# Small Banks Excel in AI Adoption While Facing Scaling Challenges

> Small banks and credit unions are outpacing their counterparts in AI adoption, yet face critical challenges in scaling its use across their operations. Discover the insights from the latest SAS study on AI readiness in the SMB sector.

**Source**: prnewswire.com | **Published**: 2026-09-25 | **Type**: research

## Key Facts

- Small banks lead AI adoption at 64%, highlighting competitive edge in tech integration.
- 39% cite infrastructure costs as a barrier, revealing vulnerabilities in scaling AI efforts.
- Governance issues affect 40%, indicating risks in compliance and security for small banks.
- Focus on efficiency and automation (30%) shows strategic shift towards cost reduction in banking.
- Fragmented data systems hinder AI progress, suggesting need for unified platforms for growth.

## Summary

A recent study by SAS, in collaboration with IDC, highlights that small banks, credit unions, and lenders are leading the adoption of artificial intelligence (AI) within the small and midsized business (SMB) sector. This trend is significant as it positions these financial institutions ahead of their counterparts in industries such as insurance, government, healthcare, and life sciences. However, while AI integration is advancing in small financial institutions, the study reveals that widespread deployment remains hindered by infrastructure issues and governance challenges.

The study, titled "AI for SMBs: Closing the Readiness-Reality Gap," surveyed 1,600 SMB leaders across 28 countries, focusing on organizations with 100 to 999 employees in the U.S. and 100 to 499 employees in other regions. It found that 64% of small financial institutions utilize AI primarily in IT functions. In contrast, AI adoption in other critical areas like finance and risk (47%), marketing (44%), customer service (42%), and product development (39%) lags significantly. This concentration of AI usage in IT suggests that while these institutions are embracing technology, they are not yet fully leveraging AI's potential across their operations.

Infrastructure readiness is a notable barrier, with 39% of respondents indicating that their existing systems are either inadequate or prohibitively expensive for broader AI implementation. Governance issues also loom large; 40% of small financial institutions cite security, privacy, and compliance concerns as significant obstacles to scaling AI initiatives. Additionally, one-third of respondents reported challenges stemming from fragmented data and analytics platforms, which complicate the integration of AI across various functions.

As SAS prepares for its participation at the Sibos conference, scheduled for September 28 to October 1, 2026, industry experts emphasize the importance of strategic resource allocation. Chris Marshall, Vice President of Financial Services at IDC, suggests that smaller institutions should focus on maximizing internal capabilities while partnering with technology providers to enhance their AI initiatives. This approach can transform the complexities of integration into a more streamlined process.

The study also identifies key priorities for small banks moving forward. These include automating core business processes, reducing costs through efficiency, improving data quality, and fostering product and service innovation. Notably, the most advanced institutions are shifting their focus from isolated AI pilots to developing comprehensive AI strategies that connect disparate initiatives. Alex Kwiatkowski, Director of Global Financial Services at SAS, points out that without bridging these "islands of innovation," the potential of AI remains underutilized.

To realize AI's full potential, small banks must expand their adoption beyond IT and into finance, risk management, and customer engagement. By doing so, they can transform isolated gains into broader institutional benefits, enhancing fraud detection, risk management, and customer experiences. 

As the competitive landscape evolves, the ability of small financial institutions to effectively scale AI will be crucial. Those that can overcome infrastructure and governance challenges while fostering a cohesive AI strategy will likely gain a significant edge in the marketplace. The implications are clear: institutions that prioritize comprehensive AI integration will not only enhance their operational efficiency but also position themselves as leaders in the rapidly changing financial services sector.

## Entities

- **Companies**: SAS, IDC
- **Products**: AI Readiness Calculator
- **Technologies**: AI
- **People**: Chris Marshall, Alex Kwiatkowski
- **Organizations**: SAS Institute Inc.

## Key Concepts

AI readiness, small banks, financial institutions, infrastructure challenges, governance concerns, AI deployment, data integration, AI strategy

## Definitions

- **AI readiness**: The preparedness of an organization to effectively implement and utilize artificial intelligence technologies.
- **SMB**: Small and midsized businesses, defined as organizations with 100 to 999 employees in the U.S. and 100 to 499 employees in other markets.
- **AI pilots**: Initial projects or trials that test the application of AI technologies within an organization.
- **infrastructure challenges**: Obstacles related to the technological framework that hinder the broader deployment of AI solutions.
- **governance concerns**: Issues related to security, privacy, and compliance that affect the scaling of AI technologies.

## Use Cases

- Automating and streamlining core business processes
- Reducing costs through efficiency and automation
- Improving data quality and integration
- Increasing product and service innovation
- Strengthening fraud and risk management
- Enhancing customer experience

## Frequently Asked Questions

**What is the current state of AI use in small banks?**

AI use in small banks is at 64% within IT, but broader deployment across other areas like finance and customer service remains limited due to infrastructure and governance challenges.

**What are the main barriers to scaling AI in small financial institutions?**

The primary barriers include infrastructure readiness, governance concerns related to security and compliance, and the fragmentation of data and analytics platforms.

**How can small banks improve their AI strategies?**

Small banks can enhance their AI strategies by focusing on integrating AI across various functions, bridging isolated initiatives, and leveraging technology partners for support.

**What is the AI Readiness Calculator?**

The AI Readiness Calculator is an assessment tool provided by SAS that helps small and midsized banks evaluate their AI maturity and identify strengths and gaps in their AI implementation.

**What are the near-term AI priorities for small banks?**

Small banks prioritize automating core processes, reducing costs, improving data quality, and fostering innovation in products and services as their near-term AI objectives.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/small-banks-excel-in-ai-adoption-while-facing-scaling-challenges)
- [Original source](https://www.prnewswire.com/news-releases/smb-study-small-banks-lead-in-ai--but-scale-lags-302890367.html)
- [SAS](https://welcome.ai/company/sas): Featured company

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Source: Welcome.AI | https://welcome.ai/content/small-banks-excel-in-ai-adoption-while-facing-scaling-challenges