The financial services industry is reaching an important milestone with AI as organizations move beyond testing and experimentation to successful AI implementation, driving business results. A recent survey highlights how financial institutions have consolidated their AI efforts to focus on core applications, signaling a significant increase in AI capability and proficiency. Companies investing in AI are seeing tangible benefits, including increased revenue and cost savings.
Nearly 70% of respondents report that AI has driven a revenue increase of 5% or more, with a dramatic rise in those seeing a 10-20% revenue boost. In addition, more than 60% of respondents say AI has helped reduce annual costs by 5% or more. Nearly a quarter of respondents are planning to use AI to create new business opportunities and revenue streams.
Prominent use cases in terms of return on investment (ROI) include trading and portfolio optimization, which account for 25% of responses, followed by customer experience and engagement at 21%. These figures highlight the practical, measurable benefits of AI as it transforms key business areas and drives financial gains. Half of management respondents said they’ve deployed their first generative AI service or application, with an additional 28% planning to do so within the next six months.
There has been a 50% decline in the number of respondents reporting a lack of AI budget, which suggests increasing dedication to AI development and resource allocation. The challenges associated with early AI exploration are also diminishing. The survey revealed fewer companies reporting data issues and privacy concerns, as well as reduced concerns over insufficient data for model training.
Ai boosts financial services efficiency
These improvements reflect growing expertise and better data management practices within the industry. As financial services firms allocate budget and grow more savvy at data management, they can better harness AI for enhanced operational efficiency, security, and innovation across business functions.
After data analytics, generative AI has emerged as the second-most-used AI workload in the financial services industry. The applications of the technology have expanded significantly, from enhancing customer experience to optimizing trading and portfolio management. Notably, the use of generative AI for customer experience, particularly via chatbots and virtual assistants, has more than doubled, rising from 25% to 60%.
This surge is driven by the increasing availability, cost efficiency, and scalability of generative AI technologies for powering more sophisticated and accurate digital assistants that can enhance customer interactions. More than half of the financial professionals surveyed are now using generative AI to enhance the speed and accuracy of critical tasks like document processing and report generation. Financial institutions are also poised to benefit from systems that harness vast amounts of data from various sources and use sophisticated reasoning to autonomously solve complex, multistep problems.
Banks and asset managers can use agentic AI systems to enhance risk management, automate compliance processes, optimize investment strategies, and personalize customer services. Recognizing the transformative potential of AI, companies are taking proactive steps to build “AI factories”—specially built accelerated computing platforms equipped with full-stack AI software—through cloud providers or on-premises. This strategic focus on implementing high-value AI use cases is crucial to enhancing customer service, boosting revenue, and reducing costs.
By tapping into advanced infrastructure and software, companies can streamline the development and deployment of AI models and position themselves to harness the power of agentic AI. With industry leaders predicting at least 2x ROI on AI investments, financial institutions remain highly motivated to implement their highest-value AI use cases to drive efficiency and innovation.
