Liv Bank · Q3_2026_Liv_1

v1 · submitted · Half-Day Executive · Digital Challengers & Neobanks

AiRi Composite Score

0.00/ 5.0

Piloting

Liv Bank

Half-Day Executive · Digital Challengers & Neobanks

44/44 metrics scored

Strengths

Data3.57
Strategy3.55
Talent3.05

Priority gaps

Payments1.10
Infra1.90
Change2.00

Pilots Are Running, but Value Is Leaking

Your institution has active AI pilots and some early wins. But these efforts are fragmented — each pilot has its own data pipeline, its own deployment process, and its own definition of 'success.' Industry data confirms this pattern: 82% of banks report 'positive ROI' from AI, but only 38% can provide specific financial metrics when pressed by stakeholders. That gap is where credibility erodes — with the board, with regulators, and with the business line leaders whose buy-in you need to scale.

Domain maturity radar

hover for insight

Gap-to-target (lowest first)

What it means

ROI was estimated at project kickoff but nobody tracked whether it materialized. Leadership hears 'we saved 2,000 hours' but nobody can tell the CFO whether that translated into headcount reduction, throughput increase, or margin improvement. Hours saved is not value captured. The data science team is stretched across multiple pilots, delivering none at production quality. Data infrastructure supports the pilot use case but breaks when a second domain is added. There is a growing 'pilot zoo' that consumes budget without scaling.

Next steps

Implement standard value measurement with banking-specific baselines: false positive rate before/after, approval rates before/after, cost-per-transaction, time-to-decision, and financial attribution for each. Invest in MLOps foundations: standardized pipelines, model registry, and API-first integration. Consolidate the portfolio — kill low-impact pilots and redirect to 2-3 high-value initiatives with clear production paths. A bank with 3 models in production is more mature than a bank with 15 pilots.

Domain breakdown

Domain	Maturity	vs. composite	Coverage	Weight
AI Strategy & Governance	3.55	+0.93	8/8	9.0
Data & Foundation Architecture	3.57	+0.95	4/4	10.0
AI Talent & Capability Building	3.05	+0.43	4/4	7.0
Model Lifecycle & MLOps	2.85	+0.23	4/4	9.0
Responsible AI & Ethics	2.67	+0.05	4/4	10.0
AI Value Measurement & ROI	3.00	+0.38	4/4	9.0
Change Management & Adoption	2.00	-0.62	4/4	5.0
Enterprise AI Infrastructure	1.90	-0.72	4/4	10.0
Customer Experience & Engagement	2.27	-0.35	4/4	12.0
Payments Intelligence	1.10	-1.52	4/4	8.0

Sutherland recommendations

AI Strategy & Governance

Advanced

Enterprise AI Operating Model

Advanced governance. Industry leadership potential. Scale through ecosystem.

Approach: Design AI-first operating model with innovation partnerships

Innovation PartnershipOngoingProof: DBS Bank AI strategy driving S$370M value

Data & Foundation Architecture

Advanced

Data Mesh Architecture

Industry-leading data architecture. Self-service analytics. Autonomous governance.

Approach: Implement data mesh with domain-driven ownership model

Innovation PartnershipOngoingProof: DBS Bank data architecture enabling 800+ models

AI Talent & Capability Building

Operationalized

Center of Excellence (CoE)

Solid AI team with defined structure. Scale through CoE model.

Approach: Build dedicated AI CoE with specialized roles and governance

Transformation Program10-14 weeksProof: Microsoft AI CoE structure

Model Lifecycle & MLOps

Operationalized

Mature ML Platform

Operational monitoring. <2-week deployment cycles. Self-healing basics.

Approach: Deploy automated retraining, A/B testing, monitoring at scale

Scaling Program12-16 weeksProof: Netflix ML platform architecture

Responsible AI & Ethics

Operationalized

Continuous Bias Monitoring

Solid responsible AI program. Operationalize continuous monitoring and governance.

Approach: Deploy continuous bias monitoring and automated governance at scale

Transformation Program10-14 weeksProof: Microsoft Responsible AI Practices

AI Value Measurement & ROI

Operationalized

AI FinOps Program

Systematic value measurement. Focus on cost optimization and portfolio management.

Approach: Implement AI FinOps for cost per inference and portfolio optimization

Scaling Program8-12 weeksProof: Google Cloud AI FinOps practices

Change Management & Adoption

Developing

AI Adoption Program

Structured change emerging. 76% adopt AI with training vs 25% without (S&P Global).

Approach: Launch AI adoption program with champion networks and incentives

Training & Capability10-12 weeksProof: McKinsey Change Management Framework

Enterprise AI Infrastructure

Developing

Basic AI Infrastructure

Basic AI infrastructure exists. Provisioning slow and reactive. No FinOps.

Approach: Deploy cloud AI services with automated provisioning and basic monitoring

Digital Engineering10-12 weeksProof: Azure AI infrastructure services