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 Your AI Projects Back 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.

Document what contributed to project outcomes.
Identify opportunities to improve future execution.
Share lessons learned across teams and initiatives.

Get and Keep Your AI Projects on Track

Tackle the five root causes of AI project failure.

Analyst perspective

AI projects face a more intense version of the challenges all IT projects face.

Recent reports suggest that AI projects often feel like they fail to deliver value.

Those faced with taking on an AI project are facing increasing pressure to implement AI technology to drive efficiency, growth, and competitive advantage despite these reported challenges. This is not the only competing objective that project stakeholders will come across when working through an AI project.

Tensions within your AI projects cause a widening gap between ambition for AI projects and their outcomes. At the same time, AI projects face numerous obstacles that can easily lead to project failure if managed ineffectively. Having multiple obstacles to navigate blurs whether a project is failing or simply facing navigable challenges and makes it challenging to avoid sunk costs for failing projects. Not all obstacles are here to stay. Pausing projects to let the technology and best practices catch up can be more effective than digging in to resolve every problem.

This guide is designed to help you get your AI project back on track by focusing on what matters. Identify the real blockers, fix what matters, and build the habits that stop it from happening again.

Jennifer Aswald
Research Analyst, Artificial Intelligence
Info-Tech Research Group

Executive summary

Your Challenge

Organizations are feeling increasing pressure to implement AI technology to drive efficiency, growth, and competitive advantage. At the same time, executive leaders face heightened scrutiny as high failure rates for AI initiatives are being reported.

These tensions within AI projects cause a widening gap between ambition for AI projects and their outcomes.

Despite scrutiny, senior stakeholders need to demonstrate skillful time management, effectiveness, and valuable results.

Common Obstacles

AI projects face a more intense version of the challenges all IT projects face:

Rapid Obsolescence: As AI progress outpaces the project, relevancy erodes.
Value Gap: Misaligned technology expectations prevent real-world impact.
Completion Challenges: Without structured readiness gates, AI projects struggle to start or close cleanly.
Fierce Opinions: Exceptionally strong advocacy and resistance slows decision-making.
Adoption Resistance: Fatigue and mistrust of AI suppress adoption.

Info-Tech's Approach

Our approach simplifies the diagnosis by consolidating the many ways a project can fail into five root cause categories, so leaders can quickly identify what is broken and where to act.

From there, a structured process helps teams execute a root cause analysis and prioritize the right fix, whether that means course-correcting or making the call to stop the project entirely.

Once your project is back on track, the same framework keeps it there with built-in checks at every stage from proof of concept through scaled rollout.

Info-Tech Insight

AI projects face a more intense version of the challenges all IT projects face. Identify the real blockers, fix what matters, and build the habits that stop it from happening again.

Get the AI Project Back on Track

The pressure is real, the scrutiny is high, and the timeline is tight.

Increasing organizational pressure to adopt AI.

Reported failure rates for AI initiatives are rising.

Senior stakeholders must still demonstrate effectiveness and deliver measurable results.

You are operating in an environment where ambition outpaces execution, but halting execution means falling behind.

Project sponsors are expected to drive AI forward and prove results even as failure rates rise.

Understand Why AI Projects Fail Differently

AI projects face a more intense version of the challenges all IT projects face

Fierce Opinions: Exceptionally strong advocacy and resistance slows decision-making.

AI Triggers Strong Advocacy & Resistance, Complicating Sponsorship Alignment

Rapid Obsolescence: As AI progress outpaces the project, relevancy erodes.

AI Creates Demand That Outpaces Intake Discipline
Project Governance Must Account for Evolving AI Regulatory and Compliance Requirements
AI Capabilities are Expanding at an Unmanageable Pace
AI Projects Fail When Success Criteria are Implicit, Narrow, or One‑Dimensional

Completion Challenges: Without structured readiness gates, AI projects struggle to start or close cleanly.

AI Projects Lack Clear Readiness Gates for Start and Closure
Use Case Demands for AI Move Faster Than Scope Controls Can Keep Up With
Resourcing AI Projects Demands Investments Beyond Initial Delivery

Value Gap: Misaligned technology expectations prevent real-world impact.

Cost Prediction Uncertainty with AI Weakens Project Cost Plan Credibility
AI Makes it Easy to Create an MVP, Breaking Traditional Build‑vs‑Buy Logic
Emergent AI Risks Overwhelm Traditional Project Controls
AI Requires Continuous, Not End‑Stage, Validation
AI Projects Depend on Data Quality to Sustain Success

Adoption Resistance: Fatigue and mistrust of AI suppress adoption.

AI Fatigue and Distrust From Previous Low-Value Projects Stalls Progress
AI Adoption Requires Judgement & Cognitive Shifts that Transcend Traditional Training

Psa Framework

We have mapped the unique, intensifying challenges inherent to AI projects today to the pillars of Info-Tech's Project Success Assurance Framework.

Info-Tech's methodology for getting your AI project back on track

	1. Assess Your Current State	2. Diagnose, Decide, and Act	3. Keep Your Next AI Project On Track
Phase Steps	You Are Not Alone Execute a Rapid Triage Conduct a Root Cause Analysis	Diagnose and Prioritize the Fix Decide. Can it be Saved? Build Your Roadmap to Get it Back on Track	Learn From the Project Keep Your Next AI Project on Track
Phase Outcomes	Gain an understanding of AI project obstacles. Identify the obstacle(s) that are preventing your project from moving forward.	Assessed AI project health Confident decision on moving forward or cancelling the project Get Your AI Project on Track Roadmap	Project postmortem Build solutions that will last Deliver solutions that provide value Get things done Prepare for objections Get people on board

Insight summary

AI projects are harder to manage
AI projects face a more intense version of the challenges all IT projects face. Identify the real blockers, fix what matters, and build the habits that stop it from happening again.
Some causes of failure aren't going to be a problem forever	Know whether your AI project is failing or simply facing a known obstacle
Increased rates of AI project failure are temporary. Many current AI obstacles are short-term growing pains. As disruption stabilizes and governance frameworks mature, these obstacles will decline.	Managing the many obstacles an AI project faces is its own obstacle. It obscures determination between an AI project that is failing or simply navigating an obstacle. Awareness of AI project obstacles and management strategies of these obstacles assists with this determination.
Determine the root cause of your current AI project's pains	Decide if this obstacle can be navigated within the project timeline
Use the fishbone analysis tool in combination with the five root causes of AI project failure to determine the root cause of your current AI project pain.	After discovering the root cause of your current AI project discomfort, diagnose the severity of their obstruction and determine if you can manage to avoid project failure.

Deliverables that GET your AI projects on track

Get Your AI Projects on Track Workbook

Diagnose and prioritize the obstacles that stand in the way of AI project success.

Postmortem Review Template

Regardless of project success or failure, you should take the time to reflect on lessons learned with your team. This postmortem review template guides project managers through a thorough analysis of the project.

Deliverables that KEEP your AI projects on track

Keep Your AI Projects on Track Workbook

Use this workbook to enable project teams to evaluate and navigate key AI project obstacles, at the right time.

Keep Your AI Projects on Track Poster

This poster will help you navigate AI project obstacles from proof of principle to the end of the pilot phase, preparing you for a scaled rollout. If you cannot answer "yes" to the questions in each stage, your AI project is in danger of falling off track.

Blueprint benefits

IT Benefits	Business Benefits
Keeping momentum on digital transformation goals. Maintaining stakeholder trust in future AI initiatives. Avoiding the cost and delay of starting over from scratch. Protecting the investment already made. Reduce sunk costs and frustration with AI projects through a clear project start or stop decision.	Ensure projects are relevant by the time they are completed. Effectively translate AI capability into real business value. Complete your AI projects within a reasonable time frame. Navigate organizational challenges effectively. Improve end user adoption.

Measure the value of this research

Are the success factors below relevant to your organization?

Success Factor	Potential Metrics	Example Target
Deliver a relevant solution	Self Check: Does the completed project deliver results that cannot be easily purchased or generated through technology that evolved during the project's creation process?	No easily bought or generated solution can quickly replace the completed project
Deliver expected business outcomes	% of AI projects that pass UAT % of AI projects that meet defined objectives	>80%
Develop start, scale, and closure gates	Completion of start, scale, and closure gates Project scope is reasonably managed	Complete set of start, scale, and closure gates
Minimize unanticipated costs	% of AI projects within budget	>80%
Deliver solutions that are used	Usage and perceived satisfaction scores	>80%

Phase 1

Assess Your Current State. Prepare to Resuscitate the Project.

Phase 1	Phase 2	Phase 3
1.1 You Are Not Alone 1.2 Execute a Rapid Triage 1.3 Conduct a Root Cause Analysis	2.1 Diagnose & Prioritize the Fix 2.2 Decide. Can it Be Saved? 2.3 Build Your Roadmap to Get it Back on Track	3.1 Learn From the Project 3.2 Keep Your Next AI Project on Track Build solutions that will last Deliver solutions that provide value Get things done Prepare for objections Get people on board.

Phase 1

Phase 2

Phase 3

1.1 You Are Not Alone

1.2 Execute a Rapid Triage

1.3 Conduct a Root Cause Analysis

2.1 Diagnose & Prioritize the Fix

2.2 Decide. Can it Be Saved?

2.3 Build Your Roadmap to Get it Back on Track

3.1 Learn From the Project

3.2 Keep Your Next AI Project on Track

Build solutions that will last
Deliver solutions that provide value
Get things done
Prepare for objections
Get people on board.

Get Your AI Project Back on Track

This phase will walk you through :

A thorough analysis of what is preventing success in your current AI project.

An understanding of the AI obstacles that will dissipate as the technology evolves, and more importantly, which obstacles are here to stay.

Save costs! Focus your long-term efforts toward shielding against the obstacles that are here to stay.

1.1: You Are Not Alone

Current reports suggest that AI projects fail more than non-AI projects, but AI adoption is not slowing.

Case study

Big players are not immune to AI project challenges

INDUSTRY: Fast Food
SOURCE: Business Insider; (Tangalakis-Lippert, 2026)

Background

Pizza Hut is a franchise known for record fast delivery times, high customer satisfaction, and consistent sales growth.

Feeling the pressure to adopt AI, they mandated a new AI-powered delivery management platform. The franchisees had little choice but to comply despite concerns about how the system would interact with its existing third-party delivery model. Rather than streamlining operations, the technology introduced new inefficiencies that the franchisee had no tools or support to address.

Change

The AI-powered delivery platform gave third-party drivers real-time visibility into kitchen order preparation. In theory, this would allow for tighter coordination between kitchen output and driver pickup.

In practice, third-party drivers used that visibility to batch multiple orders together, leaving completed pizzas sitting while they waited for a second order to be ready. The result was longer waiting times, colder deliveries, and a broken handoff process that had previously run smoothly.

Impact

The financial fallout exceeded $100 million in lost revenue and business value. What began as a technology rollout intended to improve the customer experience became a cautionary tale in AI adoption.

A gap between intended and actual value was never identified or addressed. That misalignment didn't just hurt this project; it has likely damaged franchisee trust and set back any future AI initiatives.

1.1 AI Project Failure

If your AI project is off track, you are in good company.

95%	Of enterprise level AI projects deliver no measurable value. MIT Sloan Management Review, 2025
53%	Of the population adopted generative AI in the last three years. Stanford, 2026

95%

Of enterprise level AI projects deliver no measurable value.

MIT Sloan Management Review, 2025

53%

Of the population adopted generative AI in the last three years.

Stanford, 2026

These two forces create a key tension between how AI is perceived and prioritized inside organizations.

Beware of the forces driving your AI projects off track

Adopt AI wisely! These are the five key causes of AI failure:

Rapid Obsolescence

As AI progress outpaces the project, relevancy erodes.

Value Gap

Misaligned technology expectations prevent real-world impact.

Completion Challenges

Without structured readiness gates, AI projects struggle to start or close cleanly.

Fierce Opinions

Exceptionally strong advocacy and resistance slows decision-making.

Adoption Resistance

Fatigue and mistrust of AI suppress adoption.

Each cause of AI failure is a result of mismanaging AI project obstacles

These obstacles are more intense versions of the challenges all IT projects face.

Rapid Obsolescence
As AI progress outpaces the project, relevancy erodes.

AI Creates Demand That Outpaces Intake Discipline
Project Governance Must Account for Evolving AI Regulatory and Compliance Requirements
AI Capabilities Are Expanding at an Unmanageable Pace
AI Projects Fail When Success Criteria Are Implicit, Narrow, or One‑Dimensional

Value Gap
Misaligned technology expectations prevent real-world impact.

Cost Prediction Uncertainty With AI Weakens Project Cost Plan Credibility
AI Makes it Easy to Create an MVP, Breaking Traditional Build‑vs‑Buy Logic
Emergent AI Risks Overwhelm Traditional Project Controls
AI Requires Continuous, Not End‑Stage, Validation
AI Projects Depend on Data Quality to Sustain Success

Completion Challenges
Without structured readiness gates, AI projects struggle to start or close cleanly.

AI Projects Lack Clear Readiness Gates for Start, Scale, and Closure
Use Case Demands for AI Move Faster Than Scope Controls Can Keep Up With
Resourcing AI Projects Demands Investments Beyond Initial Delivery

Fierce Opinions
Exceptionally strong advocacy and resistance slows decision-making.

AI Triggers Strong Advocacy & Resistance, Complicating Sponsorship Alignment

Adoption Resistance
Fatigue and mistrust of AI suppress adoption.

AI Fatigue and Distrust From Previous Low-Value Projects Stalls Progress
AI Adoption Requires Judgement & Cognitive Shifts That Transcend Traditional Training

Don't worry! Many causes of AI failure are only short-term growing pains

As disruption stabilizes and governance frameworks mature, these obstacles will decline.

These AI obstacles are here to stay

AI Requires Continuous, Not End‑Stage, Validation: As AI behavior changes over time due to data, technology, or user evolution, ongoing validation of business value delivery is essential to continued project success.

AI Projects Fail When Success Criteria Are Implicit, Narrow, or One‑Dimensional: AI does not respond in the same way to a request every time, so broader success measures can help businesses report on AI value delivery more effectively.

AI Makes it Easy to Create an MVP, Breaking Traditional Build‑vs‑Buy Logic: A common cause of AI project failure is underestimating the time and costs to build a solution. It can appear simple to create an MVP, but tuning the model takes time.

AI Projects Depend on Data Quality to Sustain Success: Your AI solution will only ever be as good as the quality of the data that is fed into it.

Resourcing AI Projects Demands Investments Beyond Initial Delivery: Each AI project will have reoccurring costs required to uphold the value and reliability of its output.