Like the evolution of cloud platforms in recent years, banks must go further in their digital transformation and consider the practical applications of Artificial Intelligence (AI). While there are proven examples of effective applications, many banks still consider AI to be experimental, and many of their pilot programs never move toward full-scale implementation. To be successful in the implementation and scaling process of AI-based solutions, it is important to cover the following critical areas:

Develop an AI strategy: Today many banks are defining use cases to implement AI solutions in some capacity within their organizations. However, to remain competitive, both in the short and long term, banks must scale AI as a critical component rather than treating it as a standalone initiative. Business strategies must evolve from AI implementation incrementally to organization-wide integration and focus on moving from simply being AI aware to becoming a strong competitor in the adoption and implementation of AI solutions.

Define a process based on use cases: One of the challenges for AI adoption is identifying use cases driven by business value. Defining relevant use cases and prioritizing them on a roadmap can help banks stay focused during implementation and help achieve defined objectives during the strategy phase.

Experiment with prototypes: The purpose of a prototype is generally to determine whether it is worth continuing to invest more time and money in a technology solution. AI solution use cases require prototypes to scale across the organization, and therefore initial planning and setting expectations around data, timelines, business goals, and strategy are crucial.

Build with Confidence – In AI deployments, risk and compliance assessments should be done early in the process, starting in the AI ​​strategy phase. To preserve trust, it is crucial that AI models and processes are compliant with regulations. Many of the organizations that adopt AI solutions believe that the risks associated with the use of models based on artificial intelligence are holding back the adoption of this technology, therefore, it is imperative to manage the risks throughout the implementation process.

Drive Sustainable Results – Goals after AI solution implementation should focus on system maintenance and continually learn how models react to various inputs and scenarios and identify ways to improve results. These lessons can be applied in the scaling phase to the entire organization, as well as in the development of other AI solutions.

Banks are coming to recognize the competitive differences that disruptive technologies can bring to improve their customer experience. In the long term, the competitive characteristics of banks may very well depend on building the foundations and technological processes to achieve the benefits that AI promises to offer. Still, technological advancements could outpace industry adoption, even as banks take an accelerated journey toward digital transformation. To successfully achieve the benefits that AI can deliver in the future, banks must stay the course today, which for some may be easier said than done.

The author is a Partner of Risk Advisory

Deloitte Dominican Republic

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