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From Tool to Transformation: The Growing Role of AI in Database Operations

Tushita Gupta
Head of Product Design
Redgate Software

Generative AI and large language models (LLMs) are becoming essential in data-driven businesses. With increasing pressures on time and resources, as well as growing complexity, AI offers critical support. It's not about AI replacing jobs, but rather about companies missing growth opportunities if they don't take advantage of AI tools. Businesses that fail to leverage AI may find themselves at a disadvantage compared to those that do.

But when do we make the shift to AI, and how?

We surveyed IT professionals on their attitudes and practices regarding using Generative AI with databases. We asked how they are layering the technology in with their systems, where it's working the best for them, and what their concerns are. Our 2024 survey on the State of the Database Landscape engaged 3,849 global IT professionals from all kinds of businesses, from large to small, and across 15 industry sectors.

You're Not Late Yet

We found that 20% of our respondents are already using AI in the context of database management, and 35% are planning to. We believe that many of the 32% who say they don't currently have plans to use AI will change their tune in the next few years.

Image
Redgate1

AI is not a panacea. It is a tool like any other, and it requires active and wise technological leadership to keep it aligned with business objectives. As Jeff Foster, our director of technology and innovation, says, "You need decent code reviews, human oversight and process-based guardrails to help prevent the buildup of technical debt."

How It's Used

Generative AI may be just a tool, but it is a unique one, as reflected in how people are using it in database development. More than any other task, our survey respondents are using AI for testing and development tasks that involve database schema (65%). AI is also used for generating and optimizing queries and code, and for generating sample data. These are areas where AI has unique and powerful benefits.

We believe it's critical to understand that, at the phase of AI adoption in the database business, the tool is best used to support IT professionals in the day-to-day performance of their jobs. As we can see in how Generative AI is currently being used, its greatest impact today comes from streamlining and automating tasks. Using it for generating business insights is far down on the most popular uses.

Image
Redgate2

We believe AI will ultimately be integrated into business decisions and that its use for "insights" will increase. However, that should not happen before we use the technology to make IT pros' jobs easier. Focusing on IT worker productivity will serve to get the group trained up on AI's capabilities, while providing them a time-saving benefit in their day to day.

We think it's appropriate and promising that the #1 task that organizations are looking at AI to streamline is query optimization. That's exactly the kind of work that benefits from human judgment combined with the pattern-matching skills of AI.

Image
Redgate3

Generative AI will have a significant impact in many aspects of our jobs, as well as in the fundamental products and services that companies sell. But business will always be competitive, and at all levels of employment, from entry-level coder to CEO, it will remain necessary to apply human creativity to find ways to use — not be used by — technology. The more work we do to provide our IT professionals with exposure to these tools today, the more successful they will be in the future.

Tushita Gupta is Head of Product Design at Redgate Software

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

From Tool to Transformation: The Growing Role of AI in Database Operations

Tushita Gupta
Head of Product Design
Redgate Software

Generative AI and large language models (LLMs) are becoming essential in data-driven businesses. With increasing pressures on time and resources, as well as growing complexity, AI offers critical support. It's not about AI replacing jobs, but rather about companies missing growth opportunities if they don't take advantage of AI tools. Businesses that fail to leverage AI may find themselves at a disadvantage compared to those that do.

But when do we make the shift to AI, and how?

We surveyed IT professionals on their attitudes and practices regarding using Generative AI with databases. We asked how they are layering the technology in with their systems, where it's working the best for them, and what their concerns are. Our 2024 survey on the State of the Database Landscape engaged 3,849 global IT professionals from all kinds of businesses, from large to small, and across 15 industry sectors.

You're Not Late Yet

We found that 20% of our respondents are already using AI in the context of database management, and 35% are planning to. We believe that many of the 32% who say they don't currently have plans to use AI will change their tune in the next few years.

Image
Redgate1

AI is not a panacea. It is a tool like any other, and it requires active and wise technological leadership to keep it aligned with business objectives. As Jeff Foster, our director of technology and innovation, says, "You need decent code reviews, human oversight and process-based guardrails to help prevent the buildup of technical debt."

How It's Used

Generative AI may be just a tool, but it is a unique one, as reflected in how people are using it in database development. More than any other task, our survey respondents are using AI for testing and development tasks that involve database schema (65%). AI is also used for generating and optimizing queries and code, and for generating sample data. These are areas where AI has unique and powerful benefits.

We believe it's critical to understand that, at the phase of AI adoption in the database business, the tool is best used to support IT professionals in the day-to-day performance of their jobs. As we can see in how Generative AI is currently being used, its greatest impact today comes from streamlining and automating tasks. Using it for generating business insights is far down on the most popular uses.

Image
Redgate2

We believe AI will ultimately be integrated into business decisions and that its use for "insights" will increase. However, that should not happen before we use the technology to make IT pros' jobs easier. Focusing on IT worker productivity will serve to get the group trained up on AI's capabilities, while providing them a time-saving benefit in their day to day.

We think it's appropriate and promising that the #1 task that organizations are looking at AI to streamline is query optimization. That's exactly the kind of work that benefits from human judgment combined with the pattern-matching skills of AI.

Image
Redgate3

Generative AI will have a significant impact in many aspects of our jobs, as well as in the fundamental products and services that companies sell. But business will always be competitive, and at all levels of employment, from entry-level coder to CEO, it will remain necessary to apply human creativity to find ways to use — not be used by — technology. The more work we do to provide our IT professionals with exposure to these tools today, the more successful they will be in the future.

Tushita Gupta is Head of Product Design at Redgate Software

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...