ThoughtSpot

Why does the same question you ask AI return a different answer on Monday than it did on Friday? Generic AI doesn't know what your business means (your definitions, your fiscal calendar, your logic, your rules), so it reasons from raw table names and column headers. Then, the answer shifts depending on how the question was phrased, who asked it, and what the model decided to prioritize that day. Spotter Semantics fixes this issue at the source. Define your business logic once, and every agent, app, and query works from the same governed foundation. Plus, it gets smarter the more your team uses it. The semantic layer tells your AI what your data says. Spotter Semantics tells it what your business means. See how Spotter Semantics works: https://bit.ly/4hxEWzc

Finding the issue is the easy part. Acting on it is where teams lose the most time. Where does your team get stuck? 👇

Gone are the days when annual planning takes two weeks of data prep before you get to the strategizing and decision-making.  Here's the prompt playbook finance leaders are using with Spotter, our agentic analyst, to go from numbers to next year's strategy—fast. 

From sold-out summits to stadium roundtables to cleaning up a river in the pouring rain, it’s been a busy and rewarding summer for our EMEA team. 🇬🇧 London: The Agentic Analytics Summit brought together inspiring conversations with speakers from Thrive, CWT, Snowflake, Roche, Zencargo, Grant Thornton Ireland, Dunelm, and Vita Mojo. 🇩🇪 Munich: The Agentic Analytics Playbook DACH came to life at the Allianz Arena München Stadion GmbH. onlyfy by XING shared one of the most grounded sessions we've seen, moving from static dashboards to AI-powered self-service analytics embedded directly into their product, with honest lessons on scaling from hype to real business impact. 💧 Isar River: R.I.S.E. Global Impact Week brought our DACH SE team out to clean up one of Munich's most beloved rivers. It rained, and they went anyway. The before-and-after photos speak for themselves. Grateful for every customer, partner, and Spotter who showed up this month. 🙌 #LifeAtThoughtSpot

"In the AI world of 2026, the maxim 'Knowledge is power' could be updated to 'Semantics is power,'" says Tony Baer in SiliconANGLE & theCUBE. AI agents don't apply judgment to questionable data the way human analysts do. They don't recognize that "revenue" means gross in one system and net in another, or that last quarter's churn metric was quietly recalculated midway through the year. They reason from the context they're given, so when that context is absent, the outputs are wrong in ways that are hard to detect and impossible to contain. According to Gartner, by 2028, 60% of agentic analytics projects relying solely on MCP will fail due to a lack of a consistent semantic layer. The semantic layer needs to be not only a data team priority but also an organizational one. We built a practical guide for how to audit, build, and govern an agentic semantic layer that scales with your agents, eliminates hallucinations, and gives every user answers they can trust.

There are a couple of things you don't want to build internally. Jeff Rubesin from Endpoint Clinical shares what that is with our own Ivan Seow. Plus, they discuss the build vs. buy decision to what it took to get an AI-native product to market. 📽️Watch the full conversation: https://lnkd.in/eNgpJnxK

Really enjoyed the conversation with Cindi Howson on The Data & AI Chief podcast discussing what it takes to build a trustworthy and cost effective data foundation for agentic AI. Listen here: https://lnkd.in/eggnEVXJ #TheDataAIChief /cc ThoughtSpot

Token-maxxing, running up AI usage to make adoption look successful, is the new vanity metric every Chief Data and Artificial Intelligence Officer (CDAIO) will be hearing about soon. The pattern is familiar. A POC runs cheap in the sandbox, looks great on paper, and gets greenlit for production. Then the same app jumps to a five- or six-figure daily run rate overnight. Not because usage exploded, but because nobody audited the architecture. Below is a practical checklist for CDAIOs who want to get ahead of it before procurement starts asking questions they don't have answers to.

Today we are launching the Spot30 program.  The challenge with Enterprise AI is not the technology. We have plenty of models and data stores. What we need is more adoption to drive business impact. Every AI conversation needs to be an ROI conversation. That's why we created Spot30, a time-boxed fast start program available to ThoughtSpot customers at no additional cost. We believe the first 30 days matter more than the next 365. As part of Spot30, our architects and success engineers join forces with your team from day one to build the right semantic foundation, deploy a meaningful use case into production, and establish a change management framework. Think of this as your 30-day innovation sprint. The objective is simple: to give you the strongest possible start so your team can confidently scale. We are beginning a worldwide rollout of Spot30, starting with new customers purchasing ThoughtSpot Embedded (TSE). I'm personally very excited about this. Your success is our success. Spot30 is our investment in helping you get there faster. Reach out to your ThoughtSpot team to check eligibility and program requirements. More to come.

Spotting sales trends across 365 days shouldn't require endless scrolling. In our next Behind the Chart, we show you how to compress an entire year into one scannable view. By mapping daily sales activity to color-coded slots, seasonality and recurring patterns become immediately obvious. Try this when you need to: → Show activity patterns over time → Make dense daily data easier to scan → Highlight milestone events alongside recurring work → Build a custom chart type that doesn't exist out of the box