Data gaps stall AI scale-up despite strong AIOps gains

Financial services firms are reporting strong returns from AIOps, yet most still struggle to move AI programmes beyond pilots, according to a global survey commissioned by Riverbed.

The research found that 89% of financial services organisations said returns from AIOps investments have met or exceeded expectations. Yet only 12% said AI initiatives have reached full enterprise-wide deployment, while 62% remain in pilot or development.

The survey identifies data readiness as the main barrier to scaling AI. It found that 92% of sector decision-makers agree improving data quality is critical to AI success. However, only 43% said they trust the accuracy and completeness of their organisation's data.

This gap between ambition and execution is reflected in a mixed view of organisational preparedness. Only 40% of respondents said they feel fully prepared to operationalise their AI strategy today, despite nearly two-thirds saying they have high confidence in that strategy.

Data trust gap

Financial services firms operate under tight regulatory expectations and a low tolerance for service disruption. These conditions raise the bar for data governance and auditability when AI is used in operational settings such as incident management, monitoring and service assurance.

The survey suggests many organisations have not yet built the data foundations they believe are necessary. Respondents showed the strongest agreement of any sector on the importance of data quality, but reported the lowest confidence in their underlying datasets across the industries covered.

Riverbed linked the issue to the complexity of modern IT estates, where data is spread across on-premises infrastructure, multiple clouds, edge locations and specialist platforms. This sprawl can lead to inconsistent telemetry and gaps in monitoring, weakening the inputs AI systems rely on to detect anomalies or automate responses.

Tool consolidation

In response, many firms are renewing efforts to simplify tooling and reduce vendor overlap. Financial services IT teams use an average of 13 observability tools from nine different vendors, the study found. This fragmentation can slow investigations and create blind spots across applications, networks and end-user experience.

As a result, 96% of respondents said they are consolidating tools and vendors across IT operations. Nearly all (95%) agreed a unified observability platform would make it easier to identify and resolve operational issues.

Consolidation may also reshape supplier relationships. The study found that 95% of respondents are considering new vendors as part of their consolidation plans-the highest level among the industries surveyed.

Unified communications

Unified communications performance is also emerging as an operational pressure point as firms digitise client engagement and internal workflows. Employees in financial services now spend 41% of their working week using unified communications tools, the survey found, and nearly two-thirds of respondents said these tools are essential for operating effectively.

Satisfaction was uneven. Only 47% of organisations said they are very satisfied with performance, while 44% reported regular issues across video calls, messaging platforms and collaboration workspaces.

These issues also contribute significantly to support workloads. The survey found they account for 16% of IT tickets in the sector. The average time to resolve a unified communications-related ticket is 41 minutes, with almost one in five taking more than an hour.

Open standards

The study points to OpenTelemetry as a standard firms are adopting to unify observability data across systems. OpenTelemetry is a framework for collecting and correlating telemetry-such as traces, metrics and logs-across applications and infrastructure.

Adoption in financial services was high. The survey found that 92% of organisations are already using OpenTelemetry. It also found that 96% said cross-domain correlation is critical to their observability strategy, and 99% agreed OpenTelemetry reduces vendor lock-in and increases flexibility.

For organisations trying to scale AI in operations, consistent telemetry can provide a baseline for automation and model-driven analysis. The survey found that 97% of financial services respondents view OpenTelemetry as a foundation for future initiatives such as AI-driven automation.

Networks and data movement

Attention is also shifting from AI models to the movement of data that feeds them. Financial services firms placed greater emphasis on AI data movement than any other sector surveyed: 94% said it is important to their overall AI strategy, and 37% described it as critical and foundational to how they design and execute AI.

As data becomes more distributed across public cloud, edge and co-location environments, respondents also highlighted network performance and security as central considerations. The survey found that 81% of financial services decision-makers cited network performance and security as essential, the highest of any industry included.

Looking ahead, 76% of financial services organisations said they plan to establish an AI data repository strategy by 2028, signalling continued investment in governed architectures that can meet compliance expectations while managing the operational demands of AI workloads.

Jim Gargan, Chief Marketing Officer at Riverbed, said:

"Financial Services organisations are among the most sophisticated and disciplined adopters of AI, and our research shows they're already seeing strong returns. 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 organisations as they bridge this gap and turn AI ambition into operational reality."

The survey was conducted by Coleman Parkes Research and polled 1,200 business decision-makers, IT leaders and technical specialists across seven countries and multiple industries, including financial services.