There is an emerging trend among financial institutions and other sectors to explore alternatives to SAS, with Python being the preferred open-source option. To the untrained eye, transitioning from one programming language to another may appear straightforward; however, migrating to the cloud involves much more than that. It is a labor-intensive process that requires re-engineering workflows, data streams, and operations. Companies must examine entire business processes through the lens of change and emerging technologies, with many considering generative AI as a key technology to adopt.
Anna Slodka-Turner offers an in-depth perspective on what gen AI can and cannot accomplish during a migration from SAS to Python, emphasizing the importance of leveraging gen AI for its strengths: improving efficiency and speed in the code migration aspect of the process.
Code migration refers to converting or rewriting code from one language to another, such as from SAS to Python. Gen AI excels in this area, hastening the process and minimizing errors. However, Anna points out that code migration alone cannot address the underlying workflows and processes that dictate how data flows through a system.
Two key themes emerge:
- Cloud migration is not simply a technical overhaul; it requires active participation from various departments within the organization. It necessitates a collaborative effort across different business functions to rethink data usage, implement automation, identify sources of errors in the current system, understand their underlying causes, and fix those problematic workflows to improve the entire process.
- The human aspect is critical throughout this journey. While technology plays a significant role in cloud migration, the human element is equally essential. The concept of "human in the loop" emphasizes that technology should work in tandem with expert knowledge.
Anna Slodka-Turner argues that deploying gen AI can lead to significant efficiency and operational gains for banks, with observed improvements of 30% to 40% in operational efficiency being noteworthy. "However," she notes, " while GenAI slashes the cost and effort of process migrations, it’s only a tool in a bigger transformation journey. Real gains cannot be realized without reengineering how data is managed, how processes are streamlined, and how technology aligns with the business needs."
In conclusion, leveraging gen AI in SAS to Python migration can boost operational efficiency but should be viewed as a tool within a broader transformation strategy. Success requires understanding workflows, encouraging cross-department collaboration, and integrating expert insights. By reengineering data management alongside gen AI, organizations can drive meaningful change in a digital landscape.
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