53% organisations struggle translating business context into AI systems: Alteryx

As organisations increase their investments in artificial intelligence (AI), translating business knowledge and operational requirements into AI systems is emerging as a challenge for enterprise adoption. A new global study by Alteryx found that 53% of organisations struggle to translate business context into AI systems and workflows, even as 80% expect their AI spending to increase over the next two years.

The findings are part of Alteryx’s 2026 IT Leader Research: The State of AI Ownership, Agents, and ROI, which surveyed 1,400 technology leaders globally. The study highlights a gap between investment in AI and organisations’ ability to integrate the technology into business processes and generate consistent outcomes.

While 69% of organisations reported moderate or significant returns on their AI investments, 77% of technology leaders said business context was essential for generating accurate and relevant AI outputs.

This suggests that access to data and advanced AI models alone may not be sufficient for enterprise deployment. AI systems also need to understand the business rules, policies and processes that guide organisational decisions.

Business knowledge remains scattered across enterprise systems

Much of an organisation’s operational knowledge is spread across spreadsheets, internal documents, email conversations, macros and employee expertise. This information often contains business rules and assumptions that influence everyday decisions but may not be readily accessible to AI systems.

For instance, financial forecasts depend on specific assumptions, tax processes follow defined rules and exceptions, while supply chain decisions are influenced by inventory levels and operational requirements. If an AI system cannot access or interpret this context, its outputs may not reflect the organisation’s actual business requirements.

The challenge is particularly relevant for Indian enterprises, where AI adoption is gaining momentum but data readiness remains a concern.

A separate Dun & Bradstreet AI Momentum Survey released in August 2026 found that all surveyed Indian businesses had AI-related projects underway, while 69% planned to increase their AI investments. However, only 4% said their enterprise data was fully ready to support AI at scale. Although 73% reported some measurable returns from AI investments, the findings point to gaps in data preparedness as organisations expand their AI deployments.

Commenting on the findings, Sabya Sen, Vice President, IMEA and APAC, Alteryx, said that the next phase of enterprise AI adoption would depend on how effectively businesses connect AI systems with the knowledge and processes that drive their operations.

He added that making business logic accessible, governed and repeatable would be important for organisations moving from experimentation to deployment at scale.

Limited data access affects enterprise AI adoption

The Alteryx study also identified access to enterprise data as another challenge. Only 18% of organisations said they had achieved fully self-service access to cloud data for business users.

Meanwhile, 38% reported a mixed model of data access, while 15% said business users remained largely dependent on technical teams to access information.

This can create difficulties when employees who understand business operations need to work with data to develop or improve AI workflows. If access depends heavily on IT teams, the process of retrieving relevant information and translating operational requirements into AI applications may become slower.

The findings point to the importance of making enterprise data more accessible while maintaining appropriate governance and security controls.

IT and business teams need closer collaboration

The study also highlights the role of collaboration between technology and business functions in AI implementation.

Although strategy and delivery responsibilities remain largely concentrated within IT departments, 71% of technology leaders believe AI initiatives are most successful when IT and business teams work closely together. Around two-thirds also believe AI and agent-based systems are most productive when managed within business functions.

This reflects the need to combine technical expertise with an understanding of business operations. IT teams may be responsible for building and managing AI infrastructure, but business teams provide the operational knowledge needed to define requirements, validate outputs and identify where AI can be applied.