Get and Keep Your AI Projects on Track

AI projects face a more intense version of the challenges IT projects face, contributing to high rates of failure amid growing pressure to demonstrate results. Technology decision-makers need a practical way to identify blockers to success, recover or cancel struggling initiatives, and avoid repeating the same mistakes in the future. This research provides a structured framework for getting and keeping AI projects on track.

Many AI project obstacles are temporary growing pains rather than permanent barriers to success. Understanding which challenges require intervention and which will diminish over time is critical to deciding what comes next.

1. Understand why AI projects fail differently.

AI can intensify traditional project challenges. Using the Project Success Assurance Framework to assess those pressures helps teams identify where obstacles are emerging and focus on the factors most likely to affect project outcomes.

2. Focus on the obstacles that matter.

AI projects can lose momentum for many different reasons, making it difficult to know where to focus. Understanding which obstacle is creating the greatest friction helps teams focus on the issue most likely to affect project outcomes.

3. Determine the root cause before taking action.

Symptoms rarely reveal why an AI project is struggling. Root cause analysis helps teams separate temporary obstacles from more significant issues, identify the best path forward, and make decisions with greater confidence.

Use this guide to determine whether an AI project should continue, change course, or stop.

This research includes a structured recovery framework, diagnostic and planning workbooks, a readiness assessment poster, and a postmortem review template. Together, these resources help teams assess struggling AI projects, determine whether recovery is possible, capture lessons learned, and apply those lessons to future initiatives.

Assess your current state by identifying the obstacles preventing project success.
Diagnose the root cause, determine whether the project should continue, and build a roadmap for recovery.
Apply lessons learned and establish practices that keep future AI projects on track.

Get and Keep Your AI Projects on Track Research & Tools

1. Get and Keep Your AI Projects on Track – A research framework that helps determine why AI projects struggle, whether recovery is possible, and what comes next.

This research organizes the many ways AI projects go off track into five root cause categories and provides a structured process for diagnosing obstacles, making recovery decisions, and keeping future projects on track.

Understand the five root causes of AI project failure.
Determine whether recovery is realistic.
Build a structured path toward resolution or closure.

2. Get Your AI Projects on Track Workbook – This diagnostic workbook helps identify obstacles, evaluate recovery options, and plan remediation efforts.

The workbook guides teams through triage, root cause analysis,and recovery planning using structured exercises and decision tools.

Assess available resources and project constraints.
Diagnose and prioritize the obstacles preventing project success.
Develop a remediation plan for the issues that matter most.

3. Keep Your AI Projects on Track Poster – A readiness assessment tool that helps determine when to move forward, pause, or stop an initiative.

The poster organizes common AI project obstacles into stage-specific readiness checks that help teams determine whether they are prepared to move forward.

Evaluate readiness from proof of principle through pilot.
Answer stage-specific questions before advancing to the next phase.
Identify when a project is at risk of falling off track.

4. Keep Your AI Projects on Track Workbook – Use this planning workbook to identify the risks and decision points most likely to emerge throughout the AI project lifecycle.

Designed for active AI initiatives, this workbook helps teams navigate common challenges at the appropriate stage of delivery rather than reacting to them after they appear.

Stay focused on delivering AI projects in a timely manner. Address common project obstacles throughout the project lifecycle.
Demonstrate project value as the initiative evolves.

5. Postmortem Review Template – This retrospective template captures lessons learned and strengthens future AI initiatives.

The template facilitates structured reflection after project completion, whether the initiative was successfully delivered, redirected, or discontinued.