AI In Professional Roles

Gone are the days when the only way to know something was wrong with your machinery was the ominous clunking sound it made, or the smoke signals it sent up as a distress signal. In the traditional world of maintenance, these were the equivalent of a machine's cry for help, often leading to a mad dash of troubleshooting and repair, usually at the most inconvenient times. Today, we're witnessing a seismic shift in how maintenance is approached, thanks to the advent of Industry 4.0 technologies. This new era is characterized by a move from the reactive "𝐈𝐟 𝐢𝐭 𝐚𝐢𝐧'𝐭 𝐛𝐫𝐨𝐤𝐞, 𝐝𝐨𝐧'𝐭 𝐟𝐢𝐱 𝐢𝐭"  philosophy to a proactive "𝐋𝐞𝐭'𝐬 𝐟𝐢𝐱 𝐢𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭 𝐛𝐫𝐞𝐚𝐤𝐬" mindset. This transformation is powered by a suite of digital tools that are changing the game for industries worldwide. 𝐓𝐡𝐫𝐞𝐞 𝐍𝐮𝐠𝐠𝐞𝐭𝐬 𝐨𝐟 𝐖𝐢𝐬𝐝𝐨𝐦 𝐟𝐨𝐫 𝐄𝐦𝐛𝐫𝐚𝐜𝐢𝐧𝐠 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: 𝟏. 𝐌𝐚𝐤𝐞 𝐅𝐫𝐢𝐞𝐧𝐝𝐬 𝐰𝐢𝐭𝐡 𝐈𝐨𝐓 By outfitting your equipment with IoT sensors, you're essentially giving your machines a voice. These sensors can monitor everything from temperature fluctuations to vibration levels, providing a continuous stream of data that can be analyzed to predict potential issues before they escalate into major problems. It's like social networking for machines, where every post and status update helps you keep your operations running smoothly. 𝟐. 𝐓𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐂𝐫𝐲𝐬𝐭𝐚𝐥 𝐁𝐚𝐥𝐥 𝐨𝐟 𝐀𝐈 By feeding the data collected from IoT sensors into AI algorithms, you can uncover patterns and predict failures before they happen. AI acts as the wise sage that reads tea leaves in the form of data points, offering insights that can guide your maintenance decisions. It's like having a fortune teller on your payroll, but instead of predicting vague life events, it provides specific insights on when to service your equipment. 𝟑. 𝐒𝐭𝐞𝐩 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐰𝐢𝐭𝐡 𝐌𝐢𝐱𝐞𝐝 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 Using devices like the Microsoft HoloLens, technicians can see overlays of digital information on the physical machinery they're working on. This can include everything from step-by-step repair instructions to real-time data visualizations. It's like giving your maintenance team superhero goggles that provide them with x-ray vision and super intelligence, making them more efficient and reducing the risk of errors. ******************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

As someone who's been teaching at the intersection of finance and AI for several years, I’m often asked by finance and accounting professionals where to start. There’s no shortage of AI content out there; but if you're looking for practical, finance-specific resources, these are the ones I know best. Here’s a quick rundown: 💡 LinkedIn Learning Courses Short, practical, and focused on core workflows: ▪️ Leveraging GenAI in Finance and Accounting https://lnkd.in/gymdJr5C ▪️ Automated Financial Reporting w/AI https://lnkd.in/gfui82gh ▪️ AI in Financial Forecasting https://lnkd.in/gVczvP6J ▪️ AI in Risk Management & Fraud Detection https://lnkd.in/g-UrBcC8 💻 Corporate Finance Institute® (CFI) ▪️ Finance Institute (CFI) Courses Part of CFI’s AI for Finance Specialization: ▪️ Introduction to AI in Finance https://lnkd.in/giBFxv-c ▪️ AI-Enhanced Financial Analysis https://lnkd.in/e6jrdEc9 ▪️ AI-Powered Scenario Analysis https://lnkd.in/dPTJ9Eun ▪️ Advanced Prompting for Financial Statement Analysis https://lnkd.in/e68ej_iH ▪️ GenAI Tools in Finance – ChatGPT https://lnkd.in/g7Yuh8JN ▪️ Leveraging GenAI for Risk Assessment https://lnkd.in/gqczAJPW ▪️ Foundations of ML & Deep Learning for Finance https://lnkd.in/gXVVU_s4 🏫 Duke University - The Fuqua School of Business CFO Program ▪️ I teach applied AI strategy in the Duke CFO Program, where we focus on implementation frameworks, decision-making, and data readiness. https://lnkd.in/gGGwDX63 🏫 Wharton Online FP&A Certificate Program ▪️ Address the issues of AI in finance with course developer, Christian Wattig https://lnkd.in/g5tq8uAC 📕 Books Longer-form content for deeper context and case-driven examples: ▪️ Deep Finance: Corporate Finance in the Information Age https://lnkd.in/g_FR5aky ▪️ AI Mastery for Finance Professionals https://lnkd.in/efjvaPiJ 🎙️ Podcasts To keep up with the latest trends on AI in finance: ▪️ FP&A Today, sponsored by Datarails https://lnkd.in/gddppHsQ ▪️ Future Finance, sponsored by QFlow.ai w/Paul Barnhurst https://lnkd.in/gbnbydAr Reach out if you’re looking for something more targeted or if you’re building out training for a team.

A look at how CS50 has incorporated artificial intelligence (AI), including its new-and-improved rubber duck debugger, and how it has impacted the course already. 🦆 https://lnkd.in/eb-8SAiw In Summer 2023, we developed and integrated a suite of AI-based software tools into CS50 at Harvard University. These tools were initially available to approximately 70 summer students, then to thousands of students online, and finally to several hundred on campus during Fall 2023. Per the course's own policy, we encouraged students to use these course-specific tools and limited the use of commercial AI software such as ChatGPT, GitHub Copilot, and the new Bing. Our goal was to approximate a 1:1 teacher-to-student ratio through software, thereby equipping students with a pedagogically-minded subject-matter expert by their side at all times, designed to guide students toward solutions rather than offer them outright. The tools were received positively by students, who noted that they felt like they had "a personal tutor." Our findings suggest that integrating AI thoughtfully into educational settings enhances the learning experience by providing continuous, customized support and enabling human educators to address more complex pedagogical issues. In this paper, we detail how AI tools have augmented teaching and learning in CS50, specifically in explaining code snippets, improving code style, and accurately responding to curricular and administrative queries on the course's discussion forum. Additionally, we present our methodological approach, implementation details, and guidance for those considering using these tools or AI generally in education. Paper at https://lnkd.in/eZF4JeiG. Slides at https://lnkd.in/eDunMSyx. #education #community #ai #duck

AI Field notes: models are awesome in isolation; but the superpower of AI is in combining these models to be greater than the sum of their parts. Let's dive in. ⚙️ Foundation models are one of the most important software components of the next 100 years. These remarkable models are best thought of as reasoning and integration engines. Combing these models have compounding effects, like sparks in a firework display. ⚡️ The spark: taken in isolation, each foundation model has a sweet spot. Some are capable at natural language tasks, or summarization, or handling different languages; others are really fast; others are super affordable; some work really well on text; others excel at understanding images, or whiteboards, or sketches, or speech, and so on. 📊 Bedrock is the rocket: picking the right model for the right use case makes the difference between a successful prototype, and an impactful, bottom-line-moving new feature, product, or process (it's why we make this super easy to automate in Bedrock). 🎆 Combined together, you get fireworks. The combination of foundation models - each with their own sweet spot - isn't just additive, it is a force multiplier of capability. An AI system comprising multiple models is able to tap into all of these sweet spots at once, and the result is greater than the sum of its parts. ☀️ An AI system of sufficiently advanced capability won't just benefit from the compounding effect of these abilities, it will be enabled uniquely through them. Two quick examples. 1️⃣ Imagine a legal team automating document analysis and preparation, which could combine a powerful, deep model to understand legal texts, PDFs, diagrams, etc; a fast model to automate the generation of routine legal docs, and a low-cost model to refine output from other models for consistency of style, language, and tone. The result would be a faster, more efficient way to process – say – legal contracts, or understand the risk of old life insurance policies. 2️⃣ Or a smart city traffic management app which combines powerful models to analyze traffic patterns from images, fast models to render the results in real time, and models with a balance of intelligence and speed to coordinate short-term traffic management strategies based on current and future projections. The result would be more efficient routing of traffic, or emergency vehicles in peak commute times. While the sparks are exciting in close up, the fireworks are where the show is from the perspective of the business. It's why you see model providers like Anthropic launching model families - Haiku, Sonnet, and Opus - each with a unique spark, which combined together or with models from other providers, lead to amazing results. Exciting times. #aws #genai #ai

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

At Duolingo, we dedicate two hours company-wide every Friday to learning how to use AI to change how we work. What I’ve done with some of that time is shadow software engineers to observe their use of AI coding tools. It’s been very eye-opening. Here are some of the things I’ve learned so far. > One experienced software engineer has challenged himself to not write any code himself at all. “Writing code is 90% solved. LLMs are very, very good at this stuff. What I can do as an engineer is focus on high-level architecture and use my intuition to guide things.” He described AI coding like working with a "super genius intern." He provides occasional hints while leveraging his expertise in high-level architecture, his intuition, and his knowledge of the codebase. > An intern noted that interns and new grads should prioritize learning foundational skills first. Relying heavily on AI for implementation hinders deeper understanding of intricate system interactions. She uses AI more for explanations rather than direct implementation to enhance her learning process.  > Critical thinking is very important. “Vibe coding” is amazing for unlocking prototyping and tool building for non-software engineers. Software engineers still need to apply their skills to guide AI tools. > There’s no single front-runner for AI coding tools. Engineers that are successful in using AI have figured out which tools and which models are good for which task, whether it’s debugging a stack trace, fixing a bug, building a new feature, refactoring, migrating code, understanding a repo, etc. > Tech specs are more important than ever. In fact, good prompting looks a lot like a tech spec. While use of AI coding tools like Cursor and Claude Code have taken off, it’s clear that we’re still very much in the learning phase. For all the note-worthy AI wins, there are also the AI failures that people are less likely to talk about: going down a rabbit hole trying to solve a problem with AI assistance and then having to restart from scratch the old way. We’re not yet in the stage of seeing meaningful productivity gains that translate into faster product iterations. And that’s okay. It takes time to learn new ways to do work, especially when the tools themselves are changing so quickly. #engineering

𝗧𝗵𝗲 𝗵𝗶𝗱𝗱𝗲𝗻 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗶𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 As you scale your enabled systems and integrate multiple AI models (like ChatGPT, Claude, Gemini, etc.) with enterprise tools—CRM, analytics, internal apps—something critical breaks: 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆. This is where the 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣) comes in. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗠𝗖𝗣: Each AI agent needs a separate integration with each tool—resulting in an exponential 𝙼 × 𝙽 mess. 𝗪𝗶𝘁𝗵 𝗠𝗖𝗣: A single protocol acts as a unifying layer. Each model and system integrates once with MCP—bringing order, efficiency, and scalability. Now it's simply 𝙼 + 𝙽. This is not just cleaner architecture—it’s 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. I've visualized this transition in the image below to make the value of MCP clear for technical and non-technical teams alike. What do you think—are we heading toward an AI future where 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹-𝗳𝗶𝗿𝘀𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 becomes standard?

Can an # AI #Doctor partner with clinicians? Can we please move past the AI versus doctor/clinician comparisons in taking board exams.. solving diagnostically challenging cases... providing more empathetic on-line responses to patients...? and instead focus on improving patient care and their outcomes? The authors, Hashim Hayat, Adam Oskowitz et. al. at the University of California, San Francisco, of a recent study may be hinting at this: envisioning an agentic model (Doctronic) “used in sequence with a clinician” to expand access while letting doctors focus on high‑touch, high‑complexity care and supporting the notion that AI’s “main utility is augmenting throughput” rather than replacing clinicians (https://lnkd.in/e-y3CnuF)  In their study: ▪️ >100 cooperating LLM agents handled history evaluation, differential diagnosis, and plan development autonomously. ▪️ Performance was assessed with predefined LLM‑judge prompts plus human review. ▪️ Primary diagnosis matched clinicians in 81 % of cases and ≥1 of the top‑4 matched in 95 %—with no fabricated diagnoses or treatments. ▪️AI and clinicians produced clinically compatible care plans in 99.2 % of cases (496 / 500).  ▪️In discordant outputs, expert reviewers judged the AI superior 36 % of the time vs. 9 % for clinicians (remainder equivalent). Some key #healthcare AI concepts to consider: 🟢 Cognitive back‑up, in this study, the model identified overlooked guideline details (seen in the 36 % of discordant cases; the model used guidelines and clinicians missed). 🟢 Clinicians sense nuances that AI cannot perceive (like body‑language, social determinants). 🟢 Workflow relief , Automating history‑taking and structured documentation, which this study demonstrates is feasible, returns precious time to bedside interactions. 🟢 Safety net through complementary error profiles – Humans misdiagnose for different reasons than #LLMs; so using both enables cross‑checks that neither party could execute alone and may have a synergistic effect. Future research would benefit from designing trials that directly quantify team performance (clinician/team alone vs. clinician/team + AI) rather than head‑to‑head contests, aligning study structure with the real clinical objective—better outcomes through collaboration. Ryan McAdams, MD Scott J. Campbell MD, MPH George Ferzli, MD, MBOE, EMBA Brynne Sullivan Ameena Husain, DO Alvaro Moreira Kristyn Beam Spencer Dorn Hansa Bhargava MD Michael Posencheg Bimal Desai MD, MBI, FAAP, FAMIA Jeffrey Glasheen, MD Thoughts? #UsingWhatWeHaveBetter

🧠 If AI agents can do 80% of your job... What exactly is your job title now? That question stayed with me. Because this isn’t just about automation anymore. It’s about identity. Over the last 20 years, I’ve helped companies unlock value with AI. But this moment feels different. AI agents aren’t just helping us work faster — they’re starting to own the work: → Drafting strategies → Leading meetings → Making financial decisions → Even hiring contractors and reallocating budgets And they’re learning — fast. Every prompt. Every project. Every outcome. I’m no stranger to transformation. But this shift is so fundamental, it’s rewriting job descriptions before we even have time to update LinkedIn. 📊 What’s happening now: 80% of knowledge workers already use AI to complete tasks AI agents now execute end-to-end workflows with limited oversight Companies report up to 500% productivity gains Entry-level roles in consulting, finance, and project management are vanishing Titles like Junior Analyst or PMO Coordinator may not survive 2026 In IRREPLACEABLE, we describe this as the human shift. But how we navigate it matters. 📚 And now, we have data to back it up. A groundbreaking new study from Stanford University introduces the WORKBank, surveying: → 1,500 workers → 104 occupations → 844 tasks → Alongside 52 AI experts Here’s what it found: ✅ 46% of workers want AI to take over repetitive, low-value tasks 🟥 But many don’t want AI in areas requiring judgment or human interaction 🟨 Critical mismatches exist between what workers want and what AI can do 🧭 A new Human Agency Scale (HAS) helps define how much control humans want to retain over tasks 📈 The biggest shift? From information skills → interpersonal skills This isn’t just a tech upgrade. It’s a realignment of the core competencies that define our value at work. ✅ To stay ahead, I’m doubling down on: Human-AI collaboration fluency Strategic thinking that AI can’t replicate Ethical oversight and empathy Becoming the bridge between human vision and agent execution 💥 So let me ask you: If an AI agent does 80% of your tasks… What’s your role now? Coach? Strategist? Orchestrator? Or something entirely new? 👇 Let’s debate. How are you preparing? #AI #FutureOfWork #AIagents #WorkplaceTransformation #JobTitles #Automation #IRREPLACEABLE#Stanford #WORKBank #HumanAgency #AIleadership

For those interested in the responsible use of Generative AI in professional practices, particularly in law, significant insights await! Using Generative AI to enhance law practice and other professional services is both important and urgent. However, this technology also brings inherent limits and risks. And I do believe we (finally!) have a stable draft providing sound answers for lawyers. I'm excited to share a stable draft developed by the COPRAC of the State Bar of California. This document offers comprehensive guidelines for lawyers using Generative AI. I've been steadily drafting with a working group of the Committee on Professional Responsibility and Conduct (COPRAC) of the State Bar of California on "Recommendations from Committee on Professional Responsibility and Conduct on Regulation of Use of Generative AI by Licensees". The final draft of those recommendations, including a crisp table pairing existing professional ethics rules with specific application of those rules to the use of Generative AI as part of law practice, is now publicly posted and accessible. You can view the final draft of our recommendations here: https://lnkd.in/gvA8rcar I believe this draft will be reviewed by the board next week. In the meantime, I invite and encourage all California attorneys, and others interested, to review the draft and consider its implications for your practice.  Note that the draft recommendations include a detailed table aligning professional ethics rules with Generative AI use in law. This table deserves special attention. To learn more about the work of COPRAC, check here:  https://lnkd.in/gaa2BP2m And finally, a special hat-tip to the COPRAC working group for incorporating insights from the law.MIT.edu Task Force on Responsible Use of Generative AI for Law and Legal Processes in their initial review. Discover more about the Task Force's work at law.MIT.edu/ai Dr. Megan Ma Olga V. Mack Jeffrey Saviano Aileen Schultz Shawnna Hoffman Tony Lai Pablo Arredondo Alex 'Sandy' Pentland Robert Mahari