• Banks are accelerating AI agent adoption across risk, compliance, audit, and fraud functions
• Accenture data shows more than half of banking technology leaders expect widespread deployment
• McKinsey estimates AI could reduce organizational costs by up to 20%
• Industry leaders stress collaboration between technology and business teams
• Data quality and governance remain critical success factors
• Cape & Coast Bank is using AI to support referral and growth initiatives
• Experts recommend starting with focused pilot projects before scaling
• AI assistants are viewed as a stepping stone toward broader automation
• Human oversight remains essential as banks expand AI capabilities



Financial institutions are increasingly positioning artificial intelligence agents at the center of their digital transformation strategies, with industry leaders arguing that successful deployment depends as much on collaboration and data quality as it does on technology.

Long regarded as a cautious adopter of new technologies due to regulatory pressures and complex operational requirements, the banking sector is now leveraging established cloud infrastructures to accelerate the implementation of AI-powered tools across a growing range of business functions.

The momentum is being driven by the prospect of greater efficiency, lower costs, and improved decision-making.

According to data cited by consulting firm Accenture, more than half of banking technology executives expect AI agents to become fully embedded within risk management, compliance, audit functions, fraud detection, and transaction monitoring processes.

At the same time, research from McKinsey suggests that organizations deploying AI technologies could reduce operating costs by as much as 20%, adding further incentive for financial institutions to invest in the emerging technology.

Industry practitioners say, however, that achieving those benefits requires a carefully planned approach.

Speaking during the Creatio No-Code Days Florida conference, Ken Tingle, First Vice President and Business Intelligence Manager at Cape & Coast Bank, emphasized the importance of involving both technology and business teams throughout the deployment process.

"There's a strategic component, a collaboration component," Tingle said. "You have to bring in not only technology but your sales leaders in order to deploy a solution that's going to benefit the organization."

His comments reflect a growing recognition that AI initiatives cannot be treated solely as technology projects. Instead, institutions must align AI deployments with business objectives and ensure key stakeholders are engaged from the outset.

Data governance remains another critical factor.

As organizations seek to automate increasingly complex processes, ensuring the quality, consistency, and accessibility of data has become a top priority. Tingle warned that AI agents can only be as effective as the information they receive.

Leaders across the organization should therefore play a role in supporting governance frameworks and improving data quality, he said, enabling AI systems to generate more accurate recommendations and perform tasks more effectively.

Cape & Coast Bank has already begun exploring practical applications for AI within growth-focused initiatives.

The institution deployed a referral agent using Creatio's platform, allowing it to compare the effectiveness of AI-generated recommendations against traditional employee referrals.

The project reflects a broader trend within financial services, where many AI use cases are focused on revenue generation and operational enhancement rather than wholesale automation.

Tingle advised organizations to resist the temptation to scale too quickly.

"You want to start small and focused," he said. "Start targeted with a very small audience, and then deploy slowly to the rest of your organization."

That measured approach was echoed by Drew McMonigle, Chief Technology Officer at Lake City Bank, who argued that user adoption should precede full automation.

"Ground zero is: have people use AI assistant-type use cases," McMonigle said. "You cannot fully automate something until you get people using the assisting capability."

His comments suggest that many institutions view AI assistants as an important stepping stone toward more advanced autonomous systems.