The 3 questions to answer to take AI from experimentation to impact
Summary
Moving AI from experimentation to enterprise impact requires embedding secure, governed AI agents directly into employees' everyday workflows rather than forcing them into isolated tools. Establishing unified governance frameworks and automated identity management allows teams to safely automate tasks across their full data estate without stalling adoption or creating shadow IT.
Summary generated by brickster.ai. For the full article, follow the source link above.
More from Databricks Blog
IP Functions are Generally Available, bringing high-performance network analytics to the Lakehouse
Native IP functions are now generally available on Databricks Runtime 18.3+, providing built-in support to parse, validate, canonicalize, and join IPv4 and IPv6 addresses and CIDR blocks without UDFs or regex. Optimized in Photon across SQL, PySpark, and Scala, these native functions run demanding network workloads in seconds and complete IP CIDR joins up to 3.1x faster and 6.4x cheaper than another leading cloud data warehouse.
Introducing ai_decide: make fast decisions on your governed data
Databricks has introduced ai_decide, a new AI Function that processes unstructured text to return structured decisions faster and at lower cost than traditional LLMs. Optimized for decision-making rather than text generation, it runs directly on governed data at batch scale in SQL or in real time over REST for tasks like prompt routing, metadata tagging, and agent quality evaluation.
A practical guide to cost optimization with Lakebase Postgres
Lakebase Postgres delivers low, predictable costs when you sync only the working subset of Lakehouse data, match sync modes to actual freshness needs, and right-size compute so hot data fits in cache. Combining these optimization practices with serverless scale-to-zero, tuned PITR, and shared storage ensures strong performance and availability without paying for unused resources.
How to scale agentic applications without creating AI sprawl
Scaling autonomous agentic applications without creating AI sprawl requires shared infrastructure for context, tooling, governance, and observability instead of rebuilding capabilities for every agent. Establishing centralized choice, governed enterprise context, and rigorous control ensures teams can adopt new models while enforcing scoped permissions, consistent policies, and operational tracing.
Your data, your storage, your rules: a 2026 guide to storing Unity Catalog managed tables
You can redirect where Unity Catalog managed tables land by using SET MANAGED LOCATION and moving existing data through external table conversion. Defining managed storage paths at the metastore, catalog, or schema level keeps you in control of your cloud storage to support compliance and accurate cost attribution.
How Databricks rolls out frontier models to 12,000 employees on Day 1
Databricks delivers Day 1 access to frontier AI models for all 12,000 employees to ensure cutting-edge capabilities are immediately available internally. See how the company prioritizes and operationalizes large-scale model rollouts across its entire workforce.