Many AI models never reach deployment, limiting business impact despite rising investments. To change this, organizations must follow four key steps: 1) Ensure access to the right data—it must be available, clean, timely, and compliant with regulations. 2) Collaborate across teams—data scientists must work with IT, business units, and stakeholders to ensure models align with real-world processes and decision-making. 3) Focus on relevant problems—models must address critical business needs, not just technical challenges. 4) Prepare for deployment—allocate resources for implementation, including organizational and tech changes. Success depends on building practical, integrated solutions from the start.

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