More than half of organizations investing heavily in artificial intelligence are failing to translate their business knowledge into AI systems that can actually use it, according to new research from Alteryx. While 80% of companies expect to increase AI spending over the next two years and 69% report moderate or significant returns on their AI investments, 53% say they struggle to operationalize the business context that AI systems need to deliver real value.

What's the Real Problem With Enterprise AI Adoption?

The challenge isn't about having more data or better artificial intelligence models. It's about making the rules, definitions, and operational knowledge that shape how a business actually works visible and accessible to AI systems. According to the Alteryx research, which surveyed 1,400 technology leaders globally in April and May 2026, 77% of IT leaders agree that business context is critical to producing accurate and relevant AI outputs.

Yet that same business logic remains scattered across spreadsheets, email threads, documentation, and the expertise of employees closest to the work. A financial forecast depends on specific assumptions. A tax process depends on rules and exceptions. A supply chain decision depends on inventory thresholds and timing. Without embedding this knowledge into AI workflows, the systems cannot consistently apply company-specific policies and decision criteria.

"Our research highlights a growing gap between AI ambition and enterprise-scale execution. 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," said Andy MacMillan, CEO of Alteryx.

Andy MacMillan, CEO at Alteryx

Why Are Organizations Measuring AI Success Differently Now?

The research reveals a fundamental shift in how enterprises evaluate AI investments. Technology leaders are increasingly measuring AI success through productivity improvements (53%), cost reduction (45%), and revenue growth or broader business impact (39%). More than one-third of respondents say the ability to measure AI ROI will be one of the capabilities that most distinguishes technology leaders from their peers.

This marks a transition from the earlier phase of AI adoption, where organizations simply asked whether AI works. Now they're asking whether it consistently delivers measurable business value. The stakes have risen because the investment is rising. With 80% of organizations planning to increase AI spending, accountability is becoming non-negotiable.

How to Bridge the Gap Between AI Investment and Business Outcomes

The Alteryx research points to several practical approaches that organizations can take to operationalize their business knowledge and improve AI effectiveness:

• Centralize Business Logic: Make the rules, thresholds, and decision criteria that define how your business operates visible and documented so they can be built into AI workflows and systems.
• Improve Data Access for Business Teams: Enable business users to access the data they need without waiting on IT, so employees with deep business knowledge can validate and improve AI systems directly.
• Foster IT and Business Collaboration: Ensure that AI strategy and delivery involve both technical expertise and business expertise working together, rather than keeping these functions siloed within IT departments.

The research shows that two-thirds of technology leaders believe AI and agent-based systems are most productive when managed within the line of business, and 71% say AI initiatives are most successful when IT and business teams collaborate closely. However, strategy and delivery remain concentrated within IT departments in most organizations, while business teams are typically responsible only for defining requirements.

What's Holding Back Data Access and Self-Service Analytics?

A significant barrier to embedding business context into AI systems is the limited access business users have to data. Despite years of investment in data democratization, only 18% of organizations report that business users have fully self-service access to cloud data. Most organizations continue to rely on IT or data teams for routine data access, with 38% describing a mixed model and 15% saying business users remain largely dependent on technical teams.

This dependency creates a bottleneck. The employees with the deepest understanding of how the business operates are often the same people waiting on IT to access the data needed to build, validate, and improve AI workflows. That disconnect makes it more difficult to embed business context into enterprise AI systems, limiting AI's ability to generate meaningful business outcomes.

What Do Leaders Expect From AI Agents in the Next Two Years?

Despite the challenges, enterprise confidence in AI agents remains high. According to the Alteryx research, 93% of IT leaders are confident that agentic AI, which refers to AI systems that can autonomously perform tasks and make decisions, could deliver measurable ROI for their enterprise within the next two years. This optimism reflects the growing recognition that AI agents, when properly grounded in business context, could automate complex workflows and decision-making processes at scale.

The research suggests that organizations entering this new phase of AI maturity understand that success is no longer defined by AI adoption alone, but by the ability to translate AI investment into measurable business outcomes. The organizations that succeed will be those that operationalize their business logic so it becomes visible, governed, repeatable, and ready for AI systems to use effectively.