Generative AI Use Cases

Generative AI isn’t just about knowing how to use ChatGPT or build RAG—it's a whole ecosystem of skills, tools, and techniques. To grow meaningfully in this space, you need strong roots in fundamentals, a sturdy trunk of core techniques, and expanding branches into advanced applications. Here’s the breakdown from foundation to advanced growth: 𝟭. 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 (𝗥𝗼𝗼𝘁𝘀):  • AI/ML Basics → PyTorch, TensorFlow, Keras, scikit-learn  • Python Programming → Python, Jupyter, VS Code  • Math & Data Fundamentals → Pandas, NumPy, SciPy, Matplotlib 𝟮. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗧𝗿𝘂𝗻𝗸):  • LLMs (OpenAI GPT, Claude, LLaMA, Mistral)  • Image Generation (MidJourney, Stable Diffusion, Adobe Firefly)  • Audio & Video (Runway, Descript, ElevenLabs)  • Multimodal Models (GPT-4o, Gemini, LLaVA, Kosmos-1) 𝟯. 𝗖𝗼𝗿𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 (𝗕𝗿𝗮𝗻𝗰𝗵𝗲𝘀):  • Prompt Engineering (LangChain, DSPy, FlowGPT)  • Fine-Tuning (LoRA, QLoRA, PEFT)  • RAG (Pinecone, ChromaDB, Weaviate)  • Evaluation & Guardrails (Guardrails AI, Trulens, LlamaGuard) 𝟰. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 (𝗡𝗲𝘄 𝗚𝗿𝗼𝘄𝘁𝗵):  • AI Agents (AutoGPT, CrewAI, LangGraph, Microsoft Autogen)  • Workflow Orchestration (Airflow, n8n, Zapier, Make.com) 5. Advanced Growth (Leaves): Deployment & Scaling (Docker, Kubernetes, AWS Bedrock, GCP Vertex AI) Specialization & Use Cases (Healthcare AI, FinTech AI, Creative AI, Enterprise Automation) Whether you’re just starting out or already scaling solutions, this skill tree gives you a roadmap to grow strategically in Generative AI. 👉Where are you currently on this skill tree? Are you building your roots, strengthening your trunk, or branching out into advanced growth?

Applied Intuition is paving the way to self-driving success. Applied Intuition's $600M Series F ($15B valuation) highlights the autonomous vehicle market's AI-enabled resurgence and how infrastructure companies are capturing outsized value as the industry matures. The broader autonomous driving space saw equity funding 3x last year, driven by massive rounds to Waymo ($5.6B Series C) and Wayve ($1.1B Series C). Applied Intuition's raise is the latest signal of a broader market revival, where generative AI is accelerating the timeline for full autonomous driving by removing remaining hurdles around cost, explainability, and vehicle-passenger communication. While everyone debates which manufacturer(s) will "win" autonomous driving, Applied Intuition’s "picks and shovels" strategy is paying off. They are quickly becoming the foundational simulation and validation software that everyone needs. “Everyone” includes 18 of the top 20 automotive OEMs as customers and strategic partnerships with Audi, TRATON Group, Isuzu Motors, and OpenAI. OEMs are realizing they need specialized software partners, not just in-house development. It's not just about building the cars, it's about building the tools that build the cars. Broader AV market dynamics particularly favor companies like Applied Intuition. Major OEMs like GM and Hyundai injected $1.4B into their self-driving units last year but are facing safety issues and commercialization delays. This creates opportunities for specialized software providers to offer cost-effective alternatives to in-house development. Like with many emerging tech markets, the biggest winners in AV may be the ones building the critical infrastructure that makes the end product possible, rather than the end product itself. Applied Intuition is a clear leader in the resurgent AV space, with their multi-sector approach across automotive, trucking, defense, and industrial applications giving them a sustainable competitive moat. The latest funding round positions Applied Intuition to capitalize on the autonomous vehicle market's second wave, where established software platforms become increasingly valuable as the industry moves from experimentation to commercial deployment.

Cutting through the AI noise - here are 5 use cases for using generative AI today in a law practice: 1) Having AI draft initial responses to standard discovery requests, pulling directly from client documents and past cases—turning 3 hours of document review into 20 minutes of attorney verification. 2) Using AI to analyze deposition transcripts and build detailed witness chronologies, flagging inconsistencies and potential credibility issues that could be crucial at trial. 3) Feeding settlement agreements from similar cases to AI to generate initial settlement terms, helping attorneys start negotiations with data-backed proposals rather than gut instinct. 4) Having AI review client intake forms and past matters to spot potential conflicts of interest—moving beyond simple name matching to identify subtle relationship patterns. 5) Using AI to draft routine motions and pleadings by learning from the firm's document history, maintaining consistent arguments while adapting to case-specific facts. The real value isn't replacing attorney judgment. It's eliminating the mechanical tasks that keep great lawyers from doing their best work. What specific AI applications are you seeing succeed (or fail) in your practice? #legaltech #innovation #law #business #learning

Therabot just passed the world’s first clinical trial for an AI therapy chatbot. Developed by Dartmouth over 5 years, it’s already delivering real, measurable results that could change the face of mental health care. But it wasn’t always this way. When Therabot was first created, its early versions were a disaster: - One chatbot expressed suicidal thoughts. - Another blamed everything on your parents by question five. But the team at Dartmouth didn’t give up — they spent 3 years and 100+ people crafting custom training data from scratch to get it right. And it paid off. The results of the clinical trial? 👉 A 51% reduction in depression symptoms among participants. 👉 People with moderate anxiety saw their symptoms drop to “mild” levels. 👉 Some no longer met diagnostic criteria for anxiety or depression. 👉 And most surprisingly: Participants rated their connection to Therabot as equal to a human therapist. So what matters now? It works. It’s scalable. It’s always available, unlike human therapists who book months out. But if you ask me — I don’t believe it will replace human therapy anytime soon. However, with 70% of people around the world having no access to mental health care, Therabot might be their first real shot at getting the help they need. So the future of therapy won’t be 100% AI. But it could be AI-assisted, AI-augmented, and most importantly, AI-accessible. Because mental health care isn’t broken — it’s just undersupplied. And Therabot might not fix that overnight. But it’s the most science-backed step forward we’ve seen in years. So I’m hopeful. What’s your take on AI therapists? Could they be the solution for those who currently don’t have access to mental health care? #entrepreneurship #healthtech #AI #innovation

Unlocking the Value of Generative AI in Commercial Payments 💡 Advancements in speech recognition, image generation and machine learning have allowed AI to support innovation across a range of payment use cases. The introduction of generative AI represents an inflection point in this journey, at which the technology begins to surpass human capability. This results in optimizing operational tasks, augmenting human capabilities and changing how people and organizations work. The top 3 areas banks are using generative AI in payments are for improving fraud detection, securing payments data and authentic chat. Growing competition from digital challengers, client impatience with traditional payments pain points and dependency on legacy technologies are constraining incumbent payment providers from keeping pace with an evolving market. A lack of value-added services around payments and the difficulty of adding new payment methods also contribute to higher levels dissatisfaction. Today, innovations in generative AI present a unique opportunity for addressing some of these pain points. They have the ability to drive truly personalized client experiences and rewire existing, complex payment processes to be simpler and more efficient. Generative AI can be applied across multiple functions in commercial payments, with use cases ranging from early-stage innovations like automated document processing to more mature applications such as enhanced fraud detection and tailored client interactions. 🔹 Client Servicing: Enhancing customer support through virtual assistants, providing personalized recommendations based on payment transaction history and knowledge management. 🔹 Sales and Marketing: AI-driven customer segmentation, targeted marketing and cross-sell opportunities (e.g., additional banking services like personalized wealth advisory). 🔹 Treasury and Finance: Optimizing finance and accounting functions (e.g., payment process and cashflow management) and predictive analytics for forecasting. 🔹 Fraud Prevention: Preventing fraud, especially in cross-border payments, and generating synthetic data for fraud scenarios. 🔹 Operations: Automating tasks like data entry, reporting and customer actions (e.g., for payment investigations and payments ops monitoring) to reduce costs and improve efficiency. 🔹 Regulatory and Compliance: Enhancing compliance by better interpreting and monitoring regulatory requirements and documentation (e.g., ISO messaging and scheme rules tracking and interpretation). 🔹 Product Development and Technology: AI-led code generation can accelerate the modernization of legacy payment systems and improve transaction risk ratings. Source: Accenture - https://shorturl.at/iWJV4 #Innovation #Fintech #Banking #FinancialServices #Payments #Treasury #Fraud #Compliance #AI #Data #Cloud #GenAI 

3 Healthcare AI papers I'm reviewing today 1. 📚AI in Medicine: Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions https://lnkd.in/eNV5CDSp Medical Multimodal Foundation Models (MMFMs) combine diverse data types (imaging, text, labs) to improve diagnosis, treatment planning, and precision medicine. Recent advances in large datasets and multimodal architectures (vision-only and vision-language) enable strong generalization across tasks like segmentation, classification, and clinical report generation. Key opportunities lie in holistic integration of multi-organ/multimodal data, but challenges remain in optimizing representations and scaling real-world clinical adoption. 2. 📚 BMJ Dig Health & AI - Optimising large language models for clinical information extraction: a benchmarking study in the context of ulcerative colitis research https://lnkd.in/erXhhX9k This study compared open-source and closed-source LLMs for extracting the Mayo Endoscopic Subscore from colonoscopy reports. It found that QLoRA fine-tuning improves open-source performance significantly, but GPT-4o with prompt engineering still outperforms them by 5–11% and is more cost-effective. Overall, GPT-4o is the most efficient option today, while QLoRA-optimized open-source models are viable fallbacks, though both leave room for improvement in instruction following. 3. 📚 JAMIA Open - Generative artificial intelligence for automated data extraction from unstructured medical text https://lnkd.in/ekfy8-VX A GenAI pipeline using an open-source LLM was developed to extract structured data from right heart catheterization notes with built-in guardrails and a retry mechanism. It achieved high performance (99% precision, 85% recall, 91.5% F1, 90% accuracy), with missed values as the main error and hallucinations extremely rare (<0.01%). The study shows LLM pipelines can reliably mine unstructured clinical data, improving research efficiency and clinical applications.

🌟 A Pragmatic Take on AI Applications 🌟 Generative AI is a powerful tool, but its true potential lies in practical applications that deliver real value. Here’s a thoughtful perspective on how businesses can leverage Generative AI effectively, inspired by insights from industry experts: 1. Focus on Tangible Use Cases 🎯 Generative AI should be applied to well-defined problems. For instance, in healthcare, AI can analyze medical records to identify patterns that lead to early diagnosis and personalized treatments. This targeted approach improves patient outcomes and optimizes healthcare resources. 2. Integration with Existing Systems 🔗 Rather than deploying AI as an isolated solution, it should be seamlessly integrated into existing workflows. In customer service, AI-driven chatbots can handle routine inquiries, allowing human agents to focus on more complex issues that require empathy and critical thinking. This integration enhances service efficiency and customer satisfaction. 3. Empowering Employees 🧑💼 AI should augment human capabilities, not replace them. By handling repetitive tasks, AI frees up employees to engage in more strategic and creative activities. For example, marketers can use AI to analyze customer data and develop personalized campaigns, enhancing engagement and conversion rates. 4. Leveraging Data for Insights 📊 Generative AI excels at processing large datasets to uncover actionable insights. In finance, AI can analyze market trends and predict risks, enabling more informed investment decisions. This data-driven approach reduces uncertainty and enhances strategic planning. 5. Ethical and Responsible AI Practices ⚖️ Deploying AI responsibly is crucial. This means ensuring transparency, protecting data privacy, and addressing biases in AI algorithms. Ethical AI practices build trust with customers and stakeholders, fostering a positive reputation and long-term success. 6. Practical Examples of AI in Action 🏥 Healthcare: AI models predict patient deterioration, allowing timely interventions and better resource management in hospitals. 📚 Education: AI-powered platforms personalize learning experiences, improving student outcomes by adapting content to individual needs. 🛍️ Retail: AI-driven recommendation systems boost e-commerce sales by offering personalized shopping experiences. 🤔 Final Thoughts: Generative AI’s true value emerges when it’s applied thoughtfully and strategically. By addressing specific needs, integrating seamlessly with existing systems, empowering employees, leveraging data for informed decisions, and maintaining ethical standards, businesses can unlock AI’s full potential.💡 Subscribe to the Generative AI with Varun newsletter for more practical insights: 🔗 https://lnkd.in/gXjqwQaz Thanks for joining me on this journey! #GenerativeAI #EthicalAI #Applications

Most people think you need tuition, credentials, or a sabbatical to learn how AI actually works. You do not. You need curiosity and the discipline to think clearly. Over the past 12 months, I’ve had the unique opportunity to guest lecture and keynote in partnership with Harvard University, and this year Harvard also released a series of generative AI and prompt engineering courses that anyone can access at no cost. That combination matters more than people realize. What I saw repeatedly while working with students and faculty is that the future of AI leadership has very little to do with tools and everything to do with judgment. Prompting is not clever wording. It is structured thinking. The quality of output reflects the clarity of intent, the assumptions built into the system, and the discipline of the person guiding it. AI fluency is quickly becoming part of the executive baseline. Strategy, workforce planning, education, and organizational design are already being shaped by these systems. Leaders do not need to become engineers, but they do need to understand how decisions are translated into outcomes. Harvard has made the following courses publicly available: 1. Introduction to Generative AI https://lnkd.in/gfvpRbsA 2. Prompt Engineering https://lnkd.in/gHfXdRKB 3. Beyond Chatbots: System Prompts and Retrieval Augmented Generation https://lnkd.in/gNkZebz6 4. Generative AI in Teaching and Learning https://lnkd.in/gvPUfsiN 5. Teaching with AI in the Classroom https://lnkd.in/grCn38KY 6. The Basics of Generative AI https://lnkd.in/gEyptr9w What stood out to me throughout this year is that education remains the stabilizing force during periods of disruption. When leaders invest in understanding rather than shortcuts, they make better decisions and create space for responsible innovation. I am grateful for the opportunity to contribute to these conversations and to partner with institutions that take seriously the responsibility of shaping how leaders think as we head into 2026.

Let’s talk about some real potential of Generative AI. Here are 9 Use cases a business leader should know to understand how to extract real value out of Gen AI. 𝟭. 𝗔𝘀𝘀𝗲𝘁 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 ↳ Optimize and Simulate maintenance schedules using historical use and performance data. ↳ Benefits - Cost Improvements - Better Health & Safety - Increased throughput 𝟮. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝘁𝗿𝗮𝗱𝗲 𝗽𝗿𝗼𝗺𝗼𝘁𝗶𝗼𝗻𝘀 ↳ Prepare negotiation decks and analyze vast amounts of historic unstructured data to support the negotiation process ↳ Benefits - Efficient trade promo process - Better allocation of resources - Data-driven decision making 𝟯. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 ↳Fast design iterations using design software (Creative Assistant). Add insights from historical market data. ↳Benefits - Faster Speed-to-market - ‘More Creative Bandwidth’ - Curtailing market research time 𝟰. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 ↳Locally fine-tuned models enable faster access to information through human-like interaction. ↳Benefits - Data-driven decision making - Analyze previously inaccessible unstructured data 𝟱. 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 ↳Faster migration to advanced analytics through assisting code development ↳Benefits - Short software dev lifecycle - Access to a wider knowledge base for SMEs 𝟲. 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 ↳ Generate synthetic data for testing and simulating scenarios previously unknown. ↳ Benefits - Faster AI Model deployment - Rigorous testing using scores of data 𝟳. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝘃𝗲𝘀 ↳ Using NLP, Speech-to-text deploys 24-hour Customer support. ↳ Benefits - Better customer experience - Increased human Customer Representative’s efficiency 𝟴. 𝗣𝘂𝗯𝗹𝗶𝗰 𝗦𝗲𝗰𝘁𝗼𝗿 𝗨𝗿𝗯𝗮𝗻 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 ↳ Support Governments to simulate scenarios of various infrastructure decisions. Generate 3D models for master planning. ↳ Benefits - Super-charge creativity - Better decision-making Faster ideas generation 𝟵. 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 ↳Multi-national corporations get access to huge in-house content and best practices previously in different languages ↳ Benefits Better Customer experience Best-practice sharing Standardized processes Share what else you can add. If you like the post, share it with someone who can benefit from it. --- I am Tariq Munir...My mission is to create a Tech-enabled Humanistic future for all through my talks, writings, and content. Follow me to be part of this mission and learn more about Digital Transformation, Data, and AI.

We are nearing the limits of the known antibiotic universe. For decades, progress has largely meant revisiting familiar molecules, even as resistance continues to outpace discovery. A recent effort from Massachusetts Institute of Technology changes the nature of the search itself. Instead of screening what already exists, researchers used generative AI to design tens of millions of hypothetical compounds that have never been synthesized or cataloged before. This is not deeper exploration of known space, but the creation of entirely new chemical territory. The AI generated molecules from first principles, guided by rules of efficacy and synthesizability. Several candidates that emerged are structurally unlike existing antibiotics and appear to act through a more fundamental mechanism: disrupting bacterial cell membranes. That distinction matters. Resistance often develops against drugs targeting specific internal proteins, but compromising the membrane is a broader, harder-to-defend strategy. In early studies, one AI-designed compound proved effective against drug-resistant gonorrhea by targeting a novel membrane-related protein, while another cleared MRSA infections in animal models, operating outside known antibiotic classes. The deeper shift here is conceptual. Generative models expand discovery beyond what can be searched or screened, into what can be designed. At a time when antimicrobial resistance is a growing global threat and the traditional pipeline is stagnant, this exploration-first approach offers a credible path forward. The next chapter of antibiotic development may depend less on rediscovery, and more on invention. #ArtificialIntelligence #GenerativeAI #DrugDiscovery #AntibioticResistance #AntimicrobialResistance #ComputationalBiology #AIinHealthcare #Biotech #LifeSciences #ScientificInnovation