Survey Results: 2026 AI Adoption & Maturity in Banking 

Despite a reputation as a risk-averse industry, financial institutions are under pressure to do more with less. Whether AI is the answer has become a common topic of conversation in board rooms and break rooms alike. But despite the widespread (and sometimes wide-ranging) opinions, information on AI adoption and maturity among banks can still be more anecdotal than analytical.  

This is why Wolf & Company launched our first AI Adoption & Maturity Survey to quantify whether – and how – senior executives are adopting this new technology. As Principals in the Artificial Intelligence Services practice at Wolf & Company, we’ve held our fair share of consultations and roundtables on this topic, given many board presentations, and lectured at multiple industry events.  

But with this survey, the answers are straight from the community banks themselves, and the results may surprise you. 

AI adoption is widespread – but AI maturity varies. 

Despite the reputation as a risk-averse industry, every financial institution we surveyed has adopted AI – although adoption stages vary.  

• While every financial institution is using AI, 30% noted their AI adoption is limited to ad hoc or vendor-embedded AI solutions only. 

• Even when we exclude limited or experimental AI solutions, most community banks (70%) shared their AI adoption plan is already underway – ranging from proofs of concept to scaled and governed AI programs. 

• Among the banks actively adopting AI, the maturity of their AI programs vary: 40% of survey respondents sharing they have defined pilots underway, but only 25% have moved their proofs of concept into production – and only 5% have successfully launched a scaled, governed AI program. 

While every financial institution we surveyed has already begun adopting AI, the stages of adoption vary – although we predict the gaps between early adopters and cautious adopters will close relatively quickly. 

Some early adopters are ready to scale pilots, while others still chart the path ahead. 

None of the financial institutions we surveyed are waiting to adopt AI, which indicates there aren’t many detractors from the AI movement. However, the stage of AI adoption varies by institution. Here’s what we can infer from their responses: 

• Most of the survey respondents are still early in their AI adoption journey, with 40% of respondents marking their priority as establishing an AI strategy and roadmap. 

• Meanwhile, 25% of survey respondents have moved to launching low-risk internal use cases. 

• The earliest adopters are already scaling their existing pilots, with 20% of respondents sharing this is next on their roadmap. 

• Finally, 15% of financial institutions surveyed are primarily focused on implementing governance and policies on AI usage. 

None of the financial institutions noted their primary AI-related priority as evaluating vendor AI and third-party risk – however, this doesn’t mean vendor and third-party risk is being ignored. It’s more likely that survey respondents are considering risk management as part of their larger AI strategy.  

Priority AI use cases focus primarily on optimizing internal processes. 

While AI maturity varies by financial institution, so do the priorities for AI use cases.  

• Employee productivity tools are clearly a priority for most banks’ AI adoption plan – 95% of survey respondents are actively exploring or investing in tools like Copilot. 

• 75% of financial institutions surveyed are also exploring or investing in internal process automation. There’s a clear desire among survey respondents to optimize employee productivity and drive efficiency internally. 

• For 60% of survey respondents, their institution is actively exploring or investing in data and reporting automation. 

• Banks face more risks than ever before, so it’s not surprising that 45% of survey respondents are actively exploring or investing in AI solutions to deliver fraud, risk, or compliance support. 

• Some financial institutions are also interested in customer-facing AI use cases: 35% are exploring AI solutions for customer service or their contact center, and 20% are exploring AI use cases for marketing and personalization. 

While banks seem to be prioritizing internal AI use cases, there are instances where institutions are exploring or investing in customer-facing AI solutions. It’s likely that internal use cases offer more control and oversight for early adopters. 

AI governance and risk controls remain a priority for financial institutions. 

Notably, 90% of survey respondents have some degree of confidence in the governance and risk behind both internal and third-party AI tools.  

This aligns with the banking industry’s reputation for making decisions cautiously and avoiding risk, even as they adopt new technology like artificial intelligence. 

AI-related challenges still plague financial institutions, with security and data leakage concerns taking top place. 

Despite their confidence in governance and risk controls for their AI programs, financial institutions aren’t ignoring the possible risks and challenges related to artificial intelligence.  

• Security and data leakage concerns are a top challenge for 55% of community banks surveyed. In addition, 40% of respondents are concerned about risks stemming from third-party and vendor AI solutions. 

• Inside the financial institutions, there are still AI-related challenges: 50% of respondents are concerned about limited internal expertise, 40% noted data quality and availability as a potential obstacle, and 30% cite unclear return-on-investment or business case as a challenge. 

• Industry-wide challenges also appear to be present, with 25% of respondents feeling uncertainty regarding regulatory and compliance, and 35% citing a lack of governance or operating model as a potential challenge. 

While AI adoption is high, it’s clear that banks still face challenges both internal and external. On an optimistic note for AI, 95% of financial institutions don’t cite lack of internal buy-in and support as a primary challenge. This could indicate a healthy appetite among banks for next-generation technology. 

AI isn’t the only technology financial institutions are interested in adopting. 

As consumers hold less loyalty for their financial institution, community banks are considering how to strategically maximize their competitive advantages. They’re also considering what next-generation technology is most worth the investment (and most likely to retain existing customers and attract new ones). 

• The majority of community banks surveyed are also prioritizing fintech partnerships – almost 60% of respondents noted this as a current priority.  

• Real-time payments infrastructure is also a priority for more than 50% of respondents, followed by digital assets & tokenization (nearly 40%), banking-as-a-service (greater than 25%), and blockchain and DLT (10%). These investments can be differentiators for institutions. 

• Of the survey respondents, 20% are staying focused on artificial intelligence, and aren’t prioritizing investments in other next-gen technology. 

Although these community banks are exploring different investments in technology, they are still united by their interest and investment in artificial intelligence.   

About The Survey: 

This survey was conducted between January 31, 2026 and February 28, 2026, and consisted of a sample size of 20 respondents from 17 community banks between $750 million and $25 billion. All responses are anonymized and aggregated. Of the 20 respondents, 45% are executive leaders (9 respondents). Technology leaders made up 30% of survey respondents (6 respondents), and 15% of survey respondents are risk, compliance, or audit leadership (3 respondents). Finally, 10% of survey respondents are data, analytics, and/or innovation leaders (2 respondents).