The automation cycle is an innovative AI pattern designed to automate every digital task currently performed manually. Its primary objective is to achieve 100% automation of tasks by fostering collaboration between AI and humans. By emphasizing the collection of data for complex actions and decisions, the automation cycle compels AI to tackle these challenging tasks first, rather than repeatedly addressing simpler ones. This approach not only accelerates the advancement of AI capabilities but also ensures that AI systems are equipped to handle the most demanding aspects of task automation.

Additionally, the automation cycle addresses regulations like the EU AI Act by establishing human supervision of AI when necessary or bypassing AI to allow tasks to be solved manually. This ensures compliance with regulatory requirements while maintaining the flexibility to handle tasks that may require human intervention.

Join this session and learn more about:

• How the automation cycle aims to achieve 100% automation of digital tasks by fostering collaboration between AI and humans
• Why this approach accelerates the advancement of AI capabilities and equips AI systems to handle demanding aspects of task automation
• How the automation cycle addresses regulations like the EU AI Act by establishing human supervision of AI when necessary or allowing tasks to be solved manually, ensuring compliance with regulatory requirements

• What architectural patterns are emerging as best practices for deploying AI at scale in enterprise environments?
• How can enterprises manage and govern large-scale data pipelines to ensure reliable and compliant AI model training?
• What strategies are proving most effective for monitoring, versioning, and updating models in production (MLOps best practices)?
• How do organizations balance the trade-off between model performance and interpretability in regulated industries?
• What are the current limitations of large language models in enterprise applications, and how are teams overcoming them?