Published on May 22, 2024, 8:25 pm

Generative AI is already reshaping organizations and significantly impacting IT strategies in its early stages. Language models (LLMs) are accelerating engineering agility but also potentially accumulating unprecedented technical debt. Red Monk’s founder Stephen O’Grady highlights the likelihood of generative systems increasing code volume, thereby escalating technical debt concerns. However, Juan Perez, EVP and CIO at Salesforce, emphasizes that responsibly exploring and implementing AI is crucial, viewing it akin to other applications requiring appropriate governance, security management, maintenance, and support.

As the number of AI products increases, selecting the optimal model and foundational data becomes vital in supporting the AI journey. When properly implemented, Generative AI can produce higher-quality products at lower costs. It is not about whether AI will positively impact overall business but rather how quickly and significantly it will do so.

Neil Sample, CIO of Walgreens Boots Alliance, stresses that responsible AI development necessitates both governmental regulations and corporate governance measures to be in place. Machine learning models have the potential to enable faster IT iterations by automating monotonous tasks, freeing up software developers’ bandwidth to focus on more creative and advanced work.

Generative AI tools are seen to dramatically increase development, especially in established programming languages such as Java, Python, and C++. Moreover, Mihir Rajevel, CIO at Palo Alto Networks adds that AI plays a critical role in shifting towards code testing leftward and assisting in identifying errors early in the software development cycle.

However, concerns arise regarding the quality of code generated by AI assistants due to various factors like model selection and developer skill sets. Ensuring clean code output from AI-generated code is crucial for maintaining software quality standards. Alastair Pooley from Snow Software emphasizes the necessity of continuous review and validation of outputs for ensuring operational reliability.

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