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### 2. Data issues **Challenge: ** Lack of clean, high-quality, and accessible data is a major driver of AI agent failure. According to Informatica’s 2025 CDO Insights Report, 43% of AI leaders cite data quality and readiness as their top obstacle. For example, outdated training data can lead to inaccurate answers in customer support interactions, while poor data pipelines can cause agents to hallucinate—leading to unreliable outputs that erode customer trust. … ### 3. Focusing on tech over business problems **Challenge: ** Organizations too often fixate on choosing the right AI framework or model rather than ensuring agentic AI addresses their persistent business pain points. Teams may chase higher model accuracy scores, for instance, while neglecting workflow design and integration. As a result, by the time projects reach business review, compliance hurdles feel insurmountable, and ROI remains unproven. In fact, 40% of agentic AI projects are projected to be scrapped by 2027 for failing to link back to measurable business value, according to Gartner. … ### 6. Workflow & integration failures **Challenge: ** Poor integration with legacy systems and rigid workflows can cause agents to break down mid-task, especially for cross-system workflows. For example, Salesforce admitted its Einstein Copilot struggled in pilots because it couldn’t reliably navigate across customer data silos and legacy CRM workflows, forcing costly human intervention. **Solution: ** Rather than “bolting on” AI to legacy processes, re-architect workflows around AI agents before plugging them in. McKinsey's 2025 State of AI Survey found that organizations reporting "significant" ROI from AI projects are twice as likely to have redesigned end-to-end workflows before deploying AI. … ### 8. Task complexity exceeds capability **Challenge: ** While leaders should choose enduring problems for agentic AI to solve, this evolving technology can be applied to problems too complex for its current capabilities, setting projects up for failure. Importantly, many “agentic” AI companies are overhyped (known as “agent washing”) and can’t reliably deliver enterprise-grade outcomes. … AI agents face challenges that go beyond model accuracy. Issues like data quality, integration with legacy systems, workflow orchestration, and lack of governance often cause failures. Without reliable pipelines and oversight, agents risk producing inconsistent or untrustworthy outputs that frustrate customers and undermine ROI. Vertical AI agents—built for specific industries like healthcare, finance, or **retail**—face the added complexity of domain expertise, regulatory compliance, and specialized data requirements. For example, healthcare agents must meet HIPAA standards, while financial service agents must align with strict risk and audit protocols. Tailoring to industry needs requires deeper integration and more rigorous governance.