Making AI Projects a Success

Fundamentals and Framework Conditions for Sustainable AI Projects

• A Refresher on the Basics of AI: Potential, Limitations, and Typical Applications.
• Understand the unique characteristics of AI projects compared to traditional IT projects.
• Assessing AI Readiness: Culture, Process Maturity, System Landscape, Data Management.
• Process and system inventory, capability map, identifying media breaks.
• Identify scope creep early and take action to prevent it.

Project Launch and AI Project Architecture

• Building an AI Project Team: Roles, Responsibilities, Change Agents.
• The 4-Phase Model in Practice: Discover, Design, Develop, Deploy.
• Record requirements, prioritize them, and continuously refine the scope.
• Define two success metrics: engagement and quality.
• Business Case and Budget: Realistically Calculate License and Token Costs and TCO.

AI Project Management and Stakeholder Management

• Identify typical pitfalls in AI projects early on.
• Analyze stakeholders and engage them in a targeted manner.
• Systematically identify and assess risks.
• Measure progress, identify deviations, and take effective corrective action.

Rollout, Adoption, and Sustainable Implementation

• From a pilot project to a productive system using the traffic-light system as a rollout canvas.
• Testing and Hypercare: Ensuring quality, security, and reliable results in production.
• Optimize change management and actively foster acceptance.
• Establish feedback loops and improvement cycles.
• Building data network effects: Every interaction makes the system better.