databox

How to Become an AI-Native Team

Jul 1, 2026 By Špela Jurič In Databox
A year ago we announced that Databox is becoming an AI-first company. At the time, that mostly meant what it meant for many companies in 2025: AI becoming a strategic priority. Teams were encouraged to experiment and adopt new tools, as it was clear that AI wasn’t a trend we could ignore. That was the easy part. What’s become clear over the last year is that there’s a significant difference between being AI-first and being AI-native.
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databox

Introducing the Skills Marketplace: AI analyses on your data, with expert judgment built in

Jun 30, 2026 By Peter Caputa IV In Databox
Every team we talk to has a running list of questions they wish they could get fast, reliable answers to. What changed in our performance last month and why. Which clients are showing the early signs of churn. Which channels are actually pulling weight and which ones are quietly burning budget. The pull toward AI for this kind of work is obvious. The answers should be a question away.
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hitachi vantara

How AI Inference Is Reshaping Enterprise Infrastructure

Jun 30, 2026 By Sunitha Rao and Nick Loy In Hitachi Vantara
Data center teams are skilled at solving familiar problems such as storage outages, missed forecasts, and late refresh cycles. These are known quantities. Teams have playbooks for them. But 2026 has brought a different kind of pressure. After years of enterprise AI investment concentrated almost entirely on model training, the industry has crossed a threshold: the workload that now defines AI infrastructure isn’t building models. It’s running them. Continuously. At scale. Every day.
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ThoughtSpot June Release: Customize Your Agent

Jun 30, 2026 By ThoughtSpot In ThoughtSpot
Check out what’s new in ThoughtSpot’s latest release! SpotterModel gets smarter: Build complex data models with AI formula suggestions and instant version rollbacks if you make a mistake. No stress, no lost work. Spotter Instructions: Fully customize Spotter’s persona, formatting rules, and strict guardrails. It says exactly what you want it to say—and nothing it shouldn't. Ad Hoc Analysis: Drop local files directly into Spotter for instant answers, or blend them safely with your governed enterprise data.
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astera

Foundation First: Why Model-Agnostic Data Platforms Win

Jun 30, 2026 By Hira Asif In Astera
In 2024, two of the largest data platform companies, each with billions in revenue and dedicated AI research teams, invested in building their own foundation models. One spent roughly $10 million training a 132-billion parameter model on 3,072 NVIDIA H100 GPUs. The other released a 480-billion parameter model optimized for enterprise tasks like SQL generation and code. Both achieved strong results within their compute class.
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