Workplace Trends Overview

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!

Last month, our Devsinc business analyst, accomplished something that would have seemed impossible five years ago. In just two weeks, she built a complete inventory management system for our client's warehouse operations – without writing a single line of code. The client had been quoted six months and $150,000 by traditional developers. Fatima delivered it in 72 hours using our low-code platform, and it works flawlessly. That moment crystallized a truth I've been witnessing: we're experiencing the assembly line revolution of software development. Henry Ford didn't just speed up car manufacturing; he democratized automobile ownership by making production accessible and efficient. Today's no-code/low-code movement is doing exactly that for software development. The numbers tell an extraordinary story: by 2025, 70% of new applications will use no-code or low-code technologies – a dramatic leap from less than 25% in 2020. The market itself is exploding from $28.11 billion in 2024 to an expected $35.86 billion in 2025, representing a staggering 27.6% growth rate. What excites me most is the human transformation happening inside organizations. Citizen developers – domain experts who build solutions using visual, drag-and-drop tools – will outnumber professional developers by 4 to 1 by 2025. This isn't about replacing developers; it's about unleashing creativity at unprecedented scale. When our HR manager can build a recruitment tracking app, our finance team can automate expense reporting, and our project managers can create custom dashboards, we're not just saving time – we're enabling innovation at the speed of thought. For my fellow CTOs and CIOs: the economics are undeniable. Organizations using low-code platforms report 40% reduction in development costs and can deploy applications 5-10 times faster than traditional methods. The average company avoids hiring two IT developers through low-code adoption, creating $4.4 million in increased business value over three years. With 80% of technology products now being built by non-tech professionals, this isn't a trend – it's the new reality. To the brilliant IT graduates joining our industry: embrace this revolution. Your role isn't diminishing; it's evolving. You'll become solution architects, platform engineers, and innovation enablers. The demand for complex, enterprise-grade applications will always require your expertise, while no-code handles the routine, repetitive work that has historically consumed your time. The assembly line didn't eliminate craftsmen – it freed them to create masterpieces. No-code/low-code is doing the same for software development, democratizing creation while elevating the art of complex problem-solving.

There's one use case for AI agents not being talked about enough: volatile or seasonal industries. Think about what crypto, fintech, travel, and even retail have in common. Their surges in volume (some random, some not) and customer inquiries make it extremely challenging for traditional CX systems to keep up. But where legacy systems struggle, AI systems step up. Here's how: 1. Scalability When inquiry volumes spike, AI agents can handle the influx without missing a beat. There are no delays from hiring surplus human agents to handle more volume, making AI agents both cost- and process-efficient. 2. Consistency Whether it's 1K or 1M customer inquiries, AI agents guarantee the same level of accuracy and precision every time. Humans need downtime, AI doesn't. 3. Prioritization Customer inquiries come with varying degrees of complexity. While AI agents take care of the low-hanging fruit and repeatable tasks, human agents can focus on the high-touch cases that demand personal attention. Take Coinbase’s customer support, for example. They handle $226B in quarterly trading volume in 100+ countries. Their margin of error is slim, and CX mistakes could cost billions. Instead of leaning on human CX alone, they use AI agents to: • Handle thousands of messages per hour • Reduced customer service handling time • Improve search relevance for their help center The enterprises we work with at Decagon experience the same benefits using AI customer service agents—scalable support, no gaps in performance, and higher customer satisfaction. Just because your industry is volatile doesn't mean your CX should be.

Innovations like hands-free computer operations and screen readers for the visually impaired could reshape how individuals with disabilities contribute to their work and drive business success in the coming year. Excluding people with disabilities from the workforce can cost up to 7% of a country’s GDP, according to a 2023 World Economic Forum report. Implementing assistive AI in business strategies could lead to a 28% increase in revenue and a 30% increase in profit margins for companies. "As someone who navigates life in a wheelchair, I’ve seen firsthand how tools like voice recognition and adaptive interfaces open doors that were previously closed," says diversity and inclusion leader Alister Ong. "These advancements aren’t just about convenience — they’re about giving us the autonomy to perform, contribute, and thrive." What other ways do you think AI can help make the workplace more accessible in 2025 and beyond? Weigh in below or post a video with #BigIdeas2025. And check out the rest of this year’s Big Ideas here: https://lnkd.in/gQphjPrt. ✍️ Neha Jain Kale

When we think about human-computer interaction, most of us picture fingers on a keyboard or swipes on a touchscreen. But what happens when those aren’t options? That’s the reality for millions of people living with paralysis or other mobility challenges. And it’s exactly the kind of barrier that a startup called Augmental is tackling - with a device that might shift how we all think about accessibility. Their innovation, MouthPad, is a wearable interface that sits inside the mouth and lets users control phones and computers using their tongue and head movements. It sounds futuristic, but for those who can’t rely on traditional input methods, it’s a doorway to independence. What’s powerful here isn’t just the technology - it’s the shift in mindset. Inclusive design like this doesn’t just “accommodate” people; it actively expands what’s possible. And history shows us that when we build with accessibility in mind, we often create solutions that benefit far more people than we initially imagined. Think of voice assistants, predictive text, or even video captions - many of these were originally developed for accessibility, but now serve a much wider audience. Have you seen similar efforts from startups or researchers in your part of the world? #innovation #technology #future #management #startups

Right now, everyone is rushing to learn AI—deep learning, LLMs, and complex machine learning techniques. But most companies aren’t struggling with AI... They’re struggling with basic data management, analytics, and decision-making. Yet, many job seekers believe they need to master deep learning to land a data science role when the reality is much different. Before focusing on AI, it’s essential to develop strong data fundamentals: + SQL and Data Manipulation – Extracting, cleaning, and structuring data efficiently is critical. SQL remains one of the most in-demand skills in data science. + Business-Focused Data Analysis – Companies prioritize professionals who can use data to drive decisions, optimize processes, and create measurable impact. + Data Visualization and Communication – Insights have no value if they can’t be communicated effectively. Data storytelling is an underrated skill that influences decision-making. + Problem-Solving with Simple Models – Many business problems can be solved using logistic regression, decision trees, and forecasting methods rather than complex AI models. Many businesses lack structured data, clean pipelines, and the ability to make sense of the information they already have. Before implementing AI, they need: - Better customer segmentation rather than an AI-powered chatbot - Stronger demand forecasting instead of deep learning solutions - Clearer sales and operations insights before investing in predictive modeling - Organizations are looking for data-driven decision-making. The ability to translate raw data into business impact is far more valuable than knowing how to fine-tune a large language model. Most entry-level roles don’t require deep learning. The focus is on: // Understanding and working with real-world messy data // Solving business problems through analytical thinking // Presenting insights in a way that leads to action AI is only as good as the data that powers it. Strong data fundamentals will always be more valuable than chasing the latest AI trends. Those who focus on building these skills will position themselves for long-term success.

In my conversations with business owners, one challenge comes up time and time again: the struggle of answering the same customer questions day in and day out. As someone who's dedicated their career to improving customer experiences, I get it. It's a huge time-sink, especially for small teams. That's why I'm so excited about the potential of AI chatbots. We've seen firsthand how these tools can transform customer service operations. AI chatbots are designed to handle common customer inquiries, like store hours, product availability, or booking appointments. And the best part? They work 24/7. Even when you're not around, your customers can still get the info they need instantly. That’s a game-changer for small teams, saving you time and keeping your customers happy. If you're looking to give your customers faster responses while freeing up your team’s time for more complex tasks, I can’t recommend AI-powered chatbots enough! I’ll share a few resources for those interested in the comments below. 👇

𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 1: 𝐓𝐡𝐞 𝐃𝐚𝐰𝐧 𝐨𝐟 𝐒𝐦𝐚𝐫𝐭 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐭𝐡𝐞 𝐄𝐝𝐠𝐞 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐂𝐥𝐨𝐮𝐝 In 2024, the spotlight is on smart connectivity, a critical evolution that promises to redefine IoT by enhancing the synergy between device intelligence at the Edge and cloud capabilities. This transformative approach is set to impact organizations across industries by enabling more efficient, secure, and intelligent operations. 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: 📌𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠: With the acceleration of Edge processing, organizations can leverage local data analysis for quicker, more autonomous decision-making. This reduces dependency on cloud processing, thereby minimizing latency and enhancing real-time responses. 📌𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Full-stack integration means that IoT devices will be more self-reliant, requiring less intervention and manual oversight. This leads to streamlined operations, lower operational costs, and reduced potential for human error. 📌𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: The emphasis on secure, resilient connectivity ensures that data is protected from endpoint to cloud. This is crucial for organizations dealing with sensitive information, helping them meet regulatory compliance standards like GDPR and HIPAA more effectively. 📌𝐂𝐨𝐬𝐭 𝐚𝐧𝐝 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Intelligent connectivity allows devices to select the most cost-effective and efficient network paths. This adaptability can lead to significant savings on data transmission costs and optimize network resource usage. 📢 𝐌𝐲 𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐬 The prediction of smart connectivity as a cornerstone for IoT in 2024 resonates with a growing trend toward distributed intelligence and the need for more agile, secure, and efficient operations. From an organizational perspective, this shift is not merely technological but strategic, offering a pathway to transform how businesses interact with digital infrastructure, manage data, and deliver services. 📌𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞: Organizations that embrace smart connectivity will gain a competitive edge through enhanced operational agility, improved customer experiences, and a stronger posture on security and compliance. 📌𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬: This new paradigm opens doors for innovative applications and services that leverage Edge intelligence, from advanced predictive maintenance to dynamic supply chain management and beyond. 📌𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐚𝐧𝐝 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: While the benefits are clear, organizations must also navigate the complexities of integrating this technology. This includes ensuring interoperability across diverse devices and platforms, managing the increased complexity of decentralized data processing, and addressing the security vulnerabilities that come with expanded IoT ecosystems.

When will business leaders learn that you can’t go from Excel to AI? Trying to kludge legacy tools into modern infrastructure stacks doesn’t work. Businesses must let go of tools that are older than some of their employees. I got pushback for that take in 2019, but today, my clients don’t have the technical debt that’s preventing their competitors from implementing agents. A core tenet of technical strategy is that decisions made today must amplify the value of future technology waves. Looking at BI tools strategically makes it obvious that they are AI disruptors, not amplifiers. Transitioning away from low maturity BI tools to self-service analytics platforms early set businesses up for AI success today. It freed technical resources to work on contextual data gathering and information architecture rather than spending 80% of their time on reporting and data cleaning. Data literacy and tool maturity have had years to take hold, so the business is filled with semi-technical teams. They’re early adopters of generative AI self-service tools and agent builders. They’re getting more value from AI and avoiding the hype. Products and capabilities have matured iteratively with a cohesive, holistic vision. Transformation is continuous, but a big picture view makes it consistent rather than a series of disconnected pivots and knee-jerk reactions. CIOs must position technology as a pillar of business strategy, so technology decisions must be forward-looking and prescriptive. Technical strategy must be holistic and enterprise-wide. #AI #DataEngineering #AIStrategy #Data

Ever feel like your team is stuck in an endless loop of manual data entry? (Automation Tip Tuesday 👇) That’s exactly where one of our clients — an education consulting firm — found themselves. They were juggling a whole tech stack of tools that didn’t “talk”  to each other, creating inefficiencies and double work. We started with a look into their sales workflow. 🔹 Sales data lived in HubSpot, but once a deal closed, someone had to manually update Asana to track project progress. 🔹 Internal teams worked from one Asana board, but clients needed visibility into their own project timelines — cue more manual updates. 🔹 With so much repetitive data entry, valuable time was being wasted on low-impact admin work. Here’s what we did: 🔗 HubSpot → Asana automation: We created an integration that auto-generates project tasks in Asana when a deal reaches a certain stage in HubSpot. No more copy-pasting! 📢 Internal and client boards sync: Internal progress updates in Asana now automatically reflect on client-facing Asana projects, reducing the back-and-forth. Less busywork, more productivity. By eliminating duplicate data entry, the team saved 10+ hours per week — time now spent on strategy and client success. When your tools work together, your team can focus on what really matters. Where is your team losing time? Drop a comment below! ⬇️ -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday  #automation #workflow #efficiency