Becoming an AI-driven bank requires a multi-front strategy
Figure 2. Key pillars underpinning the shift to AI-driven banking
Technology & operating model
1
Foundation: Build once scale everywhere
Unified, model-agnostic orchestration layer
Real-time, unified data platforms
Reusable, cloud-native component library
Self optimising and scalable infrastructure
2
From pilots to production
AI as core operating mode, autonomous core functions
Structured, repeatable decisions prioritised first
Central governance + embedded business-line squads
Target AI autonomy in decision making
Governance & regulation
3
Govern by design, not exception
Build governance and oversight in the architecture from start
Kill-switch / revocable permissions by design
Independent model validation & audit trails
Human–AI and AI-AI monitoring at scale
4
Design for most demanding regulations
Design for most demanding applicable requirements
Board-level accountability for high-risk systems
Customer disclosure for AI-driven decisions
People, value & trust
5
Workforce & process: Redesign the work
Organisational redesign, not a tool rollout
Daily, job-embedded upskilling and reskilling programmes
Redesign for AI driven processes
6
Impact analysis: Prove value, not just velocity
Finance-owned ROI and cost-structure measurement
KPI framework beyond productivity gains
Board reporting that separates speed from value
7
Build trust for agent customers
Delegation and consent frameworks for human-to-agent authority
Agentic payments and settlement infrastructure
Cross-institution interoperability and liability framework for agents
Source:
TABInsights