The challenge
The organisation’s core processing platform had supported the business for many years, but the environment around it had changed significantly. Data volumes had grown, the operating model had become more demanding, and the existing estate was increasingly constrained by long-running batch processing, fragmented data flows, limited observability and a heavy reliance on manual intervention. The broader architecture also carried maintainability, lifecycle and scalability concerns that made change harder and increased operational risk over time.
This is a familiar problem for many complex organisations. There was clear appetite to improve automation and explore AI, but the underlying systems and data foundations were not yet in the right shape to support that safely or at scale. The organisation needed to avoid jumping straight to isolated AI ideas and instead create a credible transformation path that reduced risk, improved clarity and established the right long-term foundations first. That is closely aligned with the Mavents approach of starting with business outcomes, reducing complexity and helping clients move from questions to clarity, and from clarity to impact.
What Mavents was asked to do
Mavents supported the organisation in turning a broad need for modernisation into a clearer and more actionable transformation direction. This included discovery and assessment of the current estate, shaping the core transformation strategy and roadmap, establishing architectural principles and governance, and leading key elements of the target solution architecture and non-functional design. It also included advising on where automation and AI could create value in a practical and sustainable way, while helping leadership keep those ambitions grounded in the realities of legacy complexity, delivery sequencing and data readiness.
Alongside this, Mavents contributed to the data and platform foundations needed to move the programme forward. That included conceptual data modelling, glossary development, proposals across metadata, lineage, data quality, observability and master data, support for analytics direction, and architectural input into related integration and CRM considerations. The work also helped prepare the organisation for formal supplier engagement and implementation planning.
Our approach
The first step was to create clarity. We helped document the current estate and its main structural issues in a balanced, practical way, without treating the existing platform as a failure. The focus was on understanding what now limited growth, transparency and adaptability, and then translating that into a more credible case for change. In practice, that meant moving the conversation away from piecemeal enhancement and towards a more deliberate redesign of the core processing model.
From there, we helped shape the future direction. Strategically, that meant playing a lead role in defining the roadmap and helping stakeholders align on the need for structural transformation rather than a simple upgrade. Architecturally, it meant leading key design activities to define a more modular, scalable target direction with stronger observability, clearer control points and a better foundation for staged processing, automation and long-term maintainability. The agreed cloud direction aligned to Microsoft Azure, with the target state focused on consolidating core transactional processing onto that platform.
Just as importantly, we helped put stronger governance around the change. A design authority was established to support architectural decision-making and ensure the programme progressed with clearer standards, principles and trade-offs. This gave the organisation a more structured way to move from strategy into design and procurement, while reducing the risk of fragmented decisions or short-term fixes shaping the future platform.
Where AI and automation fit
This is primarily an AI readiness story, supported by targeted proof of concepts rather than full-scale AI deployment.
The target direction was not to force AI into the platform from day one. Instead, the focus was on designing a more modern processing and data environment that could support automation, analytics and future AI use cases in a controlled way. The future vision explicitly included embedded intelligence, automation and workflow capability, supported by a governed and scalable architecture for future machine learning and advanced analytics.
The opportunity areas identified included advanced usage matching, matching quality assurance, anomaly detection, automated data cleansing and transformation, predictive analytics, natural language access to insight, AI-powered knowledge support, and staff or member-facing conversational assistance. The important point was sequencing: modernise the core, improve data and operational control, and create the conditions for AI to add value more safely and effectively later.
Alongside this, a small number of targeted AI proof of concepts were undertaken using Azure-based AI services. These focused on validating specific use cases such as matching optimisation, knowledge access and operational support. The aim was not to scale AI immediately, but to test feasibility, understand value and inform the longer-term roadmap in a controlled and low-risk way.
Outcomes / progress so far
The full transformation is still ahead, but the work has already created meaningful value.
- A clearer and more credible transformation direction for a significant multi-year programme
- Stronger architectural governance and better alignment on key design decisions
- A more modern target direction centred on scalability, observability and modularity
- Stronger data foundations through modelling, glossary work and proposals for quality, lineage and governance
- A more realistic and prioritised view of where automation and AI can deliver value
- Better readiness for supplier engagement, detailed design and implementation planning
- Early validation of key AI use cases through targeted proof of concepts, helping shape a more practical and prioritised roadmap
Why this matters
Many organisations want the benefits of AI and automation, but their core systems are still too fragmented, opaque or batch-bound to support that properly. This work shows a more practical route. Start by clarifying the case for change. Establish the right architecture, governance and data direction. Build the foundation in a way that reduces risk and supports measurable progress. Then scale automation and AI from a stronger position. That is exactly the kind of practical, outcome-focused work Mavents is built around.
