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AI Project Setbacks Are Often Mistaken for Failure, Finds Info-Tech Research Group
AI Project Setbacks Are Often Mistaken for Failure, Finds Info-Tech Research Group
As organizations accelerate AI adoption under pressure to deliver measurable results, many struggle to determine whether AI initiatives are truly failing or simply encountering predictable obstacles. New insights from Info-Tech Research Group identify five root causes of AI project failure and provide a structured framework to help leaders identify challenges, determine whether recovery is possible, and keep future AI projects on track. The firm's blueprint, Get and Keep Your AI Projects on Track, equips IT leaders with practical tools and readiness assessments to improve AI project outcomes.
ARLINGTON, Va., Aug. 17, 2026 — Rising reports of AI project failure have intensified scrutiny around AI investments, making it increasingly difficult for IT leaders to determine whether struggling initiatives should continue, change course, or stop altogether. To help organizations navigate these decisions, Info-Tech Research Group has recently published its Get and Keep Your AI Projects on Track blueprint, which helps evaluate struggling initiatives and establish practices that support future AI project success.
Info-Tech Research Group's Get and Keep Your AI Projects on Track blueprint is designed to help IT leaders evaluate struggling initiatives and establish practices that support future AI project success.
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According to Info-Tech's research, AI projects face a more intense set of challenges than traditional IT initiatives. While organizations often interpret project setbacks as signs of failure, many AI initiatives face predictable challenges tied to rapidly evolving technology, changing expectations, governance requirements, and adoption barriers.
"Many of the obstacles organizations are encountering today are temporary growing pains rather than permanent barriers to AI success," says Jenn Aswald, research analyst at Info-Tech Research Group. "IT leaders need to understand which issues require intervention and which will diminish as AI capabilities, governance practices, and organizational experience mature."
Key Challenges IT Leaders Face in AI Project Delivery Info-Tech's blueprint organizes the most common causes of AI project failure into the following five root cause categories:
- Rapid obsolescence erodes project relevance. AI capabilities evolve quickly, creating the risk that solutions become outdated before implementation is complete.
- Value gaps limit business outcomes. Organizations often struggle to translate AI capabilities into measurable business value and sustained operational impact.
- Completion challenges delay progress. Without structured start, scale, and closure criteria, AI projects can remain in prolonged states of uncertainty.
- Fierce opinions complicate decision-making. Conflicting stakeholder perspectives can slow sponsorship alignment and hinder effective governance.
- Adoption resistance reduces value realization. Previous low-value AI experiences can undermine trust, limit engagement, and reduce successful adoption.
Info-Tech's Three-Phase Approach to Getting and Keeping AI Projects on Track To address these challenges, the Get and Keep Your AI Projects on Track blueprint outlines a structured three-phase approach that helps organizations assess struggling initiatives, determine the best path forward, and keep future AI projects on track.
Phase 1: Assess Current State. Evaluate project health, conduct rapid triage, and conduct a root cause analysis to identify the obstacles preventing project success.
Phase 2: Diagnose, Decide, and Act. Prioritize obstacles, determine whether recovery is realistic, and build a roadmap that aligns resources to the most critical remediation efforts.
Phase 3: Keep Future AI Projects on Track. Apply lessons learned, establish readiness checks, and implement practices that reduce the likelihood of future project failure while improving long-term outcomes.
The firm's Get and Keep Your AI Projects on Track blueprint includes practical resources that help organizations assess struggling AI initiatives, capture lessons learned, and apply those insights to future projects. By using Info-Tech's approach, IT leaders can improve project relevance, strengthen business value realization, reduce unnecessary costs, and keep AI projects on track from initial concept through scaled rollout.
For exclusive and timely commentary from Info-Tech's experts, including Jenn Aswald, and for access to the complete Get and Keep Your AI Projects on Track blueprint, please contact [email protected].
About Info-Tech Research Group Info-Tech Research Group is the "get things done" partner for over 30,000 IT, HR, and marketing leaders worldwide. The fastest growing research and advisory firm, Info-Tech enables leaders to make well-informed decisions and transform their organizations through AI, strategic foresight, step-by-step methodologies, practical tools, industry-leading advisory, and training programs. For nearly 30 years, tens of thousands of private and public organizations have trusted Info-Tech to lead their most important initiatives through periods of change and deliver outcomes that truly matter.
To learn more about Info-Tech's HR research and advisory services, visit McLean & Company, and for data-driven software buying insights and vendor evaluations, visit the firm's SoftwareReviews platform.
Media professionals can register for unrestricted access to research across IT, HR, and software, and hundreds of industry analysts through the firm's Media Insiders program. To gain access, contact [email protected].
For information about Info-Tech Research Group or to access the latest research, visit infotech.com and connect via LinkedIn and X.
Summary
As organizations accelerate AI adoption under pressure to deliver measurable results, many struggle to determine whether AI initiatives are truly failing or simply encountering predictable obstacles. New insights from Info-Tech Research Group identify five root causes of AI project...