Redgate finds AI database governance lags adoption

Redgate has published research showing that 77% of organisations scaling AI in database management do not use formal data governance or quality frameworks. The findings point to a widening gap between AI adoption and operational controls.

Use of AI for database management among enterprises rose to 44% from 15% a year earlier, according to the database software company. The report drew on responses from 2,150 IT professionals globally and examined how companies are managing database estates as AI spending increases.

The study found that 44% of organisations spent more than USD $100,000 on database AI over the past year. Nearly a quarter of large enterprises spent more than USD $1 million, suggesting database-related AI projects are moving beyond small-scale trials in many large businesses.

At the same time, only 23% of firms said they had formal data governance or quality frameworks in place for these environments. That means most organisations are expanding AI use in databases without structured controls over data quality, governance and change management.

Redgate's figures also suggest many companies are accepting greater exposure as they push for faster results. The report found that 58% of surveyed organisations explicitly accept higher data security risks in exchange for efficiency gains.

That pressure is playing out across increasingly complex estates. More than a third of enterprises surveyed, or 36%, said they manage four or more database platforms, indicating that many teams are working across fragmented systems rather than a single standardised environment.

Operational practices also remain heavily manual in many organisations. The report found that 39% still rely on manual methods to test and deploy database changes, suggesting many businesses have not yet automated key parts of database operations even as they expand AI use.

Control gap

The data points to a contradiction at the heart of many AI programmes. Businesses are committing larger budgets and reporting benefits from adoption, but governance, security and deployment processes are not keeping pace with the speed of roll-out.

Almost all respondents, 99%, reported operational benefits from AI in database management. The most common gains were task automation, cited by 63%, and performance optimisation, cited by 60%.

Those returns help explain why companies continue to expand deployments despite unresolved risks. For IT teams under pressure to streamline operations and reduce the burden of managing sprawling data estates, AI tools are becoming part of day-to-day database administration rather than an experimental addition.

Yet the report suggests weak governance could undermine those gains, especially where organisations are dealing with large volumes of data across multiple platforms. The combination of fragmented systems, manual deployment processes and looser security trade-offs may increase the likelihood of errors that are harder to trace and fix.

Kellyn Gorman, engineer and advocate at Redgate, addressed that tension in the report findings. "AI didn't create fragile operational foundations, but it has exposed and amplified organizational pitfalls at a speed and scale that is impossible to ignore," Gorman said.

She added: "The hard truth is that AI without strong foundations doesn't just fail slower, it fails faster, and in ways much harder to debug, fix and explain to the business. The solution isn't to slow down delivery, but to stop pretending the gaps aren't there while we accelerate toward them."

Rising spend

The findings come as businesses across sectors look for practical uses of AI in infrastructure, software development and IT operations. Database management has emerged as one area where companies expect AI to help automate repetitive work, improve performance monitoring and support more efficient administration of growing data estates.

But Redgate's figures indicate that financial commitment to these tools is outpacing the organisational changes needed to support them. A company can spend heavily on AI software and still lack the controls required to manage underlying data quality, access and deployment discipline.

That issue matters because database environments sit close to core business systems. Problems in governance or testing can affect not only internal workflows but also customer-facing services, reporting and compliance processes, particularly in organisations running a mix of legacy and modern platforms.

The report places database AI adoption within a broader shift in enterprise technology management, where teams are expected to move faster while handling more complexity. In that environment, manual processes and fragmented controls can persist even as budgets rise and adoption widens.

For many organisations, the challenge is no longer whether to use AI in database management, but whether supporting processes are mature enough to handle the pace of change. Redgate's research suggests that for most, the answer is still no: only 23% are using formal governance or quality frameworks while adoption has climbed to 44%.