SutherlandAıRIAI Realization Index
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Citi · Q2 2026

v1·in_progress·Full-Day Workshop·Tier-1 Global/National Universal Banks·Banking

AiRi Composite Score

0.00/ 5.0

Piloting

Citi
Full-Day Workshop · Tier-1 Global/National Universal Banks
8/64 metrics scored
Strengths
  • Strategy2.45
Priority gaps
  • Strategy2.45

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

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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

DomainMaturityvs. compositeCoverageWeight
AI Strategy & Governance2.45+0.008/882.0
Data & Foundation ArchitectureN/A—0/40.0
AI Talent & Capability BuildingN/A—0/80.0
Model Lifecycle & MLOpsN/A—0/40.0
Responsible AI & EthicsN/A—0/40.0
AI Value Measurement & ROIN/A—0/80.0
Change Management & AdoptionN/A—0/80.0
Enterprise AI InfrastructureN/A—0/40.0
Lending & Credit IntelligenceSpecialtyN/A—0/80.0
Payments IntelligenceSpecialtyN/A—0/80.0
Calibrated profile
Banking · Tier-1 Global/National Universal Banks

This score is not a generic benchmark. Every domain weight, every rubric threshold, and every scoring band is tuned specifically to how Tier-1 Global/National Universal Banks organisations compete and operate in Banking.

2.45
Piloting
How your responses score under other segment lenses
Super-Regional Banks
2.45
0.00 vs your profile
Piloting
Community Banks & Credit Unions
2.45
0.00 vs your profile
Piloting
Digital Challengers & Neobanks
2.45
0.00 vs your profile
Piloting
Specialists & Wealth/Private Banks
2.45
0.00 vs your profile
Piloting
Capital Markets & Investment Banks
2.45
0.00 vs your profile
Piloting
Enablers & Banking Infrastructure
2.45
0.00 vs your profile
Piloting

Nearest peer lens: Your AI posture most closely resembles a Super-Regional Banks profile (Δ0.00 when domain weights are re-calibrated for that segment). This is a signal — not a prescription.

Sutherland recommendations

AI Strategy & Governance
Developing
Portfolio Prioritization Framework

Strategy exists but execution gaps. Limited board visibility. Portfolio management is informal.

Approach: Design AI governance structure with portfolio management discipline

Consulting Engagement8-12 weeksProof: JPMorgan AI strategy and governance model