A leading financial services institution was facing a familiar but increasingly pressing challenge: how to deliver a growing pipeline of critical engineering work without extending timelines or increasing delivery risk.
At the same time, the organisation had a clear strategic ambition. Rather than experimenting with AI in isolation, it wanted to understand how AI could be introduced into real delivery environments in a way that created measurable value. The question was not whether to use AI, but where it should be applied across the software development lifecycle to accelerate outcomes without compromising quality, governance, or control.
To explore this, the organisation partnered with BBD to take on a high-value, real-world engineering problem and reimagine how it could be delivered within a complex, tightly governed environment.
Objectives
- Explore how AI can be applied across the software development lifecycle to deliver measurable value
- Accelerate delivery timelines for a critical enterprise service
- Maintain enterprise-grade standards across architecture, integration, testing and governance
- Establish reusable patterns for future AI-enabled delivery
- Build a scalable, consumable service that could be used across the organisation
Benefits
- Rapidly designed, built and delivered a reusable, enterprise-grade service to support real-time account verification
- Built core components in one month using AI across the delivery lifecycle
- Significant acceleration in early-stage delivery without compromising quality
- Reduced manual effort across analysis, design, development and testing
- Improved collaboration through focused, cross-functional delivery practices
- Reusable AI-enabled delivery patterns and templates for future initiatives
- A scalable, enterprise-ready service capable of supporting broader business use
- A clearer understanding of where AI adds value, and where human expertise remains critical
The opportunity
The organisation had identified a critical service gap within its payments ecosystem, requiring the development of a real-time account verification capability. Traditionally, delivering a service of this nature would take several months, often slowed by fragmented collaboration, lengthy requirement translation and iterative design cycles.
Rather than follow the conventional path, this initiative became an opportunity to test a different approach: applying AI not just at the point of code generation, but across the entire software development lifecycle.
The goal was to move beyond isolated experimentation and answer a more meaningful question: Where does AI genuinely accelerate delivery, and where does it require human oversight to ensure enterprise-grade outcomes?
Approach
BBD worked closely with the organisation to design a delivery model that combined AI acceleration with structured, human-led control.
AI was introduced across multiple stages of the lifecycle, including translating extensive business requirements into concise, actionable technical specifications, supporting architectural design and artefact generation, accelerating development through AI-assisted code generation, enabling faster test planning and automation script creation, and streamlining delivery workflows and backlog management.
To support this, the team adopted an intensive, collaborative delivery model known as JAM sessions. Instead of fragmented meetings and iterative handovers, cross-functional stakeholders across business, product, engineering, architecture and testing were brought together in consecutive, focused, multi-day sessions.
Within these sessions, requirements were unpacked, refined and translated in real time, AI outputs were continuously reviewed and improved, prompting strategies were iterated to improve accuracy, and multiple disciplines worked in parallel rather than sequentially. This significantly reduced delays caused by misalignment and rework, while ensuring that AI outputs were guided by real expertise.
A key enabler was the structured use of prompt engineering, led by experienced practitioners across disciplines. Rather than expecting perfect outputs from the outset, the team applied domain expertise to frame, challenge and refine AI responses.
BBD’s cross-functional team brought together deep technical and domain expertise at each stage of the process. Business analysts translated complex requirements into meaningful inputs. Architects ensured design decisions aligned with enterprise patterns and future scale. Engineers validated feasibility and implementation quality, while test specialists identified edge cases and failure scenarios early, improving overall solution robustness.
Early outputs were often incomplete or overly generic. Through continuous refinement and expert input, quality improved significantly, enabling AI to become a meaningful accelerator rather than a constraint.
Overview of the solution
Using this AI-enabled delivery model, the team designed and built a reusable, enterprise-grade service to support real-time account verification. The service was developed as a set of modular, consumable components, enabling integration across the organisation’s broader ecosystem. It was designed to process verification requests in real time, ingest relevant industry data, and return structured, reliable responses to downstream systems.
While AI played a significant role in accelerating the creation of artefacts and code, the solution itself was engineered using established enterprise patterns to ensure scalability, reliability and secure integration within an existing, complex estate. Clear separation of concerns across services and interfaces ensured that the solution could evolve over time without introducing unnecessary risk.
Importantly, the service was built for reuse. Other teams within the organisation are able to consume the capability without needing to rebuild core functionality, supporting consistency and reducing duplication across the business.
Measured delivery and enterprise reality
One of the most significant outcomes of the initiative was the compression of early delivery timelines.
Core components of the service were designed and built within approximately one month, with much of the foundational work completed in just a few weeks. This represented a substantial improvement compared to traditional timelines.
However, the journey to production remained shaped by the realities of enterprise delivery.
In complex financial environments, delivery is influenced by more than just engineering effort. Integration across established systems, the need for traceability, and the expectations of operating within a tightly governed landscape all play a role in how solutions move from concept to production.
Integration into existing systems, operational readiness, governance processes and production hardening extended the full timeline beyond the initial build phase.
This reinforced an important principle. While AI can accelerate how quickly solutions are created, it does not remove the complexity of delivering them safely within a governed, interconnected environment. The final stages of delivery still depend on experience, coordination and deep system understanding, areas where human expertise remains essential.
Why it matters
This initiative demonstrated that the real value of AI in enterprise engineering lies not in replacing established practices, but in enhancing them in the right places.
Applied effectively, AI can accelerate the creation of foundational artefacts, reduce manual effort across the delivery lifecycle, and improve consistency in how work is executed. But it does not replace the need for experience.
The quality of outcomes remained directly linked to the people guiding the process. Domain expertise, architectural thinking, and the ability to interpret and challenge outputs were critical in turning AI-generated content into production-ready solutions.
The most effective model was not AI-led delivery, but human-led delivery, accelerated by AI.
Technology highlights
- AI-assisted development using tools such as GitHub Copilot for code generation and refinement
- Generative AI platforms supporting requirements analysis, design artefacts and documentation
- Structured prompt engineering approaches to improve output quality and consistency
- Modular service design enabling reuse across multiple business areas
- Integration with enterprise systems and external industry services
- Scalable architecture designed to support increasing transaction volumes and enterprise demand
Impact of BBD’s partnership
BBD helped the organisation move beyond theoretical AI experimentation into a real, high-impact delivery context.
By combining AI-enabled acceleration with enterprise engineering discipline, the team was able to significantly reduce early-stage delivery timelines while maintaining the standards required for production environments, crucially reinforcing the role of experienced practitioners in shaping how AI is applied, ensuring that speed gains translate into real, sustainable value.
More importantly, the engagement established a repeatable model for future delivery. The patterns, prompting strategies and collaboration approaches developed during this initiative have already been applied to subsequent projects, enabling faster and more efficient delivery across the organisation.
Rather than simply delivering a single service, BBD helped define how AI can be applied responsibly and effectively within enterprise software engineering, turning experimentation into a practical, scalable capability.
