By Michael Heffner, Head of Global Industry and Value at The financial sector is undergoing a profound transformation, driven by artificial intelligence (AI) that is modernizing operations and reshaping business models. In 2025, advancements in AI technology will expand its applications in financial services, pushing the sector into a new era of efficiency and personalization. From reshaping customer interactions to streamlining internal processes, AI drives business innovation across functions. Three emerging trends will shape how banks invest in AI:
Trend #1: Wider Adoption of AI in Financial Services
While large banks have already innovated considerably with generative AI (genAI), broader implementation will come in 2025 as large language model (LLM) capabilities become more accessible to smaller firms. Banks are seeing more options for curated environments that optimize the security and accuracy of genAI and its use of data in the organization.
Financial institutions will also adopt AI agents within their operations. Agentic AI combines powerful generative AI capabilities to not only understand context, but also plan a future outcome and take action to achieve a goal. AI agents work autonomously to make decisions and perform tasks for human review. Where genAI is not a good fit, traditional AI will continue to solve well-defined problems and perform repetitive tasks. For example, robotic process automation (RPA) and intelligent document processing (IDP) will continue to scan documents and extract data.
A financial services company managed the complex process of transferring funds to heirs, where accuracy was critical to avoid missed transfers or tax errors. They previously used optical character recognition (OCR) for PII extraction but needed a better solution. In two weeks, they built a pilot a generative AI prompt build skill in Appian to extract data from unstructured, low-quality document scans. The solution also translates documents into English. Then process key data from various identification formats, handwritten forms, and semantic descriptions. The company expects to reduce data extraction times by threefold by leveraging AI.
Trend #2: Enhanced Focus on Operational Efficiency
AI will expand into enterprise operations for additional use cases such as compliance reporting and institutional communications. Combining traditional AI, genAI and agentic AI is set to revolutionize operational efficiencies.
These integrations will help organizations handle complex data processes with greater speed and accuracy. Firms could also automate documentation, logs, and user guides on operational processes.
As AI becomes more adept at interpreting data and generating insights, it will provide deeper visibility into organizational processes. This paves the way for intelligence processes and more adaptive operational models. AI will transform operations and free up employees’ time for more strategic initiatives. This technology will be a cornerstone for improved efficiency and reduced costs for financial firms while enforcing regulatory compliance.
Founded in 1792, State Street Global Advisors is the second-oldest operating United States bank with operations worldwide. Since 2016, State Street Global Advisors has developed 18 applications with Appian for more than 900 employees. The firm has increased their client onboarding time by 20% in one year and expects a 10% increase with additional refinements. State Street is planning on integrating intelligent document processing to extract key information from contacts and put data in its system. Lastly, they plan to utilize process mining to find bottlenecks in the customer onboarding process for further efficiency improvement. State Street has documented a 30% improvement in operational efficiency and decreased losses due to errors by 50%.
Trend #3: Modernizing Data Infrastructure
As banks deepen their reliance on AI, transforming the foundational data infrastructure that supports AI technologies will become a top priority. Central to this effort is "data fabric," which enables organizations to integrate disparate data sources into a unified, easily accessible format. Because AI is only as effective as its ability to pull from a broad range of data sources, data fabric architectures allow banks to break down data silos for a seamless and integrated data flow across systems and departments. Furthermore, this infrastructure supports scalability, allowing banks to adapt quickly as AI use cases expand.
S&P Global, a financial information and analytics firm based in New York, leveraged data fabric to weave together hundreds of data sources, with tens of thousands of calls to them per day. Data fabric complements transactional data with contextual data, accelerating S&P's ability to deliver better market insights to its clients. The firm has completed more than 100M transactional tasks on the Appian Platform and automated more than 1,000 processes with more than 7,000 employees using the solution as their central command center. S&P has taken the business to new heights by unifying processes and data for automation.
As AI continues to reshape the banking landscape, 2025 promises to be a pivotal year for the industry.
All three trends will help banks further automate routine processes like loan approvals, fraud detection, and compliance monitoring to significantly reduce costs and human error. The same three transformational efforts will facilitate risk management and generate robust predictive models to anticipate market shifts and credit risks with unprecedented accuracy. These are just some ways traditional AI, genAI and agentic AI adoption in 2025 will help banks achieve faster decision-making to improve operational scalability and more competitive advantage in a rapidly evolving financial ecosystem.
