Point 1: Sharper Focus, Smarter AI — Why Defining Use Cases Narrowly Wins AI teams often jump into model selection with vague goals like "face recognition" or "customer shopping assistant." That's not a use case—it's a technology trap that leads to under-tuned models and wasted cycles. Narrow it down: A gallery retrieval system for finding missing persons favors recall to surface every possible lead, even with noise—as seen in AI surveillance systems achieving 94% accuracy

As GenAI adoption grows, new **technical and societal risks** are emerging that organizations must prepare for. Here are four additional challenges with real-world examples 👇 **8️⃣ Prompt Injection Attacks** 🔹 *Risk:* Malicious prompts manipulate AI systems into ignoring safety instructions or revealing sensitive information. 📌 *Real-world example:* Researchers demonstrated prompt injection attacks against AI-powered plugins and browsing tools, tricking models into exposin

Generative AI is transforming industries, but it comes with real risks that organizations must address responsibly. Here are some key challenges and practical mitigations 👇 **1️⃣ Nondeterminism** 🔹 *Risk:* The same prompt can generate different outputs, making reliability difficult in critical applications. 📌 *Example:* Developers using AI coding assistants noticed identical prompts sometimes produced different code implementations. ✅ *Mitigation:* Run repeated testing and

Generative AI is rapidly entering enterprise workflows. While the opportunity is enormous, recent real-world incidents highlight the governance risks boards should consider. Here are five examples that illustrate why AI oversight is becoming a board-level issue: 1️⃣ Legal Liability from AI-Generated Information In 2023, attorneys submitted a court filing containing non-existent cases generated by AI. The court sanctioned the lawyers, reinforcing that organizations remain acco