Future Of Work Technologies

5 key developments this month in Wearable Devices supporting Digital Health ranging from current innovations to exciting future breakthroughs. And I made it all the way through without mentioning AI… until now. Oops! >> 🔘Movano Health has received FDA 510(k) clearance for its EvieMED Ring, a wearable that tracks metrics like blood oxygen, heart rate, mood, sleep, and activity. This approval enables the company to expand into remote patient monitoring, clinical trials, and post-trial management, with upcoming collaborations including a pilot study with a major payor and a clinical trial at MIT 🔘ŌURA has launched Symptom Radar, a new feature for its smart rings that analyzes heart rate, temperature, and breathing patterns to detect early signs of respiratory illness before symptoms fully develop. While it doesn’t diagnose specific conditions, it provides an “illness warning light” so users can prioritize rest and potentially recover more quickly 🔘A temporary scalp tattoo made from conductive polymers can measure brain activity without bulky electrodes or gels simplifying EEG recordings and reducing patient discomfort. Printed directly onto the head, it currently works well on bald or buzz-cut scalps, and future modifications, like specialized nozzles or robotic 'fingers', may enable use with longer hair 🔘Researchers have developed a wearable ultrasound patch that continuously and non-invasively monitors blood pressure, showing accuracy comparable to clinical devices in tests. The soft skin patch sensor could offer a simpler, more reliable alternative to traditional cuffs and invasive arterial lines, with future plans for large-scale trials and wireless, battery-powered versions 🔘According to researchers, a new generation of wearable sensors will continuously track biochemical markers such as hydration levels, electrolytes, inflammatory signals, and even viruses, from bodily fluids like sweat, saliva, tears, and breath. By providing minimally invasive data and alerting users to subtle health changes before they become critical, these devices could accelerate diagnosis, improve patient monitoring, and reduce discomfort (see image) 👇Links to related articles in comments #DigitalHealth #Wearables

5 HealthTech CEO Imperatives for the Next 24 Months 1. AI is no longer a tech initiative. It’s a clinical one. The AI wave is here, but most HealthTech leaders still treat it as a back-office efficiency play. Wrong lens IMO. Generative AI will directly shape care protocols, triage flows, and patient trust. If your Chief Medical Officer isn’t AI-literate, you’re already behind. By 2026, AI will write discharge summaries and co-diagnose in live consults. It’s not a tool. It’s a co-pilot. Get your team aligned. 2. Data isn’t your moat - interoperability is. Everyone talks about “owning” data. But hoarded data is dead data. The real advantage is in how well your data moves - across partners, platforms, regulators, and patient hands. APIs, federated learning, synthetic data - these aren’t IT terms. They’re survival tools for value-based care. Health systems are only as smart as the worst data they can’t access. 3. Personalised medicine is coming - one use case at a time “Digital twins” sound like sci-fi. Until your largest client asks why you can’t simulate disease progression in silico. The question is no longer “Will this scale?” It’s “Which patients do we start with Watch genomics + wearables + real-time monitoring - this is the care stack of the future. The hardest part Operationalising it in messy clinical settings. 4. Virtual care isn’t telehealth anymore. It’s infrastructure. Post-COVID, many HealthTech firms de-invested in remote models. That’s shortsighted. Virtual consults are now just one node in a larger remote ecosystem: Asynchronous triage, at-home diagnostics, smart monitoring. The real play is integrating these into care pathways without fragmenting continuity. Remote-first care delivery models are emerging - and they’re crushing CAC (sorry for the SaaS reference) and improving outcomes. 5. Cybersecurity is not a cost centre. It’s a competitive advantage. A single breach will kill patient trust - and your next partnership. But the best firms are going further: Embedding compliance by design, automating audit trails, and using security posture as a BD asset. Trust is the new UX. Especially in multi-jurisdictional deployments and sensitive care domains like fertility, mental health, and paediatrics. This isn’t a roadmap. It’s a decision filter. The next 24 months will break companies with beautiful product vision but brittle strategy IMO. You don’t need to do everything. You need to know what not to ignore. If you’re a HealthTech CEO navigating these realities, what are you doubling down on right now?

☄️ The Atlas of AI Interaction Design (https://ai-interaction.com), a free, open source reference, with guidelines, examples, patterns, data types, constraints and touch points — to help teams align on how AI features and AI systems are designed, communicated and built. Beautifully put together by Brandon Harwood. I love Brandon’s (very!) comprehensive overview of AI capabilities broken down across inbound (sensing and structuring), internal (reasoning and deciding), outbound (expressing and creating) and interactive (acting and learning) layers, with useful examples of each. It reminds me of the AI Brainstorming Kit, put together and kindly shared by HCII of the Carnegie Mellon University, with plenty of use cases and capabilities that we often don’t consider in enough details as we are looking at a shiny text box for manual prompting: https://lnkd.in/d8Wjm4Vw. Highly recommended. – 💎 Design Patterns For AI Interfaces AI Interaction Design Patterns, by Emily Campbell https://www.shapeof.ai/ AIverse Design Patterns for AI, by Kshitij Agrawal, Dave Brown, Clair Sun https://lnkd.in/dBj86jHx Where should AI sit in your UI?, by Sharang Sharma https://lnkd.in/dyyMKuU9 Prompt UX Patterns, by Sharang Sharma https://lnkd.in/dWE8Ypbr AI UX Patterns, by Luke Bennis https://lnkd.in/dhtk6sQX Design Patterns For Building Trust, by If https://lnkd.in/eEJngtVv AI Design Patterns Catalogue, by Maggie Appleton https://lnkd.in/ebAp9Sb8 Design Patterns For AI Products, by yours truly Vitaly Friedman https://smashed.by/ai-ux — Glad to see more and more resources emerging around design patterns for AI — with a lot of experiments and interaction patterns going way beyond a text box. 🎉🥳 #ux #design #AI

In recent years, no technology has impacted HR or accelerated its digital transformation as much as Generative AI (Gen AI). Its conversational capabilities make technology more accessible than ever, overcoming historical challenges. ✨ This intuitive and highly efficient AI is enabling task automation, saving time, and significantly enhancing the employee experience. AI has revolutionized HR, presenting a unique opportunity to free up time, increase strategic value, and redefine the HR function in 2025. At íncipy and Advantage Consultores, we have identified the key trends that will shape the future of HR in 2025 and the challenges companies must embrace to maximize HR's strategic impact: 📌 The 13 Key HR Trends for 2025 1️⃣ AI’s Role in HR: HR will lead the adoption of Gen AI within organizations and its own processes. 2️⃣ Digital-First Culture: Creating a collaborative and agile environment that embraces technological innovation. 3️⃣ Training, Discovery & Roadmap: Raising awareness and training leaders and teams to integrate AI into corporate strategies. 4️⃣ Digital Tools Adoption: Maximizing the use of underutilized digital tools. 5️⃣ New Ways of Working: Enhancing productivity with AI assistants like ChatGPT and Copilot. 6️⃣ Employee Experience Journey Automation: Automating and personalizing the employee journey. 7️⃣ AI Agents for Employee Support: AI-driven agents autonomously handling employee requests. 8️⃣ AI Digital Workplace Evolution: Transforming intranets into interactive and AI-powered spaces. 9️⃣ AI in Internal Communications: Automating content creation, streamlining communication, and analyzing employee sentiment. 🔟 AI in Learning & Development: Personalizing training pathways and predicting skill gaps. 1️⃣1️⃣ AI in Recruiting & Onboarding: Automating selection processes, reducing bias, and optimizing onboarding. 1️⃣2️⃣ AI-Driven Organization: Enabling data-driven decision-making with predictive algorithms. 1️⃣3️⃣ AI Adoption & Change Management: Supporting AI implementation projects and managing change effectively. ⚡ 2025 will bring profound changes, challenging companies to innovate, accelerate, and move beyond traditional practices. This is a pivotal year for HR, providing a unique opportunity to lead AI-driven transformation and reinforce its critical role in business strategy. 🚀 Let’s make 2025 a year of innovation, leadership, and impact! 🙌 Excited to hear your thoughts! How is your company preparing for AI in HR? Drop your comments below! Post by: Mireia Ranera, Digital HR & AI Transformation Director, INCIPY Sylvia Taudien CEO, Advantage Consultores #HRTrends2025 #AIinHR #DigitalTransformation #FutureOfWork #Innovation #Leadership #AI

Reflecting on Five Years of Transformation: The Rise of Connected Care in Healthcare As we mark five years since the onset of the COVID-19 pandemic, it’s crucial to reflect on the profound changes that have reshaped our healthcare landscape. A recent article by Fierce Healthcare delves into these transformations, highlighting key shifts in the U.S. healthcare system. One of the most significant evolutions has been the accelerated adoption of connected care models. The pandemic necessitated rapid innovation, leading to the widespread implementation of telehealth services and virtual hospitals. For instance, virtual hospitals have emerged to provide remote medical care to patients using video consultations and monitoring devices, addressing challenges of geographical access and specialized resources. This shift towards digital health has not only enhanced patient engagement but also improved access to care, especially for those in remote or underserved areas. However, as we integrate these technologies, it’s imperative to address challenges such as ensuring data security, maintaining the quality of care, and bridging the digital divide to prevent disparities in access. The journey towards a more connected and resilient healthcare system is ongoing. By embracing innovation and prioritizing patient-centric approaches, we can build a future where quality care is accessible to all, regardless of location. #ConnectedCare #DigitalHealth #Telemedicine #HealthcareInnovation #PatientEngagement #VirtualHospitals #HealthTech #COVID19Lessons #HealthcareTransformation #AccessToCare https://lnkd.in/eGHVMuA3

Your workout data will run your workday. From the gym to the boardroom, we won’t just track biometric data in the year ahead – we’ll translate it into action. A new era of biometric-driven training, powered by data once reserved for elite athletes, is taking hold. Large-scale events like HYROX have normalised data-rich preparation – and Bertie Wilkins, founder of tech-forward fitness studio One City, says everyday exercisers now expect feedback and recovery plans that mirror professional sport. In 2026 and beyond, this data-driven mindset looks likely to extend beyond the gym. Workplace wellness will move from tracking to adapting – using biometric signals to shape schedules, environments and team dynamics. Imagine offices that subtly respond to collective physiology, adjusting lighting, temperature and meeting length based on energy and focus patterns. Teams could even be assembled for circadian compatibility and recovery profiles, optimising wellbeing and output. Burnout prevention could be the most compelling frontier – a predicament thought to be contributing to UK firms’ £51bn annual mental health costs. Data from companies like Whoop, Oura, Garmin and Apple Watch could guide flexible work policies, such as prompting later starts to avoid commuter rush hours. Wilkins even imagines HR roles dedicated to interpreting biometric insights. Before long, wearables could be calling the shots better than any manager ever could. ✍ Aaron Toumazou 📷 Getty Images 💡 This is one of a several ideas LinkedIn News is highlighting in our annual list of predictions. Read it here: https://lnkd.in/BI26PanEurope Join the conversation in the comments or share your own prediction in a post or video with #BigIdeas2026.

If you've followed my latest posts, you'll already know that machines are getting faster, more precise, more powerful. Yet when it comes to truly understanding us (our tone, emotions, intent...) they still fall short. This is the central theme we explored in our latest research report: “When machine precision meets human intuition.” This is the frontier of Human-Machine Understanding: the ability for machines not only to process data, but to sense, interpret, and adapt to human context in real time. Why does this matter? Because as #AI and #robotics become more embedded in our daily lives, the difference between a system that simply executes tasks and one that understands the person in front of it will be the difference between frustration and trust. Between inefficiency and real value. Think of a doctor supported by AI that senses stress and adapts its interface accordingly. Or a collaborative robot that adjusts to a worker’s gestures and fatigue. Or a customer interaction that feels seamless because the system has understood not only what you said, but how you meant it. There is still a lot to improve in this facet of human-machine collaboration. But this topic has extraordinary potential and equally important ethical, safety, and trust questions to solve. I invite you to explore the full report here: 👉 https://lnkd.in/e2sagdCR Franck Greverie Alexandre Embry Kary Bheemaiah Ali Shafti Sally Epstein Keith Williams Tim Ensor Matthew Rose

We need to continually upgrade our Humans + AI capabilities: in ourselves, our organizations, and embedded in the systems we use. The objective at all times is for humans to sharpen their cognition and grow through the interaction. This framework suggests 8 levels for Humans + AI engagement, defining the interaction style and value derived from each. This can be used both for developing skills and designing systems. The levels are: 1. TASK OUTSOURCING Vending machine AI completes discrete tasks via single prompts, providing instant results with minimal user learning or growth. 2. SMART RETRIEVAL Knowledge scanner Users retrieve targeted information or examples from AI, boosting fact-finding efficiency and potentially sparking deeper inquiry. 3. GUIDED DRAFTING Rapid composer AI drafts based on human framing, accelerating content creation while refining user judgment and voice. 4. REFLECTIVE PROMPTING Reasoning mirror Prompts elicit assumptions and counterpoints, improving argument quality and fostering self-questioning habits. 5. DIALECTIC EXCHANGE Sparring partner Human and AI engage in iterative probing exchanges, stress-testing ideas and increasing intellectual resilience. 6. COLLABORATIVE SYNTHESIS Multi-agent council Multiple AI agents present distinct views for human moderation, enhancing synthesis skills and embracing diverse expertise. 7. METACOGNITIVE ORCHESTRATION Process coach AI mirrors cognitive processes and suggests refinements, sharpening thinking workflows and bias awareness. 8. CO-EVOLUTION FLYWHEEL Symbiotic loop Continuous human-AI interaction builds an evolving knowledge graph and fosters mutual insight and mastery. How are you engaging at these levels or what improvements to the model do you suggest?

#AI used to just follow instructions. Now it’s learning to read the room.   The latest piece from Capgemini AI Robotics and Experiences Lab and Cambridge Consultants, “#Machine Precision, Human Intuition: A New Era in Human‑Machine Understanding”, explores how Human‑Machine Understanding (HMU) is transforming AI from task-doer to thought partner.   #HMU is the next leap in intelligence: AI that senses tone, behaviour, and intent in real time, understands why people do what they do, and offers support before you even have to ask.   Across sectors, from healthcare and manufacturing to retail, it's set to unlock new value: empathetic decision support, safer human‑robot collaboration, personalized customer experiences, and more. Read the report: https://lnkd.in/eKNdcj9H

If you ask most data leaders, “What drives your users to take the highest value actions in your product?”, they’ll gaze back at you with a pained look on their face. They’ll probably respond through gritted teeth, “That’s a hard question.” And they’re right. It’s not that they don't care; the opposite in fact. But it’s an incredibly complex puzzle, and they wish they had better answers. Throughout my career, understanding the drivers of high value actions has been *the* burning analytics question. At Wealthfront, we obsessed over what led customers to transfer their other investment accounts to us. At Uber, it was the factors behind frequent trips and subscription sign-ups. At Gopuff, it was what drove large orders and purchases of high-margin products. The problem is, traditional analytics tools like BI dashboards and spreadsheets can’t untangle the web of factors that lead to high value actions. Answering these questions requires high-dimensional causal factor analysis, decomposing outcomes across dozens, or hundreds, or even thousands of input variables. In other words, they require machine learning. This is what the most advanced analytics teams are doing — using ML to find the needles in the haystack and unveil unexpected relationships between behaviors and outcomes. The good news: upgrading your product analytics with ML is within reach. In our latest article, Jeremy and I break down three core techniques you can use today. The topic is on our mind because we’re coming across it frequently at Delphina. We're eager as always for feedback and reactions, and if you’re tackling a similar problem and want to brainstorm, reach out! #datascience #analytics #machinelearning #artificialintelligence