The advent of Artificial Intelligence (AI) has transformed numerous fields, and the domain of SAS (Statistical Analysis System) programming is no exception. From automating tedious tasks to enhancing decision-making processes, AI has made significant inroads into how SAS programmers work.

However, AI is not a substitute but a companion to programmers. While AI can help us focus our critical thinking, creativity, and problem-solving skills, AI needs our expertise. Domain expertise is still essential.

To understand this transformation better, here we explore key ways AI has impacted SAS programming, particularly by comparing skills of traditional to AI-assisted programming, examining the days before and after AI, and discussing the new responsibilities and skills required in the modern programming landscape.

Traditional SAS programming vs. AI-assisted SAS programming

Traditional SAS programming has long been a manual, code-intensive practice requiring a high level of expertise in statistical analysis and programming. In the earlier days, SAS programmers worked with well-defined, often repetitive tasks. The process of developing code required a deep understanding of the data and statistical methodologies, all while meticulously debugging and quality-checking code.

AI-assisted SAS programming introduces a new level of efficiency, allowing programmers to focus more on value-added tasks rather than repetitive work. Traditional SAS programming workflows are now supported by AI-driven automation tools that can generate code, optimize algorithms, and even offer suggestions for complex statistical analyses. For example, where traditional methods would require a programmer to sift through data to find patterns, AI can now analyze large datasets in seconds and offer insights that help in decision-making. This allows the SAS programmers to focus on more strategic and high-level interpretations.

In essence, the role of the SAS programmer is evolving from being a “code generator” to a “code curator” and they maintain control over every step, providing deep customization and understanding of the entire process.

AI as a companion, not a substitute

The fear of AI replacing jobs has become a common narrative, but in the case of SAS programming, AI should be viewed as a companion rather than a replacement. While AI can optimize code, automate reporting, or even suggest corrections, it is still far from replacing the creative and analytical skills of programmers. AI systems can generate insights based on patterns within datasets, but understanding the nuances of those patterns and making informed decisions based on them remains a unique programmer’s skill.

SAS programmers have a deep understanding of the data they work with, including the context, limitations, and real-world implications of their findings. While AI can handle the heavy lifting in terms of data processing and analytics, the role of the programmer is to interpret these findings, cross-check their accuracy, and ensure the outputs are aligned with business goals or research questions.

Additionally, AI’s suggestions aren’t always perfect, especially when dealing with edge cases or complex datasets with nuanced relationships. In such scenarios, a programmer’s oversight is crucial to prevent AI-driven errors from propagating throughout the analysis.

Before and after AI

The landscape of SAS programming before the integration of AI was characterized by manual coding, exhaustive debugging processes, and labor-intensive quality control procedures. Let’s break down the key changes AI has brought to these areas:

Code development

Before AI, coding was manual and depended heavily on a programmer’s syntax knowledge and experience to ensure that the code adhered to best practices for efficiency and performance. This could be a time-consuming process, especially when dealing with large, complex datasets.

In the post-AI era, code development is becoming more efficient through AI-assisted coding tools. These tools can automatically suggest code snippets based on previous coding patterns or even generate entire blocks of code tailored to the dataset. AI-driven auto-complete features and advanced libraries that recommend the best statistical models or data manipulation techniques have significantly sped up the development process.

Debugging

Debugging used to be a meticulous and painstaking part of the SAS programmer’s job. Identifying errors in code or incorrect outputs is often required by going through large blocks of code line by line, manually reviewing logic and syntax.

AI has revolutionized debugging by identifying errors in real time, suggesting fixes, and even automatically correcting syntax errors. AI tools can also track changes in code and predict where potential issues might arise based on past errors, significantly reducing debugging time and enhancing code accuracy.

Quality control (QC)

Before AI, the QC process was often manual or semi-automated, prone to missed errors, and involved peer reviews, statistical validations, and rigorous testing to ensure that the code met the necessary standards. This was particularly important in industries such as healthcare or finance, where data accuracy is critical.

Today, AI-driven QC tools can automatically verify the integrity of datasets, flag inconsistencies, and ensure that statistical models meet predefined accuracy thresholds. These tools can run tests much faster than human reviewers, allowing for quicker validation cycles and better compliance with industry standards.

AI doubles productivity, without replacing the need for programmer’s intuition and expertise, so we can opt for other developmental activities like enhancing the client outcomes, learning new skills, and mentoring to strengthen the overall team.

New responsibilities and skills for SAS programmers in the AI age

New responsibilities and skills required for AI platforms

• To understand how to work along with AI tools
• To adopt AI-driven workflows for faster development cycles
• To learn to guide and review AI-generated code
• Additional skills like data literacy, critical thinking, and ethical AI considerations are also required

Industry AI tools

• Tabnine: AI-powered code predictions
• Snyk: AI-driven security checks
• DeepCode: Real-time AI code review
• SAS Viya: Integrate existing code with AI tools

Final takeaways

AI tools are transforming the role of SAS programmers, making them faster and more effective, but human expertise remains crucial in directing AI and ensuring high-quality outcomes. The future of programming likely lies in a hybrid approach that leverages both human expertise and AI-driven efficiencies.

Interested in learning more about AI in clinical development? Watch our recent webinar:

Share Via