Checklist for Banking Leaders: Building a Future-Ready and Future-Proof Gen AI Strategy

Generative AI has become a key part of the discussions on where a financial institution aims to go. Most banks are exploring potential use cases of the technology. McKinsey estimated that Gen AI could add $200 billion and $340 billion in value annually. This technology has great potential, but it needs more than just a launch plan.

Gen AI integration in banking needs a proper strategy. It needs a checklist that lays out the end-to-end steps to define, build, govern, and scale your Gen AI initiatives so they deliver sustained value.

But while many organizations have launched pilot projects, only a few have succeeded in embedding Gen AI deeply into their DNA. Success comes not just from deploying models quickly, but from building resilience so those models continue to generate value as technology, regulations, and market dynamics evolve.

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Future Ready vs Future Proof

While future-ready and future-proof sound similar, they both address different needs.

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A successful Gen AI strategy in banking needs to be both. The competition landscape and evolving fraud and customer expectations demand ready to deploy AI solutions in the present and resiliency as time passes.

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The Six Pillars of a Future-Ready, Future-Proof Gen AI Strategy

We distill the consideration of a successful Gen AI strategy into six pillars:

Pillar 1: Data & Infrastructure

Gen AI models thrive on data and computing power. Without the qualitative and quantitative aspects of data infrastructure, even the best ideas will falter. If there is a dearth of data, the domain specificity will suffer. And if the quality is bad, the models will produce flawed and biased output, which can then lead to compliance issues.

Checklist for Data & Infrastructure:

• Data Quality & Accessibility: Audit the quality, cleanliness, and bias in your data.
• Data Governance & Security: Establish policies for data privacy and usage and ensure compliance with regulations like GDPR.
• Scalable IT Infrastructure: Assess your compute and storage needs for AI, as scalability is key.
• Cloud vs. On-Prem Strategy: Decide where to host Gen AI solutions.  

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Pillar 2: Use-Case Prioritization

This pillar is about strategic focus. Banks should identify potential use cases and align GenAI adoption with broader business goals.

Checklist – Use-Case Prioritization:

• Strategic Alignment: For each Gen AI idea, ask if it solves a real business problem or enhances a key capability.
• Impact vs. Feasibility Analysis: Rank use cases by value and viability. Quick-win projects with moderate value but high feasibility build momentum early.
• Risk & Compliance Assessment: Consider the regulatory or ethical sensitivity of each use case. Using Gen AI to generate reports may be low risk but it poses high compliance risks in credit underwriting.
• Proof-of-Concept (PoC) First: Before full rollout, conduct PoC or pilot projects for your top use cases. If deploying a Gen AI chatbot, pilot it with internal employees or a small customer segment to gather feedback and measure results.
• Defined Success Criteria: For each use case, define what success looks like up front. Is it a certain accuracy level from the AI model, a cost reduction, faster turnaround time, or user satisfaction increase? Set measurable targets.

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Pillar 3: Technology Stack & Architecture

Under the hood, a Gen AI strategy is enabled by your technology choices: the AI models, software tools, and overall architecture that glues everything together. This pillar focuses on how you will build or source the Gen AI capabilities.

Checklist – Technology Stack & Architecture

• Model Strategy (Build vs Buy): Evaluate which AI models you will use for each use case. Choose a model approach that fits your expertise, data privacy requirements, and cost structure.
• Architecture Design: Design an AI architecture that is modular and scalable. This might include an AI middleware or platform layer that handles requests between your applications and the Gen AI model.
• Infrastructure & Tools Selection: Pick the right tools for developing and serving Gen AI. Decide on an AI development platform or library (such as TensorFlow, PyTorch, HuggingFace) if doing custom model work. If you plan fine-tuning, ensure you have tools for data labeling and experiment tracking.
• Integration & APIs: Establish a robust API and integration framework. Your Gen AI services should expose APIs (securely) that internal systems or customer-facing channels can call.
• Security & Access Controls: From an architecture standpoint, embed security by design.Protect data in transit to the model (encryption, VPN for cloud API calls).
• Scalability & Performance Tuning: Test how the AI stack performs under load. If 1,000 customers ask your GenAI chatbot questions simultaneously, can your architecture handle it with acceptable response times?

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Pillar 4: Governance, Compliance & Ethics

In the rush to deploy Gen AI, banks cannot afford to ignore governance and ethics. This pillar is about putting guardrails around Gen AI initiatives to ensure AI is used in line with the bank’s risk appetite.

Checklist – Governance, Compliance & Ethics

• Governance Structure: Establish clear ownership and oversight for AI at your bank. This could mean setting up an AI governance committee or task force that includes stakeholders from Risk, Compliance, Legal, IT, and business units.
• Regulatory Compliance Mapping: Map out applicable regulations and guidelines for AI in your jurisdictions. Have a process to engage with regulators proactively if needed (some banks share their AI use cases with regulators to seek feedback).
• Risk Management & Controls: Implement controls to mitigate AI-specific risks. This includes testing for bias in AI outputs and ensuring the AI isn’t discriminating against protected groups. Put in place human-in-the-loop checkpoints for high-stakes use cases.
• Ethical AI Guidelines: Articulate an ethical framework for AI consistent with your bank’s values. This might cover principles like fairness, accountability, transparency, and explainability.
• Privacy and Security Compliance: Augment your existing data privacy program for the context of Gen AI. This means ensuring any personal data used in AI models has appropriate customer consent and is handled according to privacy laws.
• Monitoring & Incident Response: Treat your Gen AI systems like other critical processes that need monitoring and incident management.

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Pillar 5: Talent & Organizational Alignment

AI is only as good as the people who are using it. As one study put it, AI success depends more on people’s adoption than on the technology itself.

This pillar is about preparing your workforce and organization for Gen AI. It could mean building or acquiring the right talent, training existing employees, and shaping a culture that embraces AI.

Checklist – Talent & Organizational Alignment

• AI Skills Inventory & Hiring: Evaluate what skills you have in-house and what’s missing. Do you have data scientists or machine learning engineers familiar with NLP and generative models? What about ML Ops engineers to deploy and maintain models?
• Training & Upskilling Programs: At the same time, invest in upskilling your existing workforce. Not everyone needs to be a data scientist, but broad AI literacy is important. Provide training workshops or online courses for managers and employees on Gen AI.
• Cross-Functional Teams: Break down silos between business units, IT, and data science. Gen AI projects often require a multidisciplinary team – e.g., to build an AI customer service agent, you need IT folks for integration, data scientists for the model, and customer experience experts to script conversations.

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Pillar 6: Ecosystem Partnerships

This pillar encourages leveraging the AI ecosystem, so whether that includes big tech companies or an Enterprise AI technology partner.

Checklist – Ecosystem Partnerships

• Cloud & Tech Providers: Develop strategic relationships with an AI platform provider and ensure you conduct due diligence.
• Fintech and Startup Partnerships: Scan the fintech landscape for startups building AI solutions relevant to banking. This could be a company that generates synthetic data for testing models, an AI-driven fraud detection service, or a specialized NLP engine for compliance documents.
• Consulting and AI Vendors: Engage with consulting firms or AI software vendors for expertise and speed. Firms that you can leverage to shape your strategy quickly.
• Regulatory Engagement: Treat regulators as partners in innovation where possible. Engage with regulators through innovation offices or tech sprints.

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A Three-Phase Plan for Gen AI Adoption

With these strategy pillars in mind, we can now create a phased plan to integrate Gen AI in a structured way.

Phase 1: Discovery & Pilots

In this initial phase, the goal is to build foundational understanding, get quick learnings from pilots, and set the direction.

Key activities in Phase 1:

• Build Knowledge & Awareness: Educate the leadership and key stakeholders on Gen AI capabilities and trends.
• Define Vision and Objectives: Formulate a clear vision for “AI in our bank”. Set high-level objectives.
• Identify and Prioritize Use Cases: Brainstorm a list of Gen AI use cases across different business lines (retail banking, wealth management, operations, risk, etc.).
• Conduct Feasibility Analysis: For the top pilot ideas, do a rapid feasibility check – do we have the data required? What model or tool could we use? The aim is to uncover showstoppers early.
• Set Up Pilot Infrastructure: Prepare the sandbox or environment for pilots. This might involve getting API access to a Gen AI model, provisioning a cloud environment, or gathering a dataset.
• Execute Pilot Projects: Develop and deploy the pilot solutions on a small scale. If it’s a chatbot, maybe roll it out to employees first or a small customer group.
• Validate Value and Iterate: After a pilot runs for a short period, evaluate it. Did it meet the success criteria defined?  Gather qualitative feedback from users, and use this feedback to iterate on the pilot.
• Capture Lessons & Plan Next Steps: Document the findings from each pilot – technical performance, user acceptance, impact achieved, and challenges encountered.

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Phase 2: Scale & Integrate

Phase 2 is about taking the successful concepts from Phase 1 and scaling them up to enterprise-grade deployments.

Key activities in Phase 2:

• Full-Scale Implementation of Use Cases: Take each pilot that you green-lit and build it out for production use. This means hardening the solution for reliability, security, and performance.
• Enterprise Integration: Integrate the Gen AI solutions deeply into business processes. If it’s customer-facing, integrate with all relevant channels. If it’s an internal tool, embed it into the workflow.
• Strengthen Data & Infrastructure for Scale: Reassess your data and IT infrastructure now that you’re moving from pilots to scale. You may need to ramp up capacity, or optimize databases.
• Governance Rollout: Operationalize the AI governance framework in tandem with the technical rollout. At this stage, formalize policies: require every Gen AI solution to go through a review by the AI governance committee (
• Employee Training and Change Management: Now that solutions are going live, broad user groups may be affected. Conduct training sessions for end-users (employees or customers) on how to use the new AI features.
• Monitor Performance & Benefits: As the Gen AI solutions run in production, closely monitor the success metrics defined earlier. Are we seeing the expected reduction in processing time? Is customer satisfaction improving for the AI-assisted service?
• Iterate and Expand: Based on performance and feedback, refine the implementations. Maybe you need to fine-tune the model further, or adjust how the AI interacts with users. Continue an agile approach even in scale – push out improvements in sprints.

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Phase 3: Optimize & Future-Proof

This phase is an ongoing phase focused on optimization, continuous improvement, and future-proofing the Gen AI strategy.

Key activities in Phase 3:

• Performance Optimization: Continuously improve the AI models and processes.
• Scale Successful Use Cases Further: Take the use cases that have proven most valuable and consider expanding their scope.
• Continuous Monitoring & Governance: Maintain rigorous monitoring of AI systems, but now with a mature process.
• Measure and Communicate Success: Continuously track the success metrics and make results transparent.
• Future-Proofing – Stay Ahead of the Curve: Technology will not stand still. Phase 3 is where you ensure your strategy adapts to new developments.

Conclusion

The opportunity presented by generative AI in banking is enormous. From hyper-personalized customer experiences to streamlined operations, Gen AI will unlock great potential for banks. Its implementation will be key to how to avoid any hindrances.

At Arya.ai, we have built enterprise-grade AI solutions that are ideal for banks and financial institutions where balancing security with innovation is important. Connect with us to discuss how we can help.