At the same time, while these AI systems could make progress on more complex requests, they could not complete the “last mile” by themselves at the start. To overcome this, we looked at how the AI models used data to formulate responses and what sources the model called on most often. This led to more refinement and improvement in the systems alongside a human decision-maker that could understand what the AI was recommending, why it would be suitable, and where it could be improved.
Databases are essential components in the technology stack. As systems of record and sources for data analysis, they have to be reliable, available, and secure. Any decision around databases — from which database you choose for the job through to choices on management or optimization — can have a big impact. Any change has to be managed, or the result can be a broken application.
AI and the future of databases
Database management needs AI. The demand from customers for faster fixes and better performance is not going away, and those customers expect their suppliers to use AI in the same way they might use AI internally. For companies involved in service and support around IT including databases, applying AI to solve problems faster isn’t something that you can avoid. However, the human in the loop model will be essential for these service and support requirements for the foreseeable future. With databases so critical to how applications function and support the business, fully automating service with AI is not yet reliable for 100% of requests. As AI improves, the speed will benefit the majority of potential issues. However, the more complex problems will still require human expertise and control.
