Key Challenges to Operationalizing Bank AI: Why Most Projects Fail


Key Challenges to Operationalizing Bank AI: Why Most Projects Fail

AI has made significant inroads in the banking sector, transforming various aspects of operations and the customer experience. Competitive advantage has become increasingly dependent on AI-powered technology. Many banks, small to medium-sized, as well as large businesses, are investing heavily in adopting AI to their benefit.

Despite its massive growth, AI-powered and data-driven technology implementations have had a shockingly low success rate. About 90% of AI projects are never put into production and so never deliver their promised value.

Most banks fail to succeed in diffusing and scaling AI technologies throughout the organization. The most common reasons are the lack of a clear AI strategy, fragmented data sets, and an outmoded operating model.

Small and Midsize Banks Aren’t Set Up for AI and Machine Learning

Lacking AI vision and organizational culture

While small and midsize banks often have constrained resources, their mindset can also impact success or failure. A lack of organizational acceptance and culture, along with unrealistic expectations from AI, can cause your investment to go to waste. Key leaders and executives should work towards shifting from the traditional mindset and convey the urgency and benefits of AI technology. They should invest in AI education to help staff make the transition.

Insufficient access to data

AI algorithms rely heavily on large volumes of high-quality data to train models and produce accurate results. Small and midsize banks may have limited access to diverse and comprehensive data sets, making it difficult to train AI models effectively. Ensuring data quality and completeness can also be challenging due to legacy systems and data silos.

Scaling up isn’t easy

Implementing AI solutions across various banking processes and systems can be complex. Small and midsize banks may have legacy systems and processes that are not readily compatible with new AI technologies. Thus, integrating AI solutions seamlessly into the existing infrastructure and ensuring scalability can be a significant challenge.

Lacking the required expertise and talent

Developing and operationalizing AI solutions requires a skilled workforce with expertise in data science, machine learning, and AI. Small and midsize banks may face challenges in attracting and retaining top AI talent, as larger institutions and tech companies often have an advantage in this regard.

Regulatory compliance consideration

Banks operate in a highly regulated industry, and deploying AI solutions requires compliance with regulatory guidelines and data privacy laws. Implementing AI solutions introduces additional complexities. Small and midsize banks may face difficulties navigating these regulatory frameworks and ensuring compliance when using AI technologies.

Final Thoughts

To overcome these challenges, small and midsize banks must prioritize specific use cases with clear ROI, focus on data quality and governance, and adopt a phased approach to AI implementation.

Consider collaborating with AI solution providers, leveraging cloud-based AI platforms, participating in industry consortia, and investing in partnerships with fintech companies to mitigate some of the challenges.

Posted on June-15-2023

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