Oakland

Enterprise Finance AI: Transforming Financial Information at Scale

Large enterprises generate vast volumes of financial data, but turning that data into timely, actionable insight remains a challenge for many finance teams.

A large enterprise organisation, a long-standing Oakland client, came to Oakland with the challenge of how to use AI to design a secure, enterprise grade finance solution that would enable their 3,000 cost centre managers to interrogate complex financial data using natural language, surfacing inefficiencies, prioritising action, and improving financial governance at scale.

The Challenge

The organisation’s central finance function supports a workforce of approximately 30,000 employees and more than 3,000 cost centre managers operating across multiple regions. Financial information was spread across multiple transaction systems, creating a fragmented view of spend and compliance.

As a result, finance teams struggled to:

Even where issues were known to exist, the time required to investigate them manually meant many went unresolved. This is a familiar challenge in large, complex organisations: too much data, too few specialists, and no scalable way to surface insight.

Beyond the technical challenge, the organisation also recognised that delivering this kind of capability would require significant business engagement and change. Enabling thousands of managers to trust, adopt, and act on AI-driven financial insight demanded strong governance, stakeholder buy-in, and new ways of working across finance and the wider business. Oakland supported not only the technical delivery, but also the organisational foundations needed to make the solution successful.

Oakland worked closely with the organisation to build a tailored enterprise finance AI platform, designed specifically to work with structured financial data at scale rather than documents or static reports.

The solution enables users to ask questions in plain English and receive accurate, governed responses directly from live financial databases. Crucially, it enforces strict access controls, ensuring users only see the data relevant to their role and cost centres.

This is an emerging and highly advanced application of enterprise AI. While many organisations are experimenting with copilots and document-based assistants, deriving trusted, actionable insight directly from complex financial databases remains bleeding-edge. Even major platform providers are still developing mature approaches in this space. Oakland’s ability to unlock value from structured transactional data, securely and at scale, is a key differentiator.

Rather than relying on generic AI tools like Copilot, this use case required the use of a more complex AI framework to securely query and interpret millions of structured financial transactions, not documents or emails. Tools like Copilot are designed for surface-level assistance and text-based content, with limited control over how answers are generated. The organisation needed a governed solution that could generate and validate database queries, apply finance specific business logic, and enforce strict role-based access to sensitive data. That level of scale, accuracy, and control required a tailored enterprise finance AI platform rather than an off-the-shelf assistant.

How the Finance AI Platform Works

The AI solution supports both exploration and proactive insight, allowing finance teams to move from reactive reporting to preventative control.

Users can:

In parallel, the platform automatically highlights priority issues such as repeated non-compliant expense patterns, inefficient vehicle leasing arrangements or unusually high travel costs enabling cost centre managers to focus on the areas that matter most.

This combination of conversational querying and auto-generated insight ensures that valuable information is not only accessible, but actionable.

Critically, Oakland’s custom LangGraph-based orchestration enables the platform to move beyond simple question answering. It supports multi-step reasoning, governed query generation, validation, and finance-specific controls. This level of orchestration is rare in enterprise deployments today and represents a significant leap forward in how AI can be applied safely to structured financial data.

The introduction of AI-driven financial insight is already delivering tangible value across the organisation’s finance function.

Key benefits include:

  • Reduced time spent on manual data investigation
  • Earlier identification of inefficiencies and cost leakage
  • Improved support for cost centre managers
  • Scalable financial oversight without increasing team size

Finance specialists are now able to focus on higher-value analysis and decision support, while cost centre managers gain clearer visibility into their own areas of responsibility.

Technology & Architecture

The platform is deployed entirely within the organisation’s secure environment and aligns with enterprise technology standards.

Core components include:

This architecture ensures performance, security and scalability across millions of transactions.

“This work is something we’re incredibly proud of at Oakland.

“The custom LangGraph orchestration and the way we’re deriving value directly from structured database data is gold, and it’s not something that’s being done well even by Microsoft today. It’s an area where we’re really excelling and leading the way.

“What excites me most is that this goes well beyond copilots or document-based AI. We’re unlocking genuine value from complex, structured financial data at scale, and showing what enterprise AI can really do when it’s designed properly.”

 

Jack Evans, Principal Consultant at Oakland Everything Data

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