Systems | Development | Analytics | API | Testing

Imagine handing a new developer an SDK, a stack of docs, and a deadline. They read for twenty minutes, write ten lines, break something, go back to the docs, second-guess a prop name, and try again. Now multiply that by every component they'll embed, every agent, every color scheme, and every project they'll touch. That gap, between "I know what I want this to look like" and "I know the exact syntax to make it happen," is the tax every developer pays on embedded analytics.

Retailers are making heavy investments in AI. From interactive virtual shopping assistants to automated supply chain tools, the goal is simple: connect with buyers and drive growth. However, realizing real business value requires bridging a critical trust gap. So why did ThoughtSpot team up with YouGov to survey 4,833 adults across the US and the UK? It all comes back to trust.

If there's one thing that stuck with me from Yannick Misteli's session at the Agentic Analytics Playbook event in London, it's this: most AI pilots don't stall because of technology or budget. They stall because nobody answered the "day after" questions. I had the opportunity to sit down with Yannick Mistelli, Head of Engineering at Roche, the global pharma company with 100,000+ employees and heavy regulation across 25+ countries.

Seven AgentSpot agents now augment every step of our content development pipeline, shifting our team’s focus from manual operational tasks to high-value content development and strategy. Our eLearning development team at ThoughtSpot manages around 50 courses and hundreds of videos across six learning paths on ThoughtSpot University. We build enablement content for external customers: business users, business analysts, data experts, and administrators.

Our CEO recently wrote reaffirming an architectural decision ThoughtSpot made when LLMs first emerged: we do not use LLMs to directly generate SQL. My team has spent the better part of a year building AgentQL: a capability that doubles down on our decision. So let me explain what we actually built, why it doesn't just honor that architectural decision but depends on it, and the engineering choices underneath.

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.

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.

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.

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.

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.