Over the past two decades, I have built nearly every kind of enterprise data platform: data warehouses and BI stacks, data lakes, petabyte-scale cloud platforms and a data mesh. Every one of them launched with the same promise of a single version of the truth. And every one, given enough time, grew new copies of data that someone had to reconcile, secure and pay for.
So as Oracle AI World 2026 approaches, I am looking at Autonomous AI Lakehouse through one lens: does it reduce the number of places our data has to live, move and be governed before it can be used? Based on what Oracle has released so far, the answer is more interesting than a feature list suggests.
What's new
Autonomous AI Lakehouse is the next generation of Oracle’s Autonomous Data Warehouse, rebuilt around the open Apache Iceberg table format. In practical terms, five things stand out:
- Open tables, any cloud. It reads and writes Iceberg tables directly in object storage on OCI, AWS, Azure and Google Cloud, and on-premises through Exadata Cloud@Customer.
- One catalog across many. A new “catalog of catalogs” connects to Databricks Unity, AWS Glue, Snowflake Polaris and Apache Gravitino, so data governed elsewhere can be found and queried in place.
- Warehouse speed on the lake. Data Lake Accelerator adds compute on demand for large scans, and caching keeps frequently used data fast.
- AI inside the database. Select AI turns plain-language questions into SQL, and Select AI Agent lets teams build and govern AI agents in the database, connecting to tools and MCP servers.
- Private by design. Models can run through private inference, so sensitive data does not need to leave for a third-party AI service.
What's different
- It is open in both directions. Most platforms call themselves open because other tools can read their data. Oracle goes the other way: it can query tables governed by other platforms’ catalogs. When I led a data enablement program for a community of more than 3,000 data users, the hardest conversations were never about tools. They were about whose data it was and whose rules applied. Meeting data where it already lives removes a lot of that friction.
- It lets you evolve instead of migrate. When I led the migration of more than 6 petabytes of data platforms across clouds, most of the time, cost and risk sat in moving data. For organizations with years invested in Oracle warehouses, this offers an incremental path: keep what works, adopt Iceberg at your own pace, and skip the big-bang migration.
- AI goes to the data, not the other way around. Vector search, graph, spatial, JSON and machine learning all run over the same Iceberg tables, alongside in-database agents. Earlier in my career, applying NLP to customer reviews meant first building pipelines to move and prepare data for separate tools. When AI runs where the data is already governed, there are fewer pipelines to build and fewer copies to secure.
- It works with what is already there. It interoperates with Microsoft Fabric, Amazon EMR and Databricks, and offers prebuilt accelerators for E-Business Suite, Fusion and NetSuite. Very few enterprises I have worked with run on a single platform, so interoperability matters more than any one benchmark.
None of this succeeds on its own. Openness shifts the hard work from moving data to governing it: deciding which catalog’s permissions win, who maintains each table, and which data deserves to stay fast. That is where most of the value will be won or lost.
The questions that matter for enterprises
For enterprises, the questions that matter are straightforward:
- Can we make trusted data available faster?
- Can we reduce complexity and the cost of managing data?
- Can we move AI initiatives from experimentation to production with confidence?
- Can we turn insights into decisions that create tangible business value?
The last generation of data platforms competed on how much data they could hold. This one will be judged on how little data has to move to create value. I am looking forward to what Oracle shares next at AI World 2026.
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