The foundation of digital transformation in the AI era begins with clean data. At least, that will be part of the focus for Sunwest Bank Chief Technology and Strategy Officer Ben Xiang in 2026 as he unfolds his AI strategy.
“This is probably the most exciting time that I can think of to get into an industry that is really primed for innovation with AI,” Xiang told CIO Dive.
Big banks including Bank of America, JPMorgan Chase, Goldman Sachs and Citigroup are funneling significant investments into AI initiatives aimed at transforming workflows and improving productivity and efficiency. Xiang said he’s chasing similar goals for Sunwest Bank this year, with a sharp focus on improving the data layer.
“Ultimately, once you have the data in place, then you can start to automate things and you can leverage artificial intelligence in order to realize business value from that,” he said.
Sunwest Bank, a privately held commercial bank headquartered in Sandy, Utah, tapped Xiang to lead its multiyear modernization initiative and technology projects in September 2025. Xiang has also served as a member of the bank’s board of directors since 2015 and held the role of interim CIO in 2019, according to a press release.
Amid the modernization efforts, one of the bank’s projects is the rollout of Microsoft Copilot powered by OpenAI’s GPT-5 model, Xiang said. The AI tool will provide a central source of information for employees, combining publicly available and shareable information within the bank.
The next component of the bank’s AI strategy hinges on connecting disparate data sources within the bank into a data lake. Cleaning up the bank’s data and centralizing it will enable future advanced agentic workflows, Xiang said.
“By doing this, we’re able to provide some pretty comprehensive and advanced data analytics that will enable a lot of automation that we’re looking to implement throughout the bank,” he said.
In search of productivity gains
Ultimately, Xiang’s goal is to assess existing workflows that could benefit from automation and intelligence tools — while not misallocating investments into innovative projects that “won’t yield business results.”
Xiang said he’s paid attention to external research indicating how some generative AI projects aren’t translating into ROI, which is why he plans to pursue “low-hanging fruit” and projects with the highest chance of success within the organization.
“We’re really looking to uplift the overall productivity and efficiency at the bank,” Xiang said. “There are a lot of metrics we’re going to be looking at internally over the course of the next 12 months to see if the AI projects we’ve embarked on really succeeded.”
The banking industry is set to take greater advantage of agentic AI this year due to advancements in large language models, as well as the maturation of enterprise agent development tools, according to Accenture’s Top Banking Trends for 2026.
Indeed, connecting LLMs to other applications and layering agentic AI on top of that foundation is where “people are really starting to see the value,” Xiang said.
“Being able to have an AI agent that can do multiple things, that can introduce intelligence to a workflow, that has really opened up people’s eyes and opened up the doors for what you can do within an organization in order to dramatically improve efficiencies in a business,” Xiang said.
Part of the bank’s strategy for success comes down to ensuring staff members have the tools they need and are educated on AI “to really take advantage of this incredible technology,” Xiang said.
Technology leaders will need to make AI agents easy to use for long-term success, particularly as employees will likely be managing multiple agents that are working for them in the future, said Michael Abbott, senior managing director and global banking lead at Accenture.
“It has to be as easy as Excel or PowerPoint to use,” Abbott told CIO Dive. “It can’t be a mystery novel.”
