How financial institutions can invest for the next era of digital transformation

This year is shaping up to be pivotal for treasury and commercial banking. The advantage will go to financial institutions that turn AI, new payment instruments and ecosystem strategies into measurable outcomes. 

Hard data bears this out. Celent conducts global research and offers advisory services to help financial services organizations make faster and more informed technology decisions. As part of that mission, Celent runs dimensional surveys each year asking financial institutions about what's happening in technology spending and how they feel about certain market conditions. 

The most recent survey, “Technology Trends Previsory, Corporate Banking and Payments: 2026 Edition,” found:

• 54% of banks said that, year over year, it's getting more difficult to attract and retain customers 
• 64% of the institutions find the competitive threat from fintech companies is increasing 
• About a third of banks prioritize product innovation and product development as their top driver of investment 
• Financial institutions expect their IT budgets to increase 6.1%, based on a global average, in 2026

Clearly, financial institutions are feeling market pressure and looking to innovate to remain competitive. The Celent survey examined where institutions are prioritizing spending for corporate product development and the required technology. Here are survey findings and what they can mean for organizations. 
 

Product priorities

In terms of product innovation, financial institutions are concentrating on four key areas.

Digital platforms and channels – This has been the top investment priority for three years running. For corporate banking, ease of use and client experience are topping the agenda. Many banks are adding digital assistants and embedding AI-driven self-service help tools in their digital banking platforms to deliver a better experience and reduce traffic at the helpdesk. 

Payments modernization – More than 50% of banks globally say they're either in a major payments project or undergoing a full renovation of multiple payments platforms, often with new greenfield projects and solutions underway. This includes the huge and expensive effort to more broadly implement the ISO 20022 standard. 

Customer life cycle management – This used to be primarily about customer relationship management platforms and contact-center solutions. But there has been a recent surge in solutions for digital onboarding for new customers and new products.

Pricing and deal management – Banks are showing increased interest in higher levels of digitization and transparency in price negotiation, price setting and billing, features now expected by corporate customers. 

Technology priorities

As financial institutions determine their product priorities, they identify technology investments to support them. Here are the four leaders.

Advanced analytics – Improved analytics includes enabling all forms of AI. Two-thirds of the banks surveyed said their current data infrastructure, which isn't necessarily legacy infrastructure, is a hindrance to advancing their AI agenda.

Workflow automation – This category encompasses operational automation, onboarding life cycles and deal management, including the use of robotic process automation.

The adoption of a cloud approach – This is not necessarily migration to public cloud infrastructure, but rather development of cloud-native architectures using API microservices and containerized applications. It enables institutions to design their platforms for the future, either in an on-site or cloud environment.

AI agents – An AI agent is software code that uses AI to understand the business domain and use that understanding to execute actions and achieve specific goals. As an AI agent accesses data and receives instructions, it builds long-term memory to recall historical data, events and interactions. When organizations add access to foundation models, the agent develops the ability to reason and make decisions.
 

How to think about AI agents

There's a lot of hype around agentic AI, as well as fear and skepticism. But there also are real use cases and value creation. The task for financial institutions is to rethink how they will use AI in all its forms across the enterprise.

For serving their corporate customers, financial institutions can break the potential uses down into three scenarios.

Agentic AI – For bank operations, AI is the next step in intelligent process automation. Applications in this area can include client and product onboarding, prospecting for new clients and support for fraud detection efforts.

Agentic treasury – Institutions can use AI agents to help corporate customers access banking services, such as requesting a balance, accessing a statement and summarizing transactions.

Agentic banking – In this case, financial institutions deploy agents in a corporate treasury department and the bank. In essence, AI agents on each end talk to each other to fulfill banking requests. It’s still in the future, but it's not science fiction. This prospect raises questions about identity and authorization, but these are solvable problems.

There are still risks with agentic AI, but many of those can be mitigated by ensuring the technology is appropriate for the use case and guardrails are in place for development.