So You Bought AI—Now What? Companies are spending millions on AI tools. The assumption? AI will instantly transform workflows, unlock efficiency, and drive revenue. The reality? Adoption stalls. AI gets underutilized. Teams revert back to old habits. Why does this happen? Because AI isn’t just a tool—it’s a fundamental shift in how work gets done. And without a structured approach to ownership, measurement, and integration, AI turns into just another expense instead of a competitive advantage. Where AI Adoption Breaks Down Who actually owns AI within an organization? Is it RevOps? IT? Enablement? The problem is that no one is truly accountable, leading to fragmented adoption and poor usage. Without a dedicated team to oversee usage, optimization, and scaling, AI tools become “nice to have” instead of mission-critical. 📉 No Defined Success Metrics Companies buy AI tools expecting results but rarely define what success looks like upfront. Are we using AI to reduce manual work? Is it meant to enhance decision-making? Should it increase revenue per rep? AI without a business case and ROI measurement is a ticking time bomb for budget cuts. 🔄 The Post-Sales Blindspot While most AI adoption efforts focus on pre-sales, the real impact often happens after the deal closes. AI is transforming Customer Success, renewals, and pricing models, yet many companies still approach these functions manually. The teams responsible for retaining revenue need AI just as much—if not more—than the teams generating it. Fixing AI Adoption: What Works? 1️⃣ Assigning AI Ownership Companies seeing success have created dedicated AI adoption teams—cross-functional groups that ensure AI gets embedded into workflows, not just bought and ignored. 2️⃣ Defining AI Success Early Before implementing AI, the best teams define: ✅ What problem are we solving? ✅ What are the key metrics we expect AI to impact? ✅ How do we track and report these metrics? If these aren’t clear, AI adoption will always feel like a moving target. 3️⃣ Expanding AI Beyond Sales AI isn’t just for pipeline generation. In Customer Success, AI can surface risk signals before churn happens. In Pricing, AI can analyze deal patterns to optimize renewal strategies. The companies that treat AI as a full revenue lifecycle strategy—not just a sales tool—are the ones winning. Let’s Talk About It In the latest AI Advantage series, we are sitting down with Amanda Whiteside Global VP of Revenue Enablement to unpack how companies are making AI work after the purchase as she leads the AI implementation for her revenue team. We’ll dive into: 🔹 Who should actually own AI adoption inside an organization? 🔹 How AI is changing post-sales, CS, and pricing strategies. 🔹 The business case & ROI metrics Amanda uses to decide whether to keep or cut an AI tool. AI can drive massive impact—but only if you build the right adoption strategy. What’s been your biggest challenge in AI adoption so far? Come hang ;)
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