Royal London operated multiple siloed data sources across pensions, savings and operations. Reporting was manual, inconsistent and difficult to trust. Leaders across Finance, Operations and Risk struggled to get timely insight, affecting forecasting, regulatory reporting, fraud monitoring and customer performance analytics. The Board wanted to shift the business toward evidence-led decision making, but lacked a...

Across global payment channels, fraud and anomaly cases were rising in volume and complexity. Existing controls were rule-based, reactive, and created false positives that slowed customer transactions and increased manual investigation workload. Fraud losses and operational cost were a Board-level concern, and regulators demanded improved controls, auditability and faster intervention.

Engineering productivity across global teams was being held back by fragmented tooling, inconsistent documentation, slow onboarding, and unclear ownership of APIs and services. New engineers took weeks to become productive, and support teams were overwhelmed with repeated questions. Leadership wanted transformation without adding more layers of process — something scalable, self-service, and trusted across regions.

Several critical platforms were heavily dependent on third-party vendors for development, support and change delivery. Release cycles were slow, costs were high, and knowledge lived outside the organisation. When issues occurred, resolution required vendor intervention, causing delays and frustration for product and customer teams. Senior leadership wanted lower spend, faster delivery, more reliability, and internal...

Core customer and enterprise platforms were running across fragmented, aging on-prem infrastructure with limited observability and inconsistent failover. Change windows were long, recovery was manual, and capacity planning was guesswork. The business wanted better availability during peak cycles, stronger business continuity, and lower total cost of ownership—without disrupting regulatory obligations or daily operations.

Royal London relied heavily on manual processes for operational checks, data analysis, and exception handling. These manual steps created delays, operational cost, higher error probability, and inconsistency in customer and internal outcomes. There was interest in using AI/ML, but no enterprise strategy, no prioritised use cases, no platform for adoption, and limited internal capability.