Strategy Software

AI agents are only as good as the context they receive.

Without business context, they don't understand how your organization defines customers, revenue, or other critical metrics. Instead, they interpret raw schemas and make assumptions that can lead to inconsistent answers.

Join Kaitlin Qreitem on July 16 at 12:00 PM EST or Johannes Silhan on July 16 at 10:00 AM BST / 11:00 AM CEST for a live demo of Strategy Mosaic and see how a semantic model gives AI the context it needs to produce consistent, governed answers.

In this session, you'll see:
🔹 How a semantic model captures business logic once for AI, dashboards, and analytics
🔹 How an AI agent connects to Mosaic through MCP and the difference between querying raw schema versus governed business context
🔹 Why governed semantics deliver the same answer to the same question across tools and AI agents
🔹 How to build a semantic model visually in Mosaic Studio, no SQL required

Whether you're an analyst, data engineer, or AI practitioner, you'll leave with a practical understanding of why enterprise AI needs a semantic foundation and how to build one.

Register here: https://ow.ly/OaCE50ZlEF2

As AI agents move from dashboards to decisions, something has to govern how they interpret your data. That’s the semantic layer’s new job: one place where metric definitions, access policies, and business logic live — and get enforced everywhere.

A new report by Forrester Research, Make Data AI Ready Via Semantic Layer Platforms, breaks down what that shift means in practice, including why your data estate needs an independent semantic control plane above your LLMs, warehouses, and BI tools.

Because if your AI is acting on your data at scale, “close enough” isn’t a governance strategy.

Read the full Forrester report, courtesy of Strategy: https://ow.ly/v64F50ZlG0U

For Lotte Department Store, one of the largest retail chains in South Korea and Southeast Asia, the challenge wasn't a lack of data. It was making trusted insights accessible to everyone, regardless of technical expertise.

By deploying Strategy AI Agents on a governed data foundation, Lotte enabled employees to interact with data using natural language while ensuring responses remained consistent, accurate, and grounded in business context.

The results included:
→ 400% increase in advanced analytics usage across business teams
→ 9,500+ AI-powered analysis cases completed in the first six months
→ 90% improvement in efficiency, with answers delivered 10× faster
→ 8 specialized AI agents supporting key retail business scenarios

By combining AI, governance, and external market intelligence, Lotte is empowering teams to make faster, smarter merchandising and operational decisions at scale.

Read the full customer story here: https://ow.ly/Xtum50ZcMbz

💡 Everyone wants trusted AI. It starts with trusted business context.

One of the most talked-about topics in data today is the semantic layer, but what is it, and why does it matter?

Our new FREE self‑paced Semantic Layer Foundations course is a conceptual deep dive into what the semantic layer really is, why it matters, and how it solves the “which revenue number is right?” problem once and for all.

In this course, you’ll explore:
→ Why metrics end up conflicting across dashboards and teams
→ What a semantic layer actually is (and isn’t) in plain language
→ How a shared semantic layer becomes the trusted source of truth for people and AI
→ How these concepts are embodied in Mosaic, Strategy's universal semantic layer built for real‑world analytics

Using Mosaic as a real-world example, you'll gain the knowledge and vocabulary needed to lead semantic layer conversations with greater clarity and confidence across both business and technical teams.

Enroll for FREE today: https://software.strategy.com/education