Security and Governance Built In, Not Bolted On
A large Australian enterprise wanted to move beyond isolated AI experiments and team-specific chatbots to create a capability that could scale safely across the organisation and integrate AI into a much broader range of business systems, data sources and workflows.
Skillfield embedded consultants across platform engineering and business-facing use case teams to support the build of a governed, flexible and scalable AI solution.
The solution enables multiple teams to safely design and deploy new AI use cases with security, data permissions, and observability built in from the start rather than retrofitted later.
A citizen-developer approach now allows several hundred non-engineering team members to create and safely ship new AI capabilities with confidence.
The Problem
Like many large organisations, our customer wanted to move beyond early AI experimentation to a capability that could support multiple business teams and an expanding range of use cases. A single chatbot or one-off application, however impressive, would not provide the flexibility or scalability required.
AI needed to work across the organisation’s existing technology landscape, interacting with a diverse range of systems, data sources, and business processes, including databases, SharePoint, specialised applications, geospatial data, satellite mapping data, and many more.
They also wanted to avoid the duplication, complexity and risk of every new use case requiring its own bespoke integration, data access, and security controls.
The organisation needed a flexible, scalable platform that could accommodate different functional and data requirements while applying consistent controls across the enterprise. Governance, security and permissions needed to be built into the foundation from the outset, rather than retrofitted as adoption grew.
The Solution
Skillfield embedded consultants across both the platform engineering team and the business-facing use case team, creating continuity between the technical foundation and real-world delivery.
On the platform side, engineers help build and maintain the core API that exposes AI capabilities, applying disciplined, version-controlled engineering best practices and controls designed to prevent sensitive data from reaching models.
Role-based access is aligned with each user’s existing data entitlements and extended into a sandboxed environment where business users can safely create governed AI functions. Centralised observability provides visibility into model calls, tool use and cost.
In parallel, Skillfield consultants worked directly with business teams to identify, design and deliver the AI capabilities that address their specific needs.
The Outcome
The platform has matured from early experimentation into an enterprise-wide AI capability that can evolve as new requirements emerge. With governance, security and data permissions enforced centrally, teams can add use cases without rebuilding core controls each time, improving consistency and reducing delivery friction.
Importantly, the organisation is not limited to deploying AI through chat. The same governed foundation can support AI embedded across different applications, information sources and operational workflows, giving teams greater freedom to apply AI wherever it creates value.
The governed citizen-developer model has also broadened participation in AI, with several hundred non-engineering staff now safely contributing to AI use cases within defined guardrails.
This gives the organisation a scalable foundation for ongoing AI adoption while maintaining the oversight required in a complex enterprise environment.
