# Small Banks Excel in AI Adoption Amid Integration Challenges

> Small banks are leading the charge in AI adoption, but face major challenges in scaling its use. Discover the barriers and opportunities identified in the latest SAS and IDC study.

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

## Key Facts

- 64% of small banks use AI in IT, revealing a strong tech focus but limited cross-departmental impact.
- 39% cite infrastructure costs as a barrier, indicating financial constraints on broader AI deployment.
- 40% face governance issues, highlighting vulnerabilities in security and compliance for scaling AI.
- 30% prioritize automating processes, showing a strategic shift towards efficiency over novelty in AI use.
- Fragmented data systems hinder AI integration, suggesting a need for unified platforms to enhance performance.

## Summary

A recent study by SAS, in collaboration with IDC, reveals that small banks and credit unions are leading the way in artificial intelligence (AI) adoption among small and midsized businesses (SMBs). The study, which surveyed 1,600 SMB leaders across 28 countries, indicates that while these financial institutions excel in integrating AI into their operations, they face significant hurdles in scaling its use beyond IT departments. This finding is critical as it highlights both the potential and the limitations of AI in the banking sector, signaling a need for strategic focus on broader deployment.

The study, titled "AI for SMBs: Closing the Readiness-Reality Gap," shows that 64% of small financial institutions utilize AI within their IT functions. In contrast, adoption rates in other critical areas, such as finance and risk (47%), marketing (44%), and customer service (42%), are notably lower. This concentration of AI usage within IT suggests that while these institutions recognize the technology's value, they struggle to leverage it across their operations effectively.

Infrastructure and governance issues are significant barriers to scaling AI. Approximately 39% of respondents indicated that their existing infrastructure is either inadequate or prohibitively expensive for broader AI deployment. Furthermore, 40% cited security, privacy, and compliance concerns as primary obstacles. These challenges are compounded by fragmentation within organizations, with one-third of respondents reporting a lack of unified data and analytics platforms as a critical issue. 

The timing of this study is particularly relevant as SAS prepares for its participation in the Sibos conference from September 28 to October 1, where discussions will focus on AI readiness and its implications for the financial sector. Chris Marshall, Vice President of Financial Services at IDC, emphasizes that smaller institutions should not attempt to replicate the technological architecture of larger banks. Instead, he advocates for a focused approach that leverages partnerships and technology to enhance their capabilities.

Looking ahead, small banks are prioritizing practical AI applications over novel implementations. Their near-term goals include automating core business processes and improving data quality and integration. This focus on operational efficiency suggests a strategic shift towards using AI as a tool for enhancing existing processes rather than merely as a means of innovation for its own sake.

Despite the current concentration of AI use in IT, there is a clear opportunity for these institutions to expand their AI capabilities into finance, risk management, and customer-facing functions. As Alex Kwiatkowski, Director of Global Financial Services at SAS, notes, many AI initiatives within banks operate in silos, leading to isolated benefits rather than a cohesive strategy. Bridging these gaps by integrating AI across departments could yield substantial returns, enhancing fraud detection, customer experiences, and product development.

As the competitive landscape evolves, small banks that successfully navigate these challenges will likely gain a significant advantage. The ability to harness AI effectively across various functions will not only improve operational efficiency but also position these institutions as innovative leaders in a rapidly changing market. The upcoming SAS webinar on October 16 will further explore these themes, providing insights into how banks can transform AI ambitions into tangible business outcomes.

The implications of this study extend beyond individual institutions; they signal a broader trend in the banking sector. As small banks increasingly adopt AI, the pressure will mount on larger competitors to enhance their own capabilities. The focus on practical applications and integration may well define the next phase of AI evolution in banking, making it essential for all players in the market to reassess their strategies to remain competitive.

## Entities

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

## Key Concepts

AI readiness, small banks, infrastructure challenges, governance concerns, AI deployment, data integration, cost reduction, customer experience

## Definitions

- **AI readiness**: The preparedness of an organization to implement and scale artificial intelligence technologies effectively.
- **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.
- **governance**: The framework of policies and processes that ensure the effective management and control of AI technologies within an organization.
- **infrastructure**: The underlying physical and organizational structures needed for the operation of AI systems, which can impact deployment capabilities.
- **AI pilots**: Initial small-scale implementations of AI projects intended to test feasibility and effectiveness before broader deployment.

## Use Cases

- Automating core business processes
- Reducing costs through efficiency
- Improving data quality
- Increasing product innovation
- Enhancing customer experience
- Strengthening fraud management

## Frequently Asked Questions

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

AI adoption in small banks is at 64% in IT, with significant use also in finance, marketing, and customer service. However, broader deployment is hindered by infrastructure and governance issues.

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

The primary barriers include concerns over security, privacy, and compliance, as well as inadequate infrastructure and a lack of unified data platforms.

**How can small banks benchmark their AI maturity?**

Small banks can use SAS' AI Readiness Calculator, which evaluates their AI maturity across various dimensions and provides a personalized report with strengths and gaps.

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

Small banks prioritize automating business processes, reducing costs, improving data quality, and increasing innovation in products and services.

**What role does governance play in AI deployment?**

Governance is crucial for ensuring that AI technologies are managed effectively, addressing concerns related to security, privacy, and compliance, which are significant barriers to scaling AI.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/small-banks-excel-in-ai-adoption-amid-integration-challenges)
- [Original source](https://www.sas.com/en_be/news/press-releases/2026/september/idc-smb-study-banking.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-amid-integration-challenges