A finance operations team had
everything they needed—or so it seemed. Their transactional data was clean,
reconciled, and sitting in the database. Reports were accurate. Dashboards were
trusted.
Yet every week, the same questions
came up:
- “Why did receivables spike last month?”
- “Which customers are most likely to delay payments?”
- “Can you quickly explain this variance without building a new report?”
Each question triggered a familiar cycle:
- Write a new SQL query
- Add a new report
- Or ask a data analyst to interpret the numbers
The data was ready. The answers
were not.
This gap—between structured data and human-friendly insight—is exactly where Select AI, introduced in Oracle Database 23ai and enhanced in 26ai, changes the conversation.
👉 Oracle Select AI: The Architecture of Natural Language Data
This blog explains:
- What Select AI is and why it matters
- A simple enterprise use case
- The architectural thinking behind it
- Key lessons learned for production adoption
This is not a feature walkthrough.
It’s a design and value discussion for real enterprises.
What
Is Select AI (In Simple Terms)?
Select AI allows users to:
- Ask natural language questions
- Directly against enterprise data
- Using standard SQL semantics
Instead of translating business
questions into SQL manually, users can ask:
“Show me customers with increasing
overdue balances in the last 90 days.”
And the database itself:
- Interprets the intent
- Generates the query
- Returns structured, explainable results
The key shift is this:
AI is no longer outside the database. It is embedded into how data is queried.
A Simple Enterprise Use Case :Business-Friendly Analytics without Rebuilding Reports
Scenario
A finance team wants faster insight into receivables and payment
behavior—without waiting for new reports or dashboards.
Traditional approach : Analysts write custom SQL , BI teams create new reports ,Business waits days or weeks
With Select AI
- Business users ask natural language questions
- Queries run directly on governed enterprise data
- Results are returned in structured form
Example questions
- “Which customers have overdue balances increasing month
over month?”
- “What invoices are most likely to miss payment
deadlines?”
- “Summarize receivables risk by region”
No new ETL, No new semantic model & No separate AI
pipeline.
This has several important
implications.
1.
Data Gravity and Governance Remain Intact
In most AI architectures: Data is copied out ,Transformed,Sent to external AI services
This creates: Security concerns , Data duplication , Governance gaps
With Select AI:
- Data stays where it already lives
- Existing access controls apply
- No new data movement is required
For enterprises, this is not a
convenience—it’s a requirement.
2.
SQL Remains the Control Plane
Select AI does not replace SQL.
- SQL remains the execution layer
- AI assists with intent translation
- Results are still query-based and auditable
This matters because:
- SQL is explainable
- SQL is testable
- SQL fits enterprise controls
From an architect’s view, this
dramatically lowers adoption risk.
3. AI Is Used for Reasoning, Not Guessing
A critical design choice in Select
AI is bounded intelligence.
The AI:
- Translates intent into SQL
- Operates within schema, metadata, and constraints
- Does not hallucinate new data
This makes it suitable for: Finance ,Operations, Compliance-sensitive domains
In other words, Select AI is
designed for enterprise trust, not just convenience.
Based
on real enterprise patterns, a few lessons stand out.
1.
Select AI Is Not a Replacement for Analytics
Select AI works best when:
- Core data models are already solid
- Metrics and definitions are agreed upon
- Data quality is trusted
It augments analytics—it does
not replace modeling or governance.
2.
Metadata Quality Matters More Than Ever
- Table names
- Column descriptions
- Data relationships
…all become critical.
This is a data design discipline,
not an AI problem.
👉 for additional details refer our blog Why Metadata is Important Select AI
3. Start with Guided Use Cases
Successful adoption starts with:
- Well-defined domains (finance, supply chain, ops)
- Known question patterns
- Controlled user groups
This builds confidence before
broader rollout.
4.
AI Success Is an Architecture Exercise
Select AI works best when:
- Security is well-defined
- Data ownership is clear
- Usage patterns are intentional
AI does not eliminate
architecture—it raises the bar for it.
Where
Bizinsight Consulting Fits In
At Bizinsight Consulting, we
see Select AI not as a feature—but as a capability that must be designed
correctly.
- Identifying the right use cases for Select AI
- Ensuring data models and metadata are AI-ready
- Aligning AI access with enterprise governance
- Helping teams move from experimentation to production
The value is not just using
Select AI—it’s using it responsibly and effectively.
Reach out to the Bizinsight Consulting team today for expert design, development, and strategic advisory services.
