ThoughtSpot

As the head of ThoughtSpot’s international business, based in London, I have the privilege of working with some of the most forward-thinking digital leaders across EMEA. But every now and then, a customer journey comes along that perfectly encapsulates why we do what we do. At our recent Agentic Analytics Playbook EMEA event in London, I had the pleasure of welcoming Craig Haughan, VP of Data Engineering—who led the data and analytics charge for CWT—on stage alongside Jimmy Hall, who leads Snowflake here in the UK and Ireland. For those who don’t know CWT, they are an absolute powerhouse in corporate travel. If you’ve booked business flights, hotels, or itineraries, chances are CWT’s engines are running in the background. That means managing millions upon millions of complex, fast-moving data points. Historically, legacy BI kept this data locked away in rigid, pre-defined PDFs. Today, CWT puts live, conversational insights directly into the hands of over 100,000 users . In fact, their

Your team already knows when a campaign starts to underperform, when spend spikes, or when web traffic shifts. What you don't have is the speed to turn that insight into action. Someone still has to investigate, decide what to do, pull in the right people, and coordinate the work. That takes time. As a data-driven CMO, I've lived this every day. I can know the instant something changes in the data, but there's still a large gap between insight and action. Today, we're closing that gap. I’m excited to share that we’ve launched AgentSpot , the agentic workforce platform to build workflows that decide, act, and deliver across every system your business runs on, grounded in your data. Anyone on your team can now build a team of agents that turns the insights they uncover into action. A new operating model for work AgentSpot turns repeatable tasks into agentic workflows that run on live data across your systems. It has context about your business, so the people who know

The journey to AgentSpot didn't start with a traditional product roadmap or a speculative “what if” from our R&D labs. Instead, it was born out of a growing friction within our own walls and became a "frontier R&D project" fueled by engineers exploring the internal potential of generative AI. When we first launched SpotGPT, our internal genAI application (similar to ChatGPT, but trained on internal resources) we saw immediate and massive adoption. However, the phase of simple queries quickly evolved into a sophisticated problem: our teams didn’t want just a chatbot, and instead started asking more and more for specialized digital colleagues. As usage surged, our engineering and product teams were flooded with requests from across the business. Our Marketing, Finance, and HR teams weren't looking for generic AI advice but asking for agents with vertical-specific knowledge and the ability to autonomously move their unique workflows forward. They needed tools t

Foreign Filing filed 2026-08-04

Foreign Filing filed 2026-08-04

From Reactive Dashboards to Proactive AI with ThoughtSpot Every data leader knows the BI waiting room. Someone in operations needs an answer, files a request, and waits a day, sometimes three. By the time the dashboard arrives, the moment that mattered has moved on. That waiting room was the quiet subject of one of the sharpest sessions at the Agentic Analytics Playbook event in London . Brian Reynolds, VP of Embedded at ThoughtSpot, sat down with Sam Greenhalgh, Chief Revenue Officer at Zencargo, for a fireside chat titled "The Competitive Edge: Driving Business Outcomes Through Embedded Intelligence." The theme? A company that decided answers should reach its customers before anyone thinks to ask the question. Here’s how Sam's story maps to the shift you’re probably chasing right now. Why Is Real-Time Supply Chain Visibility So Difficult? Start with the freight itself. Zencargo is a digital freight forwarder, moving goods around the world by sea, air, road, and rail. &

At the Agentic Analytics Playbook EMEA event, Stefan Cotoiu, Co-Founder and Chief Product/Engineering Officer at Vita Mojo, shared how the hospitality software provider transformed its data strategy to empower its customers and unlock unprecedented leadership visibility. Vita Mojo powers the digital infrastructure for quick-service restaurants (QSRs) across Europe. Operating in a high-transaction, low-margin industry, their restaurant clients are flooded with transactional data—from click-and-collect orders and self-service kiosks to point-of-sale systems and kitchen fulfillment. However, most QSR operators are severely constrained by small head-office teams and lack dedicated in-house data analysts. Here is how Vita Mojo shifted its strategy to empower non-analysts across the long tail and drive C-suite adoption using ThoughtSpot Embedded and Spotter. 1. Empowering the "80% Long Tail" of Non-Analysts Before adopting ThoughtSpot, Vita Mojo faced a common challenge in embedded

When the Open Semantic Interchange (OSI) initiative launched last year, it set out to solve a problem every data leader recognizes: the same business metric gets defined a dozen different ways across a company's BI tools, warehouses, and now, AI agents. "Monthly active users" in the CRM rarely matches "monthly active users" in the warehouse, and every new AI copilot added to the stack makes the gap more visible, not less. That initiative has just taken its most consequential step yet. OSI has entered the Apache Software Foundation’s (ASF) incubator and, as part of that transition, has been renamed Apache Ossie (Incubating) . The specification and the community behind it haven't changed. What has changed is the governance model: Ossie now operates under ASF norms, with public mailing lists, GitHub-based development, and committership earned through contribution rather than employer affiliation. ThoughtSpot was a founding member of OSI , and we're conti

📌 Key takeaways 1. A POC's token cost does not scale linearly into production—the same app can go from free to a five or six-figure daily run rate overnight. 2. Token-maxxing (measuring AI success by volume consumed) is a vanity metric, not a value metric. 3. Inference spend, not model training, is where AI budgets are actually exploding. And it's headed toward the same formal cost controls as cloud and travel. A Head of Product at a major sportswear retailer has a brilliant idea: let’s build an app that sales staff on the shop floor can have on their tablets, and ask their questions there and then, where they serve customers. They set about building. In order for the app to answer questions, it needs to have the information from the 2026 Spring/Summer Catalogue, a mammoth manual, let’s say 300k tokens. Then the developer loads the huge PDF into the system instructions of the LLM and builds a chat interface over it. When the sales assistant in the store asks about Adidas Gazelles, the

I'll be honest: one number from the latest embedded analytics research stopped the entire planning conversation for this webinar. 57% of teams with embedded analytics report no measurable business impact , and that means not low impact or underwhelming impact, but no measurable impact at all. That stat set the stage for a candid, wide-ranging conversation between Ivan Seow from ThoughtSpot and Jeff Rubinson, VP of Product at Endpoint Clinical , during the June 25 webinar " Bridging the Embedded Analytics Revenue Gap ." What followed was part research briefing, part real-world case study, and part honest advice for anyone building analytics into a product. Here’s what stood out. The Revenue Gap Is Real, and It’s Not About Adoption If embedded analytics was struggling because nobody used it, the fix would be straightforward: improve the product, drive adoption, measure results. But the Product Led Alliance Embedded Analytics Opportunity 2026 Report featured in the webinar

For years, the "Data-Driven" dream has looked a lot like a crowded screen. We built dashboards for every department, every KPI, and every niche project. But as we reached "peak dashboard," a frustrating reality set in: we were drowning in visualizations but starving for immediate insights. At a recent fireside chat on the Agentic Analytics Stage , I sat down with Joachim Höffner, Verivox’s Lead Data & BI Engineer, to explore a provocative question: Is traditional BI dying, or is agentic analytics the progress we’ve been waiting for? For Verivox , the answer isn’t about choosing one over the other—it’s about a fundamental shift in the "operating model" of data. The "Dashboard-Driven" Trap The debate began with a blunt assessment of the status quo: Most companies are not data-driven; they are dashboard-driven. At Verivox, the team recognized this pattern early on. While dashboards are great for monitoring, they often fail at the explanation pha