AI Is Transforming SQL: How Smarter Queries are helping Companies Build Faster Applications

For decades, SQL has been the backbone of modern applications.

Every online transaction, banking operation, inventory update, customer dashboard and business report relies on databases retrieving and processing information efficiently.

But writing SQL and writing efficient SQL are two very different things.

A query that works perfectly with 1,000 records might become painfully slow when handling 100 million.

Traditionally, database engineers spent hours analyzing execution plans, identifying inefficient joins, tuning indexes and rewriting complicated SQL statements.

Artificial intelligence is beginning to change that process.

By 2026, AI is doing more than generating SQL from natural language. It is helping engineers analyze database workloads, identify performance bottlenecks, generate alternative queries and test which approaches perform better.

And companies are already reporting measurable results.

1. Taboola: When AI Made an SQL Query Run 22 Times Faster

In October 2025, digital advertising company Taboola shared how its engineering team developed Rapido, an AI-powered SQL rewriting system.

The objective was simple: automate some of the time-consuming SQL optimization work traditionally performed by experienced database engineers.

Rapido works by examining several important factors:

• SQL query structure and database schemas.

• Query execution plans.

• Data distribution and indexing information.

• Known SQL performance anti-patterns.

• Alternative query-writing strategies.

Rather than producing a single rewritten query, the system generates multiple optimization candidates, benchmarks them and validates the results.

The outcome was impressive.

One SQL query that originally took approximately 110 seconds was reduced to just 5 seconds after an AI-assisted rewrite.

That represents roughly a 95% reduction in execution time for that particular query.

In another example, a query running for almost 60 seconds was reduced to approximately 1.2 seconds using a pre-aggregation strategy.

Taboola also reported a 15% performance improvement from automatic fixes where applicable and an estimated 3,200 hours of human optimization effort saved.

These figures reflect the company’s reported results, not a guaranteed improvement across every query.

What makes this interesting?

Rapido doesn’t simply ask AI to make SQL faster.

It provides database context, generates different rewrites and measures their actual performance.

Crucially, the team checks whether rewritten queries produce equivalent results before recommending implementation.

This is AI functioning as an SQL performance engineering assistant, rather than just a code generator.

2. CRISIL: Using AI to Reduce Data Processing Costs by 70%

Faster queries are valuable. But what if AI could also reduce the cost of running large-scale data platforms?

In August 2026, CRISIL published a case study detailing how its team helped a major US investment bank optimize Databricks workloads.

The bank faced familiar enterprise data engineering challenges: inefficient processing patterns, excessive cloud resource usage, poorly configured compute clusters and inconsistent job performance.

CRISIL developed an AI-powered optimization agent to analyze scripts, execution logs, workload patterns and resource utilization.

The agent helped identify opportunities for code refactoring, incremental processing and better compute allocation.

The results

One expensive weekly processing job dropped from approximately $450 to $130 per execution.

That represented around a 70% reduction in cost.

CRISIL also reported 30–40% improvements in execution efficiency for key analytical workloads, along with improved operational stability.

The company estimated annualized savings exceeding $16,000 for the optimized high-cost job alone.

An important distinction: this was broader data engineering and Databricks workload optimization, not simply AI rewriting a single SQL statement.

Nevertheless, it demonstrates a significant opportunity.

AI can help organizations optimize not only how they query data, but also how they process and manage it.

3. How AI Is Making SQL and Database Operations More Efficient

The real opportunity lies in combining AI capabilities with established database engineering techniques.

Smarter query rewriting

AI can examine SQL statements and suggest alternative approaches involving joins, subqueries, aggregations and filters.

Intelligent indexing recommendations

By analyzing execution plans and access patterns, AI can help engineers identify where appropriate indexes might reduce unnecessary data scanning.

Faster performance debugging

Instead of manually interpreting every part of a complicated execution plan, engineers can use AI to explain expensive operations and investigate potential bottlenecks.

Improved data pipelines

AI can highlight inefficient transformations, unnecessary full-table processing and opportunities for incremental data loads.

Reduced infrastructure costs

More efficient workloads may consume less CPU, memory, storage I/O and cloud compute time.

Faster analytics development

Data analysts can translate business questions into SQL, review the results and spend more time interpreting data rather than writing repetitive queries.

These improvements aren’t automatic. Their effectiveness depends on database design, workload characteristics, data quality and proper testing.

4. What Does This Mean for Application Backends?

Consider a billing or inventory application serving thousands of customers.

Every time someone searches for a product, generates an invoice, checks inventory or opens a dashboard, the backend may execute multiple SQL queries.

Imagine a dashboard query taking three seconds to execute.

An AI-assisted performance investigation might discover that the query performs unnecessary joins, repeatedly calculates the same information or scans far more records than necessary.

After engineers implement and validate appropriate changes, that operation might execute significantly faster.

This can create a chain reaction:

Better SQL → Lower database workload → Faster API responses → Improved application experience.

For high-traffic applications, even modest improvements in frequently executed queries can be valuable.

However, query optimization alone cannot fix every performance problem. Network latency, application code, caching, concurrency and infrastructure configuration also affect backend performance.

5. Does AI Replace Database Engineers?

No. In fact, these developments show why database engineering expertise remains important.

AI-generated SQL can contain logical mistakes, perform poorly under real workloads or introduce security risks.

A faster query is not necessarily a better query if it returns incorrect data.

That is why production-ready AI optimization requires:

• Accurate schema and workload context.

• Execution-plan analysis and benchmarking.

• Verification that results remain correct.

• Proper database permissions and security controls.

• Human review, monitoring and rollback mechanisms.

The strongest approach is not AI replacing engineering judgment.

It is AI accelerating the investigation, while engineers validate and control the outcome.

6. The Future: From Writing SQL to Continuously Improving It

For years, databases relied heavily on query optimizers built into engines such as PostgreSQL, MySQL and Microsoft SQL Server.

These optimizers already perform sophisticated work to determine efficient execution strategies.

AI is introducing an additional layer of assistance.

Instead of only optimizing the execution plan for a given SQL statement, AI-assisted tools can also explore alternative SQL formulations, recommend configuration changes and help diagnose system-wide performance problems.

We are moving toward development workflows where SQL performance analysis can become a more continuous part of engineering.

Imagine an automated review identifying an expensive query before it reaches production, proposing alternatives, benchmarking them against representative data and recommending the safest improvement.

Parts of this workflow are already possible. Fully autonomous, dependable database optimization remains an engineering challenge.

Final Thoughts

The biggest contribution of AI to SQL may not be its ability to generate queries from plain English.

It may be its ability to help engineers understand why a query is slow, explore better alternatives and measure the improvement.

Taboola demonstrated how AI-assisted SQL rewriting could reduce one query’s execution time from 110 seconds to 5 seconds.

CRISIL demonstrated how AI-guided data platform optimization could reduce the execution cost of a major workload by approximately 70%.

These are different applications of AI, but both point in the same direction.

Database performance engineering is becoming more intelligent, accessible and increasingly automated.

SQL isn’t becoming obsolete in the age of AI. The way we develop, optimize and operate SQL-powered systems is evolving.

References

1. Taboola Engineering — How We Developed Our AI-Based SQL Rewrite System (October 31, 2025)
   https://www.taboola.com/engineering/rapido-sql-rewrite-system/

2. CRISIL — AI-Powered Optimization Delivers 70% Lower Job Costs and Improves Data Platform Stability (August 31, 2026)
   https://integraliq.crisil.com/en/homepage/what-we-think/all-our-thinking/case-studies/2026/08/ai-powered-optimization-delivers-70-percent-lower-job-costs-and-improves-data-platform-stability.html

Prepared for Let’s Learn by Axein Technologies.