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The Obstacles and Mandate to AI Adoption

This blog is excerpted from a white paper based on a panel discussion with Jim Marous of the Financial Brand, which also Josh Nash, SVP and Director of IT, Camden National Bank; Greg Spencer, Director of Financial Services at Passerelle; James McGeehan, Head of Banking and Capital Markets at Snowflake; Jason Bishop, Senior AI Solutions Consultant at Qlik; and Anil Sharma, Senior Partner Solutions Architect for Worldwide Banking at AWS. Read the second part in this series here.

To view the panel discussion, click here.

What is Preventing FIs from AI Adoption?

While there is no denying the buzz – and real opportunity – in AI, banks and credit unions can be reticent to make the investment. Our experts laid out the roadblocks to adoption, which ranged from cultural to technological barriers.

Banks and Credit Unions are Conservative by Nature

Josh Nash, SVP and Director of IT at Camden National Bank, explained that banks and credit unions are ultimately governed by the regulatory environment they work in.

“They’re very conservative by nature, and they’re heavily regulated,” Josh explained. When looking at new data initiatives, the first question will be, “What is this going to mean for me from a regulatory perspective?”

To be comfortable with new data applications, banking organizations must control sensitive data throughout its lifecycle. At the same time, banks and credit unions need to access an analytic data set that is large enough to be meaningful in AI applications. At Camden National Bank, work to modernize its data ecosystem created a strong data management foundation as the bank looks to add AI to its data strategy.

“We’ve been very fortunate, we’ve had the executive and CEO level support to be able to build this out,” Josh said. “Going to AI or ML models is just the next step in our path.”

With the bank’s data in Snowflake, Camden National Bank can look at core banking data alongside other bank data in a central data warehouse.

“When we go to run our models, we have a really strong data set to work with,” Josh said.

Data Quality is Essential

Poor data quality presents another obstacle to implementing Enterprise AI. At Camden National Bank, Josh focused on establishing a data governance program to make sure data was trusted and understood. As part of the bank’s data governance program, data needs are established at the start of a new project with data stewards who are embedded in the impacted business line. As data is brought into the use-case-specific data marts, it is certified by the data team. Over the last few years, Camden National Bank has added 90% of its data into a certified data mart.

“We are building business-line communities that understand their data,” Josh said.

Overcoming Data Sprawl

The rapid conflagration of data technologies in the last decade has also impacted AI-readiness at banks and credit unions. Siloed data in discreet point systems can make it impossible to access data securely. Coupled with the volume of solutions on the market, and even the most tech savvy data leader can get overwhelmed.

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Greg Spencer, Director of Financial Services at Passerelle, said the combination of legacy architecture and the demands of innovative technologies can be crippling. “Banks and credit unions have historically relied on their existing technology vendor ecosystem to provide solutions to problems versus developing competencies to grow scale and innovate on their own, and that has been a big hurdle,” Greg explained. The challenge for banking executives is to look beyond a quick fix or silver bullet solution, and instead focus on creating an architecture that can scale for any use case – AI or otherwise. “There’s a misplaced tendency by banking executives to look at AI as a tool or a solution versus a suite of technologies to enhance and drive their operations forward,” Greg said.
“We have a small team,” Josh said. “We like to punch above our weight, and we look at who we're competing against. We need something to help us out, and that's why a strong toolset that includes Generative AI will help us produce things faster and get some of the monotonous reporting out of our hands. It will help us along our path of efficiency.”
Josh Nash, SVP and Director of IT at Camden National Bank
Josh Nash
Camden National Bank

The Innovation Mandate – Why Banks and Credit Unions Should be on the Path to AI

With the obstacles for AI laid out, the panel turned to the “why,” specifically “Why should banks incorporate AI into their data strategy.” While fierce competition and changing consumer expectations are demanding action from banks and credit unions, new tools on the market have made it easier to access technology that was previously the purview of PhDs and mathematicians.

At Camden National Bank, Josh Nash is looking at AI as a force multiplier for his small team of data professionals.

“We have a small team,” Josh said. “We like to punch above our weight, and we look at who we’re competing against. We need something to help us out, and that’s why a strong toolset that includes Generative AI will help us produce things faster and get some of the monotonous reporting out of our hands. It will help us along our path of efficiency.”

Jim Marous, Co-Publisher of the Financial Brand, described this moment as a sea change, where AI will be the tide that raises all boats toward more robust, more effective data decision-making.

“The democratization of the system, of the process, of the technology, makes it so that more people will become used to how it can be deployed,” Jim said.

Jason Bishop, Senior AI Solutions Consultant at Qlik, explained that the nature of AI tools has fundamentally changed in the last two years.

“Today there are very real solutions across the board that people can not only implement, but a lot of these systems are now plug and play,” Jason said. “You don’t have to have a PhD in Applied Mathematics or have formal training with a data scientist background to be able to implement these solutions.”

Ready to get started on your AI journey? Contact Passerelle for a complimentary, 90-minute AI Readiness Workshop. 

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