Your Company AI Doesn’t Have a Problem with Intelligence. It Has a Problem with Your Data
Discover how to unify metrics, build a semantic layer, and prepare your data infrastructure so that AI delivers accurate answers instead of confident hallucinations.
Why participate?
Discover how to unify metrics, build a semantic layer, and prepare your data infrastructure so that AI delivers accurate answers instead of confident hallucinations.
AI is rapidly becoming part of BI tools, dashboards, and everyday decision-making. Management wants to chat with data. Teams want self-service analytics. And IT and data teams are expected to deliver everything quickly.
Imagine this situation:
The CEO asks AI: “What was our profit yesterday?”
AI responds with a number that looks correct. In reality, however, it selected the wrong column in the database.
Without a solid data foundation, AI starts generating answers that appear correct but actually are not.
In this webinar, we will show how to prepare enterprise data so that AI provides accurate answers, management receives consistent numbers, and the business can truly trust its data.
- Why most AI projects end up as wasted investments and how to build an agent with real business impact
- How to ensure AI does not learn from sensitive data and that information is visible only to those who are authorized to access it
- Whether AI will truly replace dashboards and how AI agents can accelerate the development of data models
- How to unify metric definitions and create a “Single Source of Truth”
- CDOs and Digital Transformation Officers: leaders responsible for modernization and turning AI into a real business tool
- Heads of BI & Data Analytics who want to unify metrics and deliver data that can truly be trusted
- Heads of Data: leaders responsible for data strategy, quality, and infrastructure for AI initiatives
Speakers
Pavol Hajastek
Chief Technology Officer,
EMARK
Andrea Janidžárová
Presales & Analytics Engineer
EMARK
Róbert Šrotýř
Chief Business Officer
EMARK
Webinar Program
Opening words
- Why Is Artificial Intelligence Alone Not Enough for Success?
Where AI Fails in Companies (and Why)
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Why AI today works with only 1% of enterprise data
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The real impact of incorrect implementation
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2 types of unsuccessful companies:
- “Legacy-based” companies with fragmented data
- Companies that implemented AI but failed
Why AI Hallucinates Over Enterprise Data
- Your AI does not hallucinate because it is “stupid” (Garbage In, Garbage Out)
How to Implement AI Correctly (Step by Step) + 2 Demo Examples
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How to define a use case with real business value
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How to integrate data across systems within a selected cloud platform
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How to create a “Single Source of Truth” that both the analytics team and AI can rely on
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How to create an AI agent alongside an analyst for instant answer verification
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And why the cloud is a necessity
Semantic Layer: The Foundation Without Which AI Cannot Function
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What a semantic layer is and why it is crucial
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How to ensure unified metric definitions (“Single Source of Truth”)
A Chatbot Is Not Enough: How to Connect AI to the Real World of Business
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Are dashboards dead?
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Internal AI agent: how to unlock the potential for intelligent work with data
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Integrating AI into the data ecosystem
Discussion and Q&A
Space for your questions and our answers