Nearly all financial services organisations are currently leveraging generative AI applications or workloads today, with a focus on real-life applications gravitating towards customer support and content development.
This is according to Nutanix, based on its seventh annual global Financial Services Enterprise Cloud Index (ECI) survey and research report, which measures enterprise progress with cloud adoption in the industry.
Commissioned by Nutniax, United Kingdom-based researcher Vanson Bourne surveyed 1,500 IT and DevOps/Platform Engineering decision-makers in the autumn of 2024.
Respondents spanned multiple industries, business sizes, and geographies, including North and South America; Europe, the Middle East and Africa (EMEA); and Asia-Pacific-Japan (APJ) region.
Results show that despite widespread generative AI adoption, financial services organisations are struggling to keep pace. Most cite a skills gap needed to manage generative AI with existing infrastructure.
Moreover, 97% of respondents admit they could do more to secure their generative AI models and applications.
This trend is also echoed in Singapore, where 89% of financial institutions have explored, piloted, or implemented generative AI in 2024.
According to the Monetary Authority of Singapore (MAS), more than 30 institutions have also established AI functions locally, with several serving as Global AI Competency Centres that incubate AI solutions in Singapore before scaling them to other markets.
However, without the right foundation, organisations may face challenges when attempting to translate these pilots into enterprise-wide transformation.
“In Singapore’s financial sector, the generative AI conversation has moved beyond pilots to focus on scale, security, and enterprise readiness. Financial institutions are no longer asking if they should adopt AI — they’re asking how to make it work seamlessly across the enterprise,” said Ho Chye Soon, Singapore country manager at Nutanix.
He said that shift is redefining infrastructure priorities, with containers and hybrid cloud emerging as essential enablers of customer value, innovation, agility, and trust in an increasingly dynamic digital landscape
“While GenAI remains a core focus, organisations in Singapore are also pushing to embed AI more deeply across operations and customer experiences—including exploring agentic AI,” he added.
Findings show that infrastructure modernisation is needed for generative AI success. Among respondents, 92% say their current infrastructure requires improvement to fully support cloud native applications and containers. Although containerization and Kubernetes are already in use, particularly for GenAI workloads, application portability and data silos persist as major hurdles.
Also, IT talent shortage could slow momentum. Nearly all (98%) respondents face challenges scaling generative AI from development to production, citing a lack of skilled personnel and integration issues. While 62% of respondents are actively hiring for generative AI expertise, training, and upskilling remain critical priorities.
In addition, ROI is a priority but it’s a long game. Among respondents, 39% anticipate potential generative AI-related losses in the next 12 months, while 58% expect gains within one to three years. This suggests that financial services leaders are embracing a longer-term view of generative AI success but also underscores the need for better tools to measure generative AI ROI.
Further, security and compliance will continue to be important. The majority (96%) of respondents say generative AI is reshaping their data security and privacy priorities. Additionally, 90% express concern about data security in the broader IT vendor ecosystem, further highlighting the complexity of securing AI deployments.
