There are a multitude of potential uses for AI with regards to database management, with analyzing and optimizing database queries or code being top of list. This aligns with our survey data where 64% of those already using AI said they had used it for query and code optimization.

Beyond that, 65% are using AI tools to help with tasks involving database schema, and 55% to generate sample data or code snippets. Along with simulating various database scenarios and automating testing scenarios, it appears that AI has already found a natural fit in the development and testing of databases.

AI tools, when used correctly, have many benefits for database professionals. The promise of making tasks quicker and easier being the main draw, as well as error detection and correction.

The survey asked respondents who have been using AI what were the main beneficial outcomes they experienced, and being more efficient via streamlining and automating tasks came top. Enhanced security and standardization followed closely after and optimizing database performance and gaining advanced insights coming behind. The results suggest that with correct application, AI can deliver on every element.

Aside from respondents who had already used AI, those who were planning to in the future were asked what kind of tasks they were interested in adopting AI for. The most popular task selected was optimizing queries and generating and reviewing code, which are obvious candidates for AI tools because, by their very nature, the tools can be trained on a vast repository of queries and code already written.

Other uses now being looked at in the database space are predictive analytics, data modelling and schema design, anomaly detection and security, and automating database management.

As with any new digital development, it doesn’t take long before a vast number of tools appear on the market. The same can be said for AI, which although it is still relatively new, already has a wide range of tools emerging, helping with a variety of tasks.

However, in terms of database and development there were five particularly popular tools cited in the survey results as already being in use. 39% of respondents are using GitHub Copilot, 26% are using Amazon CodeGuru and CodeWhisperer, 23% are using DeepCode, and 21% are using Tabnine.

While there are advantages to be gained from AI, three main areas of concern also emerged in the survey. The biggest is the practicality of actually using AI. Data security is of concern, in particular how a chatbot like ChatGPT will use any data entered into it. There then comes the accuracy of the results generated and the training and expertise necessary to write the best prompts to achieve the best results.

The ethical concerns around the data stem from information being used to train the Large Language Models behind AI chatbots, and how that impacts regulatory compliance. 2024 will see the introduction of the Regulatory AI Data Act in Europe which directly addresses these concerns and will undoubtedly, set the precedent for future AI use.

The question of training and expertise in the area is likely to become more important over time as privacy and regulatory requirements are resolved. This includes the ongoing management and support of using AI tools, the performance overhead and integrating them into existing workflows.