How Can Banks Control Costs While Implementing GenAI Analytics?

Posted on: January 16th 2025

The rapid evolution of generative AI (GenAI) presents significant opportunities for banks to enhance operational efficiency, deepen customer engagement, and refine decision-making processes. However, successfully adopting GenAI-driven analytics comes with substantial costs and complexities. 

High Costs of GenAI Integration

As banks navigate the AI technological shift, they face cost hurdles spanning infrastructure upgrades, workforce development, compliance with regulations, and bolstering security against emerging threats. 

1. Infrastructure Investments: Deploying AI at scale requires robust computational power, cloud storage, and advanced data architecture. According to Accenture, GenAI has the potential to boost banking productivity by up to 30%, but these gains often necessitate significant upfront investment in infrastructure and data modernization initiatives.
2. Talent and Skill Gaps: Banks face hurdles in hiring or upskilling teams with the expertise required to integrate and manage AI systems effectively. Ongoing training amplifies these costs as teams navigate AI’s rapid advancements.
3. Compliance and Regulation: Integrating AI escalates compliance costs, especially in banking, where meeting stringent regulatory requirements is essential. GenAI’s opacity (its “black-box” nature) complicates efforts to ensure complete explainability, uphold ethical practices, and adhere to frameworks like GDPR.
4. Security Challenges: Banks face heightened risks of cyberattacks as AI systems handle sensitive data. Data security and system integrity require continuous investments in cybersecurity measures and robust governance frameworks.

Banks deploying smaller, targeted projects demonstrate higher success rates than those attempting large-scale overhauls.

Strategies to Manage Costs and Risks

Integrating GenAI analytics is costly and complex, yet it offers transformative benefits. Banks can convert these investments into sustainable competitive advantages by employing a phased, strategic approach and establishing strong governance.

1. Focus on Strategic Use Cases: Banks should begin with pilot projects that address specific, high-impact challenges to build momentum and validate ROI. Starting small ensures better alignment with business goals.
2. Collaborate with Ecosystem Partners: Outsourcing or partnering with AI providers can mitigate the need for costly in-house expertise while leveraging established platforms for scalability.
3. Ethical and Responsible AI Frameworks: Ensuring transparency and accountability in AI decision-making mitigates risks and builds trust among regulators and customers.
4. Scalability Planning: AI solutions must adapt to evolving technologies and regulatory landscapes while maintaining cost efficiency.

A proactive approach helps banks maximize returns while effectively managing costs and complexities.

Project Management Techniques for Controlling GenAI Integration Costs

Banks can employ several project management techniques to control the costs of integrating GenAI, ensuring efficient deployment while mitigating risks. Key methods include:

1. Agile Methodology

Banks can use agile techniques to break down AI integration into more minor, manageable phases or sprints. This approach allows for continuous feedback, quick adjustments, and early identification of challenges, reducing the risk of costly rework. 

Agile emphasizes delivering high-value components first, ensuring that early investments align with critical business objectives.

2. Lean Project Management

Lean principles help banks focus resources on essential activities that deliver direct value, avoiding unnecessary features or functionalities. It fosters an environment of regular evaluation and refinement, which can optimize both costs and outcomes throughout the project.

3. Cost-Benefit Analysis (CBA)

Conducting detailed CBAs at each project stage ensures resources are allocated only to initiatives with clear, measurable ROI. This disciplined approach helps avoid overspending on low-impact use cases.

4. Hybrid Project Management

Combining traditional waterfall methods with agile or lean practices enables banks to manage fixed-scope regulatory requirements while retaining flexibility for iterative AI development. This hybrid approach balances structured governance with adaptability.

5. Stakeholder Collaboration

Involving stakeholders from IT, risk, compliance, and business functions fosters alignment, reduces silos, and ensures project priorities meet operational and regulatory needs. 

Also, partnering with AI vendors or third-party providers allows banks to leverage existing expertise and tools, reducing the need for costly in-house development.

6. Rigorous Risk Management

Banks can proactively identify and mitigate potential cost overruns due to regulatory changes, cybersecurity threats, or technological challenges using risk management frameworks. This approach ensures that contingency plans are in place.

7. Milestone-Based Funding

Allocating budgets based on specific milestones or deliverables ensures banks deploy additional resources only after achieving tangible progress. This method minimizes the risk of sinking funds into underperforming projects.

By adopting these techniques, banks can integrate GenAI effectively while maintaining financial control and aligning with strategic objectives.

Straive helps banks integrate GenAI by building robust AI infrastructure, optimizing risk management, and enhancing investment and customer operations. Our services include AI deployment, model design, and seamless integration, ensuring smooth adoption across banking functions. 

Straive also provides training, monitoring, and continuous support to maintain AI systems, ensuring scalability and performance over time. This comprehensive approach enables banks to leverage GenAI for innovation, operational efficiency, and improved decision-making.

About the Author

Sudhakaran Jampala

Sudhakaran Jampala is a Content Writer (Marketing) with Straive, specializing in the cutting-edge technology areas of data science, machine learning, and AI. He is fascinated by the art of storytelling, which transforms data into sparkling insights by revealing patterns and infusing visual narratives.