Unique AI Applications for Professionals

Agentic AI is quietly reshaping UX research and human factors. These systems go beyond isolated tasks - they can reason, adapt, and make decisions, transforming how we collect data, interpret behavior, and design with real users in mind. Currently, most UX professionals experiment with chat-based AI tools. But few are learning to design, evaluate, and deploy actual agentic systems in research workflows. If you want to lead in this space, here’s a concise roadmap: Start with the core skills. Learn how LLMs work, structure prompts effectively, and apply Retrieval-Augmented Generation (RAG) to tie AI reasoning into your UX knowledge base: 1) Generative AI for Everyone (Andrew Ng) - broad introduction to generative AI, prompt engineering, and how generative tools feed autonomous agents. https://lnkd.in/eCSaJRW5 2) Preprocessing Unstructured Data for LLM Apps - shows how to structure data for AI-driven research. https://lnkd.in/e3AKw8ay 3)Introduction to RAG - explains retrieval-augmented generation, which makes AI agents more accurate, context-aware, and timely. https://lnkd.in/eeMSY3H2 Then you need to learn how agents remember past interactions, plan actions, use tools, and interact in adaptive UX workflows. 1) Fundamentals of AI Agents Using RAG and LangChain - teaches modular agent structures that can analyze documents and act on insights. This one has a free trial. https://lnkd.in/eu8bYdjh 2) Build Autonomous AI Agents from Scratch (Python) - hands-on guide for planning and prototyping AI research assistants. This one also has a free trial. https://lnkd.in/e8kF-Hm7 3) AI Agentic Design Patterns with AutoGen - reusable architectures for simulation, feedback analysis, and more. https://lnkd.in/eNgCHAss 3) LLMs as Operating Systems: Agent Memory - essential for longitudinal studies where memory of past behavior matters. https://lnkd.in/ejPiHGNe Finally, you need to learn how to evaluate, debug, and deploy agentic systems at scale in real-world research settings. 1) Building Intelligent Troubleshooting Agents - focuses on workflows where agents help researchers address complex research challenges. https://lnkd.in/eaCpHXEy 2) Building and Evaluating Advanced RAG Applications - crucial for high-stakes domains like healthcare, where performance and reliability matter most. https://lnkd.in/eetVDgyG

AI Agents are task-specific, autonomous systems that integrate large language models with structured tools, APIs, and real-time data sources. They operate across domains such as cybersecurity, supply chain logistics, and healthcare by executing workflows that traditionally required human-in-the-loop decision making. These agents leverage vector databases, retrieval-augmented generation, and fine-tuned embeddings to enable contextual reasoning and dynamic response generation. As orchestration frameworks mature, multi-agent systems are increasingly capable of handling end-to-end processes like demand forecasting, patient triage, and adaptive tutoring with minimal supervision. The below chart shows just how broad their impact is: 1.🔹 IT & Security : Phishing filters, threat detection, patch suggestions 2.🔹Healthcare : Patient alerts, medical chatbots, symptom matching 3.🔹 Education : Flashcards, concept explainers, AI tutors 4.🔹 Sales & Marketing : Lead scoring, campaign ideas, email outreach 5.🔹Logistics : Fleet tracking, demand forecasting, inventory updates 6.🔹Manufacturing : Predictive maintenance, robotic control, energy monitoring 7.🔹 Research : Academic writing, data cleaning, topic expansion 8.🔹 Customer Support : FAQ bots, emotion detection, chat summaries 9.🔹 Smart Environments : Digital twins, voice commands, access control 10.🔹Ops Automation : Shift scheduling, system alerts, order tracking What used require significant manual effort, now takes a few smart agents. I believe it’s a great time to start exploring and experimenting in this space… #genai #aiagents #artificialintelligence

3 AI USE CASES YOUR ORGANIZATION CAN IMPLEMENT TODAY Forget the AI hype cycle—while everyone's talking about tomorrow's possibilities, forward-thinking organizations are already gaining ground with practical applications. Here are three AI implementations you can launch this week: 1. AI-Powered Meeting Analysis ➤ Deploy an AI agent to record, transcribe, and analyze team discussions—identifying action items, tracking decisions, and ensuring all voices are heard. 2. Custom Knowledge Base AI ➤ Build an AI partner trained on your organization's internal documents and processes. New team members get up to speed faster, and experts focus on high-value work instead of answering repetitive questions. 3. Collaborative Strategy Development ➤ Create a virtual strategy team with AI personas designed for different thinking styles—one analyzing data, another challenging assumptions, a third generating alternatives. The organizations that will dominate aren't waiting to see how AI develops—they're actively building the hybrid workforce today. Across industries, companies are reimagining collaboration between humans and AI, finding ways to augment team capabilities rather than simply replacing tasks. 💡What AI applications are you exploring? I'd love to hear your experiences. #ArtificialIntelligence #FutureOfWork #Strategy #business #innovation