Program Overview

Individual AI use creates value, but organizations benefit when people, data, tools, and AI work together in a reliable process. This course helps managers and leaders turn promising use cases into tested AI-enabled workflows that teams can coordinate, evaluate, and improve.

AI Literacy, Trends, and Personal Productivity is a prerequisite for this course.

Details

  • Location
    Baltimore, MD
  • Clock
    3 Days
  • Global search
    Artificial Intelligence

Investment

Upcoming Courses

In-person: December 7-9, 2026 (9:00am-4:00pm)
Advanced AI Productivity
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In-person: June 28-30, 2027 (9:00am-4:00pm)
Advanced AI Productivity
Register

Program Details

In this course, participants will learn to select and prioritize AI projects based on value, feasibility, and risk, define scope, baselines, and success measures, and choose appropriately among copilots, automation, workflows, and agents. Working with an organizational process, they will map decision points and handoffs, redesign human and AI responsibilities, define data and knowledge needs, and build a workflow or agent prototype. This course also addresses quality and reliability testing, human review and escalation, team roles, adoption, knowledge sharing, and implementation.

Course Outcomes

  • Identify and prioritize AI use cases using value, feasibility, and risk criteria
  • Define project scope, baseline performance, success measures, and an initial business case
  • Map a workflow and design effective roles for people, AI tools, and agents
  • Build and test an AI-enabled workflow or agent prototype
  • Evaluate quality, reliability, usability, risk, and operational improvement
  • Create a team-adoption plan with clear ownership, decision rights, handoffs, and iteration

This course was built for:

  • Managers leading AI, automation, innovation, or process-improvement initiatives
  • Functional, product, project, and operations leaders responsible for redesigning workflows
  • Cross-functional teams seeking a practical method for moving from an AI idea to a tested solution
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