How to Identify AI Use Cases (Backed by 600+ OpenAI Customer Use Cases) Many companies struggle to go beyond experimentation with AI. This blueprint from OpenAI’s customer success research helps you pinpoint where AI brings the most value, how to start small, and scale effectively across workflows. 1. Key Principles AI adoption succeeds when supported by leadership, focused on practical use cases, and reinforced through hands-on learning experiences. 2. Where AI Adds Value AI works best when it automates repetitive tasks, fills knowledge or skill gaps, and reduces ambiguity in complex decision-making processes. 3. Fundamental Use Cases to Fast-Track Value From content creation to research, data analysis, automation, and coding—these are proven areas where AI can deliver immediate business impact across departments. 4. Prioritize Use Cases Use the impact/effort matrix to focus on high-value, low-effort opportunities first. Build momentum with quick wins before scaling to more complex implementations. 5. Workflow Mapping Instead of one-off tools, think in terms of workflows. Break down processes, identify repeatable steps, and layer AI to improve efficiency and outcomes over time. Content Reference : Lewis Walker ➲ I help companies go from clunky operations to lean, high-output systems with AI agents, automations, and chatbots. Want 20+ hours back per week? Or 5–6 figures in added pipeline? That’s what we deliver—in 30 days. 👉 Book before my calendar closes: https://lnkd.in/dMymqhug Be honest, are you using AI to save time, or still stuck in busywork? | 29 comments on LinkedIn