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AI-powered analytics is everywhere right now. But the payoff? Not so much. Two patterns show up again and again. The first is an “AI everything” backlog that expands faster than teams can deliver. The second is an insights bottleneck that still forces the business to wait in line for basic answers while analysts drown in ad hoc requests.

Your data’s never lived in one place. Customer records might be in your CRM, while sales and operational metrics are split among data platforms. And somewhere, there's critical budget data living in a spreadsheet, owned by a single person on the finance team. Bringing it together has always come at a cost of speed vs. governance.

Every analyst has been there: Deadline looming, data in hand, and a BI tool that either requires a workflow you haven't learned or a colleague you can't reach. So you open Excel. It's familiar, it's flexible, and it works right now. So that's where the work happens—and now where insight stays, ungoverned and invisible to your team, your analytics stack, and your agents.

We launched Spotter with one goal: give every enterprise team their own analyst—an agent that reasons through business complexity, validates its own outputs, and surfaces answers you can actually act on. The response from customers made one thing clear: the ThoughtSpot foundation works. Teams trust Spotter, because it doesn’t only rely on an LLM to reconstruct your business logic on the fly—a process that produces different answers depending on how a question is phrased.

AI can now generate SQL, build dashboards, and answer questions in plain language. But generating queries isn’t the same as understanding a business. The model might not know which revenue definition finance approves, how your fiscal calendar works, or which fields require restricted access. As AI agents become the front door to analytics, the real challenge isn’t query generation; it’s semantic grounding. That’s where the Agentic Semantic Layer becomes essential.

The EMEA region is undergoing a massive transformation, driven by companies demanding instant, actionable insights embedded directly into their applications and workflows. This fundamental shift away from legacy BI has translated into record-breaking momentum for ThoughtSpot, positioning EMEA as our fastest-growing region globally. The Agentic Analytics revolution is here, and ThoughtSpot is delivering on the promise to make the world more fact-driven.

One year ago, we introduced Analyst Studio, ThoughtSpot’s unified workspace for preparing and managing AI-ready data, with a vision: to transform analysts from report generators into business catalysts. SQL, Python, and visual analysis finally worked together in one workspace, letting data teams move seamlessly between ad-hoc queries and advanced modeling, all while preparing data for the AI revolution we knew was coming.

Agentic AI is moving quickly from experiments to real work. In 2026, it shows up inside the workflows that drive outcomes: decisions, operations, and accountability. In the season 7 premiere of the Data Chief podcast, host Cindi Howson sat down with three leaders who work at the intersection of AI ambition and enterprise execution: Paul Baier (GAI Insights), Jennifer Belissent (Snowflake), and Rory Blundell (Gravitee).

Interactive analytics has a simple promise: answers should show up when people need them, not after the moment has passed. But when usage spikes, many teams end up paying twice: once in latency and again in compute. We recently published a blog introducing Snowflake Interactive Analytics and what it means for ThoughtSpot customers.

AI is changing how work gets done. But for many data leaders, it’s also creating a new challenge: managing the cloud bill. As more people (and more AI agents) query data, cloud data warehouse (CDW) spend can spike fast. Costs become harder to predict, and teams end up making tradeoffs—scaling AI insights or staying within budget. That tension creates a real bottleneck on the path to becoming AI-ready.