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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

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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

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...