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Learn how to build reliable AI automation systems that deliver $100M+ business impact through a conference talk examining practical approaches to deploying AI for business-critical processes. Explore the evolution from manual workflows to AI-powered automation, focusing on use cases that became viable only with generative AI due to their customization requirements and complex data dependencies. Discover strategies for overcoming the inherent non-determinism of generative AI to create predictable, reliable systems without surprising failure modes. Examine methods for working with existing messy data spread across multiple systems without requiring expensive centralization efforts. Understand the "Automation Paradox" and how AI solutions address challenges in language processing, DevOps, and security implementation. See a practical demonstration of AcmeQL, a domain-specific language designed for non-technical users to implement AI automation. Gain insights into replacing error-prone manual processes that previously required expensive training with automated systems that meet the high reliability standards required for business-critical operations.
