Top Emerging AI Use Cases and Their Capabilities

Not surprisingly, at Mayfield Fund we are seeing a big wave of Gen AI applications; below are 5 use case themes emerging: 1. Content Generation: LLMs producing custom content for marketing, sales, and customer success, and also create multimedia for television, movies, games, and more. 2. Knowledge CoPilots: Offering on-demand expertise for better decision-making, LLMs act as the frontline for customer questions, aiding in knowledge navigation and synthesizing vast information swiftly. 3. Coding CoPilots: More than just interpretation, LLMs generate, refactor, and translate code. This optimizes tasks such as mainframe migration and comprehensive documentation drafting. 4. Coaching CoPilots: Real-time coaching ensuring decision accuracy, post-activity feedback from past interactions, and continuous actionable insights during tasks. 5. RPA Autopilots: LLM-driven robotic process automation that can automate entire job roles. What else are we missing?

In my work at ServiceNow, we continuously host executives in our global innovation centers. AI and AI use cases are the top topics everyone wants to explore. Innovative leaders seek to understand how AI can help them reimagine operational and business models entirely. I wanted to share some interesting examples with you in this edition of AInsights. 💡 (Link at the end 👀) The world’s largest mining company used ChatGPT to analyze its leadership framework and improve employee experiences. IKEA deployed AI in customers service to optimize customer service and reduce costs, reskilled call center employees as interior designers, and generated net new revenue. Klarna AI assistant handles two-thirds of customer service chats in its first month. NVIDIA and Simulation Solutions use computer vision and AI to monitor end-to-end supply chain operations. NVIDIA and Omniverse use AI to create digital twins of manufacturing plants and distribution centers to design and optimize workflows 24/7 without having to purchase equipment or build new facilities. NVIDIA's BioNeMo AI platform helps researches analyze cell structures and dynamics to recreate cells and virtually test responses to disease pathways and drug efficiency. 👉 https://lnkd.in/g_T3UYfP

Your AI Will See You Now: Unveiling the Visual Capabilities of Large Language Models The frontier of AI is expanding with major advancements in vision capabilities across Large Language Models (LLMs) such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude. These developments are transforming how AI interacts with the world, combining the power of language with the nuance of vision. Key Highlights: • #ChatGPTVision: OpenAI’s GPT-4V introduces image processing, expanding AI’s utility from textual to visual understanding. • #GeminiAI: Google’s Gemini leverages multimodal integration, enhancing conversational abilities with visual data. • #ClaudeAI: Anthropic’s Claude incorporates advanced visual processing to deliver context-rich interactions. Why It Matters: Integrating visual capabilities allows #AI to perform more complex tasks, revolutionizing interactions across various sectors: • #Robots and Automation: Robots will utilize the vision part of multimodality to navigate and interact more effectively in environments from manufacturing floors to household settings. • #Security and Identification: At airports, AI-enhanced systems can scan your face as an ID, matching your image against government databases for enhanced security and streamlined processing. • #Healthcare Applications: In healthcare, visual AI can analyze medical imagery more accurately, aiding in early diagnosis and tailored treatment plans. These advancements signify a monumental leap towards more intuitive, secure, and efficient AI applications, making everyday tasks easier and safer. Engage with Us: As we continue to push AI boundaries, your insights and contributions are invaluable. Join us in shaping the future of multimodal AI. #AIRevolution #VisualAI #TechInnovation #FutureOfAI #DrGPT 🔗 Connect with me for more insights and updates on the latest trends in AI and healthcare. 🔄 Feel free to share this post and help spread the word about the transformative power of visual AI!

Revolutionizing Life Sciences R&D: The Power of Intelligent Automation and GenAI "Life sciences companies are using artificial intelligence (AI) to transform drug discovery by extracting concepts and relationships from data. By 2030, the time required for screening to preclinical testing will be reduced only a few months, and new potential drug candidates would be identified at more affordable prices." - Deloitte: 2023 Global Life Sciences Outlook 🧬 UiPath has released a white paper on the transformative impact of combining intelligent automation with Generative AI (Gen AI) in Life Sciences R&D. Patterns such as personalized message generation, context-driven analysis, and conversational assistant enhancements are paving the way for unprecedented efficiency and responsiveness. The synergy of automation and Gen AI is showcased through various use cases, including Pharmacovigilance processes, patient/donor screening, regulatory submissions, narrative generation, and SOP documentation queries. Key Points: 🔷 Pattern 1: Personalized Message Generation Context gathering for crafting personalized messages. Application in regulatory forms and complex responses. 🔷 Pattern 2: Context-Driven Analysis Automation analyzes data for the next best action. Embedding business context into responses or workflows. 🔷 Pattern 3: Conversational Assistant Enhancement Automation adds context and action to verbal or text queries. Initial stages with tremendous growth potential in verbal query interfaces. 🔷 Use Cases and Impact: 🔹Pharmacovigilance Process (PV): Gen AI-enhanced automation streamlines data analysis and submission processes, making them more intuitive and responsive. 🔹Patient/Donor Screening: Coordinating appointments, guiding through questionnaires, and generating follow-up documents for personalized therapies. 🔹Regulatory Submissions (NDA/BLA): Gen AI expedites the completion of new product submissions, potentially saving 10-20 weeks and generating substantial revenue benefits. 🔹Narrative Ability: Gen AI's ability to generate narratives accelerates data analysis, impacting clinical data summaries and more complex scenarios. 🔹Query SOP Documentation: Gen AI reviews extensive SOP libraries, providing real-time responses to user queries, and enhancing process execution. In 2023, we witnessed a remarkable evolution of the emergence of GenAI. In 2024 we will see the beginning of AI-enabled automation, propelling us into a new era of efficiency and discovery. #IntelligentAutomation #GenAI #LifeSciences #ResearchAndDevelopment #Innovation #uipath 𝗡𝗼𝘁𝗶𝗰𝗲: The views expressed in this post are my own. The perspectives within any of my posts or articles are not those of my employer or the employers of any contributing experts. 𝗟𝗶𝗸𝗲 👍 this post? Click 𝘁𝗵𝗲 𝗯𝗲𝗹𝗹 icon 🔔 for more!

The Future of AI in Business: 5 Emerging Trends to Watch AI is continuously evolving with new trends that have the potential to significantly impact the business world and consumers. Here are some emerging AI trends gaining traction. 1️⃣ Explainable AI (XAI) As AI becomes more complex, the need for transparency grows. Explainable AI makes AI decisions more interpretable, ensuring businesses and customers trust the outcomes. 🏦 Financial services example A financial leader used XAI to decide on an investment strategy. The AI analyzed trends, data, and risks, recommending a diversified portfolio with clear explanations of returns, risks, and assumptions. This allowed the leader to confidently present the strategy to the board, securing approval and driving growth. __________________________ 2️⃣ AI in Hyper-Personalized Marketing AI is moving beyond basic personalization to create hyper-personalized marketing experiences. By analyzing real-time data, AI tailors content, offers, and interactions to individual preferences at an unprecedented level. 🏥 Healthcare example Healthcare providers using AI to send personalized health tips and medication reminders based on individual patient data, improving patient engagement and adherence to treatment plans. __________________________ 3️⃣ Federated Learning Federated learning allows AI models to be trained across decentralized devices without sharing raw data. This trend is significant for privacy and data security, particularly in industries handling sensitive information. 🛒 Retail example Retailers using federated learning to analyze customer shopping behavior across locations without sharing personal data, enabling personalized recommendations and inventory optimization. __________________________ 4️⃣ Agentive AI Agentive AI, or AI agents, are autonomous systems that perform tasks on behalf of humans. These AI agents handle complex tasks, learn from interactions, and make decisions, freeing up human workers for higher-value activities. 🏭 Manufacturing example Manufacturing plants using AI agents to monitor equipment health, predict maintenance needs, and autonomously manage supply chain logistics, enhancing efficiency and reducing downtime. __________________________ 5️⃣ AI-Driven Business Process Automation AI-driven business process automation involves AI systems that can understand and improve business processes over time. This leads to more efficient operations, cost savings, and improved agility. 🎥 Media and entertainment example Media companies using AI to automate content distribution, optimize ad placements, and manage subscriber data, leading to more targeted marketing efforts and enhanced viewer experiences. __________________________ #AI #Data #ArtificialIntelligence #EmergingTrends #FutureOfAI #ExplainableAI #FederatedLearning #AgentiveAI #BusinessAutomation #HealthcareAI #FinancialAI #RetailAI #ManufacturingAI #MediaAI #Innovation

Generative AI has spawned thousands of new products. But outside of ChatGPT, what are everyday consumers using? What's growing, and what has flattened? I crunched the numbers to find the top 50 consumer AI web products by monthly global visits - here's my learnings: 1. Most leading products are built from the “ground up” around generative AI - of the 50 on the list, 80% are brand new as of the past year. Only five are owned by big tech companies (ex. Google, Microsoft), and of the remaining 45, nearly half are bootstrapped! 2. ChatGPT has a massive lead, for now…representing 60% of traffic to the entire list! Character.AI comes in at #2, with ~21% of ChatGPT's traffic. Compared to mainstream consumer products, even the top AI products are fairly small - ChatGPT ranks around the same traffic scale as Reddit, LinkedIn, and Twitch, but far behind Facebook and Instagram. 3. General assistants (ex. ChatGPT, Bard, Poe) represent almost 70% of traffic, but companionship (ex. Character.AI) and content generation (ex. Midjourney, ElevenLabs) are surging! Model hubs are also a category to watch, with only two companies on the list (Civitai, Hugging Face) but both in the top 10. 4. While some early winners have emerged, most categories are still up for grabs - with a <2x gap in traffic between the #1 and the #2 leading players. Use case or workflow-specific platforms are also emerging alongside more horizontal players - ex. Leonardo Ai has taken off in image generation for games assets, while Midjourney continues growing as the leading generalist platform. 5. Acquisition for top products is almost all organic - with the median gen AI company on the list seeing 99% free acquisition! This compares to 52% for the median consumer subscription company before AI. Consumers are also showing significant willingness to pay for genAI, with 90% of products monetizing, and at a ~2x higher ARPU than non-AI consumer subscription comparables. 6. Mobile is still emerging as a platform for AI products - only 15 companies on the list have an app, and just three (PhotoRoom, Speechify, Character.AI) saw >10% of traffic from their app versus website. Given consumers now spend 36 more minutes per day on mobile than desktop, we're excited to see more app-first AI products emerge soon. For the full post and more stats, check out: https://lnkd.in/gR6Paycc #ai #genai #startups

Today's deep dive: AI Clinical Summarization tools 🕵️♀️📚 One of the areas gen AI has immense potential in healthcare is its ability to surface and synthesize information from vast sets of unstructured documents and data. As Oscar Health cofounder Mario Schlosser puts it: “[LLMs] are uniquely capable at going from unstructured data into structured data, and the other direction.” So it’s no surprise that distilling information from a wide range of clinical documents and data is one of the most common applications of AI that we’re seeing come to market. There are MANY use cases for summarization across nearly every dimension of healthcare. To name a few: ↳ Pre-charting ↳ Referral summaries ↳ Discharge summaries ↳ Diagnosis and care gap identification ↳ Quality measurement and improvement ↳ Real world evidence curation ↳ Clinical trial matching ↳ Clinical registry submissions ↳ Clinical documentation improvement (CDI) ↳ Prior authorizations ↳ Legal and insurance use cases – e.g., workers comp, life insurance Despite their broad applicability, uptake of these solutions has been relatively slow, at least in comparison to ambient scribing solutions. A big reason is their perceived risk. With summarization, the AI is making a subjective call about what to include and what to leave out. There's also the potential it hallucinates something not present in the underlying information set. In a clinical context, both are major concerns. Still, between advancements in gen AI capabilities and improvements in risk mitigation techniques, we're seeing more and more organizations begin to evaluate and adopt summarization products, starting with lower risk use cases. Will be interesting to watch this space in the coming months to see how quickly the pace picks up, particularly for clinical use cases. Curious to hear what are others seeing. --- P.S. Here's our working list of AI Clinical Summarization solutions • Abstractive Health • Carta Healthcare • Credo • DigitalOwl • Layer Health • emtelligent • Fourier Health • Google Care Studio • Hona (YC W24) • Inference Analytics - Workforce Concierge • meMR Health • Mendel.ai • Navina • Oler Health • Pieces • Quench • Regard • Solstice • Wisedocs • Synthpop - Healthcare AI #healthcareai #genai #healthai #digitalhealth #healthtech

Happy New Year! If you are an Enterprise CTO, you are probably thinking about your GenAI strategy. Here's a decent write-up by Gartner: https://gtnr.it/3RQodsK. To augment that, here are some #genai trends to track and act on in 2024: --Open and Smaller Models: Open models like Llama, Mistral, BERT, and FLAN are becoming competitive with larger, closed-source models. They're suitable for many use cases and offer transparency for Responsible AI. In my opinion, open & closed models are not in a zero-sum game; BOTH should be used for the right use case. Action: Implement a clear plan for using different models. Amazon Web Services (AWS) users can leverage Bedrock & SageMaker (https://bit.ly/3vkX4qa). -- Domain-Adapted Models: Use your enterprise proprietary data to extend a large language model via continued pre-training (CPT) for domain-specific tasks. Action: Assess your use cases and data for CPT alongside Fine-tuning. Learn more: https://lnkd.in/eNjbQm-m -- Multi-modal Models (MMMs): MMMs will gain prominence in 2024. Both commercial (like GPT-4V) and open-source models (like Llava) will be popular. Action: Expand into business cases served by MMMs. More about Llava: https://lnkd.in/eTvn82iM. -- AI Agents (RAG+++): AI agents using LLMs can improve upon RAG by intelligently utilizing multiple data sources. Action: Prepare APIs and Data Sources for AI agents. More information: https://lnkd.in/eVf_J_bA. -- LLMs with Graphs: Graphs are one of the best representations of real-world knowledge which when combined with LLMs can be very effective in various domains. Action: Identify suitable business cases and explore Graphs+LLMs. Details: https://lnkd.in/eF68FbVA. -- AI Routers: Most enterprises will end up using a dozen or more models and it will become necessary to manage multiple models - Auth, Audit and Smart Model Selection. Action: Build an AI router. AWS Bedrock can assist, but more is needed. Info: https://go.aws/4aFLkyA. -- FinOps meets MLOps: Focus on cost optimization for GenAI projects. 2023 was all about GenAI POCs; 2024 will be about production & big bills! Action: Learn about GenAI business cases and FinOps for GenAI: https://lnkd.in/ezfV8NTa. --Make AI Invisible. Technology is at its best when its invisible and seamless to the end user. Action: Look at existing enterprise applications and look for ways to rethink the user experience using GenAI while keeping the tech invisible. (https://bit.ly/3vkXvAO) What are you tracking? Watch out for more on domain-focused AI trends in areas like AI for the Edge, robotics, and Drug discovery etc. in upcoming posts.

🚀 McKinsey & Company Tech Trends 2025: What Business Leaders Must Know Now. The future is arriving faster than expected—and AI is at the core of it. McKinsey’s Technology Trends Outlook 2025 is a must-read for executives, founders, and technologists looking to stay ahead. The report evaluates 15 breakthrough technologies based on adoption, investment, talent availability, and real-world momentum. Here are the key insights and strategic takeaways 👇 🔮 1. #AI is the Central Force AI is not just one of many trends—it’s a foundational technology driving others. From developer productivity to robotics, AI is now integrated across industries and functions. Use cases have matured beyond experimentation into real-world value creation. 🧠 2. Generative & Agentic AI: From Tools to Teammates Generative AI continues to surge, but Agentic AI —tools that can reason and take action autonomously—is emerging as the next frontier. These systems will move from responding to prompts to completing tasks, triggering a shift in business automation. ⚙️ 3. Next-Gen Software Development AI-assisted development environments are accelerating time-to-code and shifting how engineering teams function. Companies investing here are cutting product cycles by up to 30%, according to McKinsey insights. 📡 4. Advanced Connectivity Fuels Edge Innovation With maturing 5G, low-Earth-orbit satellites, and edge computing, advanced connectivity is unlocking real-time applications across manufacturing, logistics, and smart infrastructure. This isn't future-talk—deployment is accelerating now. 🔬 5. Applied AI in Real Operations AI-powered vision systems, robotics, and simulation tools are already optimizing everything from warehousing to agriculture. What’s new? These tools are being used at scale, not just in pilot programs. 📊 6. Trust Architecture & Responsible AI As AI grows more autonomous, McKinsey emphasizes trust architecture—governance, risk controls, and ethical design must evolve in tandem. Regulation is coming fast. Companies that prepare early will lead with confidence. 🌱 7. Sustainable Tech: From Buzzword to Bottom Line Tech is finally aligning with sustainability goals. Energy-efficient compute, circular hardware design, and green cloud are becoming investment priorities, not side projects. 💡 Leadership Takeaways ✅ Embed AI as a horizontal strategy, not a vertical investment ✅ Invest in next-gen developer tools to stay agile ✅ Build or upskill talent to lead agentic workflows ✅ Establish clear AI governance frameworks early ✅ Use advanced connectivity to optimize operations ✅ Don’t overlook trust, ethics, and sustainability—they are competitive differentiators. #McKinsey #AI #TechTrends2025 #AgenticAI #DigitalTransformation #FutureOfWork #TrustInTech #GenerativeAI #Sustainability #AILeadership #TechStrategy #BusinessInnovation 🤖📈🌐💼

Data and AI leaders: what GenAI use cases are you piloting/implementing in 2024? Here's my take on the compelling use cases. Notably, some early adopters are tackling multiple use cases at once. Check out this summary and tell us what you are doing. Also check out our recent Eckerson Group webinar with Intel Corporation, "The Next Wave of GenAI: Domain-Specific LLMs." https://lnkd.in/emF95Vaq. To boost productivity and gain competitive advantage, GenAI adopters are not just using platforms such as ChatGPT from OpenAI or tools such as GitHub Copilot. They also are building language models into proprietary applications and workflows that consume their own domain-specific data. Companies implement these domain-specific LMs, which Eckerson Group also calls small language models, in one of three ways. They do this by building an LM from scratch, fine-tuning a pre-trained LM, or enriching prompts. > Build from scratch. Data science teams design a new LM and train it on their own domain-specific use of language as well as their own facts. > Fine-tune. They take a pre-trained LM such as Llama or BLOOM and fine-tune it on their domain-specific language and facts. > Enrich prompts. They inject domain-specific data into user prompts to ensure the LM gets the facts right. By getting domain-specific, companies can reduce risks such as hallucinations or mishandling of intellectual property--provided they govern their inputs correctly! Check out this summary and tell us what you think. Also check out our recent Eckerson Group webinar with Intel Corporation, "The Next Wave of GenAI: Domain-Specific LLMs." https://lnkd.in/emF95Vaq Common use cases focus on customer service, document processing, research, sales, and marketing, as shown in the chart here. Here are early adopters that tackle a few at once. > This summer Priceline announced plans for an external chatbot to help customers book travel, as well as internal GenAI tools to help employees develop software and create marketing content. > Health providers at MEDITECH use GenAI to summarize patient histories, auto-generate clinical documents, and place orders. > Insurance provider Lemonade positions GenAI as a strategic differentiator for the entire business. Its latest letter to shareholders boasts  “we have LLMs trained to answer customer emails, review pet medical records, evaluate satellite and other imagery, read home condition reports, and more.” Wayne Eckerson Jay Piscioneri Sumit P. #generativeai #genai #ai #data Sancha Huang N. Ro Shah Arlen Reyes Tiffany Winman