In recent years, the banking industry has witnessed a significant shift towards adopting artificial intelligence (AI) technologies to enhance operations, improve customer service, and drive innovation.

When generative AI burst onto the scene in early 2023 showing positive results as well as potential risks, big banks scrambled to work out what to do with generative AI. Today, the tantalizing questions that pervades banking institutions is how and where to use gen AI most effectively, and how to ensure the applications are fully adopted and scaled within their organizations.

Scaling generative AI in banks requires traditional change management skills, senior leadership alignment, and business unit accountability, but it also presents unique challenges.

The task’s scope and the need for advanced analytics create complexities akin to the smartphone revolution. Executives must quickly learn and adapt to new AI concepts and strategies. The rapid pace of AI adoption, faster than previous technological shifts, demands swift operational adjustments. Additionally, banks face talent challenges, needing to train or recruit AI experts to integrate new AI capabilities effectively.

We know organizations are ready to roll out Generative AI (83% of AI leaders are already exploring or experimenting with it), but how can they navigate challenges around infrastructure, architecture, and governance? What’s the path of least resistance to reducing implementation hurdles?

A Brief Guide to Implementing & Scaling up Gen AI

Strategic Roadmap

• Firstly, establishing a strategic roadmap is essential, aligning senior leadership vision with clear business goals and use case prioritization. This roadmap should encompass a thorough assessment of enabling capabilities, including talent, agile operating models, technology, and data.
• Talent acquisition and development play a crucial role and leaders must equip themselves and their teams with a deep understanding of generative AI, fostering excitement and overcoming apprehension. Building consensus around high-impact use cases and addressing employee concerns about automation are vital steps in this process. Additionally, banks need to invest in upskilling employees and recruiting new talent to meet the evolving demands of AI-driven initiatives.
• The operating model must facilitate collaboration and coherence between business and technical teams. Cross-functional teams can ensure that use cases meet specific business outcomes, while processes like funding, staffing, and risk management are adapted to support agility and scalability.
• Technology decisions must align with the bank’s strategic objectives, weighing the options of building internally, buying from vendors, or partnering with ecosystem players. Integration with existing legacy systems is critical, requiring a comprehensive architecture that supports the deployment and maintenance of generative AI models.
• Data quality and governance are paramount, particularly given the reliance on unstructured data in generative AI applications. Banks must enhance their capabilities in leveraging unstructured data while maintaining rigorous standards for data quality and security.
• Risk management frameworks need to evolve to address the unique risks associated with generative AI, such as model interpretability and biased decision-making. Banks must develop new controls and validation methodologies to ensure responsible use of AI technologies.

Lastly, successful adoption and change management are essential for realizing the full potential of generative AI solutions. This entails designing user-centric AI agents, fostering a culture of experimentation and continuous learning, and implementing comprehensive change management plans that engage employees and align incentives with desired outcomes. Transparency and pragmatism are key principles in this process, ensuring that employees understand the benefits of AI technologies and are motivated to embrace them in their daily workflows.

Generative AI has the potential to significantly boost productivity for banks and other financial institutions, with new examples emerging regularly. However, scaling these solutions is challenging, and it remains uncertain how effectively banks will implement generative AI and gain full buy-in from employees and customers. To unlock the long-term promise of generative AI, banks must follow a comprehensive plan that addresses all relevant hurdles, complications, and opportunities.