Leveraging Technology For Better Productivity

Progress doesn’t always mean pushing forward; sometimes it means reinventing where you’re going in the first place. In the era of Industry 4.0, not all digital initiatives are cut from the same cloth. Sure, everything gets the “Digital Transformation” label, but there are really three different flavors—each with unique goals and metrics. 𝐌𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 𝐓𝐡𝐞 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐎𝐯𝐞𝐫𝐡𝐚𝐮𝐥 Think of this as hitting the refresh button on old systems. It’s swapping outdated equipment, moving from on-prem to cloud ERP, and digitizing paper processes. Sometimes you see an immediate ROI (yay, fewer crashes!), sometimes it’s just clearing the path for bigger changes in the future. Measuring success? Look for fewer failures, easier integrations, and faster deployment of new projects. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐁𝐞𝐭𝐭𝐞𝐫 Once you’ve modernized, it’s time to squeeze every last drop of efficiency out of what you’ve got. This is where you deploy predictive maintenance, automations, and data-driven insights. It’s all about speed, accuracy, and cost savings. If you’re hitting better cycle times, lower waste, or shorter lead times, you’re doing it right. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: 𝐑𝐞𝐝𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 Now we’re talking next-level reinvention—completely new business models, product-as-a-service offerings, or immersive digital experiences. This isn’t about cutting costs; it’s about long-term strategic impact. The metrics here revolve around adoption rate, new revenue streams, and market share growth. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐍𝐨𝐭𝐞: These don’t have to happen in perfect order. One plant might jump straight to efficiency because its core tech is already solid. Another might aim for a big transformation play to disrupt its industry. The real key? Know what you’re aiming for, and measure it the right way. If you judge a radical new business model by the same ROI metrics as an optimization project, you’ll never let it get off the ground. So before you label everything “Industry 4.0,” ask yourself: am I modernizing, optimizing, or transforming? The strategy—and the success—depends on choosing the right path. 𝐅𝐨𝐫 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐚𝐧𝐝 𝐊𝐏𝐈𝐬 𝐟𝐨𝐫 𝐞𝐚𝐜𝐡:  https://lnkd.in/eNSBCkVz ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

AI field note: my word of the year is 𝔼𝕍𝔸𝕃: celebrating the art and science of rigorous measurement of AI performance, progress and purpose. (1 of 3) This year delivered a wealth of new AI models, architectures, and use cases - all united by one thread: evaluation. Model benchmarking, evaluation, or just "eval" has evolved from a simple, singular measure to a more complex blend of stats, metrics, and measurement techniques. Today's evals help discerning practitioners make pragmatic, informed technology decisions and measures improvements as AI systems are tuned. With AI innovation accelerating, staying up to date on evals ensures informed trade-offs when building intelligent systems, agents, and applications. Let's start by looking at measuring "performance"; the best way we know how to compare model behaviors, and find the right fit-for-purpose. Defining 'good performance' now involves a sophisticated suite of metrics across diverse dimensions. ⚙️ Task eval - beyond raw performance numbers. Today's evals measure how models perform across diverse scenarios - from basic comprehension to complex reasoning, reliability, consistency, and nuanced evaluation of reasoning paths, output quality, and edge case handling. 👛 Token economics - balancing cost, efficiency, and operation. Understanding token costs - both input and output - was essential last year, but evals have evolved beyond raw price per token, to understanding efficiency patterns, batching strategies, and the total cost of operation. ⏲️ Time-to-first-token. Speed is a feature, as they say, and while streaming responses have improved user experiences, this metric has become particularly crucial as models are deployed in production environments where user experience directly impacts adoption. 🔥 Inference compute: The amount of compute used for prediction shapes what problems a model can solve. More compute enables greater complexity but increases costs and latency - making it a pivotal benchmark for 2024. For some light holiday reading to explore this further: Service cards (OpenAI, Amazon), Meta's Llama 3 paper, and Anthropic's evaluation sampling research (links below).

I know how easy it is to feel overwhelmed by the constant flow of communication. I’ve built several practical systems using AI that dramatically improve how I manage information without drowning in it as a CEO. Here’s what works for me: 📥 Make your way to inbox zero. Reaching inbox zero is essential for my mental clarity. With a clear inbox, I find myself more present and receptive to new ideas. But it’s a lot easier said than done! Measuring my progress works really well for me. I track my inbox zero status using a Gmail integration in Coda, which creates a progress tracker inspired by Wordle that gives me an immediate visualization. You can create your own tracker here and check out some of the rituals that help me maintain inbox zero: https://lnkd.in/g5j6ppni. 📑 Turn meeting notes into action drivers. My top tip is to use AI to auto-draft summaries for each audience, get a concrete list of actions, and then send personalized recaps to attendees. This is super helpful and ensures my meeting notes actually serve a purpose instead of just going into a filing cabinet, never to be surfaced again. 📤 Set up forwardable notes as an alley-oop for your team. I have a solid structure in place that helps me reach customers, partners, and candidates my team wants to connect with. It’s a pretty simple idea: instead of writing a note that I will send, write a note to me, and I will forward the email after adding a small personal addition. And an important note is that for this to be effective, the notes should be brief and clearly articulate the ask and all details. Those processes for inbound, outbound, and in-person communications work for me today, but I’m constantly refining them and exploring new tools that might make them even better.

Most managers focus on performance once a year. The best managers improve performance daily. After 25 years of managing teams, I've learned, The difference between good and great managers isn't: • Effort • Skill • Tools It's having the right information at the right time. But most people managers work from scattered and stale data: • Random, handwritten notes from 1-on-1s • Performance reviews from 6 months ago • Gut feelings about who's struggling • Occasional frustrated feedback And that's if they can make time to manage at all. Here's where AI comes in. AI won't manage your team.  But it will help you know them. Here's why you need an AI-powered employee dashboard: HIGH-FIDELITY PROFILES | See The Whole Person ↳ Combine resumes, assessments, and feedback into one view ↳ Understand strengths, gaps, and motivations ↳ Predict where they'll thrive and where they'll struggle CONNECTED TO EXPECTATIONS | Align Profile to Performance ↳ Link their capabilities to role requirements ↳ Identify natural fits and development needs ↳ Spot blind spots before they become problems WITH TARGETED DEVELOPMENT | Focus on What Matters Most ↳ Build 90-day plans for 2-3 key capabilities ↳ Adjust based on real data, not assumptions ↳ Track progress with specific milestones INSTANT PATTERN RECOGNITION | Spot What You'd Otherwise Miss ↳ Upload weekly updates, KPIs, and meeting notes ↳ Get early warnings on performance shifts ↳ Let AI identify trends across time PERSONALIZED COACHING | Tailor Your Approach ↳ Get AI-suggested coaching topics for each person ↳ Customize feedback delivery to their profile ↳ Make every 1-on-1 more impactful The AI advantage: It never forgets context.  It spots patterns across months of data.  It grounds your coaching with homework, not guesswork. The 7-step framework: 1. Create high-fidelity employee profiles 2. Connect profiles to role expectations 3. Build targeted development plans 4. Upload ongoing performance data 5. Get AI-suggested coaching 6. Tailor feedback to their profile 7. Track progress and iterate [Get my starter prompts from the carousel below] Better yet: Join our Free Lighting Lesson next week.  And we'll build one together.  In under 30 minutes. November 13th at 1 PM ET: https://lnkd.in/e3h3aRDa A few more tips: • Keep one AI thread per employee for context • Upload data weekly, not just when problems arise • Use AI insights to inform your coaching, not replace it The truth about great management: Most managers react to problems after they happen. Great managers predict and prevent them. Better information leads to better decisions. Better decisions lead to high-performing teams. 📕 Save in case you want to build this out later.  ♻️ Share to help other managers connect with their people. 🔔 Follow Dave Kline for more AI-powered management strategies.

Early Sunday mornings are usually my time I make space to think more deepy about few key areas. Today I looked at two things — global LNG market trends, and ADNOC Gas own market performance. For both, I used AI agents I’ve built in Copilot, where I’ve been feeding in analyst reports, market updates, and our own data (within secured platform). What used to take me hours is now done faster and with a wider perspective. But I never take it at face value — human judgment, context, and experience are still critical. For me, this is a real example of AI for People (one of ADNOC’s AI Strategy Pillars) in action: giving us tools that make us sharper and more efficient, while still relying on our own and expert’s judgment to make the right call. The second pillar is Energy for AI. AI itself is hugely energy-intensive, and data centers are only growing. Here, ADNOC Gas plays a central role: we already supply 60% of the UAE’s gas needs, and we’re investing to increase capacity by 30%. Supplying the energy that powers AI is part of our contribution to this transformation. Finally, there is AI for Energy — using AI to run our operations smarter, safer. This is where we’ve built focused programs across our business: Planningai, Operationsai, Maintenance/HSEai, and Corporateai. Two examples from ADNOC Gas show what this looks like in practice: • The Centralized Predictive Analytics Diagnostics CPAD system, which monitors more than 500 rotating machines to catch problems before they become failures, cutting costs and avoiding downtime. • The Neuron 5 platform, already running on 20% of our critical equipment, using deep learning on sensor data to predict maintenance needs. These are not Ideas or conepts — they are already part of daily operations, helping us improve efficiency, safety, and reliability. Step by step, this is how ADNOC Gas is becoming an AI-native company. Reuters events published special report on how ADNOC Group is embedding AI across its downstream operations worldwide to accelerate innovation and performance (report attached) What about you? How do you see AI being integrated into your life and the operations of your business? #AI #EnergyTransition #ADNOCGas #PredictiveMaintenance #OperationalExcellence

Your CFO wants to know the return on your software development budget? Here are 5 metrics that actually matter in the boardroom - and they're not story points. As a CTO, I've found these key metrics create a meaningful fitness function for your development organization: 1. Business Value per Feature: Don't just ship features - measure their impact. That new checkout process? Track how it changes conversion rates and order values. 2. Lead Time from Idea to Impact: Understand your value stream. Sometimes a 30-minute deployment is stuck behind weeks of stakeholder meetings. 3. Throughput and its composition: Monitor the balance between new features, maintenance, and bug fixes. When maintenance exceeds 25%, it's time to invest. 4. Quality Signals: Track customer experience, operational efficiency, and technical health. These are your early warning system. 5. Team Health: Happy teams deliver better results. Regular pulse checks predict delivery performance weeks before metrics show issues. But never compare teams through these metrics. Each team operates in a unique context with different challenges. Instead, help each team understand and improve their own trends. Metrics should drive improvement, not punishment. Use them as a compass, not a hammer. What metrics do you use to measure development success?

As a CTO who has successfully scaled AI and tech products, I’ve refined productivity strategies that can transform your leadership workflow and enhance your team’s output. If you’re leading in the tech industry, and grappling with overwhelming demands, the 3 targeted tactics I’m about to share are tailored for the unique challenges you face. My guiding principle each week is the 'Rule of Three': identifying three top priorities that serve as my North Star. These aren't just scribbled in a planner but physically placed on my office wall, a constant visual reminder of my core focus. This practice not only keeps me centered amidst the whirlwind of daily tasks but also ensures that every action is a step toward our most critical goals. Sharing these priorities with my direct reports does more than foster transparency — it aligns our efforts, synchronizes our strides, and forms the bedrock of our collective pursuit. It's a simple yet profoundly effective strategy that has continually steered us toward meaningful progress and impactful results. Next, time blocking has been a critical strategy. Carving out dedicated blocks for deep work, meetings, and even unexpected tasks allows me to create a rhythm amidst the chaos. This isn't just about sticking to a schedule; it's about allocating mental space and ensuring that high-priority projects get the uninterrupted attention they deserve. I always check each Friday that my time blocked schedule appropriately reflects the work I need to accomplish for my top three priorities. Lastly, I leverage automation and delegation. By automating routine tasks and delegating effectively, I maintain focus on what truly requires my expertise. It's not just about offloading work; it's about empowering my team by entrusting them with responsibilities that aid their growth while freeing me to lead more effectively. A framework I really like using is the Eisenhower matrix around categorizing work based on its urgency and importance. I try and focus as much of my work as I can on the important and urgent tasks. Implementing these strategies hasn’t just boosted my personal productivity; it sets a precedent for the whole team. When leaders manage their time effectively, it cascades down, fostering a culture of efficiency and clarity. Remember, in the world of tech and AI, where the ground shifts daily, these strategies aren't just nice-to-have—they're essential for survival and success. If you're leading in this space and looking to refine your approach to productivity, let's connect and share insights that propel us forward! #techleadership #productivitytools #teamleader

Ask any technologist what makes for a great work experience, and their answer will most likely mirror this phrase: “the simpler, the better.” At Chase, we challenge ourselves to really simplify our monitoring mechanisms and automate our manual processes. One area that we have found best serves not only our employees, but also our customers, is automating our certificate renewals process. Why is this something I fully support? - More time to ideate: It’s vastly more complicated to work in software engineering compared to when I started my career decades prior. There’s so much more to do and monitor in our daily jobs. Finding ways to automate manual processes is only going to make our lives easier. - Minimal customer downtime: Manual processes leave us open to easily avoidable errors. By automating our manual processes, we’re less likely to miss a needed renewal, and in turn avoid downtime for our customers. - Maximum security: Failure to renew important certificates leaves us open to data breaches and security vulnerabilities. It takes a long time to build trust, yet seconds to destroy it. Keeping our data secure is of most value, and it’s something we owe to our customers. Do you have any thoughts on cert automation? Reply below! #tech #security #innovation

90% of companies track cloud cost. But they miss the real signals of infra performance. Your AWS bill is not a KPI. It’s a lagging symptom. If you're tracking just the cost, you’re 2 incidents too late. Here are 7 𝐊𝐏𝐈𝐬 𝐰𝐞 𝐭𝐫𝐚𝐜𝐤 for every single business. 1. 𝐔𝐧𝐢𝐭 𝐂𝐨𝐬𝐭 𝐩𝐞𝐫 𝐃𝐞𝐩𝐥𝐨𝐲 How much infra are you burning per release? If this isn’t improving, your platform isn’t scaling. It’s bloating. 2. 𝐈𝐝𝐥𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐑𝐚𝐭𝐢𝐨 Not just CPU or memory. We measure provisioned vs. consumed by workload class. This tells us exactly where to optimize capacity without touching autoscalers. 3. 𝐌𝐞𝐚𝐧 𝐓𝐢𝐦𝐞 𝐓𝐨 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 (𝐌𝐓𝐓𝐑) 𝐛𝐲 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 Nobody wants to own this. But we break it down per team + service. The gaps reveal way more than your uptime reports ever will. 4. 𝐑𝐨𝐥𝐥𝐛𝐚𝐜𝐤 𝐑𝐚𝐭𝐢𝐨 (𝐩𝐞𝐫 100 𝐝𝐞𝐩𝐥𝐨𝐲𝐬) We don’t just track how often it happens. We track why it happens (infra drift, pipeline bugs, or config issues) Rollback is the silent killer of developer trust. 5. 𝐇𝐨𝐭 𝐏𝐚𝐭𝐡 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐕𝐨𝐥𝐚𝐭𝐢𝐥𝐢𝐭𝐲 Not average latency. Not p95. We track volatility across hot paths. Especially during deploys, autoscaling, or burst traffic. This exposes hidden platform debt. 6. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐒𝐚𝐟𝐞𝐭𝐲 𝐒𝐜𝐨𝐫𝐞 A composite we built: - Change failure rate - Config diff size - Service dependency blast radius - Rollback history - Team experience Helps us predict risk before a deployment happens. 7. 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐂𝐨𝐯𝐞𝐫𝐚𝐠𝐞 % Not just “we use Datadog”. We audit how much of the infra is actually traced, logged, and alerted on.. Most setups cover 20–30% of what really matters. If you're not tracking these, you're not running infra. You're running blind. Want to actually get proactive about your platform? Fix your KPIs first.

When we think about improving an online shopping experience, we often jump to new features or a better design. But behind the scenes, web performance—how fast and smoothly a site loads and responds—is one of the biggest drivers of user satisfaction and business impact. In a recent blog post, Walmart Global Tech’s Engineering team shared their journey to improve their site’s performance systematically, and the lessons are highly relevant for anyone building digital products today. Their journey began with choosing the right metrics. Instead of relying solely on backend or infrastructure-level indicators, they shifted toward Core Web Vitals, a set of user-centric metrics that reflect the real customer experience. These include how quickly content loads (Largest Contentful Paint), when the page becomes interactive (Interaction to Next Paint), and how stable the layout is during loading (Cumulative Layout Shift). By anchoring their efforts in these three metrics, the team ensured that any optimization directly improved what customers actually felt. From there, they focused on how to move these metrics across a massive user base meaningfully. The team set goals based on the 75th percentile, ensuring that improvements benefited most users and weren’t overly influenced by outliers. They also embedded web performance into company-wide decision-making: Core Web Vitals are integrated into Walmart’s experimentation platform, incorporated into the release review process, and included in leadership discussions. In other words, performance isn’t just an engineering KPI—Walmart turned it into a shared organizational priority. This work is a great reminder that improving performance isn’t just an engineering task; it’s about building a culture where user experience is measurable, visible, and owned by everyone. Their approach shows that when a company aligns around the right metrics and integrates them into everyday workflows, even small performance gains can compound into meaningful business results. #DataScience #Analytics #Metrics #CoreWebVitals #Optimization #WebPerformance #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gqsfix-p