Data Readiness Assessment

Artificial intelligence is no longer the domain of large enterprises alone. Small and medium-sized businesses are increasingly exploring AI tools and AI agents to improve efficiency, deliver better customer experiences, and unlock new revenue streams. Yet the success of these initiatives depends on something far more foundational than algorithms or models: the state of your data.

Recent studies highlight the gap between ambition and reality. According to Harvard Business Review Analytic Services, while 91% of business leaders agree that reliable data is essential for AI adoption, only 10% say their organizations are fully ready. This disconnect illustrates why data readiness for AI adoption has become a make-or-break factor. Without reliable, accessible, and compliant data, AI systems cannot deliver accurate insights or scale effectively.

For SMBs, the stakes are even higher. Limited budgets and lean teams leave little room for trial-and-error. Issues like inconsistent data, siloed systems, or unclear ownership can quickly derail AI projects, resulting in wasted investments, compliance risks, or missed opportunities. A structured data readiness assessment is therefore not optional, it is the most practical safeguard to ensure AI investments deliver measurable value.

This white paper introduces a framework to evaluate the core dimensions of data readiness - quality, accessibility, governance, and scalability, while offering a hands-on way to take the first step. Included you will find a link to a free data readiness template in Excel, designed to help technology leaders systematically map their data landscape, identify gaps, and prioritize improvements.

By applying this approach, SMBs can transform AI adoption from risky experimentation into a confident, scalable strategy. With the right data foundation, AI moves from hype to impact, empowering organizations to act decisively and achieve sustainable results.