Among financial services decision-makers responding to a new survey 92 percent agree that improving data quality is critical to AI success.
Yet the study, from AIOps specialist Riverbed, shows progress remains uneven. Only 12 percent of AI initiatives have achieved full enterprise-wide deployment, while a significant 62 percent still remain in pilot or development stages.
Despite this the financial services sector continues to demonstrate strong confidence in the value of AI and AIOps, with 89 percent of organizations reporting that ROI from their AIOps investments has met or exceeded expectations, reinforcing the industry’s reputation for disciplined, value-driven technology adoption. Nearly two-thirds (62 percent) of respondents also express a high degree of confidence in their AI strategy. But despite this optimism, Financial Services organizations continue to be affected by AI implementation gaps. Amid mounting pressures to optimize operations, strengthen compliance, mitigate risk, and deliver superior digital experiences, the industry is increasingly constrained by data readiness, operational complexity, and the ability to scale AI beyond pilot initiatives.
“Financial Services organizations are among the most sophisticated and disciplined adopters of AI, and our research shows they’re already seeing strong returns,” says Jim Gargan, chief marketing officer, at Riverbed. “However, the sector operates under unique pressures, including rigorous regulatory scrutiny, zero tolerance for downtime and a critical need for data accuracy. What’s clear is that success now depends on simplifying IT, consolidating observability tools and vendors, improving data quality, embracing open standards like OpenTelemetry, and ensuring network and application performance can support AI at scale. At Riverbed, we are actively supporting some of the world’s largest financial services organizations as they bridge this gap and turn AI ambition into operational reality.”
The research shows that just 40 percent of financial services organizations feel fully prepared to operationalize their AI strategy today. Data remains the most significant constraint as only 43 percent are fully confident in the accuracy and completeness of all their organizations data, the lowest level of confidence across all industries surveyed.
Operational complexity is part of the issue. On average, IT teams currently have 13 observability tools from nine different vendors, creating blind spots across applications, networks and user experience. As a result, 96 percent of organizations in this sector are actively consolidating tools and vendors across their IT operations, with 95 percent agreeing that a unified observability platform would make it easier to identify and resolve operational issues.
You can find out more and get the full report on the Riverbed site.
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