Automation Implementation Tips

𝗜𝗳 𝘆𝗼𝘂 𝗳𝗼𝗹𝗹𝗼𝘄 𝘁𝗵𝗲 𝗻𝗲𝘄𝘀, 𝘆𝗼𝘂’𝘃𝗲 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝘀𝗲𝗲𝗻 𝗶𝘁 𝗮𝗹𝗹: 𝗔𝗜 𝗶𝘀 𝗯𝗼𝗼𝗺𝗶𝗻𝗴. 𝗔𝗜 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱. 𝗔𝗜 𝘄𝗶𝗹𝗹 𝘀𝗮𝘃𝗲 𝘂𝘀. 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗱𝗲𝘀𝘁𝗿𝗼𝘆 𝗷𝗼𝗯𝘀. The Stanford University AI Index 2025 cuts through all of it. Produced by the Institute for Human-Centered Artificial Intelligence, it’s one of the most respected and data-driven reports on the state of AI today. Over 400+ pages of concrete insights — from technical benchmarks and real-world adoption to policy shifts, economic impact, education, and public sentiment. 𝗧𝗵𝗲 2025 𝗲𝗱𝗶𝘁𝗶𝗼𝗻 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 12 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 𝗮𝗿𝗲 𝗯𝗲𝗶𝗻𝗴 𝗰𝗿𝘂𝘀𝗵𝗲𝗱. ➝ AI performance on complex reasoning and programming tasks surged by up to 67 percentage points in just one year. 2. 𝗔𝗜 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘀𝘁𝘂𝗰𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗮𝗯. ➝ 223 FDA-approved AI medical devices. Over 150,000 autonomous rides weekly from Waymo. This is mainstream adoption. 3. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝗮𝗹𝗹-𝗶𝗻.  ➝ $109B in U.S. private AI investment. 78% of organizations using AI. Productivity gains are no longer theoretical. 4. 𝗧𝗵𝗲 𝗨.𝗦. 𝗹𝗲𝗮𝗱𝘀 𝗶𝗻 𝗾𝘂𝗮𝗻𝘁𝗶𝘁𝘆—𝗖𝗵𝗶𝗻𝗮’𝘀 𝗰𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘂𝗽 𝗼𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆.  ➝ Chinese models now rival U.S. models on MMLU, HumanEval, and more. Global AI is becoming a multi-polar game. 5. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗶𝘀 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻. ➝ Incidents are rising, but standardized RAI benchmarks and audits are still rare.   Governments are stepping in faster than vendors. 6. 𝗚𝗹𝗼𝗯𝗮𝗹 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗺 𝗶𝘀 𝗿𝗶𝘀𝗶𝗻𝗴—𝗯𝘂𝘁 𝗻𝗼𝘁 𝗲𝘃𝗲𝗻𝗹𝘆.   ➝ 83% of people in China are optimistic about AI. In the U.S., that number is just 39%. 7. 𝗔𝗜 𝗶𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗰𝗵𝗲𝗮𝗽𝗲𝗿, 𝘀𝗺𝗮𝗹𝗹𝗲𝗿, 𝗮𝗻𝗱 𝗳𝗮𝘀𝘁𝗲𝗿.  ➝ The cost of GPT-3.5-level inference dropped 280x in two years. Open-weight models are nearly matching closed ones. 8. 𝗚𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴.  ➝ From Canada’s $2.4B to Saudi Arabia’s $100B push—states aren’t watching from the sidelines anymore. 9. 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴—𝗯𝘂𝘁 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗹𝗮𝗴𝘀. ➝ Access is improving, but infrastructure gaps and lack of teacher training still limit global reach. 10. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗶𝘀 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁.   ➝ 90% of top AI models now come from companies—not academia. The gap between top players is shrinking fast. 11. 𝗔𝗜 𝗶𝘀 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘀𝗰𝗶𝗲𝗻𝗰𝗲.   ➝ AI-driven breakthroughs in physics, chemistry, and biology are earning Nobel Prizes and Turing Awards. 12. 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝘁𝗵𝗲 𝗰𝗲𝗶𝗹𝗶𝗻𝗴.   ➝ Despite all the progress, models still struggle with logic-heavy tasks. Precision is still a challenge. You can download the full report FREE here: https://lnkd.in/dzzuE5tN

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.

Automation, AI workflow, or AI agent? To always 𝘬𝘯𝘰𝘸 𝘸𝘩𝘪𝘤𝘩 𝘰𝘯𝘦 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥, follow this 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬: Remember when I explained why many "𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴" shared on LinkedIn are actually 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 or 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 in disguise? Turns out: understanding the difference is only partially helpful. The real challenge is knowing 𝘸𝘩𝘪𝘤𝘩 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦. So I built this framework to help you decide. There are 6 key dimensions to consider - working in pairs: 𝐏𝐚𝐢𝐫 #1: 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 ↔️ 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐯𝐨𝐥𝐯𝐞𝐦𝐞𝐧𝐭 aka. how decisions are made - and how much human intervention is required: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: You make ALL decisions upfront when designing your automation, which means that no human intervention is needed after. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: You set boundaries for the AI to operate within; humans occasionally review outputs or intervene when the system encounters edge cases. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: You set high-level goals, and AI determines its own path; this means humans need to provide ongoing feedback to ensure it makes the right decisions. 𝐏𝐚𝐢𝐫 #2: 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ↔️ 𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 a.k.a which type of data the system should process - and how adaptable it has to be: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Requires strictly predefined data formats with no deviation; breaks when encountering unexpected inputs and needs to be re-engineered when processes change. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Handles mostly structured data with some variability allowed; can adjust to parameter variations within defined parameters but needs guidance for significant changes. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Processes diverse unstructured data across multiple sources with varying formats; independently adapts to different inputs and shifting environments without reprogramming. 𝐏𝐚𝐢𝐫 #3: 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 ↔️ 𝐑𝐢𝐬𝐤 𝐓𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞 a.k.a how predictable the outcomes must be - and what level of risk is acceptable: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Delivers highly consistent, predictable results every time; ideal for mission-critical processes where errors cannot be tolerated and predictability is essential. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Produces mostly reliable outcomes with occasional variations in edge cases; balances flexibility with guardrails to prevent major errors while allowing some adaptability. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Creates outcomes that can vary significantly between iterations; optimized for scenarios where discovering novel approaches and adaptability outweigh the need for consistent results. How to use this framework: Always 𝘴𝘵𝘢𝘳𝘵 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘭𝘦𝘧𝘵 and move right only when necessary. 1. Start with automation 2. Move to AI workflows when you need more flexibility within guardrails  3. Only move to agents when you need high adaptability Don’t fall for the AI agent hype - most processes can be automated without agents.

My biggest takeaways from Fei-Fei Li: 1. Just nine years ago, calling yourself an AI company was considered bad for business. Nobody believed the technology would work back in 2016. By 2017, companies started embracing the term. Today, virtually every company calls itself an AI company. 2. The modern AI revolution started with a simple but overlooked insight from Fei Fei: AI models needed large amounts of labeled data. While researchers focused on sophisticated mathematical models and algorithms, she realized the missing ingredient was data. Her team spent three years working with tens of thousands of people across more than 100 countries to label 15 million images, creating ImageNet. This dataset became the foundation for today’s AI systems. 3. The human brain’s efficiency vastly exceeds current AI systems. Humans operate on about 20 watts of power—less than any lightbulb—yet accomplish tasks that require AI systems to use massive computing resources. Current AI still can’t do things elementary school children find easy. 4. Simply scaling current approaches won’t be enough. While adding more data, computing power, and bigger models will continue advancing AI, fundamental innovations are still needed. Throughout AI history, simpler approaches combined with enormous datasets consistently outperformed sophisticated algorithms with limited data. 5. Breakthrough technologies often start as toys or fun experiments before changing the world. ChatGPT was tweeted by Sam Altman as “Here’s a cool thing we’re playing with” and became the fastest-growing product in history. What seems like play today might transform civilization tomorrow. 6. Spatial intelligence is as crucial as language for real-world applications. In emergency situations like fires or natural disasters, first responders organize rescue efforts through spatial awareness, movement coordination, and understanding physical environments—not primarily through language. This is why world models that understand three-dimensional space represent the next frontier beyond text-based chatbots. 7. Physical robots face much harder challenges than self-driving cars, which took 20 years from prototype to street deployment and still aren’t finished. Self-driving cars are metal boxes moving on flat surfaces, trying not to touch anything. Robots are three-dimensional objects moving in three-dimensional spaces, specifically trying to touch and manipulate things. This makes robotics far harder than creating chatbots. 8. Everyone has a role in AI’s future, regardless of profession. Whether you’re an artist using AI tools to tell unique stories, a farmer participating in community decisions about AI deployment, or a nurse who could benefit from AI assistance in an overworked health-care system, you can and should engage with this technology. AI should augment human dignity and agency, not replace it—which means both using AI as a tool and having a voice in how it’s governed.

Don’t Automate Complexity... Simplify and Error-Proof Instead When problems arise, it’s tempting to think automation is the magic fix. But automating a broken or complex process just means you’re speeding up the production of errors. The smarter approach? Simplify the process and error-proof it (Poka Yoke) before thinking about automation. Here’s why simplification often beats automation and how you can apply it. Why You Should Simplify Before Automating: 1️⃣ Faster, Cheaper Improvements Simplifying a process through standardization and removing unnecessary steps often solves problems more quickly and at a lower cost than automation. 2️⃣ Avoid Automating Waste If your process is full of waste (like waiting, overprocessing, or rework), automating it only speeds up inefficiency. Fix the process first, then think about automation. 3️⃣ Built-In Error Proofing With Poka Yoke solutions (like jigs, fixtures, or guides), you can design processes to prevent errors from happening in the first place—without needing expensive sensors or software. 4️⃣ Flexibility and Adaptability Simplified processes are easier to adjust and improve, while automated systems can be rigid and costly to change once implemented. How to Simplify and Error-Proof a Process: 🔍 Map the Current Workflow: Identify unnecessary steps, bottlenecks, and areas prone to errors. ✂️ Eliminate Waste: Remove any steps that don’t add value to the product or service. 📋 Standardize Work: Create clear, repeatable instructions that everyone can follow. 🔧 Introduce Poka Yoke: Physical Error-Proofing: Use jigs, fixtures, or alignment guides to prevent incorrect assembly. Visual Cues: Use color-coded labels or visual templates to guide operators. Sensors or Alarms: Only when needed, use low-cost technology to detect errors in real time. Example of Simplification and Poka Yoke in Action: A warehouse team was dealing with frequent errors when picking products for orders. Instead of implementing a costly automated picking system, they: 1. Introduced a color-coded bin system (Poka Yoke) to help operators select the correct items. 2. Simplified the picking route to reduce unnecessary walking and waiting time. Result: Picking errors dropped by 80%, and productivity increased by 15%—all without expensive automation. When to Consider Automation: Once the process is simplified and stabilized with minimal variation, automation can enhance speed and efficiency. But it should support an optimized process, not mask its problems.

~30% of my pipeline comes from Closed Lost opportunities. So when an opportunity is Closed Lost, don’t let it go cold. If you have a sales engagement tool, set up an automation rule to auto add the primary contact into a Closed Lost Cadence, if not, just do this manually. Here’s an example cadence: 🔹 Step 1 (30 days post-CL) → Manual email (personalised) Summarise their focus, why the deal was lost, and let them know you’ll stay in touch. 📩 Example: "Hey Billybob, really enjoyed working with you and learning more about [initiative], like increasing conversion rates from 12% → 15% and driving $100K pipeline per AE. Appreciate other priorities took precedent, but I’ll stay in touch until timing makes sense". 🔹 Step 2 (55 days post-CL) → Automated email (deposit) Share a relevant resource. 📩 Example: "Pipeline is a challenge for most teams - thought this 30MPC webinar on account segmentation might be useful". 🔹 Step 3 (80 days post-CL) → Evaluate next steps Any team growth? Leadership changes? Priority shifts? No change → Stay in Closed Lost cadence. Key changes → Move to a prospecting cadence & re-engage. 🔹 Step 4 (105 days post-CL) → Phone call + LinkedIn touch (check-in). 🔹 Step 5 (130 days post-CL) → Automated email (new product update). 📩 Example: "See how Salesloft Rhythm incorporates AI into workflows to prioritise prospects most likely to convert into meetings [link]". 🔹 Step 6 (155 days post-CL) → Call (check-in). 🔹 Step 7 (180+ days post-CL) → Final review & decision No movement/changes? Pause outreach or move to a light nurture cadence. New priorities? Add to outbound cadence with a tailored approach. The goal? Stay relevant without being intrusive - so when timing aligns, you’re already on their radar. Are you keeping tabs on your Closed Lost Opps, or letting them slip? #sales #cadences #closedlost

I just saved a company $9.7M on their AI project. Here's the $300K solution they almost spent $10M on: [The Million-Dollar AI Implementation Truth] Their original plan: • $5M AI transformation • $3M cloud migration • $2M digital transformation What they actually needed: • Month 1: Fixed Excel hell ($50K) • Month 2: Cleaned data ($50K) • Month 3: Basic automation ($100K) Results: • 300% ROI in 90 days • Problems actually solved • No AI magic required Reality check: Your "$10M AI project" is probably: • Messy spreadsheets • Broken processes • Bad documentation • Excel doing heavy lifting • Someone manually copy-pasting Why companies waste millions: • "AI" sounds better than "Excel" • "Transformation" beats "cleanup" • "Innovation" > "maintenance" • Executives need big projects • Consultants love big budgets The truth about enterprise AI: Small team + clean data > Big budget + AI dreams Want proof? • Company A: Spent $10M on AI, failed • Company B: Spent $300K on basics, won • Same problem, different approach Save this before your next AI project. Your career might depend on it. #EnterpriseAI #DigitalTransformation #RealTalk P.S. VPs of Innovation are typing angry responses. But first, show me your ROI metrics.

If you’re learning AI automation without a roadmap, you’re guaranteed to get overwhelmed. People usually “learn AI automation” by jumping straight into tools… and then wonder why nothing works consistently. Real automation requires structure - thinking, logic, testing, and a gradual build-up of skills. This 18-day roadmap breaks down the exact sequence to go from zero → confidently building automations with AI, APIs, tools, and no-code platforms. Here’s the full breakdown, day by day: Day 1 - AI Automation Fundamentals Learn what automation really means, how it differs from AI and agents, and see real examples. Day 2 - Automation Thinking Break work into steps, triggers, and outcomes - the mindset behind every good automation. Day 3 - APIs & Webhooks Basics Understand how apps communicate and how events trigger workflows. Day 4 - No-Code Automation Platforms Explore Zapier, Make, n8n - and how no-code tools actually run workflows. Day 5 - Build Your First Automation Create a simple trigger-action workflow and connect two apps. Day 6 - Data Handling Pass data between steps, map fields, and work with text, numbers, and dates. Day 7 - Logic & Error Handling Add filters, conditional logic, retries, and fallbacks to keep automations reliable. Day 8 - AI Model Basics Learn prompts vs system instructions, tokens, limits, and LLM behavior. Day 9 - Using AI Inside Automations Insert AI steps into workflows and parse structured AI outputs. Day 10 - Prompt Design for Automation Write consistent prompts and reduce hallucinations with JSON outputs. Day 11 - Text-Based Task Automation Automate email replies, summaries, CRM updates, and document tasks. Day 12 - Knowledge Automation (RAG Basics) Connect AI to internal documents and fetch accurate answers from real data. Day 13 - AI Agents Basics Understand agent planning, tools, and identify use cases for agents. Day 14 - Business Use Case Automation Automate lead qualification, ticket routing, and internal processes. Day 15 - Sales & Marketing Automation Personalize outreach, repurpose content, and automate follow-ups. Day 16 - Operations Automation Manage approvals, notifications, and repetitive operational tasks. Day 17 - Monitoring & Optimization Track workflow success, cut costs, and improve performance. Day 18 - Build & Ship Your System Design, test, document, and finalize a complete end-to-end automation. You don’t master AI automation by learning tools, you master it by learning systems thinking, data flow, and structured execution. Follow this roadmap, and you’ll build automations that are reliable, scalable, and business-ready.

GenAI adoption is all about people, not about tools. Pharma giant Novo Nordisk offers a great case study of working out what supports useful uptake of AI across a large organization. A case study in MIT Sloan Management Review uncovers a range of useful lessons. Here are some of the most interesting. 🚀 Recognize a mid-cycle drop as normal. Novo Nordisk grew Copilot use from a few hundred to 20,000 users in just over a year, with 23% becoming frequent users within one month. However, by month three or four, 15% of early adopters dropped off and average time saved per week declined. Recognizing this dip as natural helped avoid panic and kept the focus on re-engagement strategies rather than getting staff to try tools for the first time. 🛠 Deliver function-specific training through champion networks. Generic AI onboarding failed to meet the needs of specialized roles. Novo Nordisk succeeded by creating domain-specific training, leveraging internal champions to contextualize AI use, and allowing teams to shape guidance based on their actual work. This addressed “AI shaming” and bridged confidence gaps across functions. 🤝 Use internal champions to overcome cultural resistance. Skepticism wasn’t solved by policy, it was shifted by influence. Novo Nordisk identified trusted, high-status employees to openly adopt and advocate for AI tools. Their visible endorsement encouraged hesitant peers to try AI without fear of judgment or failure. 📈 Treat adoption as a change process, not a tech rollout. Rather than pushing a one-time launch, Novo Nordisk framed GenAI as a long-term transformation. This meant investing in ongoing communication, support structures, and iterative learning. The approach acknowledged that adoption would ebb and flow, and prepared the organization to adapt accordingly. 🎯 Emphasize strategic value over time saved. Though average users saved about 2 hours per week, the most meaningful wins came from higher-quality work—more strategic thinking, clearer writing, and better planning. By highlighting these human-centric gains, Novo Nordisk built a stronger case for AI’s workplace relevance beyond mere productivity. 📊 Use employee data to shape the deployment strategy. Over 3,000 employee surveys and interviews helped Novo Nordisk spot where and why adoption lagged. This feedback guided real-time adjustments—like where to invest in new use cases, where to scale back, and how to tailor messaging. It also surfaced which functions became tool-reliant versus those needing more support.

SAP S/4HANA Greenfield Implementation – End-to-End View A SAP S/4HANA Greenfield implementation is a "new implementation" approach, building a fresh, optimized ERP system from scratch rather than upgrading old systems. It uses SAP Activate methodology, involving phases from preparation to go-live to adopt best practices, redesign processes, and migrate only master/open data. This approach allows for maximum innovation but requires significant change management Key Aspects of Greenfield Implementation Approach: Starts with a clean slate, leaving behind old customizations (Z-objects) and historical data. Methodology: Follows SAP Activate, which includes Prepare, Explore, Realize, Deploy, and Run phases. Data Migration: Uses tools like the SAP S/4HANA Migration Cockpit to load master data and open items (e.g., open POs, GL balances). Process Improvement: Focuses on adopting standard, modern best practices rather than replicating old, inefficient processes End-to-End Implementation Phases Prepare: Project initiation, planning, defining, and system installation (Sandbox, Development, Quality, Production). Explore: Conducting workshops to map business requirements to SAP standard best practices. Realize: Incremental build cycles ("Sprints") to configure, test, and integrate the system. Deploy: Data migration, user training, cutover activities, and moving to the production environment. Run: Post-go-live support and continuous improvement. Pros and Cons Pros: Modernized, agile system with reduced technical debt. Cons: Higher cost, longer timelines, and significant change management for users Success in S/4HANA is not about configuration alone — it’s about structured execution. Below find the complete SAP Activate methodology for a Greenfield implementation into a single visual cheat sheet covering: 1. Discover to Run phases 2. Fit-to-Standard approach 3. Cross-module integration (FI, CO, MM, SD, PP, QM, EWM) 4. Data migration & RICEFW governance 5. Testing strategy & Cutover planning 6. Clean Core & S/4HANA differentiators Greenfield implementations demand clarity, discipline, and alignment across business and IT. A well-governed Activate framework makes that difference. If you’re leading or preparing for an S/4HANA journey, this structured view may help anchor your roadmap.