Generative AI Use Cases

🚀 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

The AI landscape is evolving beyond traditional models. We’re now entering the Agentic AI era, where autonomous agents don’t just respond to queries but plan, coordinate, and execute complex workflows—bringing true intelligence to automation.  Agentic AI refers to AI systems composed of multiple autonomous agents that can:  • Decompose complex tasks into subtasks   • Collaborate through structured workflows   • Leverage external tools & data for enriched decision-making   • Self-optimize based on feedback & environmental changes  Unlike standard AI models, Agentic AI doesn’t wait for human prompts—it takes initiative, makes decisions, and dynamically adjusts its actions based on real-time data.  𝗛𝗼𝘄 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗪𝗼𝗿𝗸𝘀:  ➟ The Actor (Initiator) – The system or user triggering the workflow.   ➟ The Supervisor (Orchestrator) – Manages tasks, delegates work, and monitors execution.   ➟ AI Agents (Executors) – Autonomous units that perform tasks, collaborate, and adapt based on outcomes.   ➟ External Tools & Data (Enhancers) – Includes RAG (Retrieval-Augmented Generation), search engines, computational resources, and APIs to augment knowledge and improve results.  Think of it as an AI-powered assembly line, where different agents specialize in specific jobs, ensuring efficiency and scalability.  𝗪𝗵𝘆 𝗗𝗼𝗲𝘀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗠𝗮𝘁𝘁𝗲𝗿?  ➟ Scalability – AI agents work in parallel, handling multi-step processes efficiently.   ➟ Adaptability – They adjust dynamically to changing inputs, making them more reliable than static AI models.   ➟ Autonomous Decision-Making – Unlike traditional AI that waits for instructions, Agentic AI actively solves problems and suggests improvements.   ➟ Enhanced Productivity – By integrating external knowledge sources like RAG, search, and APIs, Agentic AI learns in real-time and delivers more accurate results.  𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜  ➟ AI-powered ETL Pipelines – Automating data extraction, transformation, and loading with autonomous workflow execution.   ➟ AI-Driven Research Assistants – Multi-agent systems retrieving and synthesizing information from external sources.   ➟ Autonomous Software Development – Agents writing, debugging, and deploying code without human intervention.   ➟ Customer Support Automation – AI agents that dynamically adjust responses, perform transactions, and resolve issues without human escalation.  This is just the beginning of Agentic AI. As AI agents become more autonomous, we will see systems that:   ➟ Self-improve by learning from failures and adapting to new challenges.   ➟ Collaborate across different domains—AI agents working alongside humans in business, healthcare, finance, and tech.   ➟ Expand reasoning capabilities through multi-modal data processing, integrating text, images, audio, and more.  𝗔𝗿𝗲 𝘆𝗼𝘂 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝘄𝗮𝘃𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻?

If AI in your company still lives inside chat windows… you haven’t started the Agentic journey yet. Today’s Agentic AI systems don’t just answer questions. They observe signals, make decisions, trigger tools, coordinate workflows, and continuously improve outcomes. Instead of assisting humans one task at a time, these agents run end-to-end business operations across sales, support, finance, engineering, HR, and marketing. This is what production-grade Agentic AI actually looks like inside modern organizations: - Customer Support Agents Handle FAQs, resolve tickets, process refunds, update CRM systems, and escalate complex issues automatically. - Sales Ops Agents Qualify incoming leads, enrich prospect data, update pipelines, generate follow-ups, and notify sales teams in real time. - Marketing Automation Agents Plan campaigns, analyze audiences, generate content, schedule outreach, track performance, and optimize future runs. - Data Analysis Agents Convert business questions into SQL, clean datasets, analyze trends, generate insights, and deliver visual summaries. - Reporting Agents Pull metrics, validate data, create dashboards, write narratives, and distribute reports across stakeholders automatically. - QA / Testing Agents Generate test cases, execute regressions, detect failures, log bugs, and recommend fixes without manual intervention. - DevOps Agents Monitor infrastructure, detect anomalies, run diagnostics, apply rollbacks, notify teams, and assist deployments. - Finance Ops Agents Process invoices, categorize transactions, reconcile records, flag anomalies, and generate financial summaries. - HR Ops Agents Manage resume intake, screen candidates, schedule interviews, update HR systems, and respond to employee queries. - Research Agents Search documents and web sources, extract key findings, compare references, and summarize insights. - Content Creation Agents Outline topics, draft content, optimize for SEO and branding, publish assets, and track engagement end-to-end. - Internal Tools Agents Act as company copilots - understanding employee requests, calling internal APIs, executing actions, and confirming results. The real shift? These agents don’t just respond. They reason. They orchestrate tools. They execute workflows. They learn from feedback. They operate continuously. This is how organizations move from isolated automation to connected, outcome-driven AI systems. Not experiments. Not demos. Not pilots. Real production systems.

AI Agents and Agentic AI are not the same thing. A useful review paper analyzes the literature to offer a structured conceptual taxonomy and application mapping to clarify the distinctions, use cases, and challenges There is plenty of useful detail and analysis in the paper, it is worth a look (link in comments). Here are the high-level insights: 🧠 Generative AI was only the starting point. Generative AI systems are reactive and stateless—they generate content when prompted but lack autonomy, persistent memory, or self-directed reasoning. These limitations spurred the development of AI Agents, which integrate tools, maintain limited memory, and execute goal-oriented tasks using structured planning loops. 🛠️ AI Agents are modular executors, not thinkers. AI Agents perform narrowly defined tasks using tool calls, reasoning chains, and APIs. They rely on LLMs for language understanding and integrate external functions like web search or data queries to complete operations such as scheduling, email triage, and customer support automation. 🤝 Agentic AI means systems that collaborate. Unlike single AI Agents, Agentic AI comprises multiple agents with specialized roles—planners, retrievers, synthesizers—that communicate through shared memory or orchestration layers. These agents coordinate to decompose complex goals and adapt strategies dynamically in tasks like robotic coordination and research automation. 🔁 Agentic systems support persistent memory and reflection. Key architectural advances in Agentic AI include long-term memory buffers, recursive reasoning, and orchestrators (meta-agents) that assign roles and resolve dependencies. These features enable them to manage workflows across sessions and adjust to partial failures or new information in real time. 📊 Real-world applications split along complexity lines. AI Agents handle tasks like enterprise search, customer support, and scheduling—well-bounded, low-complexity domains. Agentic AI tackles multi-step goals like drafting research proposals or coordinating robot swarms, where task decomposition, inter-agent communication, and dynamic planning are essential. ⚠️ Risks grow with autonomy and coordination. AI Agents face issues like hallucinations and brittle prompt responses. Agentic AI introduces higher risks: inter-agent misalignment, error propagation, unpredictability, and governance challenges. These demand solutions like retrieval-augmented generation (RAG), causal modeling, and robust evaluation frameworks. 📈 Clear taxonomy reduces misapplication. The paper emphasizes that misapplying an AI Agent where Agentic AI is needed (or vice versa) can lead to under-engineering or over-complication. A structured taxonomy aligns design choices with problem complexity, supporting scalable and maintainable deployments.

The use-cases for AI and GenAI are truly limitless.    One of the new ways Deloitte is leveraging #GenAI is by supporting internal audit teams in their development of #AI strategies and applied capabilities. Not only are these tools supporting teams in the day-to-day audit process, but they are allowing them to build toward future-state operating models.    Here are a few of the ways Deloitte is offering AI-powered tools for the audit process:    Dynamic Risk Assessments – We utilize AI to develop end-to-end assessment capabilities to create more proactive models, resulting in a dynamic and iterative #risk assessment lifecycle that evolves with the org’s needs.    AI-on-Demand PODs – Our AI-on-Demand Product Oriented Delivery (POD) service delivery model consists of a team of engineers and designers to help clients develop customizable AI solutions that follow our Trustworthy AI Framework ™ (https://deloi.tt/3ywy7K8).    Automated SOX Scoping – We work with our clients to utilize AI to increase efficiency and save time during the Sarbanes-Oxley (SOX) scoping process. The statistical algorithms we put into place help clients develop a more accurate and risk-aligned scope for their SOX programs.    You can read more about how AI is changing the #audit landscape, here: https://deloi.tt/4d4xRBa Chris Griffin, Trevear Thomas, Dipti Gulati, Lynne Sterrett

Johnson & Johnson, the world’s biggest pharmaceutical company by revenue, revealed details on its AI strategy. After a year of experimentation with over 900 AI applications, they kept on using the ones that drove the most value: A life sciences division uses a generative AI sales assistant that delivers compliant, product-specific insights tailored to each customer. It’s now being adapted for complex medtech sales like robotics and implants. AI is speeding up pharma R&D—from optimizing chemical synthesis steps to spotting promising compounds using image-based models. A predictive AI tool scans for disruptions in the supply chain from fires to material shortages so managers can act before delays hit. Clinical trials are getting a boost from AI: algorithms now match diverse patients to studies faster and even double enrollment rates in some programs. A company-wide chatbot is helping employees navigate HR policies and benefits with instant answers and direct links. Separate AI and data governance units ensure ethical development and scalability, while staff receive hands-on training, including in generative AI. Do you know about other similar use cases at pharma companies? Source: https://lnkd.in/evfrcTaq

NEW: Ar. June Chow and I created a 235-page ebook: 📓 50 GenAI Use Cases for Architects 📓 What is this about? Generative AI is incredibly helpful for architects, but many have little idea where to start, what it can help with, and how to use it safely. This ebook equips architects with the latest knowledge of generative AI and practical cases. What does it cover? ✔️ Fundamentals of generative AI: Top tools, safety, and ethics ✔️ 50 practical AI use cases for architects: From hardcore design to tedious research ✔️ 6 quick-start guides: ChatGPT, Midjourney, Copilot, Gemini, Meta AI, and Prome AI ✔️ Exact prompt templates and real prompt examples Who is this ebook for? Anyone in a profession related to architectural design or urban planning. It's beginner-friendly, and while some use cases or tools may appear sophisticated, they can be easily mastered with some training. Please watch our updates for new trainings or reach out to us directly. For those who joined our workshop yesterday, thank you so much for your participation! We are thrilled to hear the positive and constructive feedbacks! Get the full ebook 👉 https://lnkd.in/dwB3zg6q Another exciting update coming soon!

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

🤖 Key Highlights from the Systematic Narrative Review on Generative AI in Clinical Services 🔍 Generative AI (GenAI) Usage Trends in Healthcare 🔹 GenAI tools are increasingly utilized by healthcare professionals as knowledge aids, dialogue facilitators, and training resources. 🔹 Applications are focused on disease detection, diagnosis, and screening, especially in radiology, cardiology, gastrointestinal medicine, and diabetes care. 💡 Preliminary Insights 🔹 Knowledge Accessibility: 87.6% of reviewed studies emphasize GenAI's role in making crucial medical knowledge accessible and actionable. 🔹 Automation Gap: Only 11.18% of studies reviewed report on GenAI’s role in automating clinical services, highlighting the need for further development in this area. ⚠️ Challenges and Missed Opportunities 🔹 Service Value Creation: Current implementations of GenAI do not fully realize their potential to create direct value in healthcare, particularly in advancing systems and personalized medicine through knowledge reuse. 🔹 Drug Development Potential: Despite being outside the primary scope, integrating GenAI into drug development and clinical trials remains a significant untapped opportunity for value creation. 🏥 Pathways for Patient-Centered GenAI Adoption 🔹 User-Centered Design: A design approach focused on user needs can help GenAI transcend traditional models based on basket data or end-point labels, enhancing its relevance in clinical research and healthcare service delivery. 🔹 Organizational Capabilities: Establishing robust organizational frameworks is essential for effectively embedding GenAI into healthcare systems and unlocking its full potential. This review highlights the importance of validating GenAI's role in clinical service delivery and emphasizes the need for patient-centered approaches to maximize its transformative impact in healthcare. Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024;12:e52073, doi: 10.2196/52073 🔗 https://lnkd.in/dxrEs7ey #GenerativeAI #AIInHealthcare #DigitalHealth #ClinicalInnovation #PatientCenteredCare #PersonalizedMedicine #InnovationInHealthcare #KnowledgeAccess #AIAdoption