Reclaiming Self-Service in the Age of AI-First Analytics

Business users don't want another tool to log into. They want answers where they already work, inside the applications they use every day. But for most organizations, self-service analytics still means leaving a workflow, opening a separate BI platform, and hoping the dashboard someone else built is close enough to what they actually need. That gap is the problem. And it's one that most analytics vendors have quietly deprioritized as they race to lead with AI messaging. The self-service story has stalled, and the people who need it most are still stuck context-switching to find answers that should be one click away. What's getting lost in the AI race is a more foundational question: when an executive asks an AI agent for revenue figures, or a field rep queries an embedded dashboard in your application, are they getting the same answer? The interface has changed. The underlying problem has not. Without a governed semantic layer translating raw data into consistent business meaning, AI becomes fast and unreliable simultaneously, and unreliable intelligence is unusable intelligence. In this session, we'll explore how Logi Symphony and Simba Intelligence from insightsoftware Data + Analytics take a fundamentally different approach: embedding self-service analytics directly inside the products and systems users already operate in, while ensuring the data powering those experiences is AI-ready, governed, and consistent across every interface. You'll learn how a single Logi Symphony deployment can serve every user type simultaneously, from occasional viewers to power analysts, each with an interface matched to their role and capability, governed from a single platform, and running entirely within the infrastructure your organization controls. And you'll see how Simba Intelligence connects that embedded experience to a governed data foundation that works whether insight is delivered through a dashboard, an application, or an AI agent asking the same question. What you'll take away:

• Why self-service analytics fails when it lives outside the workflow and how embedded delivery changes that equation

• How Logi Symphony supports differentiated user experiences across roles, tenants, and use cases within a single deployment

• Why the semantic layer is now the most strategic asset in enterprise analytics and what "AI-ready data" actually requires in practice

• What "self-service that feels native" means for ISVs building in regulated industries vs. teams developing internal data products

• How to build the case internally for an analytics stack that serves today's embedded workflows and tomorrow's AI interfaces from the same governed foundation