Published on November 22, 2023, 7:21 am

Generative artificial intelligence (AI) is not just a solution in search of a problem. In fact, it is already demonstrating its value in the area of software development productivity. According to a recent survey conducted by O’Reilly, nearly half of technology professionals are utilizing generative AI to build applications, with one-third of IT staff utilizing it for data analytics.

The survey, which involved over 2,800 technology professionals, revealed that 44% of respondents are actively using AI in their programming work, while an additional 34% are experimenting with it. Data analysis also emerged as a significant use case for generative AI, with 32% of IT professionals employing it for analytics and another 38% exploring its possibilities.

Mike Loukides, author of the O’Reilly report, highlighted that the most common application of generative AI is in programming. Tools such as GitHub Copilot and ChatGPT have played a pivotal role in driving this trend. However, what surprised Loukides was the extent of adoption within the industry.

The report also emphasized the existence of a burgeoning ecosystem surrounding generative AI tools. To quote Loukides’ analogy: “As was said about the California Gold Rush, if you want to see who’s making money, don’t look at the miners; look at the people selling shovels.” This suggests that there are considerable opportunities for companies creating supportive tools and technologies in this field.

Loukides further noted that automating the process of building complex prompts has become routine. Techniques like retrieval-augmented generation (RAG) and tools such as LangChain have been instrumental in advancing generative AI capabilities. Additionally, there are tools available for archiving and indexing prompts for reuse and vector databases for retrieving relevant documents required by an AI to answer specific queries.

The survey revealed that around 16% of IT professionals reported their companies are building on top of open-source models. This suggests that even organizations with prohibitive policies regarding the use of AI will have a difficult time stopping the adoption of AI tools by programmers who are determined to enhance their productivity.

The report predicts that the demand for professionals with AI expertise will continue to rise. Specifically, there is increasing demand for those skilled in AI programming (66%), data analysis (59%), operations for AI/ML (54%), and general AI literacy (52%). However, users must also develop a critical understanding of generative AI tools to avoid potential issues caused by hallucinations or incorrect answers.

While generative AI has shown promise in data analytics and customer-support applications, the report cautions that there are still risks associated with deploying it directly in customer-facing interactions. Incorrect responses, biased behavior, and other documented problems can lead to considerable damage.

One challenge inhibiting the broader adoption of generative AI is finding appropriate business use cases. For non-users, this is cited as the primary roadblock by 31% of respondents, while users identify it as an issue for 22%. It is important to approach the implementation of AI solutions thoughtfully, taking into account their potential repercussions and ensuring they align with organizational goals.

Another reason why formulating business use cases takes time is because integrating AI requires reevaluating traditional approaches to various aspects of an organization. Recognizing suitable use cases for AI involves reimagining how businesses operate and embracing new perspectives.

Lastly, it’s crucial to remember that despite advancements, AI technology is still relatively new. The survey indicates that 38% of IT professionals have only been working with AI for less than a year. Furthermore, while cloud-based foundation models like GPT-4 offer convenience by eliminating the need for custom model development or infrastructure provision, fine-tuning a model for specific use cases remains a significant undertaking.

In conclusion, generative AI has already proven its worth in software development productivity and data analytics. Its adoption by programmers is expected to grow, regardless of management policies. However, careful consideration of appropriate use cases and the recognition that AI challenges traditional approaches are essential for successful implementation. With a rising demand for professionals skilled in AI, continued advancements in generative AI tools will pave the way for innovation and transformation across various industries.


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