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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

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

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

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.