Alteryx has released new research revealing that while enterprises are ramping up investment in AI and automation, trust and data challenges continue to slow adoption. The research finds a growing disconnect between AI ambition and real-world impact. Despite heavy investment in AI, most organizations are failing to move AI beyond pilot programs, held back by low trust in AI outputs, poor data quality, and legacy technology that can’t support scale. Fewer than one in four AI pilots successfully operationalize into production.
Key Findings:
- Trust remains a major barrier to adoption: While nearly half of respondents say they trust AI to automate repetitive tasks, draft content, and monitor systems, fewer trust it for strategic decisions. Only 28% trust AI to support decision-making, and just 27% trust it to facilitate forecasting or planning, highlighting a significant gap in confidence for high-impact applications.
- Data quality is critical for agentic AI impact: Nearly half (49%) of leaders cite high-quality, accessible, and well-governed data as the top factor for agentic AI to achieve its full potential.
- AI workflow ownership is shifting across the business: Business and IT leaders expect responsibility for AI workflows to move away from centralized teams to individual lines of business by 11% over the next three years.
- Growing AI adoption: 48% of leaders plan to boost AI spending on AI infrastructure and tools, with 89% maintaining or increasing budgets in 2026. AI platforms now make up a larger portion of data stacks, projected to grow from 33% in 2024 to 51% in three years.
Together, the findings point to a deeper issue behind stalled AI initiatives: trust breaks down when AI is deployed without the business context and logic required to produce consistent and explainable results. Many organizations are layering generative AI directly on top of raw data sources, leading to hallucinations, inconsistent outputs, and responses that change from one query to the next, undermining confidence in AI for real business decisions.
As a result, organizations should double down on the foundations needed to make AI trustworthy at scale. This includes governed data, defined metrics and workflows that combine the creativity of generative AI with deterministic rules – and the ability for the business to quickly adapt them as needs change. In fact, 28% of leaders plan to prioritize data governance improvements.
“AI adoption is accelerating fast,” said Andy MacMillan, CEO of Alteryx. “Our research shows that compared to a year ago, two-thirds of business and IT leaders are using AI more in their roles. We’re also seeing AI move closer to individual departments. Over the next three years, leaders expect responsibility for AI workflows to shift to specific lines of business, rising from 22% today to 33% by 2028. The most advanced organizations are doubling down on improving data quality and integrating AI across their operations.
