Benchmarking Salesforce’s Financial Services Cloud Performance in 2026

What benchmarking data says about Financial Services Cloud today

Missing field descriptions are limiting AI readiness

Hubbl’s analysis of thousands of Salesforce environments highlights a consistent issue across Financial Services Cloud: critical metadata context is missing. Key benchmark findings include:

• Nearly 70 percent of all fields are custom, rather than standard.
• The average organization carries more than 4,700 custom fields.
• 69 percent of custom fields lack descriptions, leaving critical context undocumented.

The absence of field descriptions is the most consequential finding. As organizations move toward AI and automation, context becomes essential. AI relies on metadata, including field names and descriptions, to interpret data correctly. When that context is missing, outputs become harder to trust and scale.

Platforms may continue to function, but undocumented metadata increases governance effort and limits how confidently teams can extend the system as analytics and AI usage grows.

Package risk is rising faster than governance

Package management presents another systemic issue, particularly in regulated industries.

Benchmark data shows:

• 94 percent of Salesforce orgs run at least one out-of-date installed package.
• 38 percent of packages have never been security reviewed.

In Financial Services Cloud environments, where data sensitivity and regulatory scrutiny are high, these gaps represent more than technical debt. They introduce operational and audit risk that often goes unnoticed because it does not immediately disrupt daily workflows.

Over time, unmanaged packages increase the cost and risk of platform change, limiting how confidently organizations can modernize or adopt new capabilities.

Automation investment is growing, but legacy logic still slows change

Automation maturity tells a more nuanced story. Progress is measurable, but uneven. Benchmark findings show:

• Adoption of record-triggered Flow automation increased from 14 percent to 39 percent, a 179 percent year-over-year gain.
• Legacy automation tools remain deeply embedded across many FSC orgs.

This overlap creates competing logic paths that slow delivery and increase fragility. While automation investment continues, the benefits are often diluted by the complexity of maintaining multiple generations of automation simultaneously.

Taken together, these findings point to platforms that still work, but carry growing friction. Financial Services Cloud environments have evolved faster than they have been actively managed, making them harder to modify, harder to govern, and increasingly expensive to change. The issue is not failure, but drag, and over time that drag erodes the return on every new initiative.

What financial institutions can do next

The benchmark data points to a consistent conclusion. Improving ROI from Financial Services Cloud is no longer about adding more capability. It starts with understanding the condition of what already exists.

For many institutions, that means moving beyond assumptions about how Salesforce should work and establishing clarity around how it is actually built, governed, and used today. The most effective teams focus on three fundamentals:

1. Health: Establishing a baseline of platform health
2. Process: Understanding how work actually flows through Salesforce
3. Prioritization: Using that insight to prioritize remediation and investment

This is where health checks and process analytics become essential.

1. Salesforce Health Check

A Salesforce health check establishes a baseline by showing how the platform is configured today. It surfaces structural signals such as metadata volume, automation patterns, package risk, and security posture, offering an objective view of where complexity and fragility exist.

This baseline replaces perception with evidence, making it easier to identify where governance has weakened and where technical debt is quietly increasing.

Learn more about the role of a health check in AI readiness

2. Process Analytics

Process analytics adds the missing dimension by focusing on behavior rather than configuration. By analyzing real system events and user actions, it reconstructs how records actually move through Salesforce.

Instead of reflecting the intended sales or service process, process analytics shows what happens in practice, where steps repeat, approvals stall, or handoffs loop backward. These patterns rarely appear in documentation, yet they directly affect cycle time, user effort, and customer experience.

3. From insight to prioritization

Together, health checks and process analytics connect structure to behavior. Health data explains what is fragile in the platform. Process analytics explains why outcomes break down once users interact with it.

Organizations that apply this approach tend to follow a consistent pattern. They baseline platform health, map real-world process behavior, and use that combined insight to prioritize remediation and investment. In some cases, this informs annual planning. In others, it becomes an ongoing backlog-planning and monitoring discipline.

When insight replaces opinion, prioritization becomes clearer. Teams can focus on the changes that reduce risk, simplify operations, and create the strongest foundation for future investment.

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