# How to Identify and Scale High-Impact AI Use Cases > [!NOTE] Fuente original: [carlosluengo.com](https://carlosluengo.com/posts/blog-post-3) What if the fastest way to create value with AI isn’t by chasing a single killer app—but by spotting dozens of everyday moments where AI can quietly make life easier? That’s the approach behind the OpenAI guide *"Identifying and Scaling AI Use Cases"*, based on insights from over 300 deployments and 2 million users. This post breaks down the most practical lessons, ready for teams who want to move from “playing with AI” to *delivering real impact*. ### TL;DR – Use Cases Are Everywhere (If You Know What to Look For) - Start with **repetitive tasks**, **skill bottlenecks**, and **ambiguous work**. - Teach teams the **6 AI primitives** that unlock productivity across any role. - Use a simple **impact/effort matrix** to prioritise use cases that matter. - Think beyond single tasks—*map full workflows*. - Build a culture where people experiment, test, and share their own use cases. ### Key Principles for Finding Great Use Cases - **Lead from the top.** Senior sponsorship is key. - **Start simple.** Avoid over-engineering. Quick wins build momentum. - **Make it social.** Hackathons, Slack channels, and peer GPTs help spread adoption. ### The 3 Work Areas Where AI Excels **1. Repetitive, low-value tasks:** - Writing summaries - Updating dashboards - Answering common questions **2. Skill bottlenecks:** - Running queries without a data team - Drafting designs or mockups - Creating reports without an analyst **3. Ambiguity blockers:** - Brainstorming ideas - Getting unstuck when starting a task - Structuring complex plans ### The 6 AI Use Case Primitives These are reusable patterns that work across most teams: - ✍️ **Content creation** – Emails, documents, campaigns, scripts - 🔎 **Research** – Market scans, benchmarks, summaries - 💻 **Coding** – SQL, Python, HTML, debugging - 📊 **Data analysis** – Trends, harmonisation, visuals - 💡 **Ideation & strategy** – Brainstorms, plans, feedback - 🤖 **Automation** – Scheduled reports, smart summaries, GPT flows ![[_assets/carlosluengo/assets/img/202504_post_infografia.png]] ### Prioritising with Impact vs Effort Use a simple 2x2 to decide what’s worth scaling: | | Low Effort | High Effort | |---------------|------------------|------------------| | High Impact | ✅ Quick Wins | 🚀 Big Bets | | Low Impact | 🤏 Self-serve | ❌ Deprioritise | Examples: - **Quick win:** Auto-summarise meetings - **Big bet:** Custom multilingual GPT for credit risk - **Self-serve:** Personalised SQL queries - **Deprioritise:** Replacing tools that already work well ### From Tasks to Workflows The best users don’t stop at a single task—they link AI across steps: **Example: Marketing Campaign Flow** - Research trends → 2. Analyse audience data → 3. Brainstorm ideas → 4. Create assets → 5. Automate localisation ### Building AI Culture Great AI rollouts aren’t just technical—they’re cultural: - Run hackathons or "use case olympics" - Create GPT labs like Estée Lauder’s cross-functional teams - Set up shared spaces for ideas, templates, and prompts ### My Take This guide is gold for teams trying to make AI useful *today*. The "6 primitives" idea is so practical—it turns vague possibilities into tangible action. If you want to democratise AI inside your org, this is the playbook. 📎 [ Full Guide PDF ](#) ## También te puede interesar - [[bbva-chatgpt-enterprise-journey|How BBVA Scaled ChatGPT Enterprise]] - [[2025-04-19-generative-ai-workplace-happiness|Generative AI, Productivity, and the Future of Happiness at Work]]