As artificial intelligence (AI) reshapes healthcare, laboratories housing both the structured data essential for AI development and the ground truth needed for validation must evolve. Laboratory information systems must progress beyond traditional specimen tracking to become comprehensive data stewards. Through examining current challenges in data connectedness, interoperability, and implementation, a framework emerges for navigating the initial wave of AI adoption while building robust foundations for future innovation.