AI In Professional Roles

The Agentic Enterprise is driving profound change across every industry, but nowhere are the stakes higher than in healthcare. There is an incredible opportunity to elevate the work of healthcare professionals and deliver stronger care for patients around the world. In an essay for TIME, Murali Doraiswamy, professor of medicine at Duke University, and I discuss how AI is revolutionizing medicine, including: • Flagging subtle abnormalities in scans and slides that a human eye might miss. • Speeding up the discovery of drugs and drug targets. • Providing patients faster and more personalized support, from scheduling to flagging side effects But we’ve also seen that over-reliance on AI can lead to “deskilling” — in which medical professionals become less effective. That underscores the importance of approaches that keep humans at the center, such as the Intelligent Choice Architecture (ICA), where AI systems don’t make decisions but nudge providers to take a second look at results, weigh alternatives, and stay actively engaged in the process. The future of work is humans and AI agents working together. If we commit to designing systems that sharpen our abilities, we can combine the promise of AI with the critical thinking, compassion, and real-world judgment that only humans bring. https://lnkd.in/gqkTUfb6

Microsoft just released a 35-page report on medical AI - and it’s a reality check for healthcare. The paper, “The Illusion of Readiness”, tested six of the most popular models (OpenAI, Gemini, etc)… across six multimodal medical benchmarks. And the verdict? The models scored high on medical exams. But they’re not even close to being real-world ready. Here’s what the stress tests revealed: ▶ 1. Shortcut learning Models often answered correctly even when key information, like medical images, was removed. They weren’t reasoning - they were exploiting statistical shortcuts. That means benchmark wins may hide shallow understanding. ▶ 2. Fragile under small changes Making small tweaks caused big swings in predictions. This fragility shows how unreliable model reasoning becomes under stress. In visual substitution tests, accuracy dropped from 83% to 52% when images were swapped - exposing shallow visual–answer pairings. ▶ 3. Fabricated reasoning Models produced confident, step-by-step medical explanations - but many were medically unsound… or entirely fabricated. Convincing to the eye, dangerous in practice. And more importantly, healthcare isn’t a multiple-choice exam. It’s uncertainty, incomplete data, and high stakes. So Microsoft’s team calls for new standards: - Stress tests that expose fragility - Clinician-guided guidelines that profile benchmarks - Evaluation of robustness and trustworthiness - not just leaderboard scores The takeaway is simple: Medical AI may ace tests today. But until it proves reliable under stress, it’s not ready for the clinic. When do you think popular LLMs will be clinic-ready? #entrepreneurship #healthtech #AI

We continually hear promises that AI will reduce clinicians’ burdens. Yet, we almost never hear about the new burdens AI creates. This NEJM AI perspective explains that because LLMs are imperfect, they create a new and tedious burden of high-stakes proofreading and editing. Meanwhile, disclaimers to cover liability shift accountability from developers to physicians. Reading this piece made me think of two examples of AI making our work harder: 1️⃣ Chinese radiologists who used AI heavily felt more emotionally exhausted and burned out. It turns out that AI often increases interpretation times, especially when abnormalities are reported. [doi:10.1001/jamanetworkopen.2024.48714] 2️⃣ UCSD primary care physicians who used ChatGPT to respond to patient messages paradoxically spent 22% more time on the task. [doi:10.1001/jamanetworkopen.2024.3201]. Of course, AI often makes our work easier. For example, AI scribes often speed up documentation. Also, as I explained in two recent Forbes articles, trustworthy AI summaries help us process medical literature and patient information more effectively and efficiently. The point is that we must remain clear-eyed and continually ask what we gain and what we lose with AI. Only then will we be able to intelligently decide whether, when, and how to use AI.

The Executive Assistant manages calendars, filters information, handles logistics, and serves as the critical interface between leaders and everyone seeking access to them. Today, #AI can automate perhaps 80% of these tasks with remarkable efficiency. But I believe automation will not eliminate this role, it will elevate it from #assistant to genuine partner. The future EA will spend less time on bookings and more time on judgment calls: deciding what deserves the executive's attention, prioritizing competing demands, analyzing plans before they reach the decision-maker, and anticipating needs before they become urgent. Throughout my career, my executive meetings were scheduled twelve months in advance, requiring thoughtful planning rather than reactive scheduling from assistants who knew what truly mattered. The qualities that make an exceptional EA cannot be automated: making #leadership possible, providing the human touch that keeps intense schedules bearable, and demonstrating the loyalty built through years of consistent judgment. These are delicate, well-compensated roles that are becoming more valuable as AI handles the routine and frees these professionals to focus on what truly differentiates them. What roles in your organization will AI elevate rather than eliminate?

𝗗𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗻𝗲𝗲𝗱 𝗮 𝗖𝗵𝗶𝗲𝗳 𝗔𝗜 𝗢𝗳𝗳𝗶𝗰𝗲𝗿 (𝗖𝗔𝗜𝗢)? A new global study from IBM, Oxford Economics, and DUBAI FUTURE FOUNDATION looked across 2,300+ companies to figure out when — and how— CAIOs actually move the needle. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗯𝗼𝗹𝗱 𝘀𝘁𝗮𝘁: Organizations with a CAIO see 10% higher AI ROI. Yet only 26% have one (!). This isn’t a vanity title. It’s a signal for maturity — and a catalyst for action. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗳𝗼𝘂𝗿 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁: ⬇️ 1. 𝘊𝘩𝘪𝘦𝘧 𝘈𝘐 𝘖𝘧𝘧𝘪𝘤𝘦𝘳𝘴 𝘮𝘰𝘷𝘦 𝘵𝘩𝘦 𝘯𝘦𝘦𝘥𝘭𝘦 𝘰𝘯 𝘙𝘖𝘐  → CAIO-led orgs generate 10% more return on AI spend. And they’re 24% more likely to outperform peers on innovation. 2. 𝘈𝘤𝘤𝘰𝘶𝘯𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘢𝘯𝘥 𝘪𝘯𝘧𝘭𝘶𝘦𝘯𝘤𝘦 𝘮𝘢𝘵𝘵𝘦𝘳  → 57% of CAIOs report directly to the CEO or Board. 76% are consulted by other CxOs on critical AI decisions. This role is about authority—not just evangelism. 3. 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 𝘴𝘩𝘢𝘱𝘦𝘴 𝘴𝘶𝘤𝘤𝘦𝘴𝘴  → A centralized or hub-and-spoke model led by the CAIO sees a 36% ROI boost over decentralized ones. AI governance and orchestration aren’t optional. 4. 𝘐𝘮𝘱𝘢𝘤𝘵 𝘮𝘦𝘢𝘴𝘶𝘳𝘦𝘮𝘦𝘯𝘵 𝘪𝘴 𝘩𝘢𝘳𝘥—𝘣𝘶𝘵 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭  → 72% say lack of metrics puts them at risk of falling behind. Still, 68% launch AI initiatives anyway—because the biggest bets are often the hardest to quantify. 𝗦𝗼 𝘄𝗵𝗲𝗻 𝗱𝗼𝗲𝘀 𝗮𝗻 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱 𝗮 𝗖𝗔𝗜𝗢? → When AI efforts grow beyond pilots → When complexity from tooling, data silos, and vendor chaos paralyze progress → When there’s a need for someone who can speak both Python and P&L 𝗪𝗵𝗮𝘁 𝗱𝗼 𝗖𝗔𝗜𝗢𝘀 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘀𝘂𝗰𝗰𝗲𝗲𝗱? → Clear mandate and reporting lines → Direct alignment with the CEO, CTO, CHRO, and CFO → Budget control (61% already own it) → Cross-functional support across security, data, HR, and innovation How can they deliver higher AI ROI? → Move from scattered projects to coordinated portfolios → Build small, expert teams embedded in the business—not isolated shadow IT → Define broader success metrics beyond technical ROI: revenue, productivity, trust Great report with some sharp insights. This isn’t just about adding another title to the C-suite. It’s about closing the gap between AI ambition and actual business value. Full report below or here: https://lnkd.in/dKFbm7-5 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

We sent 4,495 AI SDR emails in 2 weeks and achieved the #1 response rate on our platform. But here's what nobody tells you about making AI SDRs actually work... The Metrics: ✅ 4,495 personalized messages sent in 14 days ✅ Highest response rate on our entire platform ✅ $700,000 of pipeline opportunities opened ✅ Meetings booked daily (literally got one this morning) ✅ Outperformed all our historical human SDR averages — mostly ✅ Better results than some of our human AEs The Reality Check First We had unfair advantages. SaaStr has been around since 2012, we've sold $100,000,000 in sponsorships, and people know our brand. We targeted our existing database—website visitors, past attendees, lapsed accounts—not cold lists. We spent 2 weeks doing basically nothing else: 90 minutes every morning, 1 hour every evening training our AI, plus real-time responses throughout the day. 👉What Actually Works: 1️⃣ Your AI has to add real value, not just volume There's no way we could send 4,495 good emails ourselves manually in two weeks. The key is each one has to be at the level we would write ourselves. Bad: "Hey [NAME], saw you visited our website" Good: "Congrats on your new VP role at Oracle. Since you attended SaaStr London last year, thought you'd want to know about our 2025 VC track with speakers from a16z and Sequoia..." 2️⃣ Your data is messier than you think We trained our AI on 20+ million words of SaaStr content, but still found: - Opportunities never logged in Salesforce - Missing context from AEs who never used the system - Customer relationships that existed nowhere in our CRM We literally spend time every day finding things that were missing and manually adding them to AI's knowledge base. 3️⃣ Human-in-the-loop isn't optional When prospects respond to your AI, YOU have to respond instantly at the same quality level. We have it hooked up to Slack—our phones go off at all hours because SaaStr is global. The AI creates an expectation of responsiveness. You better match it or they'll know it was "just an AI email." 5️⃣ This is additive, not replacement We still do personal emails, marketing campaigns, and have human SDRs. Results by campaign type: - Website visitors: Hit or miss - Cold outbound: Ranked 4th out of 4 campaigns - Lapsed renewal accounts: Really good results 🏋🏽♀️ The Uncomfortable Truth: It's MORE work, not less. You get 10x better output, but it requires S-tier human orchestration. E.g., we're running 30+ personas across different campaigns. 🔮 Bottom line: AI SDRs work incredibly well, but only with proper training and orchestration. After 60 days of daily improvements, you'll have something you're proud of. But you can't skip the daily 30-45 minute audit process. Full breakdown with all our tools and processes at link in comments.

Year-End Reflection: AI in Healthcare Isn’t About What Everyone Thinks It Is !!! As 2025 winds down, I’m struck by how narrow the conversation around AI in healthcare has become. We keep circling the same points: documentation relief, scheduling efficiency, smoother workflows. These are helpful, yes. But let’s not confuse efficiency gains with paradigm shifts. These tools solve pain point. Thet do not redefine healthcare. 1. AI’s real power isn’t in the hospital setting. It will come from the continuous, context-rich, patient-generated data that never enters the hospital at all. When care happens at home—when conditions are monitored passively and interventions are nudged in real time—AI becomes less of a convenience and more of a clinical force multiplier. Hospitals won’t be the center of gravity. Homes and clinics will. 2. The scribe narrative is a distraction. Documentation has become the mascot of AI in healthcare because it’s easy to visualize and easy to celebrate. But is better notes what we are striving for? If AI is only good for writing the note, we’ve missed the point. The real capability lies in synthesizing signals, detecting patterns long before symptoms surface, and shaping decisions—not summarizing visit summaries. Scribes help clinicians do the work. AI will eventually help clinicians rethink the work. 3. Experience and wisdom are about to be redefined. We’re entering an era where every clinician will walk into the room with not just their training—but an AI tool that has absorbed millions of trajectories, outcomes, cases. So what becomes of “clinical intuition”? Ai should not be viewed as a threat. It’s a rebalancing. Physicians will bring judgment, empathy, nuance, and the ability to navigate uncertainty. AI will bring breadth, precision, and relentless recall. It will augment our decision-making – not replace it. The blend—not the competition—will define excellence. 4. The critical question for 2026: What is the role of the physician? Not “will AI replace us?” That’s too simplistic. The more nuanced question is: What uniquely human functions will clinicians double down on, and what will we gladly offload? Medicine has always been a human profession supported by tools. In 2026, we’ll need to actively decide which parts remain human—and which parts are simply better performed by systems built for pattern recognition at scale. It’s a tough discussion – yet, it’s one we fundamentally must have. We’re stepping into the most important transition in modern medicine. Not because AI will replace clinicians, but because it will reveal what is the role of the physician in this evolving ecosystem. #ai #aihealth #ama #digitalhealth

The pace of change in Accounting and Compliance over the past three months is materially ahead of what many incumbent firms and their investors are willing to publicly acknowledge or, indeed, are even aware of. From where we sit, partnering with several AI native startups in this space, the shift is not theoretical. It is visible in real time. These are technology first businesses hiring senior Accounting leaders not to manage delivery teams but to commercialize platforms built to absorb large portions of routine accounting and compliance work. The role of the accountant is moving from executor to validator and builder. Goldman Sachs deploying Claude to automate Accounting and Compliance work should be read through that lens. This is not a marginal efficiency play. When a systemically important financial institution embeds frontier AI into core finance and control processes, it signals that automation is moving from pilot to production. The direction of travel is structural. Accounting is not physics. It is a rules based discipline applied to structured financial data. Much of that data is already labelled through XBRL and regulatory reporting frameworks, which makes it highly trainable. The question is no longer whether AI can assist but how much of the workflow it can absorb and how quickly the economics reprice. Retrofitting AI into a legacy pyramid is fundamentally different from building an AI native delivery model from day one. The productivity gap is widening. The uncomfortable reality is that the sector is heading into margin compression and business model reset. We are already seeing tech led platforms willing to use commoditized compliance services as a loss leader to win higher value analytics and advisory revenue. If value is still being underwritten on utilization and leverage rather than net contribution after automation, the signal is being missed. The firms that respond early will redesign pricing, talent mix and partner economics. The rest will discover that apparent stability was masking erosion.

New evidence says discourse on how AI will reshape work is getting it wrong. It’s not that some jobs get automated away while others are augmented. Automation and augmentation are playing out in the same roles at the same time. In other words, AI is reshaping work within jobs rather than eliminating them. The “winners vs. losers” frame doesn’t hold. Our latest research at The Burning Glass Institute mines millions of job postings before and after the advent of LLM’s to track how AI is already reshaping skill demand. The finding is striking: we found a 0.87 correlation between the roles experiencing the greatest automation effects and those experiencing the greatest augmentation effects, meaning the jobs most vulnerable to automation are also those most empowered by AI. Tasks are disappearing and intensifying simultaneously—within the same roles, at the same time. In fact, we find that skills most exposed to AI automation were 16% more likely to see demand decline than baseline skills. Skills most exposed to AI augmentation were 7% more likely to see demand increase.   Project managers aren’t disappearing, but our analysis shows that spreadsheet-heavy tasks are fading while strategic, judgment-intensive work is growing. Financial analysts aren’t getting replaced, but model-building is automated while interpretation and decision-making matter more. The unit of change isn’t the job. It’s the task mix inside the job. Our paper, "Beyond the Binary", offers some of the first empirical evidence from the AI Tracking Hub, a multistakeholder initiative led by the Burning Glass Institute to move the AI–work conversation from forecasts to observation. If jobs aren’t vanishing but transforming from within, the real question isn’t “Which jobs are safe?” It’s whether our institutions—education, training, workforce policy—are built for continuous change rather than one-time transitions. You can find the report on https://lnkd.in/ej5FJu2J. I so enjoyed the collaboration with coauthors Benjamin Francis, Shrinidhi Rao, and Gwynn Guilford, and I am grateful as always to Gad Levanon and Stuart Andreason for their work to bring data-driven, empirical understanding to the workforce impacts of AI. #AI #artificialintelligence #jobs #economics #work.

Will Accounting Be Replaced? 🤖 💼 Everyone's asking if AI will replace accountants... Let me settle this once and for all. ➡️ WHAT WILL TRANSFORM ADVISORY SERVICES are becoming the heart of what we do. Gone are the days when accountants just crunch numbers. Now we guide strategic decisions using real data insights. Companies need advisors who understand both numbers AND business strategy. FORENSIC ACCOUNTING gets supercharged with advanced analytics. Finding fraud used to be like searching for a needle in a haystack... With AI-powered anomaly detection, we spot patterns humans would miss. The fraudsters are getting smarter, but so are our tools. AUDIT & RISK ASSESSMENT will never go away, but everything about it is changing. Instead of sampling transactions once a year, we're moving to continuous auditing with real-time data. AI review systems flag issues as they happen, not months later when it's too late. FINANCIAL ANALYSIS & FORECASTING is where accountants shine brightest. Sure, AI can run calculations, but humans bring context to numbers. Our forecasting is getting enhanced by predictive analytics and scenario modeling that processes variables faster than ever before. CLIENT COMMUNICATION is shifting completely. We're moving from transaction processors to trusted advisors. ➡️ WHAT WILL BE REPLACED Let's be honest... some parts of accounting are tedious and perfect for automation. MANUAL DATA ENTRY is already on its way out. AI-driven data capture and OCR tools process invoices and receipts in seconds, without the errors humans make after hours of monotonous work. ROUTINE BOOKKEEPING tasks are getting automated through cloud accounting software. Bank feeds, automatic categorization, and machine learning mean the days of manually reconciling every transaction are numbered. BASIC TAX PREPARATION for standard situations will be handled by smart platforms. E-filing tools get smarter every tax season. The complex tax strategy work? That's still all us. INVOICE MATCHING & RECONCILIATION is perfect for automation. AI bots can match thousands of invoices to purchase orders in minutes, with real-time reconciliation systems keeping everything in sync. COMPLIANCE MONITORING no longer needs accountants to manually check every rule. Automated alerts and built-in compliance checks flag issues instantly, letting us focus on solving problems rather than finding them. ➡️ THE FUTURE ACCOUNTANT The accountants who will thrive aren't fighting against technology... They're embracing it. The future belongs to those who combine technical accounting knowledge with: - Strategic thinking - Business acumen - Technology fluency - Communication skills === What parts of your accounting job do you think will change the most with AI? Which skills are you developing to stay ahead? Join the discussion in the comments below 👇