Most AI projects do not have a technology problem. They have a data problem.
According to Gartner, organizations are expected to abandon 60% of AI projects unsupported by AI-ready data in 2026. The reason is simple: AI can only work with the information it can access, and many businesses are trying to build AI capabilities on fragmented, inconsistent or poorly structured data.
For SMEs, that data may be scattered across a CRM, an ERP, spreadsheets, emails and other systems. Different departments may use different definitions, reports may still require manual corrections, and two systems may produce different answers to the same business question.
Adding AI on top of that fragmentation does not solve the underlying problem. Putting a smarter faucet on bad plumbing will not give you cleaner water.
An AI agent asked to identify the most profitable product, forecast inventory needs or analyze sales performance still needs reliable information to produce a reliable answer. If the underlying data is incomplete, inconsistent or difficult to access, AI inherits those limitations.
Is Your Data Ready for AI?
Before investing in a new AI solution, SME leaders can assess their foundations with three simple questions:
Can the necessary data be easily accessed?
If answering a business question requires manually combining information from several systems, those systems may need to be connected first.
Do different systems produce the same answer?
Sales, finance and operations should not have conflicting figures for the same business metric.
Can the data be trusted without manual intervention?
Recurring spreadsheet corrections, manual exports and last-minute adjustments are signs that the underlying data processes may not yet be ready for automation.
AI readiness does not require rebuilding an entire technology ecosystem. A practical starting point is to identify one business question or use case, determine which data is required to support it, and ensure that information is connected, structured and reliable.
For SMEs eager to adopt AI, choosing the technology may be the most visible part of the journey. But it is rarely the most important.
Before investing in the AI, make sure it has something reliable to work with.
Author – Damien Van Steenberge
Damien Van Steenberge is a seasoned analytics and technology executive with more than 25 years of experience across North America and Europe, recognized for transforming complex data and enterprise systems into strategic assets that drive business growth. With deep expertise in Business Intelligence, data governance, and large-scale system modernization, he has led impactful initiatives across global enterprises, consulting firms, and startups. As a founder, CEO, and academic lecturer, Damien combines technical mastery with pragmatic leadership, helping organizations harness data responsibly while empowering teams, strengthening governance, and enabling smarter, data-driven decision-making.
