Why Most AI Project Setbacks Are Not True Failures

The pressure to show immediate results is driving many firms to pull the plug on AI projects that are actually salvageable. According to Info-Tech research analyst Jenn Aswald, the current landscape of AI development is prone to specific, recurring hurdles—ranging from rapid technological obsolescence and value gaps to internal friction and adoption resistance. These issues are often mistaken for terminal failure when they are, in reality, manageable obstacles that diminish as governance and organizational maturity improve.

To help leaders distinguish between a dead-end project and a recoverable one, the firm released its "Get and Keep Your AI Projects on Track" blueprint. The framework forces a shift from reactive decision-making to a structured three-phase assessment. This process involves a rapid triage of current project health, a rigorous diagnosis of root causes, and the establishment of readiness checks for future cycles. By categorizing roadblocks—such as conflicting stakeholder opinions or vague business outcomes—IT departments can move away from uncertainty and toward measurable, long-term impact.