Launching a Successful AI Initiative

This 1-day course will equip you with the practical knowledge needed to successfully launch an AI project at any scale. Teams, work groups and individuals will learn how to select the right use case, build internal support, avoid common pitfalls, and create a framework to track and scale AI efforts. Real-world examples and simple tools such as ChatGPT and Microsoft Copilot will be used to show how ideas can quickly move from concept to prototype.

• Understand the core components of a successful AI project
• Recognize common reasons AI projects fail and how to avoid them
• Learn how to gain leadership buy-in and cross-team support
• Practice framing and pitching AI use cases
• Gain confidence using tools such as ChatGPT and Copilot to jumpstart ideas
• Leave with a practical kickoff checklist for your own AI initiative

• Refining your AI-driven use case emphasizing feasibility and impact
• Focusing on the needed data, algorithms and platforms to drive AI project success
• Obtaining stakeholder buy-in in all forms
• Familiarizing yourself with existing tools such as ChatGPT and Copilot to support quick wins
• Establishing the framework to manage AI projects through iteration, checkpoints and metrics
• Understanding the common pitfalls that hinder success and how to mitigate them

Learning Objectives

• Identify AI use cases by evaluating feasibility and business impact.
• Assess readiness across business clarity, data, technology, people, governance, and measurement.
• Recognize why AI projects fail and avoid common pitfalls.
• Frame AI project ideas to win leadership support.
• Use AI tools to rapidly prototype and refine project ideas.
• Apply an iterative framework to drive quick wins and scalability.
• Build a personalized kickoff checklist to launch your AI initiatives.

Why AI Projects Fail and What Successful Launches Do Differently

• Recognize the organizational and execution patterns that cause AI projects to fail.
• Distinguish problem-first thinking from tool-first thinking.
• Apply five early checks to pressure-test an AI project.
• Identify the two or three risks most likely to stall their use case.

Selecting the Right AI Case

• Identify and compare AI use cases against business value and feasibility.
• Determine whether AI or simpler automation is the better solution.
• Select one use case using impact, feasibility, and adoption criteria.
• Write a specific problem statement and measurable outcome.

Readiness, Data, and Feasibility

• Evaluate an AI use case across six readiness dimensions.
• Identify data, technology, process, people, and governance gaps.
• Select the lightest viable platform or solution approach.
• Estimate the time, cost, and effort required to move from pilot to production.
• Execute the Plan

Stakeholder Buy-In, Sponsorship, and Governance

• Identify the stakeholders who must approve, fund, use, support, or govern an AI project.
• Anticipate sources of resistance and develop practical responses that support adoption.
• Define decision rights, accountability, and governance controls for an AI initiative.
• Map stakeholders and prepare a concise sponsor pitch that communicates the problem, value, plan, and specific ask.

From Concept to Prototype with AI Tools

• Distinguish a demo, prototype, pilot, and scaled solution.
• Use AI tools to clarify and structure an AI use case.
• Create a prototype workflow with explicit human review points.
• Identify assumptions and risks that require testing before a pilot.

Evaluation, Pilot Planning and Scaling

• Define pilot objectives, metrics, and thresholds before testing.
• Evaluate business value, quality, risk, and user adoption.
• Decide when to proceed, pause, pivot, or stop.
• Create a practical plan for adoption, monitoring, and scaling.

Final Kickoff Checklist and 30 Day Action Plan

• Convert the day’s work into a launch-ready checklist.
• Sequence the first 30 days of project activity.
• Identify the first stakeholder conversation and sponsor request.
• Commit to one next action, one stakeholder, and one risk mitigation step.

Download Extended Seminar Outline PDF