Building the Foundations for Scalable AI Agent Delivery in UK Transport

A UK transport organisation set out to accelerate its AI journey, building on early experimentation with Microsoft 365 Copilot and initial agent development in Copilot Studio. There was already strong internal momentum, with multiple teams exploring how AI could improve productivity, streamline operations, and enhance decision-making.

However, this momentum brought a critical inflection point. Rather than rushing into rapid deployment, the organisation recognised the need to establish the right foundations—ensuring that any AI capability developed could scale securely, consistently, and under clear governance.

The engagement focused on moving from isolated experimentation to a structured, repeatable, and IT-led AI delivery model.

Challenge

The organisation faced a familiar but complex challenge: balancing speed of innovation with the discipline required for enterprise-scale delivery.

There was strong ambition to build AI agents quickly, but without the right guardrails, this risked fragmented solutions, inconsistent standards, and potential governance issues. At the same time, growing interest across teams created pressure to define ownership, responsibilities, and a clear path forward.

Key challenges included:

• Early experimentation without a standardised delivery model
• Lack of clearly defined governance, roles, and responsibilities across IT and Infosec
• Risk of siloed knowledge and reliance on a small number of individuals
• Need to upskill internal teams to confidently build and manage AI solutions
• Absence of a scalable environment for safe experimentation and deployment

Without addressing these challenges, the organisation risked losing control of its AI estate—leading to inefficiencies, duplication of effort, and increased security or compliance exposure.

Solution

The approach centred on a “done with” model—working side-by-side with internal teams to build capability while simultaneously delivering tangible outputs. This ensured that knowledge was embedded, not outsourced.

1. Education & Capability Building

Hands-on education sessions were delivered to upskill the IT team across key areas, including:

• AI fundamentals and prompting techniques
• Microsoft 365 Copilot best practices
• Agent design and development using Copilot Studio
• Azure AI and Microsoft Foundry capabilities

These sessions were practical and applied, enabling teams to immediately translate learning into action.

2. Environment & Technical Foundations

A secure and scalable technical foundation was established to support ongoing AI development.

• A dedicated Azure AI Foundry sandbox environment was configured for safe experimentation
• Structured connectors and data integrations were defined to enable reliable agent performance
• Policy controls and guardrails were implemented to ensure secure and compliant usage

Microsoft Foundry played a key role as a unified platform for managing AI models, agents, and data integration—enabling a consistent and scalable development approach.

3. Governance & Operating Model

A core focus of the engagement was designing governance that could scale with demand.

• A clear RACI matrix was co-developed to define responsibilities between IT and Infosec
• Governance frameworks were established to manage agent development, deployment, and oversight
• Standardised workflows were introduced to ensure consistency across projects

This provided the structure needed to maintain control without slowing down innovation.

4. Co-Creation of a Repeatable Delivery Model

Rather than delivering a one-off solution, the engagement focused on creating a repeatable blueprint for AI delivery.

• An end-to-end AI delivery model was co-designed, covering ideation through to deployment
• Documentation was created to ensure environments, agents, and governance could be replicated
• An AI Centre of Excellence (CoE) model was established to support long-term capability

This ensured the organisation could scale AI initiatives independently, without ongoing reliance on external support.

5. Hands-On Collaboration

The work was delivered in close partnership with the IT team:

• Shadowing sessions enabled real-time knowledge transfer during environment setup
• Collaborative workshops ensured alignment across stakeholders
• Continuous feedback loops refined the approach as capability matured

This embedded both confidence and ownership within the internal team.

Key Outcomes

The engagement delivered both immediate value and long-term capability, positioning the organisation for scalable AI adoption.

Quantitative & Tangible Results

• A fully configured Azure AI sandbox environment for safe experimentation
• Defined governance structure and RACI matrix across IT and Infosec
• Comprehensive documentation covering agent development and environment setup
• Structured AI delivery model ready for replication across future use cases
• Prioritised pipeline of AI opportunities aligned to business value

Qualitative Impact

• Increased confidence within the IT team to independently build and deploy AI agents
• Clear ownership and accountability across teams, reducing ambiguity
• Shift from ad hoc experimentation to a structured, scalable approach
• Stronger alignment between innovation and governance
• Foundations established for an AI Centre of Excellence

Before vs After

• Before: Disconnected experimentation with limited governance and unclear ownership
• After: A structured, IT-led delivery model with defined guardrails, processes, and scalability

Conclusion

By focusing on foundations rather than speed alone, the organisation has positioned itself to scale AI agents in a way that is both controlled and sustainable.

The combination of capability building, governance design, and technical enablement has created a platform for long-term success—where AI can be developed confidently, securely, and at pace.

With a clear delivery model, established guardrails, and an empowered internal team, the organisation is now equipped to move beyond experimentation and into enterprise-scale AI adoption—turning early momentum into lasting transformation.