AI ready data: Forging the path to reliable and scalable AI

Build a Strong Foundation for AI Ready Data

With the emergence of mainstream LLMs like GPT-3, Google Gemini, and DeepSeek, big models are becoming a commodity. As LLMs continue to train on the same public datasets, companies must focus on achieving AI-ready data—rather than just integrating with the OpenAI API—to maintain a competitive advantage.

In this session, Lior will share why he believes that the value of AI lies in the data – not the model. We will discuss how various industries and companies can achieve AI ready data to overcome the “first-party data-pocalypse.” We will share predictions for what might happen if companies don’t heed this warning and start investing in the underlying data collection and infrastructure powering this muscle.

What we will cover:

• Gain a high-level understanding of the current state of reliable AI across enterprise industries.
• Learn how to navigate the evolving data and AI stack, the critical role of first-party data in the AI pipeline and the foundations of data and AI observability.
• Uncover a deeper understanding of the data architectures and processes required to build reliable, value-driving generative AI applications, including RAG and fine-tuning.
• Get real-world examples of best-in-class data teams who are successfully running customer-facing GenAI with AI ready data.
• Discover best practices for operationalizing detection, triage, and resolution of data incidents that could impact the reliability of GenAI products with AI ready data.