Implementing Kaizen In Workplace

Most companies building AI agents today skip the most important step: Designing the Standard Operating Procedure (SOP). This LangChain visual breaks down the end-to-end lifecycle of building a production-grade agent. And it quietly highlights why most projects fail to move past the prototype stage. Everyone wants to jump into prompt engineering or integration. But the real unlock comes earlier: 𝗖𝗮𝗻 𝘆𝗼𝘂 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗮 𝗵𝘂𝗺𝗮𝗻 𝘄𝗼𝘂𝗹𝗱 𝗳𝗼𝗹𝗹𝗼𝘄? That’s what the SOP stage is for. It forces you to: 1/ Translate messy business logic into structured steps 2/ Capture the tacit knowledge hidden in teams 3/ Build something reliable, repeatable, and scalable Without that foundation, you’re just building a brittle demo. I’ve seen this play out across email agents, finance co-pilots, HR assistants...you name it. LangChain gets this right. I like their 6-stage framework. It isn’t flashy, but it’s operationally practical. Where do you see most AI agent efforts getting stuck?

🌱 Sustainable UX Toolkits & Resources (https://lnkd.in/eT6ZR3qz), a large (!) repository of toolkits, Figma templates, books, case studies, articles on sustainable UX — throughout the entire product design process. Kindly put together by the SUX - The Sustainable UX Network, via Thorsten Jonas. Sustainable UX Database (Notion) https://lnkd.in/eyZjigBx As designers, we often are left wondering how to integrate sustainable practices into our design work. Most environmental impact happens on our user’s devices, so we can help our users by reducing waste. Typically, when we speak about sustainability, we mean at least 4 facets of it: 🌱 Reducing waste ← In publishing, heavy visuals, animation, PDFs, 🌻 Deleting content ← Un-publishing outdated, misleading content/flows, 🐝 Maximize reusability ← UI components, flows, processes, templates, 🌳 Sustainable defaults ← Help people make more sustainable choices. In practice, we could use simple but impactful design patterns: 1. Always prefer the lightest mode of communication. 2. Aim to reduce session duration instead of increasing it. 3. Encourage the reuse of existing templates and presets. 4. Auto-delete after 365 days what hasn’t been used once. 5. Discourage users from PDF exports in favor of URLs. 6. Always provide audio-only and transcript for videos. 7. Be intentional with default settings for your users. 8. Highlight key insights to create understanding faster. 9. Skip unnecessary pages: drive users to results faster. 10. Show filters/presets in autocomplete, not just keywords. 11. Nudge users to delete old files for 10% off that month. 12. Establish an archiving, deletion and clean-up policies. 13. Encourage and reward users for trying out dark mode. 14. Question font weights, stock photos, parallax, 4K-videos. 15. Question collected data, if it’s used and when it’s deleted. Individual actions can drive changes at scale. But they need a momentum. And momentum often comes through small changes: better defaults, reused filters and templates, reduced time on task. That’s also just good usability — and can have tangible impact for users and businesses at scale. Useful resources: Sustainable UX Toolkits, by yours truly https://lnkd.in/ePya82v3 Designing For Planet Knowledge Hub (Notion) https://lnkd.in/eiHtpkJH Product Design for Sustainability (+ Google Doc template), by Artiom Dashinsky ↳ https://lnkd.in/dDnujb-t ↳ https://lnkd.in/d95FWb4r *HUGE* thanks to Thorsten Jonas, Isabel Pettinato, Christoph Stark, Alice M., Bavo Lodewyckx, Poppe G., Stine Ramsing and all wonderful contributors to the project. Your effort doesn’t go unnoticed! 👏🏼👏🏽👏🏾 #ux #design

As an engineer, you must understand the performance and memory ramifications of the code you’re producing. Sometimes naive algorithms can yield terrible results and simple changes can yield massive improvements. This becomes even more important as more of us leverage LLMs to write code... and think less... Have you ever heard of Reservoir Sampling? I found this little gem yesterday - it’s an oldie but timeless example. Back in 2013, I was facing a little technical problem. I needed to keep track of minute-level metrics for a load generator: things like counters for overall number of transactions executed, num of transactions failed, and p0, p50, p90, p99, p100 latency of each transaction type. Counters were easy, just a running sum. But percentiles were more complex, because you need all data points to calculate them. These were critical to make decisions, such as: if the system under test is compromised (too many transactions failing, too much latency), abort the run and fail the test. My initial implementation for percentiles was pretty naive. It simply kept track of each individual metric in an arraylist and every minute a background thread went through all data points and calculated the percentiles. As we started using the load generator to generate larger loads, it was clear that that piece of code was not going to cut it. It started bottlenecking at 3000 TPS (transactions per second). That made sense. Let’s say I was keeping track of 6 different metric types: 3000 x 60 x 6 = over a million doubles in memory every minute!!! That was both memory intensive and CPU intensive. The JVM had to run the Garbage Collector frequently, and even the OS had to page things out to disk. And that wasn’t even the main task… it was a background thread! Reservoir Sampling is a randomized algorithm for selecting k items from a stream of n items, where n is either very large or unknown beforehand. The key feature is that it maintains a uniform random sample at any point during the stream processing, with constant memory. It provided a simple tradeoff: give up a statistically negligible amount of precision but gain constant running time and memory usage. Here’s the “before” and “after”: it went from maxing out at 3000 TPS to maxing out at 8000 TPS. Reservoir Sampling is not a complex algorithm. It’s actually a trivial one (adding it in comments). But you had to (1) be aware that your naive approach was rudimentary and flawed, and (2) know that there were a set of algorithms that could help. Understanding memory and CPU usage of each line of code you’re producing is even more important today than ever before - as we rely more and more on our GenAI Overlords.

Unnecessary bureaucracy at work is not only time-consuming, it is also deeply annoying, and sometimes even experienced as demeaning. What if workers get an opportunity to express which bureaucratic rules and procedures they find most annoying and unnecessary? And what if employers took these concerns seriously and reconsidered these bureaucratic elements? Five economists recently put this idea to the test. They worked together with a large bakery chain in Germany where workers are required to complete a large number of checklists on a regular basis. A survey among workers identified two checklists that were considered as particularly time consuming and low-value. Next, the company removed these two checklists in a random half of the bakeries. Guess what happened... As compared to the 'business-as-usual' control bakeries, bakeries where checklists were removed saw: 🔹sales increase by 2.7% 🔹no observable increase in wasted food, coordination failures, or employee misbehavior 🔹higher customer satisfaction 🔹increased worker trust and commitment 🔹lower attrition of store managers No surprise the company decided to remove the two checklists in the other half of the bakeries as well. What is better than removing unnecessary bureaucracy? Having no unnecessary bureaucracy in the first place. A way to achieve this is to first test new rules and procedures at small scale, and only after proven success implement them. Randomized experiments can play an important role here. Economists are happy to help organizations design such field experiments. Read the full study here: Guido Friebel, Matthias Heinz, Mitchell Hoffman, Tobias Kretschmer, and Nick Zubanov (2024), Is This Really Kneaded? Identifying and Eliminating Potentially Harmful Forms of Workplace Control, working paper: https://lnkd.in/dqvFAKeT

KEY SIX SIGMA TOOLS VS. THEIR PURPOSES: DMAIC (Define, Measure, Analyze, Improve, Control) – A structured problem-solving approach for process improvement. DMADV (Define, Measure, Analyze, Design, Verify) – Used for designing new processes/products with Six Sigma quality. SIPOC Diagram – Identifies Suppliers, Inputs, Process, Outputs, and Customers to understand process scope. Process Mapping – Provides a visual representation of workflows to identify inefficiencies and improvement areas. Pareto Chart – Prioritizes problems using the 80/20 rule, focusing on major issues first. Fishbone Diagram (Ishikawa) – Categorizes potential root causes of problems for root cause analysis. 5 Whys – A simple method to identify root causes by repeatedly asking "Why?" Failure Mode and Effects Analysis (FMEA) – Identifies potential failures and their impact, allowing preventive actions. Control Charts – Monitors process stability and variations over time using statistical control methods. Histogram – Displays data distribution to analyze process performance and variations. Regression Analysis – Determines relationships between variables to optimize process outcomes. Gage R&R (Repeatability & Reproducibility) – Evaluates measurement system accuracy to ensure reliable data collection. Design of Experiments (DOE) – A statistical approach to optimize process settings and analyze factors affecting performance. Value Stream Mapping (VSM) – Identifies waste in processes and highlights opportunities for Lean improvement. Poka-Yoke (Error Proofing) – Prevents defects by designing foolproof mechanisms into processes.

Sustainability Maturity Self-Assessment 🌎 Understanding the level of sustainability integration within an organization requires structured analysis across multiple operational dimensions. Moving beyond isolated initiatives, this approach provides a clearer view of internal alignment and areas requiring systemic improvement. Disclosure practices are a key area of focus. Integrated reporting that connects sustainability and financial data, alignment with frameworks such as TCFD, and preparation for new regulatory requirements indicate a higher level of maturity. Effective organizations establish clear sustainability targets. These targets are measurable, time bound, and supported by transition plans and internal accountability. They serve as reference points for strategic planning and operational execution. Governance is another critical pillar. The presence of formal structures, leadership ownership, and cross departmental coordination reflects whether sustainability is embedded into core decision making processes. Board oversight acts as a signal of institutional prioritization. Regular engagement, monitoring through defined indicators, and integration into enterprise risk management processes are all essential components. Data quality underpins all sustainability decisions. Organizations are evaluated based on their ability to collect, estimate, and validate key metrics, particularly emissions data aligned with recognized methodologies. Value chain visibility expands the lens beyond internal operations. The ability to monitor sustainability performance upstream and downstream indicates a broader understanding of impact and risk exposure. Procurement strategies also reflect the depth of integration. When sustainability criteria shape supplier selection and guide collaborative initiatives, procurement becomes a tool for driving environmental and social outcomes. This type of evaluation does not produce a static score. Instead, it highlights capability gaps, supports internal benchmarking, and informs priorities for systems level improvements aligned with strategic sustainability objectives. #sustainability #sustainable #esg #business

Throughout my 30+ years journey leading textile and manufacturing operations, I've witnessed firsthand how the Kaizen philosophy has revolutionised organisational culture. It's not about grand, sweeping changes – it's about the compound effect of small, continuous improvements. The true essence of Kaizen lies in its simplicity and accessibility: • It transforms workplace culture from "That's not my job" to "How can I help?" • Empowers every employee to become a problem solver • Creates a sustainable framework for innovation • Builds resilience through continuous adaptation The most powerful transformations often begin with the smallest steps.  When every team member contributes daily improvements, the collective impact becomes extraordinary. Based on decades of leadership experience, here are three proven pillars of successful Kaizen implementation: 1. Leadership Through Gemba Walks Leaders must be visible on the shop floor. When we observe and engage directly with processes and people, real transformation begins. 2. Front-line Empowerment Your operators know the processes best. Give them the tools and authority to solve problems and watch innovation flourish. 3. Celebrate Progress Recognition drives repetition. Make it a habit to acknowledge improvements, no matter how small. Remember: Excellence is not a destination; it's a continuous journey of improvement. #leadership #team #peoplemangement #culture #kaizen #organizationculture #LeadwithRajeev

Improving productivity is a crucial aspect of enhancing company efficiency. Generative AI (GenAI) tools like ChatGPT and tailored LLM models hold great promise in achieving this goal. A recent blog post by Intuit's data team explores their study investigating the impact of these tools on data analyst productivity. The study recruited several internal analysts from different business units, spanning various tenures and levels of analytics experience, to ensure diverse participation. Half of the analysts were given access to an internal GenAI tool and tasked with completing representative work assignments within an hour. The study carefully balanced tasks involving both familiar and unfamiliar domains to account for domain expertise. The results revealed a significant productivity increase among GenAI tool users, with SQL tasks being completed 2.2 times faster, or a 55% reduction in time, compared to the control group. Interestingly, the study found that junior analysts experienced the most substantial productivity gains, as well as those tasks involved handling unfamiliar data. This study sheds light on effective approaches to measuring productivity enhancements in the data analyst domain. Despite potential issues with hallucination and accuracy in GenAI tools, their integration with proper user experience interaction proves highly beneficial for productivity enhancement. As more industrial-customized LLM models emerge, they may herald a forthcoming trend in elevating productivity in the analytical domain. #data #analytics #llm #generativeai #productivity #experiment – – –  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:    -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Spotify: https://lnkd.in/gKgaMvbh https://lnkd.in/gAxNBbCg

I don't wish this realization for all, but in case you have it, make sure to get a way out as soon as possible. The feeling of not being satisfied by the overall functioning at your organization. I get this stinging feeling that there is more that can be implemented to achieve prime efficiency While trying to learn a way out of this, I found the Kaizen 7-step approach. The whole process has proven to help my entire team with their functionality and productivity in the workplace. Here’s a breakdown of the Kaizen 7-step approach and how it transformed my work environment: 1️⃣ Identify the problem: Initially, we try to understand the issue at hand and clearly define the objectives. This could be anything from process inefficiencies to quality concerns. Accurate problem identification is crucial for effective resolution. 2️⃣ Analyze the current situation: As we identify the problem, we gather related data and understand the current state of the problem. This analysis helps us to understand the root cause and impact of the issue. 3️⃣ Develop solutions: With the data, we brainstorm further for potential solutions and evaluate their feasibility. In this step, involving team members helps to get diverse perspectives and innovative ideas. 4️⃣ Plan and implement: With the solution in hand, we assign responsibilities, set timelines, and ensure all necessary resources are in place. Implement the solution in a controlled and monitored manner. 5️⃣ Evaluate the results: After implementation, we assess the impact of the solution. We collect data and feedback to determine if the problem has been resolved and if the desired improvements have been achieved. 6️⃣ Standardize the solution: If the solution is successful, we standardize it by integrating it into regular workflows and processes. Then the documentation is done for the new standard procedures so that all team members are trained accordingly. 7️⃣ Review and continue improvement: This might be the last step, but all the above steps come down to the continuous process of improvement. We regularly review the processes, seek feedback, and look for further areas of improvement. Involving team members at every step has helped to resolve issues. At the same time, this practice also empowers employees, boosts their morale, and enhances overall productivity. Have you tried implementing the Kaizen approach in your workplace? #kaizen #workplace #productivity #management

💸 We spent ₹3,500 on a video. It brought us ₹1 Crore in ARR. . . Read that again.  Yes, one of our smallest bets turned out to be one of our biggest wins. Back in 2022, at the Programming Hub, we decided to run a quiet little experiment. We added 15-second explainer videos at the end of lessons - quick summaries that helped users recall what they just learned. No crazy production. No deep UX redesign. Just bite-sized clarity at the right moment. 🎬 Cost per video? ₹3,500 ⏱ Time to test? 2 weeks 📈 Result? +6% boost in conversions 💰 Impact? ₹1 Crore+ in extra ARR All from content that took less than a day to make. What this taught me (and still sticks with me today): ✅ Every big idea deserves a micro-experiment first: If it can’t be tested lean, it's probably not test-worthy yet. 🧮 Frameworks > gut feel: We used ICE (Impact, Confidence, Ease) to score this before we built anything. That kept the bias out. 📊 Always be measuring: Even the “obvious wins” should show up in the data. Numbers > opinions. 🎓 Learning isn’t over when the lesson ends: Those 15-second explainers acted like memory glue - and it worked. Honestly, this was a wake-up call. We often chase “big levers” thinking the answer is a major overhaul. But sometimes, your next inflection point is hiding in a ₹3.5K test buried at the end of a lesson. If you’re building a product right now, let me leave you with this: What’s the smallest experiment you can run this month? Try it. Measure it. You might just find your ₹1 Cr moment. 👇 Drop your most surprising experiment story in the comments! #ProductGrowth #Experiments #ABTesting #ICEFramework #StartupLessons #ARRGrowth #LeanThinking #ProductManagement #GrowthHacks