As organisations accelerate investments in artificial intelligence (AI), ensuring that AI systems understand business requirements and operational processes is emerging as a major challenge. According to global research by Alteryx, an AI-ready data and analytics company, 53 per cent of organisations struggle to translate business context into AI systems and workflows, even as 80 per cent 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, based on a survey of 1,400 technology leaders globally. The study highlights a growing gap between AI investments and organisations' ability to translate them into consistent, measurable business outcomes.
While 69 per cent of organisations report moderate or significant returns on their AI investments, 77 per cent of technology leaders believe business context is essential for generating accurate and relevant AI outputs. This indicates that successful AI implementation requires more than advanced models and access to data. AI systems also need to understand the business rules, policies and processes that guide everyday organisational decisions.
The challenge is particularly relevant for Indian enterprises, where AI adoption and investments are 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 per cent planned to increase their AI investments. However, only 4 per cent said their enterprise data was fully ready to support AI at scale. Although 73 per cent reported some measurable returns from AI investments, the findings point to the need for stronger data foundations as businesses move towards wider AI deployment.
Much of an organisation's business knowledge remains scattered across spreadsheets, internal documents, email conversations, macros and employee expertise. For instance, financial forecasts depend on specific assumptions, tax processes follow defined rules and exceptions, while supply chain decisions rely on inventory levels and operational requirements. Unless such knowledge is accessible to AI systems, their outputs may not always reflect an organisation's actual business requirements.
Commenting on the findings, Sabya Sen, Vice President, IMEA & APAC, Alteryx, said, "India is moving rapidly towards wider enterprise AI adoption, with organisations increasingly investing in AI to improve productivity, efficiency and business outcomes. However, the next phase of this journey will depend on how effectively businesses connect AI with the knowledge and processes that drive their operations. AI systems need more than access to data; they need the right business context to generate outputs that are relevant, reliable and aligned with organisational requirements. As enterprises move from experimentation to deployment at scale, making business logic accessible, governed and repeatable will be critical to translating AI investments into measurable business value."
