Is AI Making SQL Redundant? The Evolving Role of SQL in Oracle Analytics

Introduction

Structured Query Language (SQL) has been the backbone of analytics for decades, enabling users to extract, transform, and analyse data efficiently. However, with the rise of AI-driven analytics, features like AI Assistant, Auto Insights, and Contextual Insights are allowing users to interact with data without writing SQL.

Does this mean SQL is becoming redundant? The answer isn’t a simple yes or no. AI is certainly abstracting SQL from business users, making analytics more accessible, but SQL still plays a critical role behind the scenes. This blog explores how AI is changing SQL’s role, where SQL is still essential, and what the future might hold.

How AI is Reducing SQL’s Visibility in Oracle Analytics

AI-powered features in Oracle Analytics allow users to explore data without manually writing SQL. Three key capabilities demonstrate this shift:

1. Contextual Insights: Auto-Generated SQL Behind the Scenes
• Example: A sales manager sees an unexpected spike in revenue on a dashboard. Instead of running queries, they click on the data point, and Contextual Insights automatically surfaces key drivers and trends.
• What happens in the background? Oracle Analytics generates SQL queries to identify correlations, anomalies, and patterns, but the user never sees or writes them.

2. AI Assistant: Querying Data Without SQL
• Example: A marketing analyst wants to compare Q1 and Q2 campaign performance. Instead of writing a SQL query, they ask the AI Assistant:
“Show me campaign revenue for Q1 vs Q2.”
• What happens in the background? The AI Assistant translates the request into SQL, retrieves the data, and presents a visual answer.
• Why it matters: Business users get answers instantly, without needing SQL expertise.

3. Auto Insights: Surfacing Trends Without Querying Data
• Example: A finance team wants to understand profit fluctuations. Instead of manually querying revenue data over time, they use Auto Insights, which highlights key trends and anomalies.
• What happens in the background? Oracle Analytics runs SQL queries to detect significant changes and patterns.
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These features make SQL less visible but not obsolete. In fact, AI relies on SQL to function effectively, which leads to the question—where is SQL still essential?

Why SQL is Still Essential

While AI is making SQL more accessible, it hasn’t eliminated the need for SQL expertise. Several areas still require manual intervention:

1. Handling Complex Joins & Business Logic
• AI struggles with complex queries that involve multiple joins, subqueries, and conditional logic.
• Example: A financial analyst wants to calculate profitability by region, requiring a multi-table join across sales, inventory, and expenses. AI might generate an inefficient or incorrect query.

2. Performance Optimisation
• AI-generated SQL isn’t always the most efficient. SQL tuning (e.g., indexing, partitioning) still requires human expertise.
• Example: AI might generate a query that performs a full table scan instead of leveraging an index, slowing down performance.
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3. Explainability & Trust
• AI-generated queries can sometimes produce unexpected results, making it difficult for users to validate the logic.
• Example: If AI Assistant returns an unusual data trend, an analyst may still need to inspect the underlying SQL to ensure accuracy.

SQL remains a crucial tool for data engineers, analysts, and DBAs who need control over data processing, query performance, and governance. However, as AI continues to evolve, could it overcome these challenges?

The Role of the Semantic Model in AI-Driven Analytics

One of the key features of Oracle Analytics is its semantic model, designed to abstract the complexity of source systems from end users. Instead of writing raw SQL queries against complex database structures, users interact with a logical layer that simplifies relationships, calculations, and security rules.

Why the Semantic Model Exists

The semantic model serves several purposes, including:

• Hierarchies & Drilldowns: Defining business hierarchies (e.g., Year → Quarter → Month) for intuitive analysis.
• Logical Joins & Business Logic: Providing a structured way to join tables without requiring users to understand foreign keys or database relationships.
• Row-Level Security: Enforcing access control so users only see the data they are authorised to view.

This abstraction enables self-service analytics while ensuring data governance, performance, and accuracy.
Will AI Make the Semantic Model Redundant?

AI-powered analytics features in Oracle Analytics Cloud (OAC)—such as Contextual Insights, AI Assistant, and Auto Insights—are reducing the need for manual query writing. But does this mean the semantic model is no longer needed? Not quite.

AI currently relies on the semantic model to:

• Ensure accurate and governed data access—AI cannot enforce security rules or business logic without a structured data layer.
• Interpret user queries correctly—When an AI Assistant generates SQL, it uses predefined joins and relationships from the semantic model.
• Maintain consistency—Without a semantic layer, different AI-generated queries might return inconsistent results due to varying assumptions about data relationships.

The Future: AI-Augmented Semantic Models?
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Rather than replacing the semantic model, AI could enhance it by:

• Auto-generating relationships & joins based on data patterns.
• Improving performance optimisation, recommending indexing strategies or pre-aggregations.
• Enhancing explainability, showing why certain joins or hierarchies were applied.

AI and the Semantic Model Will Coexist

While AI reduces the need for manual SQL, the semantic model remains essential for structured, governed, and performant analytics. The future is likely AI-assisted semantic models rather than their elimination.
The Future of AI in SQL GenerationAI will likely become more sophisticated in handling SQL, but rather than eliminating it, AI will enhance SQL’s role. Here’s what the future might look like:

1. AI-Powered Query Optimisation
• AI could not only generate SQL but also analyse and optimise it for better performance.
• Example: Future AI Assistants might suggest indexing strategies, rewrite inefficient queries, or recommend materialised views.
2. Better Handling of Complex Joins & Business Logic
• AI could integrate knowledge graphs or semantic layers to better understand relationships between tables, improving the accuracy of generated SQL.
3. Explainable AI for SQL Generation
• AI might offer query rationale explanations, showing users why a specific query was generated and suggesting alternative approaches.
4. AI Agents & Autonomous Databases
• AI Agents could work alongside SQL experts, automating routine queries while letting humans handle complex cases.
• Oracle’s Autonomous Database could play a larger role in self-optimising SQL execution.
​While AI will continue to reduce the need for manual SQL writing, it is more likely to enhance SQL rather than replace it.

Final Thoughts: Adapting to the AI-Driven Analytics LandscapeAI is shifting SQL from a tool business users interact with directly to something that powers insights in the background. However, this doesn’t mean SQL is going away. Instead:

• Business users will rely more on AI-driven insights without needing SQL knowledge.
• Data engineers and analysts will still need SQL expertise to optimise performance, manage governance, and handle complex queries.
• The future is AI-Augmented SQL, not SQL-Free Analytics.

For professionals in the analytics space, this means embracing AI while still sharpening SQL skills. AI will make SQL more powerful, but those who understand both will be best positioned to leverage the full potential of Oracle Analytics.
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What Do You Think?

Do you see AI replacing SQL in your analytics work, or do you think SQL will remain a core skill? Let’s discuss in the comments!

Next Steps

• If you’re interested in seeing these AI-driven analytics features in action, explore Oracle Analytics’ AI Assistant, Auto Insights, and Contextual Insights.
• Stay tuned for more insights on AI’s role in modern analytics on Elffar Analytics.