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Overcoming techology hurdles to adopt AI

Mary Ellen Biery
January 20, 2026
0 min read

This article covers these key topics: 

AI adoption challenges tied to technology and data

Many financial institutions face AI adoption hurdles because of their legacy core systems, manual workflows, and fragmented data. The right technology partner can help put banks and credit unions on the path to speed and precision.

Systemwide challenges to adopting AI

For some financial institutions, the promises of artificial intelligence (AI) seem unattainable due to practical challenges. Technology and data considerations, such as legacy core systems, manual workflows, and fragmented data environments, often stand in the way.  

How can banks and credit unions overcome tech hurdles to modernize operations and tap the potential of AI to keep pace with customer expectations, increasing fraud risk, and regulatory complexity? Implementing AI isn’t simple, but the right technology partner can help make the path clear and practical. 

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Legacy core systems can challenge AI implementation

Even though most believe AI will have a large impact on banking, banks and credit unions identify a number of barriers to adopting AI, according to a recent Abrigo survey of nearly 300 bankers. While concerns range from compliance to budget to return on investment, nearly a third of bankers identified data quality or data accessibility as adoption obstacles.  

Technology and related issues around data are an understandable challenge for many financial institutions looking to tap AI’s benefits. Many institutions still rely on decades-old core systems. Often such systems weren’t designed to support the data volume or flexibility that modern AI requires. In general, these aging infrastructures often lead to siloed data, limited integration options, and rigid architectures that slow innovation. 

How financial institutions can ease the path to AI: 

  • Look for cloud-based solutions that can integrate with legacy core systems, allowing institutions to modernize incrementally without undergoing a full core replacement. 
  • Seek out platforms that centralize data across departments to improve visibility, reduce duplication, and enable faster, more strategic decisions. Platforms that can share data across lending, credit risk management, balance-sheet management, and compliance simplify the complexity of managing multiple data stacks.  
  • Choose partners with a proven track record of helping community financial institutions navigate modernization in a regulated environment. For example, with more than 2,400 financial institution customers, Abrigo understands the operational and regulatory realities institutions face and helps them modernize without disruption. “Our AI-powered suite is designed to be modular, so banks and credit unions can adopt AI at their own pace, starting where it will have the most impact and scaling over time,” said Ravi Nemalikanti, Abrigo’s Chief Product & Technology Officer. 

Reliance on manual processes hinders AI adoption 

Manual processes are another challenge facing some banks and credit unions looking to benefit from AI technology. Manual workflows limit scalability and delay getting the kinds of strategic insights that AI could generate in real time. The impacts include missed opportunities, increased regulatory risk, and staff tied up with redundant data entry, disjointed compliance tasks, and repetitive reviews instead of strategic thinking or innovation.   

How to clear the roadblock to AI: 

  • Look for automation capabilities in high-impact areas like lending, credit risk review, and fraud detection to streamline operations and reduce the risk of human error. “Lending and financial crime are the areas where banks feel the most friction, and they’re also the best starting points for AI to make a meaningful impact,” Nemalikanti said. 
  • Choose lending solutions that automate data entry, credit analysis, and compliance tracking to improve consistency and save time. 
  • Work with partners that offer expert guidance or advisory support to help assess existing workflows and identify automation opportunities that improve efficiency without adding headcount. Optimizing an existing process can be a good first step toward layering in AI. 

Data quality and accessibility can block AI efforts   

AI models are only as strong as the data behind them. For many financial institutions, years of collecting data in spreadsheets, core extracts, and siloed systems have resulted in fragmented datasets. Those are not only difficult to reconcile on a regular basis but are also nearly impossible to analyze effectively and provide data-driven strategies.  

However, building a data warehouse internally can quickly turn into a complex, expensive endeavor that requires technical expertise many community institutions don’t have. “It takes effort to get data warehousing 100% right,” said Nathan Myers, Vice President of Integration and Client Care at Abrigo. “And unfortunately, in some cases, data that is not 100% right is 0% useful.” 

How a technology partner can help: 

  • Some data platforms eliminate the need for expensive in-house data warehousing and reduce the burden of reconciling inconsistent data across systems. 
  • Solutions like those on the Abrigo platform can naturally improve data accuracy as users engage with them daily. “The benefit of a strong data platform is that it pulls data from a suite of analytical and operational software that actually uses the data every day,” Myers said. “Since users actively and consistently use these tools, the underlying data will naturally be more reliable and accurate without any additional effort required for financial institutions.” 
  • Tools on a unified platform simplify integration and data cleansing, and those that provide user-level access controls and AI-driven reporting make it easier to surface insights and visualize data for measuring performance and risk.  
  • Some technology partners can provide strategic guidance on how to build a clean, actionable data foundation. Abrigo, for example, has decades of experience helping banks and credit unions with changes that require data integrity such as: 
  1. Adopting the current expected credit loss (CECL) accounting changes. 
  2. Automating transaction monitoring for anti-money laundering efforts. 
  3. Implementing small business lending data collection requirements under the CFPB’s 1071 rule). 

Building a technology roadmap to AI readiness 

Even with the right tools, many institutions still ask: Where do we begin? “Financial institutions need AI that’s transparent, explainable, and compliant,” Nemalikanti said. “At the same time, leaders worry about disrupting day-to-day workflows or overextending resources with large-scale transformations.” 

How to move from frozen to AI-forward: 

  • Start with a partner that offers readiness assessments to identify gaps in your institution’s infrastructure, processes, and data maturity. Download this AI readiness checklist to prepare for responsible, successful AI implementation.  
  • Look for modular, assistive AI capabilities from a single provider that can deliver early wins—such as AI-generated CECL narratives, fraud case summaries, or SAR narrative suggestions. These solutions align with a phased AI strategy, allowing your institution to test, learn, and scale adoption over time. 
  • Seek partners who can help you prepare for the next wave of innovation. Abrigo, for example, has helped financial institutions for more than 20 years as they adapt to changing needs such as adopting the current expected credit loss model (CECL), offering Paycheck Protection Program (PPP) loans efficiently, and adopting small business lending data collection requirements under the CFPB’s 1071 rule.  

As Nemalikanti explained, the real value of a technology partner like Abrigo comes “from our deep understanding of where our customers spend the most time and what tasks consume them day to day. With that insight, we can embed AI into the repetitive and deterministic areas, freeing our customers to focus their energy on the work that truly drives impact.” 

Address tech complexity and build AI for scale 

With increasing regulatory scrutiny, evolving customer demands, and competitive pressure from all sides, financial institutions can’t afford to wait on adopting AI. But they also can’t afford missteps. 

By addressing legacy technology, simplifying manual processes, and improving data readiness, institutions can set the stage for meaningful innovation. And banks and credit unions don’t have to navigate AI on their own. The right technology partner will help institutions prepare, pilot, and progress in their AI implementation. 

Access Abrigo's AI Hub for solutions, resources, and important information to help your institution.

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About the Author

Mary Ellen Biery

Senior Strategist & Content Manager
Mary Ellen Biery is Senior Strategist & Content Manager at Abrigo, where she works with advisors and other experts to develop whitepapers, original research, and other resources that help financial institutions drive growth and manage risk. A former equities reporter for Dow Jones Newswires whose work has been published in

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About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

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