Singapore – Small banks, credit unions and lenders are leading AI adoption among small and midsized businesses (SMBs), but infrastructure, governance and fragmented technology are limiting wider deployment, according to a study by SAS and IDC.
The AI for the study surveyed 1,600 SMB leaders across 28 countries in five global regions. It defines SMBs as organisations with 100 to 999 employees in the US and 100 to 499 employees in other markets.
Among the five industries examined, financial institutions reported the strongest strategic alignment, established governance practices and integration of AI into day-to-day operations. However, the findings indicate that adoption remains concentrated in specific functions rather than being deployed across institutions.
AI adoption remains concentrated in IT
Nearly two-thirds (64%) of small financial institutions surveyed use AI in IT, compared with 47% in finance and risk, 44% in marketing, 42% in customer service and 39% in product development.
Infrastructure is another constraint, with 39% of respondents saying their infrastructure is either not ready or too costly for broader AI deployment.
Security, privacy and compliance concerns were identified as the leading barrier to scaling AI, cited by 40% of small financial institutions. A further 33% pointed to the lack of a unified data, analytics and AI platform as a challenge.
“For smaller financial institutions, scaling AI does not mean recreating the technology architecture of a global bank,” said Chris Marshall, Vice President of Financial Services at IDC.
“The smarter path is to focus internal resources where they create the most value and lean on technology and implementation partners to extend what the institution can accomplish on its own. The right ecosystem and the right collaborators can turn integration complexity into a manageable, even accelerated, path forward,” Marshall continued.
Focus shifts towards practical AI applications
The study found that small financial institutions are prioritising practical applications of AI, with 30% identifying automation and streamlining of core business processes as a near-term priority. An equal proportion cited reducing costs through efficiency and automation.
Improving data quality and integration was identified by 28% of respondents, while 26% cited increasing product and service innovation.
The findings also indicate that banking is ahead of the other SMB industries surveyed in moving beyond individual AI pilots. The study suggests that more advanced institutions are focusing not only on increasing use cases but also on building the data, governance and infrastructure needed to connect them.
“AI pilots often become islands of innovation. One team builds a high-impact fraud use case. Another launches a sharper risk model. A third automates a process. Each initiative delivers tangible benefits, but they don’t add up to an AI strategy,” said Alex Kwiatkowski, Director of Global Financial Services at SAS.
“Real transformation requires organisations to bridge those islands – connecting siloed functions and decisioning capabilities through shared data, governance and infrastructure – so AI travels farther and faster across the organisation,” Kwiatkowski continued.
While IT currently accounts for the highest level of AI use, the study points to potential gains from expanding adoption into finance and risk, marketing, customer service and product development.
The report comes as SAS participates in an event in Miami, where the company is discussing AI readiness, trust, agentic AI, payments and financial crime.
AI readiness benchmarking
Small and midsized banks and credit unions can assess their AI maturity using SAS’ AI Readiness Calculator, an 11-question assessment based on the SAS and IDC AI Readiness Index.
The assessment evaluates organisations across planning, building, enabling and executing, with participants receiving a report covering areas of strength, readiness gaps and potential next steps based on their maturity stage, priorities and industry.
