Productivity

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

🔎 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗱𝗲 𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹 AMD 𝗰𝗵𝗶𝗽! 😲 Here's a bit of a Ryzen processor made on TSMC's 7-nanometer node. You can see the web of interconnects, the metal wires that connect the transistors (that bottom layer) on a chip to harness their computing power. The image was taken with a new 𝗽𝘁𝘆𝗰𝗵𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗫-𝗿𝗮𝘆 𝗹𝗮𝗺𝗶𝗻𝗼𝗴𝗿𝗮𝗽𝗵𝘆 (𝗣𝘆𝗫𝗟) technique out of the PSI Paul Scherrer Institut, University of Southern California and ETH Zürich. The technique currently has 4 nanometer resolution and the scientists have a path to get to 1 nm resolution. The cool thing about this technology is its non-destructive imaging power to help find defects in chips. Today’s chips are so complicated that electrical tests alone can no longer pinpoint where a defect is: chipmakers use a mix of optical imaging and other methods to zero in on potential problem areas. They then image such areas with a slow but very high-resolution scanning electron microscope. Finally they might take a slice of a chip for further imaging with a transmission electron microscope (TEM). When they find the flaw, they can then go back and correct their design. But with PyXL, they have another tool to pinpoint defects without destroying the chip. ✨

Either you control it, or it will control you! Our bodies and minds have limits, and ignoring the need for rest can lead to significant consequences. When we push ourselves too hard without taking regular breaks, we risk burnout, decreased productivity, and health problems. This forced downtime often occurs at the worst possible moments, disrupting our personal and professional lives. So, please: Schedule Regular Breaks: Integrate short breaks into your daily routine. For example, use the Pomodoro Technique—work for 25 minutes, then take a 5-minute break. After four cycles, take a longer break of 15-30 minutes. Prioritise Sleep: Ensure you get 7-9 hours of sleep each night. Good sleep hygiene, such as a regular bedtime and limiting screen time before bed, can improve sleep quality. Take Vacations: Plan and take regular vacations to recharge. Even short getaways can significantly impact your mental and physical health. Listen to Your Body: Pay attention to signs of fatigue, stress, and burnout. If you feel overwhelmed, take a step back and rest, even if it's just for a few hours. Incorporate Wellness Activities: Engage in activities that promote relaxation and well-being, such as exercise, meditation, hobbies, or spending time in nature. Set Boundaries: Learn to say no and set boundaries to protect your time and energy. Avoid overcommitting and ensure you have time for rest and recovery. By proactively scheduling breaks and prioritising self-care, you can maintain your health, enhance productivity, and avoid inconvenient and disruptive forced breaks.

In companies where productivity has increased by 50%, creativity has doubled, and employee satisfaction is at an all-time high, one surprising change stands out: ditching the outdated obsession with time tracking. Too many managers are stuck in an outdated paradigm, fixating on: • When employees clock in • How long they sit at their desks • Micromanaging daily schedules But we’ve hired smart, capable professionals. Treating them like children who need constant supervision is not just demeaning – it's counterproductive. However, it's crucial to maintain a balance. While micromanagement is detrimental, companies still need to ensure discipline and focus on key priorities. The goal is to empower employees while aligning their efforts with organizational objectives. That’s why one needs to focus on result-focused management: 1. Shift your metrics: Focus on project milestones, work quality, and client satisfaction instead of hours logged. 2. Embrace flexibility: Allow flexible hours and remote work when possible. Trust employees to manage their time effectively. 3. Cultivate a culture of trust: Communicate openly about priorities and challenges. Reward results, not face time. Promote work-life balance and well-being. Companies like Netflix, Basecamp, and Atlassian have implemented results-only work environments (ROWE) with remarkable success. They report higher employee engagement, better outcomes, and a more dynamic, innovative workplace culture. What's one positive outcome you've experienced (as a manager or employee) when given more autonomy at work? #Leadership #EmployeeEmpowerment #WorkplaceCulture

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

Want to stay motivated every single day? Borrow a strategy from Harvard. Then borrow another from stand up comedy. Together, they’re a powerhouse for momentum, motivation, and mastery. Here’s how it works: Let’s start with Harvard. Researcher Teresa Amabile studied 12,000 daily work diaries across 8 companies. She wanted to know: What truly motivates people on a day to day basis? What she found changed how we understand drive. The #1 driver of daily motivation wasn’t: Money Praise Perks It was progress. The days people made progress on meaningful work were the days they felt the best. Progress isn’t a luxury. It’s a psychological necessity. So how do we make progress feel visible especially on days when it’s not? Use a “Progress Ritual.” → At the end of the day, pause. → Write down 3 small ways you moved forward. → That’s it. No fanfare. Just ritual. This works because we rarely notice our progress in real time. It gets buried under busyness, meetings, and mental noise. The act of looking back gives your brain the reward it needs to keep going. Momentum builds from meaning. Now let’s add some comedy. Young Jerry Seinfeld had one goal: write new material every day. To stay on track, he created a brilliant system. Each day he wrote, he put a big red X on his calendar. Soon, a chain of Xs formed. And here’s the key: Don’t break the chain. One red X becomes two. Two becomes ten. Ten becomes identity. Whether you’re writing, coding, or training Daily action + visual chain = long-term motivation. Summary: The Two-Part Motivation System From Harvard: Record 3 ways you made progress each day. From Seinfeld: Mark an X for each day you show up then don’t break the chain. Progress fuels purpose. Consistency fuels confidence. Apply both and you’ll stay on track especially on the tough days. Because when your days get better, your weeks get better. When your weeks get better, your months get better. When your months get better, your life gets better. It starts with one small win today.

People often ask how we manage complex projects as a team of 100 people in 35 countries, and since I'm currently revamping our documentation on this subject, that info is top of mind. Here's 29 pages of content condensed into 1 LI post for a sneak peek into our DO (Doist Objectives) System 👀 It starts with our annual roadmap, which the leadership team builds in Q4 of the prior year. To execute that plan, we organize our work into four areas of priority (Strategic Priorities, aka SPs), each running multiple initiatives simultaneously in quarterly "cycles", and overseen by a Directly Responsible Doister (DRD): • Brand (DRD: CMO): Marketing campaigns, brand evolution, growth initiatives • Product (DRD: Head of Product): New features, user experience improvements, product strategy • Engineering (DRD: CTO): Platform stability, performance optimization, technical infrastructure • Doist (DRD: 🙋🏻♂️): Internal tools, company operations, team effectiveness Planning kicks off four weeks before each quarter when the CXOs provide the DRDs with general guidance and goals. We respond by proposing general plans for DOs (Doist Objectives; projects/initiatives) in line with our annual roadmap. Two weeks before the new quarter begins, the DOs are agreed upon and the team Heads assign team members to cross-functional "Squads" as "Squad Leaders" and "Squad Members". **See photos below to illustrate the squad infrastructure. Each SP typically runs 2-5 major DOs per quarter, meaning we're executing 12-16 significant projects at any time. The quarter begins with a two-week "Foundation Phase", where squads: • Deep dive into the challenges and opportunities their squad faces • Conduct user research • Create comprehensive specs detailing their proposed solutions • Align on execution approach • This phase ensures we have the space to avoid diving too deep into the upcoming cycle while working on the current cycle From there, squads maintain momentum for the following 10 weeks in the "Execution Phase" through established rituals: • Weekly "snippets" in Twist for progress updates and transparency (our version of an async standup meeting) • Bi-weekly recorded demos to showcase work in-depth • Monthly retrospectives on squad health for continuous improvement • Monthly companywide updates on each strategic priority's DOs • Monthly strategic reviews/adjustments by the leadership team • Expectation = each squad should "ship" something weekly Of course, we manage most of this using Twist for communication and Todoist for project management, but more so than the tools, this system works for us because we emphasize clear ownership/autonomy, transparent communication, and just enough processes to stay coordinated without slowing the team down. That was a lot to digest, but I hope it's helpful. Let me know if I can expand on anything or answer any other questions 👇

Innovation isn’t just about upgrading your tools—it’s about reinventing how you create, deliver, and capture value. Digital business models are reshaping industries by creating value in ways unimaginable a decade ago. These aren't your grandparent’s business models with a digital veneer—they're transformative, leveraging tech to disrupt markets, engage customers, and redefine competition. This revolution is captured brilliantly in the book: 𝐷𝑖𝑔𝑖𝑡𝑎𝑙 𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑀𝑜𝑑𝑒𝑙𝑠 𝑓𝑜𝑟 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 4.0: 𝐻𝑜𝑤 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 𝑎𝑛𝑑 𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑆ℎ𝑎𝑝𝑒 𝑡ℎ𝑒 𝐹𝑢𝑡𝑢𝑟𝑒 𝑜𝑓 𝐶𝑜𝑚𝑝𝑎𝑛𝑖𝑒𝑠. 𝐅𝐨𝐮𝐫 𝐏𝐢𝐥𝐥𝐚𝐫𝐬 𝐨𝐟 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐌𝐨𝐝𝐞𝐥𝐬: • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥𝐥𝐲 𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐕𝐚𝐥𝐮𝐞 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧: Value driven by tech, not just supported by it. Think smart thermostats optimizing energy, not just controlling it. • 𝐌𝐚𝐫𝐤𝐞𝐭 𝐍𝐨𝐯𝐞𝐥𝐭𝐲: New offerings or ways of doing business—like predictive maintenance or on-demand manufacturing. • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐓𝐨𝐮𝐜𝐡𝐩𝐨𝐢𝐧𝐭𝐬: Customer relationships built through apps, IoT, and connected services. • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥𝐥𝐲 𝐃𝐞𝐫𝐢𝐯𝐞𝐝 𝐔𝐒𝐏: Unique selling points rooted in data and digital capabilities. But how do we map the revenue streams emerging from these shifting dynamics? I’ve come to see it through three essential components: • 𝐂𝐨𝐫𝐞 𝐕𝐚𝐥𝐮𝐞 𝐏𝐫𝐨𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 (What is being offered?) • 𝐕𝐚𝐥𝐮𝐞 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦𝐬 (How is value created?) • 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐒𝐭𝐫𝐞𝐚𝐦𝐬 (How is value captured?) 𝐑𝐞𝐚𝐝 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/ewhRUM28 ******************************************* • 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!

My biggest takeaways from Ethan Smith on how to win at AEO (i.e. get ChatGPT to recommend your product): 1. Being mentioned most often beats ranking first. In Google, the #1 blue link wins. In ChatGPT, the answer summarizes multiple sources—so appearing in five citations beats ranking #1 in one. Ethan’s strategy: get mentioned on Reddit, YouTube, blogs, and affiliates. Volume of mentions matters more than any single placement. 2. LLM traffic converts 6x better than Google search traffic. Webflow saw this dramatic difference because users who come through AI assistants have built up much more intent through conversation and follow-up questions, making them highly qualified leads. 3. Early-stage startups can win at AEO immediately, unlike with SEO. Traditional SEO requires years of domain authority. But a brand-new Y Combinator company mentioned in a Reddit thread today can show up in ChatGPT tomorrow. The playing field is finally level. 4. The long tail of AEO is 4x bigger than SEO. People ask ChatGPT questions with 25 or more words (vs. 6 in Google). Ethan found gold in queries like “Which meeting transcription tool integrates with Looker via Zapier to BigQuery?”—questions that never existed in search but are perfect for AI. Own these micro-niches. 5. Reddit is proving to be the kingmaker for AI visibility. ChatGPT trusts Reddit because the community polices spam better than any algorithm. Ethan’s exact playbook: make one real account, say who you are and where you work, give genuinely helpful answers. Five good comments can transform your visibility. No automation, no fake accounts—just be helpful. 6. YouTube videos for “boring” B2B terms are a gold mine for AEO. Nobody makes videos about “AI-powered payment processing APIs”—which is exactly why you should. While everyone fights over “best CRM software,” the high-value, zero-competition long tail is wide open in video. 7. Your help center is now a growth channel. All those “Does your product do X?” questions flooding ChatGPT can be answered by help-center pages. Move them from subdomain to subdirectory, cross-link aggressively, and cover every feature question. Ethan calls this the most underutilized opportunity in AEO. 8. January 2025 was the inflection point in AEO growth. That’s when ChatGPT made answers more clickable (maps, shopping cards, citations) and adoption exploded. Webflow went from near zero to 8% of signups from AI. This channel is accelerating faster than any Ethan’s seen in 18 years. 9. The AEO playbook: (1) Find questions from competitor paid search data, (2) set up answer tracking, (3) see who’s showing up as citations, (4) create landing pages answering all follow-up questions, (5) get mentioned offsite via Reddit/YouTube/affiliates, (6) run controlled experiments, (7) build a dedicated team. This exact process is driving real results at scale.

McKinsey & Company shows how Danone turns operations into a growth engine. A sharp interview by Pierre de la Boulaye and Søren Fritzen with Vikram Agarwal highlights a structural shift across the FMCG industry. For decades, operations were treated as a cost center. That paradigm is changing. Leading companies now position operations as a driver of growth and competitiveness. The transformation at Danone shows how AI, digital manufacturing and advanced supply chains are reshaping the sector. Several insights stand out. 1) AI turns factories predictive Operators increasingly monitor production lines via tablets instead of control rooms. AI systems detect potential equipment failures before they occur, for example overheating motors in packaging lines. Maintenance shifts from reactive repair to predictive intervention, improving uptime and efficiency. 2) Capacity planning becomes strategic Danone distinguishes three ways to build manufacturing capacity: • Release capacity from existing assets • Transform capacity by converting underperforming lines • Create capacity through new production investments Transforming existing lines enables growth with much lower capital intensity than building new factories. 3) AI reshapes supply chains Danone uses AI models to forecast ingredient costs and supply chain dynamics across global agricultural markets. Instead of analyzing thousands of variables, systems process millions of data points. For a company managing roughly €13.7B in COGS, forecasting accuracy becomes a competitive advantage. 4) Digital manufacturing at scale Danone’s Digital Manufacturing Acceleration program already covers 80+ factories, with 40 more joining soon, across 140+ production sites globally. The ambition goes beyond Industry 4.0 toward Industry 5.0, combining machines, AI and human expertise. 5) People remain central Danone employs 47,000+ people in operations, about half of its workforce. Through its Industry 5.0 Academy, the company has already trained around 20,000 employees in digital manufacturing capabilities. Why this matters The global FMCG industry generates over $4 trillion in annual sales and operates on tight margins. Even small improvements in forecasting, manufacturing efficiency or capacity utilization can translate into billions in value creation. As demand shifts toward health, high-protein and plant-based products, supply chains must become faster and more flexible. AI-driven operations are becoming a strategic advantage. The signal for FMCG leaders is clear: Competitive advantage is increasingly built beyond brands and marketing — in operations. #operations #manufacturing #ai #digitaltransformation #foodindustry #foodtech #retailtech #innovation #procurement #datadriven #danone #france #europe #startup #investors #marketing #sales #technology #logistics