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

It’s simple math 🧐 I use to think that motivation was the key to monumental success. Long story short, it’s not. It’s about the little things you do every day that will take you from reasonable to slightly unreasonable to completely unreasonable progress. Your future is not defined by how motivated you are, but by your daily routines and systems. I believe in this so much that we named our company Butterfly 3ffect to reflect the value of incremental gains. we believe that that’s how the best people and brands grow. Here’s how you grow the small way: 1. Start by setting achievable goals, like reading one chapter of a book each day or going for a short walk 2. Practice gratitude by writing down three things you're thankful for every night before bed 3. Engage in daily self-reflection, even if it's just for a few minutes, to assess your thoughts and actions 4. Incorporate small acts of kindness into your daily routine, like holding the door for someone or offering a genuine compliment 5. Learn something new every day, whether it's a fun fact, a new word, or a new skill 6. Prioritise self-care by getting enough sleep, staying hydrated, and taking breaks when needed 7. Surround yourself with positive influences, whether it's uplifting books, supportive friends, or inspiring podcasts 8. Embrace failure as a learning opportunity and a stepping stone to growth 9. Stay consistent and patient, knowing that small progress over time adds up to significant improvement 10. Celebrate your achievements, no matter how small, to stay motivated and encouraged along the way.

Do you feel guilty about taking time off? I used to spend weekends, trips, and lunch breaks (!!) terrified that I was falling behind. I had to constantly fight the compulsion to get back to my inbox. Now I remind myself: Your mental health is the foundation for your ability to do great work. We often think of vacations or breaks as rewards we need to earn. This is backward thinking. Your wellbeing is what allows you to achieve your goals. A successful career depends on you having rested enough to be creative, show up for others, and make good decisions. It sounds obvious but it bears repeating: When you fail to take the time you need to recharge, you set yourself up to fail.

The ability to create clarity when there’s no shortage of chaos, opinions, and competing priorities is a rare skill. In any reasonably competent company, this skill alone will help take you quite far, fairly quickly. Concretely, this means creating clarity on the main problems, clarity on the right solutions, and clarity on the action plan & priorities. Very few people can do this well even though most people possess the intelligence necessary to do it. This is because most people in the workplace have been conditioned to add more information, sound more clever, satisfy more stakeholders, and feign more precision & certainty than is possible. Few understand that clarity in a chaotic situation can only emerge from subtraction, never from addition. Clarity comes from communicating what stands out as most important, why it is most important, how it will be achieved, and last but not the least, giving people a way of thinking about why it is okay, even great, that we aren’t doing All The Other Things.

I built an AI agent that handles my entire inbound system. (And I used to be against automation). Here's how I did it: I used two tools: --> Make: For automation workflows --> Relevance: For AI agents Here's what my AI agent handles: When someone fills our form, it- --> Analyzes their LinkedIn profile --> Reviews their website --> Checks if they match our criteria --> Makes a decision in seconds For qualified leads: --> Sends personalized pitch deck --> Books discovery calls --> Handles initial questions For non-qualified leads: --> Sends a thoughtful rejection --> Explains why we're not the right fit --> Keeps the door open for future The best part? My team and I can focus on what matters - strategy and client success - instead of spending hours on admin work. No more: -Manual lead checking -Back-and-forth emails -Calendar scheduling headaches -Just high-quality conversations with pre-qualified founders. Want to know the biggest lesson? Automation isn't about replacing the human touch. It's about creating more time for it.

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.

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

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!

By now, the "95% failure rate" of GenAI financial returns (ref MIT's Project NANDA) is part of all consulting decks. The report blames the incorrect approach as the primary reason, rather than model maturity, etc. The key is to understand what #ROI metrics are used to determine the financial returns. I asked #Copilot on this, and here's what it told me: --- Here are three examples of ROI frameworks that enterprises are using to evaluate and scale GenAI adoption effectively: 1. Business Outcome-Based ROI Framework (Gartner) Summary: Gartner recommends aligning GenAI initiatives with measurable business outcomes such as cost reduction, revenue growth, or productivity gains. For example, a retail company using GenAI for automated product descriptions tracked a 22% increase in conversion rates and a 15% reduction in content creation costs. The framework emphasizes setting baseline metrics before deployment and tracking improvements post-implementation. 🔗 https://lnkd.in/dER7cTeF 2. Time-to-Value and Efficiency Metrics (BCG) Summary: Boston Consulting Group suggests using time-to-value (TTV) and operational efficiency as key ROI indicators. In one case, a logistics firm used GenAI to optimize routing, reducing delivery times by 18% and fuel costs by 12%. BCG’s framework includes pre/post comparisons, automation impact, and employee productivity metrics to quantify GenAI’s contribution. 🔗 https://lnkd.in/da2zcSfW 3. Model Performance vs. Business KPIs (McKinsey) Summary: McKinsey advocates for linking GenAI model performance directly to business KPIs. For instance, a financial services firm used GenAI for customer support automation and tracked resolution time, customer satisfaction scores, and call deflection rates. The framework includes continuous monitoring of model accuracy, relevance, and business impact. 🔗 https://lnkd.in/dA6zEGuS 🔑 Key Message Summary Effective GenAI ROI frameworks combine technical performance metrics with business impact indicators. Leading approaches include tracking cost savings, productivity gains, time-to-value, and alignment with strategic KPIs. Enterprises that define success upfront and monitor outcomes continuously are more likely to scale GenAI successfully. --- The direction taken seems to be well-intentioned. However, the measure of success is not quite what might lead to real solid business outcomes! Individual productivity improvements are just that! They don't scale across the organization unless "vertically scaled" top-to-down an entire process delivering bottomline improvements, which then need to be further "horizontally scaled" end-to-end across the entire value chain of the firm to deliver topline value! My forthcoming book on Cognitive Chasm provides actionable guidance to practitioners on this.