SME-Led Enterprise AI: Making Front Arena Expertise Reusable and Actionable

There is a common risk in enterprise AI programmes: introducing AI first and asking domain experts to validate it later.

For complex banking and capital-markets platforms, a more effective approach is to start with domain expertise and operational evidence.

AI needs to understand the platform before it can meaningfully assist the people operating it.

Domain Expertise Is More Than Technical Documentation

An experienced Front Arena specialist understands relationships and patterns that may not be obvious from individual logs or system outputs.

They know:

• Which components typically depend on each other
• Which warnings are routine and which may indicate a larger issue
• Which batch delays have meaningful business impact
• Which report differences are acceptable
• Which change patterns have previously resulted in incidents
• Which workarounds are safe
• Which temporary fixes may create additional operational risk
• Which platform behaviours require deeper investigation

This knowledge is accumulated through years of operating, supporting, upgrading and engineering the platform.

It is precisely this context that can make operational intelligence useful.

AI Without Domain Context Can Create Noise

A model may produce an answer that is technically plausible but operationally irrelevant.

It may correctly interpret a log message while missing the relationship between that event and a critical business process.

It may identify a configuration difference without understanding whether that difference is expected.

It may recommend an action that appears reasonable but conflicts with established operational practices or governance controls.

The issue is not necessarily the capability of the AI model.

The issue is the context available to it.

For complex enterprise platforms, useful AI needs to be grounded in the knowledge of the people who understand how the platform actually operates.

The Changing Role of the SME

This does not mean SMEs should remain responsible for answering every incident indefinitely.

Their role can become more strategic.

Instead of being the only people who can interpret specialist knowledge, SMEs can help transform that knowledge into reusable intelligence.

This can include:

• Platform operating knowledge
• Component relationships
• Known issue patterns
• Validation rules
• Troubleshooting approaches
• Operational runbooks
• Business-impact context
• Upgrade scenarios
• Governance boundaries
• Approved and restricted actions

The result is a shift from individual expertise to organizational capability.

The Thinking Behind FAIR™

This is one of the principles behind FAIR™.

The objective is not to bypass Front Arena expertise.

It is to make that expertise more:

Searchable.
Reusable.
Contextual.
Actionable.
Available across engineering teams.

An intelligence layer can help connect SME knowledge with operational signals, historical incidents, validation evidence and platform behaviour.

This can reduce repetitive investigation while allowing experienced specialists to focus on the decisions that genuinely require human judgment.

Keeping People in Control

Enterprise AI in banking also needs clear governance boundaries.

AI-assisted intelligence should support engineering decisions without removing accountability from the people responsible for the platform.

A practical model is:

SMEs define the knowledge --> AI connects and applies the context --> Engineers investigate and evaluate --> Accountable people make the decision.

This preserves human oversight while making specialist knowledge more scalable.

Reducing Dependency Without Reducing Expertise

There is an important distinction between reducing dependency on an individual and reducing the importance of expertise.

The first is a valuable engineering objective.

The second can create operational risk.

If a platform depends entirely on one or two people knowing how everything works, the organization has a knowledge-scaling problem.

The answer is not to eliminate those experts.

It is to capture what they know and make that knowledge reusable while retaining their role in defining, validating and governing the intelligence layer.

Building AI Around the Platform

For banks and capital-markets organizations, successful enterprise AI will require more than selecting a capable model.

It will require collaboration between AI teams and the people who understand the underlying platforms, workflows and operational risks.

The earlier those specialists are involved, the more effectively their knowledge can shape the intelligence system.

For complex Front Arena environments, this means building AI around the platform and its operating knowledge, rather than expecting a generic model to understand the platform after the fact.

The future of enterprise AI is not simply about making models more capable.

It is also about making them more relevant, contextual and governed within the environments where they operate.