AI tools like ChatGPT, GitHub Copilot, and Azure AI are already used by millions, but mostly at a surface level. This talk is designed for professionals who want to move beyond basic queries and unlock the full productivity potential of AI in daily work. In this practical session, we will present actionable strategies to transform AI into a daily work assistant, thought partner, and strategic driver. You'll learn how to use voice input for deeper prompting, build custom assistants, leverage persistent memory, and apply critical thinking patterns to improve quality and consistency. We’ll also explore how Azure AI expands these possibilities with integrated services like Azure OpenAI, AI Translation, AI Search, and custom copilots built with Copilot Studio. These tools make it possible to deploy enterprise-grade AI systems with secure data, role-based context, and scalability across teams. This talk is ideal for non-technical professionals and technical teams alike who want to turn AI into a scalable, reliable productivity engine.

This session explores how to deploy a complete Microsoft Fabric architecture using Terraform and Azure DevOps. The focus is on applying infrastructure as code (IaC) principles to data architecture, making every environment setup consistent, repeatable, scalable, and fast. We start by introducing the structure of a Microsoft Fabric deployment, including capacities, workspaces, and associated resources. Then we look at how to model this architecture using Terraform, leveraging the Fabric provider to define Fabric-specific resources. The Terraform code is modular and designed to be reused across different environments. By defining variables for project names, environment tags, and other settings, the same code can be used to deploy dev, test, and prod setups with minimal changes. We configure Azure DevOps to run the Terraform code through multi-stage pipelines. These infra-structure pipelines handle validation, plan, apply, and include optional manual approval gates for production stages. The full CI/CD flow ensures that deployments are automated, controlled, and aligned with best practices for managing infrastructure in enterprise environments. A live demonstration shows the end-to-end process: cloning the code, updating variables, triggering the pipeline, and deploying the Microsoft Fabric components in Azure. This approach allows data teams to treat infrastructure as code, integrate it into version control, and reduce the time and risk involved in setting up Azure and Fabric objects manually.

When does it actually make sense to build an AI agent and when are you just adding complexity you do not need? This session focuses on practical use of AI agents with GitHub Copilot and Agent Mode, showing how to automate documentation, interact with data platforms such as Microsoft Fabric, and create reusable workflows. We’ll cover: -When to use agents instead of prompts or simple automations -Real examples for day-to-day work -Common mistakes (like overcomplicating with too many agents) -How to keep solutions simple and maintainable The goal: clear, practical criteria to help you use agents efficiently, no hype, just real data.