Technology

Hot take: the #legalengineer is now the most critical role in the in-house legal department. Not the GC. Not the deputy. Not the head of legal ops. The person who sits at the intersection of legal process expertise, technology fluency, and change management and who can re-engineer how legal work gets done as AI reshapes what's possible is what separates the teams that will come out of this period ahead from the ones that will have a lot of expensive technology and not much to show for it. In-house legal is redesigning itself right now. What goes to outside counsel? What does AI handle? How do we staff? You can't answer those questions or execute on the answers without someone who can architect the new model. I've been in this space for over two decades. This is the role I'd prioritize above almost anything else right now. https://lnkd.in/gCy6tQr5

How can businesses go beyond using AI for incremental efficiency gains to create transformative impact? I write from the World Economic Forum (WEF) in Davos, Switzerland, where I’ve been speaking with many CEOs about how to use AI for growth. A recurring theme is that running many experimental, bottom-up AI projects — letting a thousand flowers bloom — has failed to lead to significant payoffs. Instead, bigger gains require workflow redesign: taking a broader, perhaps top-down view of the multiple steps in a process and changing how they work together from end to end. Consider a bank issuing loans. The workflow consists of several discrete stages: Marketing -> Application -> Preliminary Approval -> Final Review -> Execution Suppose each step used to be manual. Preliminary Approval used to require an hour-long human review, but a new agentic system can do this automatically in 10 minutes. Swapping human review for AI review — but keeping everything else the same — gives a minor efficiency gain but isn’t transformative. Here’s what would be transformative: Instead of applicants waiting a week for a human to review their application, they can get a decision in 10 minutes. When that happens, the loan becomes a more compelling product, and that better customer experience allows lenders to attract more applications and ultimately issue more loans. However, making this change requires taking a broader business or product perspective, not just a technology perspective. Further, it changes the workflow of loan processing. Switching to offering a “10-minute loan” product would require changing how it is marketed. Applications would need to be digitized and routed more efficiently, and final review and execution would need to be redesigned to handle a larger volume. Even though AI is applied only to one step, Preliminary Approval, we end up implementing not just a point solution but a broader workflow redesign that transforms the product offering. At AI Aspire (an advisory firm I co-lead), here’s what we see: Bottom-up innovation matters because the people closest to problems often see solutions first. But scaling such ideas to create transformative impact often requires seeing how AI can transform entire workflows end to end, not just individual steps, and this is where top-down strategic direction and innovation can help. This year's WEF meeting, as in previous years, has been an energizing event. Among technologists, frequent topics of discussion include Agentic AI (when I coined this term, I was not expecting to see it plastered on billboards and buildings!), Sovereign AI (how nations can control their own access to AI), Talent (the challenging job market for recent graduates, and how to upskill nations), and data-center infrastructure (how to address bottlenecks in energy, talent, GPU chips, and memory). I will address some of these topics in future posts. [Original text: https://lnkd.in/gbiRs2mi ]

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

We’ve all heard about AI’s potential to boost productivity. But what truly matters to me is whether it’s making work better for the people who show up every day. At Cisco, our People Intelligence team, in collaboration with IT, has been exploring this very topic, and the findings are fascinating. Here are five key insights from our research that leaders should take seriously: 1. Leaders are key to adoption. At Cisco, employees are 2x more likely to use AI if their direct leader uses it. 2. Generic AI training doesn’t work. Role-specific, practical training accelerates AI use. 3. Confidence gaps exist among senior leaders. Directors at Cisco often feel less confident with AI than mid-level employees, underscoring the need for tailored support at all levels. 4. Employee autonomy fuels adoption. Hybrid work environments are powerful accelerators for AI adoption, while mandates can hinder it. Employees who voluntarily go to the office are more likely to use AI, while those who are required to work on-site have lower adoption. 5. AI use is linked to employee well-being, but the relationship is complex, with both benefits and trade-offs that require thoughtful navigation. This is just the beginning. Next, we’re looking at how AI is transforming the way teams operate. For now, one thing is clear, employees who use AI aren’t just more productive. They’re also more engaged, better aligned with company strategy, and empowered to focus on meaningful work. #AIAdoption #EmployeeExperience #FutureOfWork

I think a very visible observation at this year's Restaurant Show was logical tech instead of theoretical. There was less "glimpses into the future" and more "proof of concept." Here's one of those in action: For two and a half years, Wingstop has worked on a new Smart Kitchen that forecasts demand in 15-minute increments, telling the store how many wings to drop. The system takes into account more than 300 variables tailored to each unit, like weather, sales trends, and sports. It also features digital touch-screen displays at every work station instead of paper chits and an order-ready screen at the front so consumers can keep up with their order. Another feature: there are now sticker print outs that identify what flavors are in each package. At restaurants where the technology has been installed, wait times have been cut in half to about 10 minutes, and there have been notable improvements in guest satisfaction, accuracy, consistency, and employee turnover. In the delivery channel, Wingstop has been able to show up in under 30 minutes. Why is this important? Shorter wait times allow the brand to become a greater consideration. Instead of serving as a destination—with an average frequency of just three times per quarter and once a month—the quicker service could entice guests to visit more often, especially during on-the-go periods like the afternoon daypart. The Wingstop Smart Kitchen is in 400 restaurants and the chain hopes to complete the rollout by the end of the year. Again, real-time innovation in the back of the house. That seems to be the battleground right now. More here: https://lnkd.in/eMHMUkmZ

By 2030, these 11 abilities will decide who gets hired Most don’t show up on resumes yet. The World Economic Forum just revealed the top skills for 2030 in the Future of Jobs Report 2025. And it’s a wake-up call. Today's celebrated tech skills? AI will do those better by 2026. Those certifications? Outdated in 18 months. But here's the good news: The skills that matter most in 2030? Technology can't replace them. Start mastering these skills to stay relevant and be recognized: 1. AI and Big Data 🤖 ❌ Passively watch AI replace jobs ✅ Make AI your competitive edge → Use AI to automate weekly reports → Build self-updating dashboards and summaries 2. Analytical Thinking 🧠 ❌ Drown in opinions and noise ✅ Let data drive key decisions → Identify root causes before reacting → Monitor metrics that reveal blind spots 3. Resilience, Flexibility and Agility 🐆 ❌ Break down under shifting priorities ✅ Adapt fast and lead through change → Stay steady during messy execution → Pause, breathe, ask: “What’s the next best move now?” 4. Motivation and Self-Awareness 👤 ❌ Burn out chasing urgency ✅ Work in sync with your energy → Track your energy every 3 hours for a week → Schedule focus work when your mind feels sharp 5. Curiosity and Lifelong Learning 🔍 ❌ Stick to your job description ✅ Learn a complementary skill to your role → If you're in marketing, study basic product design → If you're in finance, explore storytelling with data 6. Leadership and Social Influence 🌟 ❌ Rely on your title for respect ✅ Build trust by how you think, speak and act → Explain why you made a tough call, not just what you decided → Share a client insight that helped your team level up 7. Technological Literacy 💻 ❌ Run to the IT helpdesk for every issue ✅ Build and adapt your own stack → Automate one repetitive workflow today using AI → Use familiar tools more efficiently (Excel, Slack) 8. Systems Thinking 🔧 ❌ React to broken processes ✅ Design workflows that scale → Improve one repeated but inefficient process this week → Ask: “Can this run without me?” 9. Empathy and Active Listening 🎧 ❌ Talk to be heard ✅ Listen to support, inspire and lead → Listen without needing to speak more in 1:1s → Decode what’s really being said 10. Creative Thinking 🎨 ❌ Wait for inspiration ✅ Build innovation into routine → Ask: “What’s another way to solve this?” → Try a small change to test a new idea 11. Talent Management 👥 ❌ Try to do it all ✅ Delegate and develop future leaders → List 3 tasks to delegate now → Improve hiring processes to onboard the right talent 💡 It’s not about doing more. It’s about evolving how you think, lead, and grow. Because the future expects you to. Which one are you focusing on this month? -- ♻ Share this with someone you’d want on your 2030 team. ➕ Follow me (Meera Remani) for future-ready leadership strategies. 🔔 My best insights for transforming your leadership career? Join my exclusive email list. Link below.

I’ve been headhunting in the CPG industry for the past decade, and I’ve never seen a post-inflation market like we’re in right now. For the past three years, customers have been capitulating to price hikes by extending their budgets. But now, they’re at a breaking point. American families, already tethering on edges of their budgets, do not have the ability or the desire to expand their budget in order to accommodate increased prices. I’m sure you’d agree with this, because my family certainly does. With grocery bills through the roof, we’d rather skip on groceries and essentials rather than paying a premium right now. A couple things led us here, starting the pandemic and the post-pandemic impact on spending and savings. Secondly, the wave of AI and tech developments that caught us off guard. So, where do the companies go now? Once the “price increase” playbook is done, CPG brands can only win in both value and volume by shifting gears. In my chats with executives, I’m sensing a change in tone. To stay competitive, they’re looking for ways to shift from the post-pandemic survival mindset to a growth-focused one that accommodates the customer as well. Rather than hiking prices, the focus is now on bringing down costs, and getting to terms with consumer’s limited budgets and increasing product choices. Layoffs aren’t the only way to bring down costs. In my view, CPG companies do have the leeway to embrace data-driven innovation and efficiency to cut costs. Here are some of the ways in which companies can use AI and ML to achieve targets in 2025 and beyond: 1/ Predicting the demand: Post-pandemic behavior is tough to predict, especially in CPG markets. With AI, the companies can now leverage real-time insights from sources like point-of-sale systems, social media, and even economic indicators to see future trends more clearly. PepsiCo, uses Tastewise to track what consumers are eating across 60+ million touchpoints and making decisions that align with local preference. 2/ Inventory management: With AI-powered predictive analytics, companies are now turning inventory management into a science. Procter & Gamble’s Supply Chain 3.0 initiative is one example of this shift. 3/ Increased personalization: Leaders are tapping into geographical intelligence to connect meaningfully with audiences. Estée Lauder has a voice-enabled makeup assistant for visually impaired customers, reaching a new market while boosting brand loyalty. Bottom line is: customers are no longer meeting brands where they’re at. It’s high time that companies start caring about customers and their shrinking bottom lines. Are you excited to see your grocery bill go down in the next few months? #CPG #AI #ML #fmcg #marketing #trending

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!

💎 Accessibility For Designers Checklist (PDF: https://lnkd.in/e9Z2G2kF), a practical set of cards on WCAG accessibility guidelines, from accessible color, typography, animations, media, layout and development — to kick-off accessibility conversations early on. Kindly put together by Geri Reid. WCAG for Designers Checklist, by Geri Reid Article: https://lnkd.in/ef8-Yy9E PDF: https://lnkd.in/e9Z2G2kF WCAG 2.2 Guidelines: https://lnkd.in/eYmzrNh7 Accessibility isn’t about compliance. It’s not about ticking off checkboxes. And it’s not about plugging in accessibility overlays or AI engines either. It’s about *designing* with a wide range of people in mind — from the very start, independent of their skills and preferences. In my experience, the most impactful way to embed accessibility in your work is to bring a handful of people with different needs early into design process and usability testing. It’s making these test sessions accessible to the entire team, and showing real impact of design and code on real people using a real product. Teams usually don’t get time to work on features which don’t have a clear business case. But no manager really wants to be seen publicly ignoring their prospect customers. Visualize accessibility to everyone on the team and try to make an argument about potential reach and potential income. Don’t ask for big commitments: embed accessibility in your work by default. Account for accessibility needs in your estimates. Create accessibility tickets and flag accessibility issues. Don’t mistake smiling and nodding for support — establish timelines, roles, specifics, objectives. And most importantly: measure the impact of your work by repeatedly conducting accessibility testing with real people. Build a strong before/after case to show the change that the team has enabled and contributed to, and celebrate small and big accessibility wins. It might not sound like much, but it can start changing the culture faster than you think. Useful resources: Giving A Damn About Accessibility, by Sheri Byrne-Haber (disabled) https://lnkd.in/eCeFutuJ Accessibility For Designers: Where Do I Start?, by Stéphanie Walter https://lnkd.in/ecG5qASY Web Accessibility In Plain Language (Free Book), by Charlie Triplett https://lnkd.in/e2AMAwyt Building Accessibility Research Practices, by Maya Alvarado https://lnkd.in/eq_3zSPJ How To Build A Strong Case For Accessibility, ↳ https://lnkd.in/ehGivAdY, by 🦞 Todd Libby ↳ https://lnkd.in/eC4jehMX, by Yichan Wang #ux #accessibility