Introduction: Oracle AI Database 26ai Ushers in the AI for Data Era
Oracle’s latest innovation, Oracle AI Database 26ai, represents a paradigm shift in enterprise data management — an era where AI is architected directly into the core of the database.
Building upon the foundation set by Oracle Database 23ai, the new release brings deep AI integration, agentic automation, and multimodal vector intelligence into a single unified architecture. At Oracle AI World 2025, experts emphasized that AI Database 26ai is not just an upgrade — it’s a re-architecture of how enterprises store, process, and reason with data.
Derik Harlow, Senior Product Manager for DB Observability & Management, led a compelling session titled “Seven Highly Effective Habits of the AI DBA [THR2978]”, offering a glimpse into how modern DBAs can evolve alongside Oracle’s AI ecosystem.
Seven Highly Effective Habits of the AI DBA
The session opened with a roadmap for the “AI-empowered DBA” — a new breed of professionals capable of leveraging AI-driven automation and insights to enhance database performance and governance.
Key Habits for the Next-Gen DBA:
- Following trends — staying current with database and AI convergence.
- Adopting new technologies — integrating AI tools like AWR.ai and SQLPerf.ai.
- Keeping current with capabilities — mastering built-in observability and analytics.
- Being prepared for upgrades — using Oracle’s automated tools like ORADiff.
- Utilizing new AI features — embracing AI-native capabilities in 26ai.
- Leveraging existing tools — maximizing Enterprise Manager and APEX.
- Not overcomplicating ML — applying AI practically without unnecessary complexity.
These habits align with Oracle’s broader message: the DBA’s role is transforming from maintenance-focused to strategic data intelligence leadership.
Adopt the Converged Database: A Unified Foundation
Oracle reinforced its long-standing philosophy of the Converged Database, now fully realized through 26ai. A Converged Database is multi-model, multi-workload, and AI-native, enabling organizations to manage all data types and workloads in one integrated environment.
Benefits of Adopting the Converged Database
- Simplified Architecture: Consolidates relational, JSON, vector, spatial, and graph workloads within a single platform — eliminating the need for separate specialty databases.
- Enhanced Security & Compliance: Built-in encryption, advanced security controls, and consistent policies across data types.
- Reduced Complexity & Cost: Lowers licensing and management overhead while reducing data duplication.
- Faster Innovation: Allows teams to use modern data models and analytics capabilities natively.
- High Performance & Scalability: Optimized for high availability, RAC, and elastic scaling.
- Future-Proof Foundation: Prepared for emerging workloads and AI-driven applications.
Oracle’s strategy ensures that enterprises future-proof their application ecosystems by unifying every data workload — from OLTP to AI/ML — under one robust engine.
Oracle AI Database 26ai: Built for Every Data Type, Tool, and Workload
This diagram presented at Oracle AI World visually captured how Oracle AI Database 26ai acts as the nucleus connecting data types, developer interfaces, and AI-driven workloads.
Core Highlights:
- Data Types: Vector, Spatial, JSON, Text, XML, and Relational.
- Workloads: AI/ML, IoT, Analytical, Streaming, OLTP, Graph, Blockchain, and Distributed systems.
- Developer Interfaces: SQL, REST, MongoDB API, PL/SQL, Java, Python, Go, C, Node.js.
- Tools: APEX, ORDS, AutoML, SQL Developer, Enterprise Manager, VS Code extensions.
- Deployment Options: Runs anywhere — On-premises, Containers, Oracle Cloud, Cloud@Customer, or on major hyperscalers (AWS, Azure, Google Cloud).
This “runs anywhere” flexibility underscores Oracle’s multicloud openness, a competitive differentiator as hybrid architectures dominate enterprise IT strategies.
AI Vector Search: Redefining Enterprise Intelligence
Oracle’s AI Vector Search was one of the most discussed innovations at the event. It transforms unstructured data — such as text, images, and audio — into multi-dimensional embeddings, enabling context-aware and semantic search directly within the database.
How It Works
- Data is embedded into vector representations.
- Queries are converted to vectors and compared using mathematical similarity (e.g., cosine distance).
- The result is contextually relevant, semantic retrieval at scale.
Key Capabilities
- Supports semantic search — understanding meaning, not just keywords.
- Enables recommendations, anomaly detection, and contextual reasoning.
- Uses SQL syntax for seamless developer integration.
- Includes high-performance vector indexes like IVF-Flat and HNSW.
Oracle demonstrated this with a practical example: a “National Parks Image Search” application, where users could query semantically across images and text to retrieve similar scenes.
Oracle AI Vector Search – The Future of Semantic Intelligence in Oracle Database 23ai
AI Vector Search in Action: SQL + LLM Integration
Oracle’s integration of LLMs and SQL inside the database showcases its vision for AI + Data unification.
The process is fully SQL-driven:
- Generate the question vector using
VECTOR_EMBEDDING(). - Perform Vector Search using
VECTOR_DISTANCE(). - Compose and run the LLM prompt using SQL.
- Retrieve the LLM response directly in SQL output.
This integration highlights how Oracle AI Database 26ai allows DBAs and developers to query, reason, and generate insights using familiar SQL commands, bridging the gap between data engineering and AI reasoning.
Prepared for Upgrades: Smarter Lifecycle Management
Oracle showcased ORADiff, a purpose-built tool to streamline Oracle Database upgrades and patching.
Capabilities of ORADiff
- Compares database releases (e.g., 19c → 23ai or 26ai).
- Evaluates system privileges, parameters, and time model statistics.
- Highlights deprecated and changed parameters.
- Integrates with Real Application Testing for performance validation.
- Provides detailed analysis for like-for-like workloads, ensuring confidence before production rollout.
This automation-first approach is part of Oracle’s mission to make upgrades “worry-free” — allowing DBAs to focus on optimization rather than manual migration tasks.
AWR.ai: Intelligent Performance Insights
Oracle’s AWR.ai (Automatic Workload Repository with AI) introduces AI-assisted performance diagnostics.
Key Benefits
- No More Complex Queries: Users can ask natural-language questions instead of writing SQL.
- Enhanced UX: A conversational interface for querying AWR data.
- Smarter Answers: The AI can interpret nuanced questions, correlate multiple snapshots, and identify anomalies across workloads.
In one example, users could simply ask, “Show the SQL with the highest regression”, and AWR.ai generated the insight instantly.
This marks a significant evolution from static performance reports to intelligent workload analysis powered by Oracle Generative AI.
SQLPerf.ai: Simplifying SQL Optimization
Oracle’s SQLPerf.ai is a generative AI assistant embedded within SQL Monitoring, designed to help DBAs identify, analyze, and resolve SQL performance bottlenecks.
Capabilities
- Root-cause analysis for poorly performing queries.
- Identifies bottlenecks and join inefficiencies.
- Provides actionable recommendations for query tuning.
- Integrates directly into the SQL Monitoring dashboard.
- Suggests AI-driven improvements such as index usage, plan hints, and join reordering.
SQLPerf.ai in Action
The live demo of SQLPerf.ai illustrated AI-assisted diagnostics inside Oracle Enterprise Manager 24ai.
In this example:
- SQLPerf.ai detected cardinality under-estimates for a query.
- Automatically suggested gathering table statistics and creating extended statistics for improved optimizer performance.
- The interface integrated AI prompts directly within Enterprise Manager, merging human expertise with automated intelligence.
AskEM: GenAI for Observability
Another key AI innovation was AskEM, Oracle’s Generative AI-powered assistant for Enterprise Manager.
It transforms how DBAs interact with telemetry data, offering conversational access to complex performance metrics.
AskEM Capabilities
- Uses advanced AI chatbot technology trained on EM repository metadata.
- Provides context-aware responses grounded in Oracle documentation and metrics.
- Supports telemetry, logs, and documentation queries through natural language.
- Shortens the learning curve for new DBAs by offering interactive diagnostics.
A live example showed AskEM answering:
“What is the performance of my database?”
and immediately providing recommendations with drill-downs into telemetry charts.
AskEM in Enterprise Manager
Oracle’s Enterprise Manager now embeds AskEM directly within its monitoring dashboards.
Users can interact through a GenAI-powered chat interface while simultaneously visualizing metrics such as:
- Active Session History
- Database Time per Second
- Buffer Cache Hit Ratios
- PGA Cache Efficiency
This dual interface combines telemetry visualization with AI-driven explanation, redefining observability for the AI-driven DBA.
The Rise of the AI DBA
The overarching theme at Oracle AI World 2025 was clear: the role of the Database Administrator is being redefined.
The AI DBA is no longer limited to maintenance or troubleshooting. Instead, they are evolving into data strategists, responsible for ensuring performance, governance, and intelligence through automation and AI collaboration.
What Sets the AI DBA Apart
- Uses AI assistants (AWR.ai, SQLPerf.ai, AskEM) for proactive management.
- Designs AI-augmented architectures using Oracle AI Database 26ai.
- Understands vector search, embeddings, and RAG pipelines.
- Masters multicloud orchestration using Oracle’s unified data framework.
- Balances automation with strategic decision-making.
The modern DBA’s toolkit now extends beyond SQL — it includes LLM integration, semantic search, telemetry analytics, and AI observability.
Conclusion: Oracle’s AI Vision for the Data-Driven Enterprise
Oracle AI Database 26ai redefines the meaning of “AI for Data”.
It is not about adding AI tools around databases — it’s about embedding AI directly within them, making intelligence native to data management.
With innovations such as AI Vector Search, AWR.ai, SQLPerf.ai, and AskEM, Oracle empowers DBAs and developers to work smarter, not harder.
These technologies collectively transform enterprise data platforms into self-optimizing, self-analyzing, and AI-driven ecosystems.
For enterprises seeking to modernize their data strategy, Oracle’s message is clear:
“AI changes everything — especially your database.”
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