Financial services · AI strategy
Scaling AI beyond experimentation
Challenge. AI initiatives were emerging as fragmented, one-off solutions across lines of business.
Approach. Shaped an enterprise AI direction around reusable capabilities and agents embedded in core business journeys; established an AI Citizen approach and AI Labs with guardrails for responsible autonomy.
Outcome. An investment case that helped secure dedicated funding for enterprise AI and agentic foundations, and business teams able to experiment safely.
Banking · Data & predictive AI
From data hub to measurable business value
Challenge. Analytics teams lacked shared, trusted data, and AI use cases stalled before production.
Approach. Core team member defining an enterprise Data Hub: platform strategy, streaming and integration patterns, and prioritized high-value use cases.
Outcome. Data democratized across lines of business; a predictive retention model in wealth management enabled proactive intervention and protected significant business value.
Airline loyalty · Real-time data
Real-time customer intelligence
Challenge. Batch analytics couldn't support contextual, in-the-moment decisions during a major loyalty program integration.
Approach. Led the data & analytics target architecture and designed a near-real-time data and AI platform for streaming loyalty and flight data.
Outcome. Event-driven decisioning, including next-best-offer recommendations, and a clear modernization path for data, analytics and AI.
Digital banking · Business architecture
Capability-led platform modernization
Challenge. Multiple delivery squads with unclear boundaries, overlapping investments and a multi-year roadmap to align.
Approach. Introduced BIAN and domain-driven design to define business capability boundaries and connect priorities to systems and execution.
Outcome. Clearer product and squad ownership, an aligned investment roadmap, and a stronger product operating model.
