Generative AI Use Cases

𝗧𝗵𝗲 𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲 𝗚𝗲𝗻𝗔𝗜 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 ❗️ Generative AI is transforming industries, but scaling it comes with challenges. Deloitte‘s State of Generative AI in the Enterprise (Q4 2025) surveyed 2,773 AI-savvy leaders across 14 countries and six industries to explore how organizations are piloting and implementing GenAI. ❗️ 𝗪𝗢𝗥𝗞𝗙𝗢𝗥𝗖𝗘 𝗔𝗖𝗘𝗦𝗦 𝗜𝗦 𝗟𝗔𝗚𝗚𝗜𝗡𝗚 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Less than 40% of workers currently have access to GenAI tools, significantly limiting adoption and impact. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Broaden workforce access through targeted training initiatives and streamlined onboarding processes to integrate GenAI tools effectively into daily workflows. ❗️ 𝗕𝗔𝗥𝗥𝗜𝗘𝗥𝗦 ��𝗢 𝗣𝗥𝗢𝗚𝗥𝗘𝗦𝗦 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Major challenges include mistakes with real-world consequences (35%), difficulties scaling advanced initiatives, and data quality issues. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Implement robust governance frameworks, prioritize data management strategies, and adopt a phased scaling approach for GenAI projects to mitigate risks and improve outcomes. ❗️ 𝗥𝗢𝗜 & 𝗨𝗦𝗘 𝗖𝗔𝗦𝗘𝗦 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: While nearly 74% of advanced GenAI projects are meeting or exceeding ROI expectations, many organizations struggle to identify relevant use cases or lack clarity on how to apply AI effectively. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Conduct industry-specific workshops and pilot programs to help organizations uncover impactful use cases, such as cybersecurity, IT automation, and customer engagement, and develop tailored AI strategies. 📰 … and what’s currently on the table? 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜! Autonomous AI agents tackling specific tasks like sales research. 𝟱𝟮% 𝗼𝗳 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 are exploring Agentic AI. FOOD FOR THOUGHT! 💭 What’s the biggest challenge your industry faces in scaling GenAI? How can we overcome that together? Let’s discuss in the comments! ———————————— ♻️ Share this to inspire others to break barriers and start using AI for Good. 💡 Follow me, Lara Sophie Bothur, for more inspiration on innovation & technology!

Generative AI on SAP BTP Architecture Most people think SAP BTP is complex. But with Generative AI - it becomes powerful. What you’re looking at is more than just an architecture. It’s a blueprint for building intelligent, scalable, real-world apps on a secure and trusted SAP ecosystem. ✅ At the top: the User Interface layer ➔ Built with SAP UI5 and Web Components ➔ Powered through the SAP Cloud Application Programming Model (CAP) ✅ In the middle: the Generative AI Hub ➔ Uses SAP AI Core for prompt registry, trust, and control ➔ Orchestrates everything from data masking to I/O filtering ✅ At the heart: SAP HANA Cloud ➔ Vector Engine + Knowledge Graph Engine ➔ Harmonized with AI models for contextual insights ✅ The network: SAP and Partner Foundation Models ➔ Built-in, Partner-hosted, SAP-hosted ➔ All secured via HTTPS and SAP Destination Service This is SAP’s vision for enterprise AI Secure. Composable. Explainable. You can move fast without compromising on governance You can build AI into business processes not as an add-on but as a core capability And the best part It’s not just future ready It’s enterprise ready today P.S. Save this if you're building AI on SAP and want a roadmap that actually works Save 💾 ➞ React 👍 ➞ Share ♻️ Follow Alok Kumar for everything related to SAP

🚀 12 Real Use Cases of Customers using Generative AI at Amazon Web Services (AWS) Many people ask me recurrently, is there a hype around Generative AI? My answer: Yes and no... Here's why! If you look at TV, newspapers, or casual conversations with family and friends, it definitely seems like there’s a Generative AI hype. This buzz is mostly from non-tech people who are just getting familiar with the concept, often starting their AI journey with the release of ChatGPT. But when I talk to clients, the story is different. Generative AI is truly transforming how businesses interact with their end customers or boosting employee productivity. From my perspective at Amazon Web Services (AWS), there’s no hype—just exciting, real-world applications of AI making a big impact. Here are some great examples to illustrate this: Intuit: Intuit Assist is a generative AI-powered assistant that offers personalized insights to help users make smart financial decisions (more info 👉 https://lnkd.in/dbaxwfXd) BT Group leverages GenAI (CodeWhisperer) to provide coding assistance to its software engineers (more info 👉 https://lnkd.in/dgJafDCC) Accor enhances travel planning and booking, offering personalized recommendations and intuitive, conversational advice (more info 👉 https://lnkd.in/dUYhnQeh) Perplexity: reimagining search by providing personalized answers using generative ai, instead of link lists and generic results. (more info 👉 https://lnkd.in/dAUAEv6S) BMW Group: in-Console Cloud Assistant (ICCA) solution designed to empower hundreds of BMW DevOps teams to streamline their infrastructure optimization efforts (more info 👉 https://lnkd.in/dGBYB4NJ) Booking.com: delivering destination and accommodation recommendations that are tailored and relevant to customers (more info 👉 https://lnkd.in/dZnQNX43) Pfizer accelerates research, predicts product yield, and helps it deliver more medicines to patients (more info 👉 https://lnkd.in/dhHd9t6Q) Toyota Motor Corporation uses generative AI to respond in seconds to driver emergencies (more info 👉 https://lnkd.in/djQWfJ4D) United Airlines: intelligent airport operations powered by generative AI (more info 👉 https://lnkd.in/d9WueKtk) Netsmart: HIPAA-eligible service that automatically creates clinical notes from patient-clinician conversations using generative AI (more info 👉 https://lnkd.in/d8JaeDTh) Amazon Pharmacy: Q&A chatbot assistant to empower agents to retrieve information with natural language searches in real time (more info 👉 https://lnkd.in/dM9NmnTd) Amazon Ads: AI-powered image generation to help brands produce richer creative new content (more info 👉 https://lnkd.in/dCn7xG3t) #ai #genai

How are companies deploying #generativeAI? Most companies are in pilot mode investing in “no regrets” use cases in areas that are accessible without fundamental enhancements to their data and digital core in areas such as IT, marketing, customer service, sales and finance. But Reinventors, representing only 9% of companies, are going further. They’re scaling the technology to power enterprise-wide Reinvention transforming capabilities end to end with a clear 360 value business case. They are deploying #GenAI in no-regret areas while also investing more aggressively in strategic bets across broader segments of the enterprise including supply chain, R&D, engineering, asset management and capital projects where the benefits are significant. These investments offer competitive advantage and will reshape how industries operate. Read our in-depth research report to see how Reinventors are pulling ahead, and how you can leapfrog today's leaders by applying #generative AI across the enterprise. https://lnkd.in/g_YQ3T5m   Oliver Wright Muqsit Ashraf Michael Moore Karen Fang Grant

𝗟𝗲𝗮𝗿𝗻 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜: 𝗔 𝗖��𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 Generative AI is reshaping industries—from customer support to marketing—and it's only set to grow. If you’re ready to dive in or deepen your expertise, here’s a structured pathway to help you build robust skills while keeping ethics and impact in mind. 𝗚𝗲𝗻𝗔𝗜 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀: 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝗖𝗼𝗿𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗕𝗹𝗼𝗰𝗸𝘀 - Artificial Intelligence & Machine Learning: The foundations of generative AI, enabling machines to analyze and generate new content. - Transformers: The architecture behind today’s advanced models. - Prompt Engineering: Fine-tuning inputs to maximize the quality of model outputs.    𝗨𝘀𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 𝗔𝗣𝗜𝘀: 𝗚𝗲𝘁 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝘄𝗶𝘁𝗵 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆-𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗧𝗼𝗼𝗹𝘀 - API Best Practices: Learn efficient, scalable API usage. - OpenAI, Hugging Face, Vertex AI: Explore powerful platforms for generative tasks. 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 Generative AI is revolutionizing several sectors: - Education & Learning: Enabling personalized tutoring and content generation. - Business & Finance: Driving insights, automating analysis, and streamlining workflows. - Customer Support: Powering AI-driven response systems for instant support. - Marketing: Creating content at scale and enhancing customer engagement. 𝗠𝗮𝗸𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀 𝗬𝗼𝘂𝗿 𝗢𝘄𝗻: 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲 𝗮𝗻𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 - Fine-Tuning: Adapt pre-trained models to meet your unique needs. - Retrieval-Augmented Generation (RAG): Enhance model responses with real-time, relevant data. 𝗘𝘁𝗵𝗶𝗰𝘀 & 𝗗𝗮𝘁𝗮 𝗣𝗿𝗶𝘃𝗮𝗰𝘆: 𝗕𝘂𝗶𝗹𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 As you explore Generative AI, remember that ethical practices and data privacy are crucial. It’s essential to ensure fairness, transparency, and respect for user data to build trust and create responsible AI solutions. 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Generative AI is evolving fast, so continuous learning is key. Collaborate with data scientists, engineers, and product managers to bring your AI solutions to life. Experimentation and prototyping can also help uncover model strengths and limitations early on. By following this roadmap, you can build a solid foundation in Generative AI and gain the skills to make a real impact. Let’s finish the year strong and get ready to tackle new challenges in AI!

GENERATIVE BIOLOGY AI just wrote genetic instructions that cells actually followed – a breakthrough that turns biology into a programming language. For the first time ever, researchers at the Center for Genomic Regulation created AI-generated DNA sequences that successfully controlled gene expression in healthy mammalian cells. Think of it as writing software, but for living organisms. Why this matters: → The AI can design custom 250-letter DNA fragments with specific instructions like "activate this gene in stem cells becoming red blood cells but not platelets" → These synthetic enhancers worked EXACTLY as predicted when tested in mouse blood cells → Unlike previous efforts focused on cancer cells, this team worked with healthy cells, uncovering subtle mechanisms that shape our immune system → The researchers built a library of 64,000+ synthetic enhancers tested across seven stages of blood cell development Most fascinating was discovering "negative synergy" - where two factors that individually activate genes can completely shut them down when combined. This unlocks precision we never had before. The implications are enormous for gene therapy. Instead of being limited to DNA sequences evolution produced, we can now design ultra-selective gene switches customized to specific cells and tissues - potentially making treatments more effective with fewer side effects. Full paper: https://lnkd.in/en3bGZP9 Follow-up with @EricTopol's post about curing rare diseases with the existing genomic technology stack https://lnkd.in/eGCYMjGJ

How might generative AI support nonprofit workplace learning and upskilling? When OpenAI introduced ChatGPT Study and Learn Mode, it addressed a common concern in education: that AI makes it too easy for students to skip the thinking and jump to the answers. Study Mode turns ChatGPT into a learning buddy designed to help users articulate goals, reflect, and build skills step by step. Study Mode helps students explain what they know, identify where they’re stuck, and engage in a guided learning process. These same principles translate powerfully into nonprofit workplace learning. For example, a program manager preparing a theory of change can use Study Mode prompts to encourage deeper reasoning: "What assumptions are built into your model? How would you measure success?" By replacing instant answers with reflective dialogue, ChatGPT Study Mode discourages shallow thinking and could help staff strengthen strategic instincts. It’s a smart way to reinvest the time saved through AI automation. Nonprofit staff facing increased pressure to do more with less often turn to AI for automation to save steps on drafting content, summarizing meeting notes, or analyzing reports.AI can and should make our work more efficient.  But they’re only one way to collaborate with generative AI. Nonprofits also need to use AI augmentation or working with it collaboratively to support human intelligence.  AI can be a thinking partner, not just a productivity hack. When used well, generative AI can: Encourage staff to reason through problems Support learning through adaptive feedback Create space for deeper planning, strategy, and interpersonal connection Generative AI is primarily valued for speed and being frictionless. Cognitive offloading may save time in the short term, but over-reliance can dull strategic instincts and reduce our ability to make meaning across complex situations. In a sector where human judgment, pattern recognition, and values-based decision making matter deeply, that’s a risk we can’t afford. We have an opportunity to use generative AI tools to support upskilling strategies that enhance staff capability alongside human-to-human learning such as mentoring, team dialogue, and on-the-ground practice. AI isn’t a replacement, but it can be a partner in nonprofit workplace learning. https://lnkd.in/gs_rzEtR #AIAugmentation #HumanAICollaboration #AIskilling #Upskilling #humanskills #learning #workplacelearning Philip Deng Rachel Kimber, MPA, MS Meenakshi (Meena) Das Tim Lockie Kaz McGrath John Kenyon Chantal (Coco) Forster

From chatbots to code generation, and from creative design to personalized automation, Gen AI is transforming how machines understand, reason, and create. Generative AI is not just a buzzword anymore, it’s the engine driving innovation across industries. Here’s the break down 👇 Core Concepts - Gen AI runs on foundation models like GPT and Gemini that learn from massive datasets. - Concepts like Prompt Engineering, RAG, and Chain-of-Thought help AI think and plan effectively. - Techniques like Few-Shot, Zero-Shot, and Multi-Modal Learning enhance flexibility and cross-domain intelligence. How It Works - Gen AI uses Transformers and attention mechanisms to understand and generate context-aware output. - It relies on embeddings, sequence modeling, and RLHF to refine responses through feedback and learning. Applications - From text generation and AI chatbots to code writing and content creation, Gen AI boosts productivity and creativity everywhere. - It’s also revolutionizing design, education, and automation with intelligent, adaptive solutions. Challenges - Despite its power, Gen AI faces issues like bias, hallucination, and data privacy risks. - Scalability, context limits, and ethical use remain ongoing challenges for developers and businesses. The Future - Gen AI is evolving into Agentic AI, systems that can plan, reason, and collaborate independently. - Expect smarter models with memory, context awareness, and autonomous decision-making in the near future. Popular Tools - Top platforms include OpenAI, Anthropic Claude, Google Gemini, Meta LLaMA, and Mistral AI - Frameworks like LangChain, Hugging Face, and Stability AI simplify Gen AI development. In short: Gen AI bridges automation and intelligence - combining creativity, logic, and adaptability to shape a smarter future. #GenAI

AI and Protein Localization! 🧬🔬 The journey from understanding protein structures to predicting their precise locations within cells has taken a monumental leap forward. Introducing ProtGPS, a cutting-edge machine-learning model developed by researchers at the Whitehead Institute and Massachusetts Institute of Technology's CSAIL, led by Professor Richard Young and his team. Why is this a game-changer? 🤔 🔹 Predictive power: ProtGPS accurately forecasts where proteins will localize in cells, crucial for understanding both their functions and the mechanisms of diseases. 🔹 Disease insight: By examining over 200,000 proteins with disease-associated mutations, ProtGPS uncovers profound links between mis-localization and disease, paving the way for novel therapeutic strategies. 🔹 Generative potential: Beyond predictions, ProtGPS creates new proteins, designing sequences to target specific cellular locales. This innovation could revolutionize drug design by enhancing precision and minimizing side effects. 🔹 Experimental validation: Unlike many AI models, ProtGPS's predictions have been validated in real cell experiments, bridging the gap between computational design and biological application. The potential applications? Endless.....From developing targeted therapies to uncovering fundamental cellular mechanisms, the implications of this research are vast. ProtGPS isn't just a tool; it’s the start of a new era in biological exploration and therapeutic innovation. #AI #MachineLearning #Biotechnology #Proteomics #ResearchInnovation #Therapeutics #MIT #WhiteheadInstitute

How do you navigate the complex ecosystem of Generative AI applications? Generative AI is revolutionizing industries, but building impactful applications requires a deep understanding of the tools and infrastructure that power them. To help simplify this, I’ve mapped out the Generative AI Application Ecosystem—a comprehensive overview of how the pieces fit together. Here’s a detailed breakdown of the key components: 1. Frontend: Where Users Interact • App Hosting: Platforms like Vercel and Streamlit make it easy to deploy and manage user-facing applications. • Chatbots and Playgrounds: Frameworks like Amazon Lex enable dynamic user interactions. • Orchestration: Tools like LangChain and LlamaIndex streamline the integration of various Generative AI components. 2. Backend: The Core Engine • LLMs APIs and Hosting: • Open-source models (e.g., Hugging Face, Replicate) provide flexibility. • Proprietary APIs (e.g., OpenAI, AI21 Labs) deliver state-of-the-art capabilities. • ML Infrastructure: Built on cloud providers (e.g., AWS, GPU instances) for scalable and efficient computation. • LLMCache: Tools like Redis and GPTCache optimize performance and reduce latency. • MLOps and Monitoring: Frameworks like Weights & Biases and SageMaker ensure reliable deployment and monitoring of AI models. 3. Tools: Enhancing the Workflow • Embedding Models/VectorDB: Pinecone, FAISS, and Weaviate offer fast and accurate search capabilities. • Validation Frameworks: Tools like Nemo-Guardrails and ConstitutionalChain ensure outputs are trustworthy and safe. • Developer Tools and Plugins: APIs and performance metrics help refine applications and enhance usability. • Annotations/RLHF: Reinforcement learning techniques are critical for improving AI responses. Why this ecosystem matters: Understanding these interconnected layers enables developers, data scientists, and product teams to design, deploy, and monitor robust Generative AI applications that can scale to meet user needs. What tools or frameworks have you found invaluable in your journey with Generative AI? Let’s discuss in the comments! Join our Newsletter with 137000+ followers — https://lnkd.in/dbZPj6Tu Follow me for more detailed insights like this. #data #ai #agents #theravitshow