Organizations are mostly seeing a return on their AI investment, new research shows, but many are struggling to translate business context into AI systems and workflows.
Eight-in-ten IT leaders globally expect AI spending to increase over the next two years, according to Alteryx's 2026 IT Leader Research: The State of AI Ownership, Agents, and ROI report.
While 77% agree business context - the rules, definitions, and operational knowledge that shape how their organizations operate - is critical to producing accurate and relevant AI outputs, 53% say they struggle to incorporate this within AI systems and workflows.
The result here is that AI models are forced to make generic assumptions that could lead to mistakes, false insights, or incorrect metrics – and that has a big impact on business efficiency.
More than a third of respondents told researchers that the ability to measure AI ROI will be one of the capabilities that most distinguishes technology leaders from their peers.
Notably, firms are increasingly measuring AI success through productivity improvements (53%), cost reduction (45%), and revenue growth or broader business impact (39%).
“Our research highlights a growing gap between AI ambition and enterprise-scale execution,” said Andy MacMillan, CEO of Alteryx.
"Organizations have proven they're willing to invest in AI, and many are already seeing returns. But scaling AI requires more than better models. It requires making the business knowledge people use every day available to the systems making decisions.”
Lacking context
While AI can analyze information and generate responses, it can't consistently apply company-specific rules, policies, thresholds, and decision criteria unless that knowledge is built into the workflows it uses to make decisions.
Part of the problem is limited data access, Alteryx found, with only 18% of organizations reporting that business users have fully self-service access to cloud data.
Indeed, most are still relying on IT or data teams for routine data access and analytics, with 38% describing a mixed model and 15% saying business users remain largely dependent on technical teams.
Two-thirds of technology leaders say AI and agent-based systems are most productive when managed within the line of business, with 71% saying that AI initiatives are most successful when IT and business teams collaborate closely.
However, strategy (37%) and delivery (38%) remain concentrated within IT, while business teams are most often responsible for defining requirements (30%).
"The organizations creating lasting value from AI will be the ones that operationalize their business logic so it becomes visible, governed, repeatable, and ready for AI," said MacMillan.
What’s driving AI ROI?
The AI investments that appear to be delivering the best ROI are workflow automation and autonomous agents, cited by 27%, followed by copilots or assistants, at 16%, and AI-powered customer experience at 14%.
Virtually all IT leaders (93%) told Alteryx they were confident agentic AI could deliver measurable ROI for their enterprise within the next two years.
The first processes to be automated by agentic AI, they reckon, will be IT operations and incident management, cited by 47%, followed by customer support and service workflows at 39% and data analysis and reporting at 36%.
"Within our research, a small group of organizations self-identified as leading the way in AI innovation, confirming a clear path to achieving meaningful business value with AI technologies," the researchers concluded.
"These organizations point to a clear set of priorities behind their progress: rigorous measurement of AI's business impact, treating AI rollout as a strategic and operational priority, and building the governance and literacy needed to scale with confidence."
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