Most enterprises are layering AI on top of broken processes and wondering why it's not working. Fernando Cerenza, Senior Director of Product Management at Box, sits down with Emily Meade, Director of AI Innovation, and Allen Mann, Senior Director of Enterprise AI, both at Slalom, to unpack what it truly means to become an AI-first organization. They challenge the common misconception that AI can be rolled out like traditional software, and make the case for treating adoption as a full business transformation, not a tech implementation. The conversation digs into how data silos and poor metadata quietly sabotage AI deployments, why Slalom's zero legacy approach helps enterprises prioritize modernization without costly rip-and-replace projects, and how to design workflows where humans and AI genuinely complement each other. They also tackle the governance gap that trips up even the most ambitious AI rollouts, and why tying every use case to a quantifiable business outcome is the only way to build lasting executive buy-in. Key Moments: -Rethinking what AI-first really means: True AI-first organizations redesign processes from the ground up around human and AI collaboration, not just layer tools on top of existing workflows. -Rolling out AI like traditional software is the biggest mistake: Unlike a standard tech deployment, AI adoption requires real change management, business fluency, and a full transformation mindset. -Data silos silently kill AI implementations: Without sound data governance and consistent metadata, even well-built AI systems start delivering inconsistent and unreliable results. -Zero legacy over rip-and-replace: Slalom's approach focuses on small, quantifiable modernization steps tied to business outcomes, rather than expensive overhauls that preserve the same old problems. -Governance can't be an afterthought: Organizations that skip governance planning before going to production may see early wins, but the cracks show fast without clear accountability structures in place. -Tie every use case to dollars and cents: The AI projects that earn executive buy-in and scale successfully are the ones that connect directly to cost avoidance, revenue lift, or measurable ROI from day one.

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