53% of Organizations Struggle to Translate Business Context Into AI

Alteryx, Inc., an AI-ready data and analytics company, today released its “2026 IT Leader Research: The State of AI Ownership, Agents, and ROI” report, revealing that organizations are entering a new phase of AI maturity where success is no longer defined by AI adoption alone, but by the ability to translate AI investment into measurable business outcomes.

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Among 1,400 IT leaders surveyed globally, 80% expect AI spending to increase over the next two years, while 69% report moderate or significant ROI from their AI investments. Yet despite growing investment and early returns, more than half (53%) say their organization struggles to translate business context into AI systems and workflows. At the same time, 77% agree business context is critical to producing accurate and relevant AI outputs, underscoring a widening gap between AI ambition and operational readiness.

Key Findings at a Glance:

• 80% of organizations expect AI spending to increase over the next two years.
• 69% report moderate or significant ROI from their AI investments.
• 93% of IT leaders are confident agentic AI could deliver measurable ROI for their enterprise within the next two years.
• 77% agree business context is critical to accurate, relevant AI outputs.
• 53% say their organization struggles to translate business context into AI systems and workflows.
• Only 18% of organizations have achieved fully self-service access to cloud data for business users.

AI Investment Is Accelerating. Expectations Are Rising Even Faster.

AI investment continues to accelerate as organizations move beyond experimentation toward enterprise-scale deployment. Eighty percent of organizations expect AI spending to increase over the next two years across infrastructure, workflow automation, data platforms, and governance.

With that investment comes greater accountability. 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 (35%) say the ability to measure AI ROI will be one of the capabilities that most distinguishes technology leaders from their peers.

The findings suggest AI has entered a new phase where organizations are no longer asking whether AI works. They are asking whether it consistently delivers measurable business value.

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Business Context Is Emerging as a Barrier to Enterprise AI

As AI becomes embedded in everyday business processes, organizations are discovering that models alone are not enough. According to the research, 77% of IT leaders agree that business context, including the rules, definitions, and operational knowledge that shape how their organizations operate, is essential for producing accurate and relevant AI outputs. Yet more than half (53%) say their organization struggles to translate that business context into the systems and workflows AI depends on.

The challenge isn’t simply giving AI more data. It’s giving AI the business logic that tells it how the business actually works. While AI can analyze information and generate responses, it cannot consistently apply company-specific rules, policies, thresholds, and decision criteria unless that knowledge is built into the workflows it uses to make decisions.

Much of that business logic still lives in spreadsheets, macros, documentation, email threads, and the expertise of the people closest to the work. A financial forecast depends on assumptions. A tax process depends on rules and exceptions. A supply chain decision depends on inventory thresholds and timing. AI should be grounded in the rules and logic the business already trusts.

“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.”

Limited Data Access Continues to Slow AI Adoption

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 and analytics, with 38% describing a mixed model and 15% saying business users remain largely dependent on technical teams.

The findings suggest this dependency extends beyond productivity. 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.

Enterprise AI Works Best When IT and the Business Work Together

The research also points to a growing consensus that AI performs best when technical expertise and business expertise work together. Two-thirds of technology leaders say AI and agent-based systems are most productive when managed within the line of business. Additionally, 71% believe AI initiatives are most successful when IT and business teams collaborate closely.

This sentiment correlates with past Alteryx research. Yet, strategy (37%) and delivery (38%) remain concentrated within IT, while business teams are most often responsible for defining requirements (30%). That disconnect between where business knowledge resides and where AI systems are built continues to slow enterprise AI adoption.

MacMillan concluded, “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.”

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