Systems | Development | Analytics | API | Testing

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.

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.

What an incredible day at the Agentic Analytics Playbook EMEA event! It might have been baking hot outside, but inside the Nobu Hotel, the energy was absolutely electric.

Monthly active user rates stuck at 23%. A Slack message about another client who can't find the data they need. A support ticket your team has started to joke about: “Can you export this to Excel?” Once upon a time, embedding dashboards inside your product was a differentiator. Today, this is what the feedback looks like when your analytics stop working. AI agents can now respond to complex questions with meaningful insights in seconds.

You ask your agent a question. The answer is slightly off. You point out the gap. Spotter fixes it, and that fix doesn't disappear when the session ends. Your team doesn't re-explain the same thing tomorrow. The next analyst doesn't start from scratch. The correction stays, and the work gets better from here. That's what memory makes possible. Not just for you. For everyone who comes after.

I spend my days helping organizations modernize analytics to drive tangible business outcomes and liberate analytics teams from legacy processes. At this year’s Snowflake Summit ’26, I had the pleasure of sitting down with AJ Subbarao, Director of Business Intelligence & AI at Intuitive Surgical (ISRG).