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.

My task was to define and execute an enterprise AI/ML strategy that would modernise operations, automate high-impact workflows, and demonstrate measurable business value. This required aligning C-suite stakeholders, building capability, selecting the right technology platform, identifying credible use cases, and ensuring secure, compliant deployment.

• Conducted a discovery exercise with operations, data and customer teams to identify high-value use cases and prioritised them based on impact, feasibility and risk.

• Designed an end-to-end enterprise AI/ML strategy including architecture, governance, data pipelines and MLOps for secure deployment.

• Built a cross-functional team of data engineers, ML engineers and business SMEs, while coaching existing staff to increase internal capability.

• Delivered a repeatable framework for model training, deployment, monitoring and drift control to ensure reliability and ongoing performance.

• Partnered with Risk and Security to ensure all models met regulatory and data privacy requirements.

• Delivered 15 AI/ML solutions into live production within 12 months.

• Reduced manual processing effort by 40%, saving thousands of operational hours.

• Reduced error rates by 60%, improving customer accuracy and regulatory confidence.

• Enabled real-time insight and decision-making for 5,000+ users across the business.

• Positioned AI/ML as a trusted capability, not a one-off experiment — leading to further investment and expansion.

• BI & Analytics

• Financial Services