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<\/p>\n As more organizations race to showcase AI capabilities, it\u2019s easy to feel pressure to move fast. But AI readiness isn\u2019t about speed. It\u2019s about building a foundation that lasts. <\/p>\n <\/strong><\/p>\n It\u2019s easy to feel like you\u2019re already behind on generative AI,\u00a0especially if your only reference point is the ambitious deployments from big tech. In reality, the broader business landscape tells a more nuanced story.<\/p>\n While 71% of organizations recently reported of Gen AI in at least one business function, another study found that 40% of enterprise-scale companies are with Gen AI usage in a broader business context, but have not yet taken their solutions to production.<\/p>\n As public awareness and promotion of generative and agentic AI intensifies, so does the urgency to act. But speed without strategy is risky. Too often, organizations rush from pilot to rollout without ensuring their data is complete or their teams are prepared to support AI at scale\u2014resulting in tools that fail to solve real problems, strategies that stall out, and low user adoption driven by issues like hallucinations.<\/p>\n The real race isn\u2019t to be the first to adopt AI. It\u2019s about preparation and readiness, which goes beyond plugging in new tools. It means preparing your people to work with AI, ensuring your data can support it, and establishing the governance and operational frameworks to scale it responsibly.<\/p>\n Successful leaders in this space won\u2019t be the first to deploy AI. They\u2019ll be the ones who move with purpose and build the foundation to turn AI into real business value.<\/p>\n Mistaking \u201cdata in place\u201d for \u201cdata that\u2019s ready\u201d<\/p>\n Trustworthy AI starts with trustworthy data. Yet, while 75% of executives say high-quality data is the most valuable ingredient<\/a> for enhancing Gen AI capabilities, many organizations are struggling to meet that bar. Nearly half of CXOs (48%) admit their organizations lack the high-quality data<\/a> needed to operationalize GenAI efforts.<\/p>\n The issue often stems from data immaturity. When internal data is siloed, lacks clear definitions, or is missing the context required for meaningful outputs, it undermines the foundation AI models rely on.<\/p>\n Data governance is another common friction point for responsible AI deployment. Without clear, consistent rules around access, data classification, and usage, organizations risk exposing sensitive data\u2014whether through direct misuse or by failing to establish controls for using these systems effectively.<\/p>\n As AI becomes embedded in more everyday workflows\u2014from customer service to internal reporting\u2014those risks rise unless strong governance is in place. Real-time decisions made on flawed or improperly sourced data can compromise brand trust, security, and compliance.<\/p>\n Third-party data adds another layer of complexity. While external datasets can enrich AI performance, they must be credible, current, and appropriately structured. Without proper vetting, they can introduce bias or decisions that jeopardize the reliability of outputs.<\/p>\n When poor-quality data powers your AI systems, the consequences are immediate and make it difficult to build trust. In time-sensitive scenarios, like resolving a customer issue, there\u2019s little opportunity to verify accuracy. And without strong access controls and governance, you expose your organization to unnecessary risk and customer satisfaction issues.<\/p>\n Here are some important steps to build a strong data foundation for AI.<\/p>\n Getting your data in order is only the beginning. Turn readiness into results with a structured, phased approach to AI experimentation and adoption.<\/p>\n\n