Despite strong momentum, AI adoption challenges remain significant.
Key takeaways
Without consistent standards and governance, AI can amplify existing weaknesses.
Data highlights the importance of aligning AI investments with broader modernization strategies.
Artificial intelligence has moved quickly from experimentation to execution in the financial services industry. New RSM survey data suggests that for many organizations, AI has become embedded in how work gets done.
In the RSM Middle Market AI Survey 2026, 87% of the 193 respondents from financial services organizations reported that AI is at least partially integrated into their operations, including 41% who reported full integration, with AI embedded across core operations and processes.
Despite strong momentum, AI adoption challenges remain significant. The survey shows that the leading inhibitors to AI deployment in financial services are:
- Security and privacy concerns (33%)
- Data quality and availability issues (32%)
- Integration with legacy systems (27%)
These challenges may be amplified by the industry’s regulatory environment and the sharing of data across third-party vendors and fintech partners. Without consistent standards and governance, AI can amplify existing weaknesses rather than solve them.
“Financial services firms are rich with data, but that’s also the challenge. You need a single source of truth,” says Erin Sims, a financial services senior analyst at RSM UK. “Data governance is critical, but so is treating data like a business risk and a product, with key performance indicators around accuracy, completeness and timeliness.”
While adoption levels are high overall, maturity can differ across financial services subsectors. Banks and other large financial institutions tend to be further along, driven by larger technology budgets and clearer near-term returns. In contrast, insurers and wealth and asset managers often face heavier constraints from legacy systems, which can slow progress.
The survey data reinforces that AI is being used across a wide range of capabilities. Among financial services respondents:
69%
are using generative AI
60%
are using prediction AI
55%
are using language AI
51%
are using agentic AI
AI and implications for the tax function
To better understand AI adoption within tax functions, the survey included a set of tax-specific questions. These questions were asked only of respondents who reported that their organization currently uses AI in tax, were familiar with its application, and indicated that their organization is pursuing or planning AI-enabled tax use cases over the next 12 months.
Among respondents from the financial services industry, tax functions stand out as an area of particularly strong uptake: 88% said their tax departments already use AI tools, formally or informally. Asked how they expect AI to change the nature of work within their organization’s tax function in the next two to three years, respondents’ top picks were:
AI orchestrators
Tax professionals will primarily manage, train and oversee AI systems (51%).
Strategic tax advisors
The focus will shift from compliance execution to strategic planning and business partnership (44%).
Tax data scientists
Priorities will be data quality, model governance and analytics (41%).
Hybrid specialists
A combination of tax expertise and AI skills will become standard (38%).
These findings reflect the structured, repeatable nature of many tax processes and the growing availability of AI-enabled tax planning and compliance tools.
AI investment indicates sustained commitment
Financial services organizations are backing their AI ambitions with investment. Seventy percent said their organizations plan to invest at least $1 million in AI in the current fiscal year, including 31% expecting to spend $3 million or more.
Respondents reported that the majority of their organization’s AI investment currently focuses on the following areas:
- AI software and embedded AI solutions (52%)
- Data platforms/infrastructure (36%)
- AI research/innovation (36%)
- Upskilling internal talent (28%)
Looking ahead, most organizations expect AI spending to continue rising. Eighty-two percent of financial services respondents anticipate increasing AI spend next fiscal year, with the majority (73%) expecting increases of up to 25%.
However, increased AI investment comes with potential trade-offs. Among organizations expecting higher AI spend in the coming fiscal year, respondents most often said funding would be reallocated from:
- External consulting and advisory services (42%)
- Business intelligence or analytics initiatives (41%)
- Legacy application modernization (39%)
For financial services leaders, this data highlights the importance of aligning AI investments with broader modernization strategies to avoid creating new technical debt while chasing near-term gains.
Satisfaction is high, but success depends on fundamentals
Nearly all financial services respondents (98%) said they are at least somewhat satisfied with the business value delivered by their AI solutions, including 63% who reported being very satisfied. When asked why AI initiatives are working, organizations most often pointed to having the following: the right AI technologies to meet business needs (39%); sufficient technology/infrastructure for AI workloads (34%); the ability to measure return on investment (33%); established governance and policies (33%); a clear and well-defined AI strategy (33%); and appropriate AI use cases (33%).
Among those who reported moderate or limited success with their AI pilots in the prior 24 months, the top reasons cited were:
48%
Data quality issues
46%
Integration challenges
32%
Cost of production
31%
Unclear ROI
“If you choose a tool that sits outside the workflow, people simply won’t use it,” says Sims, focusing on the integration challenges that many organizations face. “Where AI works best is when it’s embedded end to end in the process, rather than bolted on.”
Practical steps to address AI adoption challenges
Financial services organizations can take several practical steps to strengthen AI outcomes. These include:
Treating data as a business asset and risk: Invest in data governance, standardization and quality metrics before scaling AI.
Integrating AI into existing workflows: Focus on embedding AI into end-to-end processes rather than deploying stand-alone tools.
Clarifying ownership and accountability: Define who owns AI strategy, governance and ROI measurement across the enterprise.
Investing in change management and skills: Upskill employees and build cross-functional teams that bring together operations, technology, risk and compliance.
Strengthening third-party oversight: Enhance governance and controls around vendors and partners that use or provide AI-enabled solutions.
“If you choose a tool that sits outside the workflow, people simply won’t use it. Where AI works best is when it’s embedded end to end in the process, rather than bolted on.”
Erin Sims, Financial Services Senior Analyst, RSM UK
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