Generative AI (GenAI) has moved from curiosity to commitment across global banking. With projected impact of $200 – 340 billion in annual value for the sector, boards and executive teams are accelerating efforts to test and deploy the technology.
However, many institutions remain stuck in the early stages of implementation – running limited proof-of-concepts, struggling to demonstrate value, and navigating unclear governance.
Our research across leading banks in North America, Europe, and the Middle East reveals a clear pattern: the most successful deployments begin with narrow, operational use cases that solve real problems, are grounded in measurable outcomes, and are governed by a structured feedback loop.
This blog highlights four high-impact GenAI experiments currently underway, how financial institutions are measuring success, and what separates scalable pilots from stalled ones.
The Context: AI Strategy Is No Longer Optional
Three forces are converging to create a tipping point in GenAI adoption:
- Executive expectation is rising: According to McKinsey’s 2024 global banking survey, 68% of financial institutions have made GenAI a board-level priority – yet only 14% report measurable business value to date.
- Vendor pressure is increasing: Technology providers are integrating GenAI into platforms by default, making it harder to delay experimentation without risking obsolescence.
- Regulatory tone is shifting: Supervisors are increasingly calling for clarity on model governance, explainability, and usage policies – particularly for customer-facing or decision-support applications.
Against this backdrop, institutions must find a path to move from “testing” to “traction”
What Leading Banks Are Doing Differently
Among institutions showing progress, we observe five shared principles:
| Principle | Description |
|---|---|
| Use-case led | Start with an operational pain point, not a technology push |
| Data-responsible | Experiments run on production-like data - often using synthetic datasets to simulate real-world conditions while preserving privacy and compliance. |
| Human-in-loop | Design for oversight, not full autonomy |
| Metrics-aligned | Tie success to business KPIs, not model performance |
| Scale-aware | Structure PoCs to anticipate scale - with integration paths, security controls, and governance requirements built in from the outset. |
Among institutions showing progress, we observe five shared principles:
Four GenAI Experiments Banks Are Running Today
Each solving a real operational challenge with measurable outcomes
1. Conversational AI: Instant Answers for Users
- The challenge: Traditional interfaces slow down access to insight. Whether it’s a relationship manager retrieving product info or a customer trying to understand terms, the journey is often clunky and multi-step.
- The experiment: Test natural language tools that provide contextual answers in real time - from policy lookups to account queries - using internal knowledge bases and structured data.
- Strategic value: Improves customer experience, reduces training burden, and increases internal productivity by turning fragmented content into a unified conversational layer.
2. Contract Summarisation: Automating Legal Review
- The challenge: Reviewing contracts manually is time-consuming and error-prone - creating backlogs, compliance risk, and inconsistent interpretation.
- The experiment: Evaluate GenAI tools that extract key terms, obligations, risks, and anomalies from legal documents - enabling faster review and escalation where needed.
- Strategic value: Accelerates time-to-signature, improves consistency, and reduces risk exposure in lending, procurement, and onboarding workflows.
3. Cloud Service Provider Evaluation: Exploring AI Capabilities
- The challenge: Choosing the right cloud platform for AI workloads involves trade-offs across performance, cost, tooling, and regulatory posture - with no clear, low-risk testing path.
- The experiment: Run side-by-side PoCs across cloud providers using synthetic data and common workloads - evaluating infrastructure suitability under real-world constraints.
- Strategic value: Supports vendor selection with evidence, derisks long-term platform decisions, and ensures alignment with internal security and compliance frameworks.
4. Automated Software Engineering: Accelerating Product Delivery
- The challenge: Manual coding, testing, and documentation processes slow innovation, stretch developer capacity, and increase time-to-market for internal tools and digital products.
- The experiment: Test tools like Copilot to automate code generation, unit testing, refactoring, and documentation - especially on low-priority backlog or internal scripts.
- Strategic value: Boosts engineering efficiency, reduces technical debt, and frees up senior developers for high-impact work - without compromising code quality or security.
The Metrics That Matter
Executives evaluating GenAI pilots should focus on five categories of evidence:
| Metric | What it demonstrates |
|---|---|
| Time saved | Operational efficiency and productivity uplift |
| Quality of output | Consistency and reliability across teams. |
| User satisfaction | Adoption likelihood and perceived utility |
| Override frequency | Human correction and risk exposure levels |
| Auditability | Compliance readiness and governance traceability |
These metrics help cross-functional stakeholders – including Risk, Legal, and Finance – make informed decisions on scale-up readiness.
The Metrics That Matter
1. Prioritise operational value, not proof-of-concept volume.
A few well-run, tightly measured experiments are more valuable than a broad portfolio of exploratory projects.
2. Align experiments to real workflows and owned data.
Use cases with direct links to internal systems and processes outperform those reliant on new or ungoverned data inputs.
3. Involve control functions early.
Risk and compliance teams must co-own experimentation – especially where outputs affect customers, regulatory reporting, or financial decisions.
4. Build toward scale from day one.
Experiments should account for eventual requirements around integration, access control, audit, and deployment architecture.
Taking the Next Step
GenAI is no longer confined to the innovation lab. The banks realising early value are those starting with tightly scoped experiments, grounded in operational reality and governed from day one.
Whether you’re evaluating platforms, reducing cycle time, or improving frontline productivity, the path forward is clear: Define the problem, structure the experiment, measure what matters.
At NayaOne, we help financial institutions run secure, production-like PoCs – with real tools, governed data, and enterprise-grade controls – to validate GenAI solutions before making scale commitments.
Book a discovery session to explore which GenAI use case aligns best with your priorities – and how to move from insight to implementation in weeks, not quarters.
