Master AI-native transformation from strategy to sustained execution. Learn to design adaptive strategy loops, architect value-proving systems, apply agentic engineering, and embed continuous organizational learning. This 3-hour practitioner course with hands-on labs helps you shift from AI projects to living intelligence systems. Aligned with AEBOP T5.4.
AEI members save 20% with code MEM_C24_20.
In this module, you will learn why most AI and digital transformation efforts fail—and how to avoid repeating the same mistakes. You will gain the ability to recognize when traditional engineering, static strategy, and automation-first thinking are actively working against AI outcomes. Instead of guessing why pilots stall or why leadership loses confidence, you’ll be able to diagnose transformation failure with clarity and precision.
You will also learn how to turn strategy into a living, executable system. By the end of the module, you will be able to convert high-level goals into runtime strategy loops that generate evidence, adapt continuously, and improve daily. You won’t just “set direction”—you’ll know how to design strategy that runs, learns, and updates itself in real time.
In this module, you will learn how to design AI transformations that prove their value instead of defending themselves after the fact. You’ll understand why transformation initiatives fragment across teams and how to architect end-to-end systems that connect operations, engineering, governance, value, and strategy. This gives you the ability to see where evidence breaks, where decisions lose signal, and where scale quietly becomes impossible.
You will also learn how to build value proof that survives executive, financial, and governance scrutiny. By the end of the module, you’ll be able to design evidence pipelines, cognitive value chains, and board-ready metrics that clearly demonstrate risk reduction, efficiency gains, and strategic advantage. Instead of arguing for AI investment, you’ll know how to make the value self-evident.
In this module, you will learn how to bridge the gap between successful AI pilots and scalable, enterprise-grade systems. You’ll identify the three engineering gaps—architecture, governance, and data intelligence—that silently multiply cost and risk as systems grow. More importantly, you’ll learn concrete Agentic Engineering patterns that fix these gaps by enabling systems to learn, adapt, and govern themselves through evidence.
You will also learn how to operate an organization that learns while it works. By the end of the module, you’ll be able to redesign decision flows, replace escalation with evidence, and structure teams as intelligence units rather than silos. This equips you to move from running AI projects to running an AI-native operating system.
In this module, you will learn how to prepare humans—not just systems—for AI-native work. You’ll understand which human capabilities matter most in cognitive systems and how culture functions as a measurable control surface. Instead of vague change-management initiatives, you’ll gain the ability to design specific rituals, incentives, and leadership behaviors that allow autonomy to scale safely.
You will also learn how to make transformation continuous instead of temporary. By the end of the module, you’ll be able to design continuity loops, maturity dashboards, and evidence portfolios that keep learning compounding long after initial success. You won’t just complete a transformation—you’ll know how to ensure it never quietly stops.