• Conversational banking is becoming a strategic priority, using context-aware engagement to improve speed, CX, and loyalty.
• Digital platforms such as ebankIT provide financial institutions with an AI‑ready digital banking platform that unifies data, workflows, channels, and intelligence across every interaction.

Driving ROI from AI

Financial institutions face mounting pressure to turn Artificial intelligence (AI) from experimentation into measurable ROI. To win, they must identify the strategies that scale deploying AI efficiently across processes, channels, and customer journeys.

From the perspective of Maria José Gonçalves, Chief Operating Officer, AI marks a pivotal strategic and operational shift, enabling banks and credit unions to deliver faster, safer, and more human‑centered services across every touchpoint.

At the same time, Pedro Azevedo, Chief Product Officer at ebankIT, views AI as a catalyst for building more agile, data‑driven organizations. It empowers financial institutions to deliver relevant, personalized experiences at scale, ensuring they remain competitive as new players enter the market.

Together, these two viewpoints reveal a simple truth. AI is fundamentally transforming financial services to improve the lives of banking users and banking employees.

Optimize banking operations with Agentic AI  

Pedro Azevedo explains that the shift to Agentic Process Automation (APA) breaks through the limitations of traditional automation constrained by siloed systems and predefined logic. Agentic AI can autonomously execute complex workflows and reduce manual workloads by 30–50%, leading to operational cost reductions of 20% or more.¹

APA accelerates tasks such as loan processing, onboarding, KYC, fraud checks, and customer queries, delivering instant or near‑instant responses. This dramatically improves service levels and customer satisfaction.

Maria José adds that when routine tasks like onboarding are handled autonomously through end-to-end workflow automation, it minimizes errors while freeing human teams to focus on higher-value work. 

Delivering hyper‑personalization at scale   

Maria José Gonçalves notes that AI‑driven hyper‑personalization can drive significant revenue growth for financial institutions. McKinsey research reinforces this point, showing that effective hyper‑personalization can boost banking revenues by 10–15% by delivering the right product or message to the right customer at precisely the right moment.²

Customers increasingly expect their bank to understand them as well as digital platforms do. AI makes this possible by enabling individualized offers, smarter cross‑sell strategies, and deeper engagement.

Pedro Azevedo highlights that AI-powered personalization engines allow financial institutions to deliver individualized experiences to millions simultaneously, creating significant operational leverage without expanding headcount. As a result, institutions that deliver this level of personalization consistently achieve higher Net Promoter Scores and see significantly lower customer attrition.

The rise of conversational banking

Forrester shows conversational banking is becoming a strategic priority as banks shift to natural‑language, proactive, context‑aware engagement.³ Pedro Azevedo emphasizes that financial institutions that scale conversational AI gain faster service, stronger CX, and improved loyalty.  

Conversational AI grants account holders’ immediate support with an around‑the‑clock service across mobile, web, and voice. It reduces friction, eliminates rigid menus, and provides real‑time answers, improving customer experience overall. Maria José Gonçalves notes that these systems restore the personalized service customers felt they lost during digital transformation.

Pedro Azevedo adds that when conversational systems perform well, customers readily adopt them in financial decisions.  

Real use cases of banks setting the standard with AI

JP MorganChase

JP Morgan Chase implemented EVEE Intelligent Q&A, an AI system that provides agents with real-time, context-aware responses. By integrating policy, transaction, and loan data, EVEE significantly reduced handling times and errors. The bank reallocated staff to higher-value areas such as fraud prevention and proactive outreach, with accuracy improving over time through continuous learning.⁴ 

Banca Transilvania

As an early AI adopter in Romania, Banca Transilvania now sees 60% of customer interactions handled by AI-powered tools. Its decentralized approach supports multiple use cases across departments, including a wallet assistant that guides users through services. The bank plans to expand automation and embed AI deeper into internal and customer-facing processes.⁵

Bank of America 

Bank of America’s virtual assistant, Erica, manages over 58 million interactions per month and achieved a 98% containment rate in 2025, resolving most inquiries autonomously. With more than 50,000 updates since launch and over 3 billion interactions, Erica has reduced call-center demand, improved retention, and contributed to a 19% earnings boost.⁶

For a comprehensive look at additional use cases, download the full report

How ebankIT can help lead in the AI era

ebankIT empowers financial institutions to modernize operations, deliver hyper‑personalized experiences, and scale conversational banking by providing an AI‑ready digital platform that unifies data, workflows, channels, and intelligence across every customer and employee interaction. The result is a cohesive, future‑ready banking ecosystem that drives both efficiency and engagement.

“We’re not just building for the banking of today; we’re building the foundation for the bank of tomorrow,” says Pedro Azevedo.

Maria José Gonçalves adds, “AI is not here to replace staff, it is here to empower them, equipping teams with intelligent tools that enhance judgment, elevate service, and accelerate decision‑making.”

With ebankIT’s architecture, AI‑driven systems also scale elastically, enabling institutions to handle spikes in demand without increasing headcount. This reduces operational costs, minimizes errors, and strengthens the overall resilience of the bank’s operating model.

In architectural terms, this evolution is further enhanced by the adoption of the Model Context Protocol (MCP), an open‑source standard that allows AI systems to securely connect with external tools and data sources.

By enabling a universal interface for richer data access and more intelligent task execution, MCP significantly elevates the quality, context, and consistency of the AI experience across the ebankIT platform.

➜ Learn more about ebankIT solution

The institutions that act now will define the next decade of banking

AI is a present-day competitive advantage, and financial institutions need to be moving beyond pilots and proofs of concept to embed intelligence deeply and responsibly.

From modernizing back-office services to empowering frontline staff with tools that make them more effective, banks and credit unions that embrace AI thoughtfully will deliver better experiences, stronger operations, and deeper community impact.

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