Automation Implementation Tips

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.

I’ve killed 50+ ERP rollouts before kickoff. Always for the same 6 reasons. And your vendor will never tell you these. If you're about to start an ERP project, pause. Run this 6-question checklist first. (1) Is your CFO actively leading this project or is IT running the show? If Finance isn't in charge, you're building the wrong thing for the right price. (2) When was the last time your Chart of Accounts was redesigned? If it’s older than your finance manager, you're about to migrate legacy chaos. (3) Are you asking for a “like-for-like” system or rethinking broken workflows? If the goal is to copy-paste the past, why even switch? (4) Is Procurement part of your ERP planning team? No? Who’s mapping landed cost, freight margins, supplier controls? (5) Have you audited your master data before selecting the ERP? Or are you planning a $1M migration with duplicate SKUs and ghost vendors? (6) Did the vendor say, “You can customize that later”? That means they don’t understand your business. At all. If you answered “No” or “Not sure” to even 2 of these, stop the rollout. You’re not ready.

If you’re an AI engineer trying to understand and build with GenAI, RAG (Retrieval-Augmented Generation) is one of the most essential components to master. It’s the backbone of any LLM system that needs fresh, accurate, and context-aware outputs. Let’s break down how RAG works, step by step, from an engineering lens, not a hype one: 🧠 How RAG Works (Under the Hood) 1. Embed your knowledge base → Start with unstructured sources - docs, PDFs, internal wikis, etc. → Convert them into semantic vector representations using embedding models (e.g., OpenAI, Cohere, or HuggingFace models) → Output: N-dimensional vectors that preserve meaning across contexts 2. Store in a vector database → Use a vector store like Pinecone, Weaviate, or FAISS → Index embeddings to enable fast similarity search (cosine, dot-product, etc.) 3. Query comes in - embed that too → The user prompt is embedded using the same embedding model → Perform a top-k nearest neighbor search to fetch the most relevant document chunks 4. Context injection → Combine retrieved chunks with the user query → Format this into a structured prompt for the generation model (e.g., Mistral, Claude, Llama) 5. Generate the final output → LLM uses both the query and retrieved context to generate a grounded, context-rich response → Minimizes hallucinations and improves factuality at inference time 📚 What changes with RAG? Without RAG: 🧠 “I don’t have data on that.” With RAG: 🤖 “Based on [retrieved source], here’s what’s currently known…” Same model, drastically improved quality. 🔍 Why this matters You need RAG when: → Your data changes daily (support tickets, news, policies) → You can’t afford hallucinations (legal, finance, compliance) → You want your LLMs to access your private knowledge base without retraining It’s the most flexible, production-grade approach to bridge static models with dynamic information. 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

Fixing a leaky roof with duct tape might stop the drip, but it won’t weather the storm. In manufacturing, it's tempting to go for quick fixes when problems arise. But these temporary solutions can end up costing you more time and money in the long run. Instead of just putting out fires, let's focus on sustainable improvements. 𝐐𝐮𝐢𝐜𝐤 𝐖𝐢𝐧 𝐯𝐬 𝐐𝐮𝐢𝐜𝐤 𝐅𝐢𝐱:  A quick win is a small, achievable change that brings immediate benefits and aligns with long-term goals. A quick fix is a temporary solution that addresses symptoms but not the underlying problem, often leading to more issues later. Imagine integrating AI into your production line. A quick fix would be using AI to temporarily automate a specific task without fully understanding or addressing how it integrates with other processes. This may provide short-term relief but could create new issues down the line. A quick win, on the other hand, would involve piloting AI to optimize a particular segment of the manufacturing process, such as predictive maintenance. By experimenting with AI to predict when machines need servicing, you can immediately reduce downtime and maintenance costs. This approach not only showcases the technology’s potential benefits but also aligns with long-term goals of increased efficiency and innovation. 𝐇𝐞𝐫𝐞'𝐬 𝐒𝐨𝐦𝐞 𝐀𝐝𝐯𝐢𝐜𝐞: 𝟏. 𝐓𝐡𝐢𝐧𝐤 𝐋𝐨𝐧𝐠-𝐓𝐞𝐫𝐦: Invest in technology and processes that will scale with your business. Quick wins are great, but make sure they're steps toward lasting solutions. 𝟐. 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐐𝐮𝐚𝐥𝐢𝐭𝐲: Enhancing the quality of your products or processes can lead to significant long-term value. Quality reduces waste and improves customer satisfaction. 𝟑. 𝐄𝐦𝐩𝐨𝐰𝐞𝐫 𝐘𝐨𝐮𝐫 𝐓𝐞𝐚𝐦: Equip your workforce with the tools and training they need to innovate and excel. An empowered team is your best asset for driving sustainable change. Remember, it's all about enhancing, not just extending. Let’s build a future that's strong and resilient. ******************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

McKinsey's ERP warning for CFOs: 1. 70% of ERP transformations fail     Most ERP projects run over budget and underdeliver. Why? Because companies underestimate complexity. Finance expects a big bang switch. Instead, they get endless data cleanups, mismatched chart of accounts, and broken workflows. In finance, a 90% rollout isn’t a win. If one close process breaks, the whole system stalls.     2. It's your design, not your tech     CFOs blame vendors. But the real issue is design. Too many teams lift-and-shift old processes into new systems. That hardcodes inefficiency. The 30% who succeed don’t copy the past. They redesign approvals, reconciliations, and controls before go-live. ERP isn’t a tool migration. It’s an operating model redesign.     3. Finance feels the pain first     In sales, if CRM misses a field, people workaround. In finance, if ERP misses a journal entry, you misstate results. Month-end closes, audits, and compliance magnify every flaw. That’s why ERP failures show up in finance before anywhere else. Unless you engineer accuracy and reliability from day one, the CFO’s credibility is at risk.     4. The gap turns critical     McKinsey calls it out: 70% stuck, 30% pulling ahead. The stuck companies run digital systems that replicate legacy pain. The winners embed automation, shared data models, and continuous improvement. Over time, that gap compounds into faster closes, lower costs, and better decision-making.     TAKEAWAY ERP failures don’t just cost money at go-live. They lock in inefficiencies for years. Every close takes longer. Every audit is harder. Every board deck gets delayed. The reverse is also true. When ERP is designed right, benefits compound: - Faster closes free capacity - Automation creates leverage - Cleaner data sharpens insight The real gap isn’t visible at launch. It shows up quarter after quarter, year after year.

How the 1-2 Year ROI Expectation Stifles Automation & Robotics In manufacturing, automation projects are often evaluated through a single lens: Will this pay for itself within 12-24 months? While this short-term ROI focus ensures financial prudence, it often prevents companies from investing in transformative technologies. Many promising solutions—like robotics, advanced AI, or flexible automation systems—can’t meet these stringent payback demands, especially in industries with complex workflows or lower margins. This narrow window creates a paradox: the technologies that could yield the most value over the long term are rejected in favor of minor process improvements. To break this cycle, we need to rethink how we measure automation success. Instead of looking solely at immediate cost savings, consider broader impacts like: • Improved resilience to labor shortages. • Increased productivity and throughput. • Enhanced quality and reduced defects. • Competitive positioning for future growth. ATI Industrial Automation and Celera Motion, A Novanta Company support the advancement of robotics. #robotics #automation

I've been building and deploying RAG systems for 2+ years. And it's taught me optimizing them requires focusing on 3 core stages: 1. Pre-Retrieval 2. Retrieval 3. Post-Retrieval Let me explain - Most people focus on the generation side of things. But optimizing retrieval is what really makes the difference. Here's how to do it: 𝟭/ 𝗣𝗿𝗲-𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 This is where we optimize the data before the retrieval process even begins. The goal? Structure your data for efficient indexing and ensure the query is as precise as possible before it's embedded and sent to your vector DB. Here’s how: - 𝗦𝗹𝗶𝗱𝗶𝗻𝗴 𝘄𝗶𝗻𝗱𝗼𝘄: 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦 𝘤𝘩𝘶𝘯𝘬 𝘰𝘷𝘦𝘳𝘭𝘢𝘱 𝘵𝘰 𝘳𝘦𝘵𝘢𝘪𝘯 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘢𝘯𝘥 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭 𝘢𝘤𝘤𝘶𝘳𝘢𝘤𝘺. - 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗴𝗿𝗮𝗻𝘂𝗹𝗮𝗿𝗶𝘁𝘆: 𝘊𝘭𝘦𝘢𝘯, 𝘷𝘦𝘳𝘪𝘧𝘺, 𝘢𝘯𝘥 𝘶𝘱𝘥𝘢𝘵𝘦 𝘥𝘢𝘵𝘢 𝘧𝘰𝘳 𝘴𝘩𝘢𝘳𝘱𝘦𝘳 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭. - 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮: 𝘜𝘴𝘦 𝘵𝘢𝘨𝘴 (𝘭𝘪𝘬𝘦 𝘥𝘢𝘵𝘦𝘴 𝘰𝘳 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘐𝘋𝘴) 𝘵𝘰 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘧𝘪𝘭𝘵𝘦𝘳𝘪𝘯𝘨. - 𝗦𝗺𝗮𝗹𝗹-𝘁𝗼-𝗯𝗶𝗴 (or parent) 𝗶𝗻𝗱𝗲𝘅𝗶𝗻𝗴: 𝘜𝘴𝘦 𝘴𝘮𝘢𝘭𝘭𝘦𝘳 𝘤𝘩𝘶𝘯𝘬𝘴 𝘧𝘰𝘳 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨 𝘢𝘯𝘥 𝘭𝘢𝘳𝘨𝘦𝘳 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘴 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘧𝘪𝘯𝘢𝘭 𝘢𝘯𝘴𝘸𝘦𝘳. - 𝗤𝘂𝗲𝗿𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴 𝘭𝘪𝘬𝘦 𝘲𝘶𝘦𝘳𝘺 𝘳𝘰𝘶𝘵𝘪𝘯𝘨, 𝘲𝘶𝘦𝘳𝘺 𝘳𝘦𝘸𝘳𝘪𝘵𝘪𝘯𝘨, 𝘢𝘯𝘥 𝘏𝘺𝘋𝘌 𝘤𝘢𝘯 𝘳𝘦𝘧𝘪𝘯𝘦 𝘵𝘩𝘦 𝘳𝘦𝘴𝘶𝘭𝘵𝘴. 𝟮/ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 The magic happens here. Your goal is to improve the embedding models and leverage DB filters to retrieve the most relevant data based on semantic similarity. - Fine-tune your embedding models or use instructor models like instructor-xl for domain-specific terms. - Use hybrid search to blend vector and keyword search for more precise results. - Use GraphDBs or multi-hop techniques to capture relationships within your data. 𝟯. 𝗣𝗼𝘀𝘁-𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 At this stage, your task is to filter out noise and compress the final context before sending it to the LLM. - Use prompt compression techniques. - Filter out irrelevant chunks to avoid adding noise to the augmented prompt (e.g., using reranking) 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: RAG optimization is an iterative process. Experiment with various techniques, measure their effectiveness, compare them and refine them. Ready to step up your RAG game? Check out the link in the comments.

RAG isn’t just about connecting a model to a vector database. It’s a complete system — with 9 moving parts that must work together to deliver reliable, context-aware responses. Over the last few months, I’ve refined this architecture while working on production-grade GenAI pipelines. Each layer has its own purpose — from ingesting and preprocessing data to evaluating and improving retrieval and generation. Here’s how it breaks down: ➟ Ingest & Preprocess: Collect, clean, and normalize data from multiple sources. ➟ Split Into Chunks: Use semantic-aware chunking to preserve meaning. ➟ Generate Embeddings: Choose embedding models based on task and domain. ➟ Store in Vector DB: Maintain a scalable vector store and metadata index. ➟ Retrieve: Combine dense, semantic, and sparse retrieval for best recall. ➟ Orchestrate the Pipeline: Use tools like LangChain or Vertex AI to automate flows. ➟ Select LLMs for Generation: Route queries to the best-fit model or gateway. ➟ Add Observability: Track performance, latency, and prompt quality. ➟ Evaluate & Curate: Continuously test retrieval and fine-tune your system. What most people miss is that RAG is iterative — not a one-time setup. Observability, evaluation, and feedback loops are what turn it from a demo into a production-ready system. If you’re building GenAI workflows, this blueprint can serve as your foundation — then adapt, optimize, and evolve it based on your data and use cases.

𝗪𝗵𝘆 𝗱𝗼 𝘀𝗼 𝗺𝗮𝗻𝘆 𝗘𝗥𝗣 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗮𝗶𝗹? 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘁𝗿𝗲𝗮𝘁 𝗶𝘁 𝗹𝗶𝗸𝗲 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗽𝗮𝘁𝗰𝗵, not the business transformation it truly is. Listening to my network, there seems to be a rush to complete ERP migrations, as fast as possible, with SAP S/4HANA plans driving most of it. But an ERP system is more than just an IT upgrade. It’s a chance to redesign how your business operates and build a solution architecture that supports agility and innovation. While necessary, these migrations often become redundant without proper alignment to business goals. Something, I've seen happen! Here some get rights to consider: ◉ 𝗔𝗹𝗶𝗴𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝘁𝗲𝗰𝗵 𝗴𝗼𝗮𝗹𝘀 Ensure that IT and business leaders are on the same page. ERP systems serve broader business objectives, such as innovation, improving procurement strategies, and enhancing supplier relationships. ◉ 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗼𝗼𝗹𝘀. Instead of getting caught up in the technology itself, be clear about the business benefits you'd like to achieve. New ERP functionality can be of support to achieve goals like efficiency, cost reduction, and agility. ◉ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗻𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 Don't just migrate complex, outdated processes but streamline them end-to-end. Reevaluate processes for efficiency and desired outcomes. ◉ 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗶𝗻 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 ERP migrations often fail due to poor user adoption. Beyond training, invest in communication & ongoing support showing the value and relevance of the system to users. ◉ 𝗜𝗻𝘃𝗼𝗹𝘃𝗲 𝗰𝗿𝗼𝘀𝘀-𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗲𝗮𝗺𝘀 ERP impacts every area of the business, so cross-team collaboration is essential. Involve stakeholders from finance, procurement, IT, and operations ensures the system meets everyone’s needs. ◉ 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 - 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗺𝗽𝗿𝗼𝗺𝗶𝘀𝗲 An ERP system is only as good as the data it processes. Ensure that data is clean, consistent, and reliable before migration. Dirty or incomplete data is one of the biggest challenges post-go-live. ◉ 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘀𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗳𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗼𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Choose an architecture which allows for future-proofing and integration of new features, scalability and integration. Business models evolve, and your ERP must evolve with them." ◉ 𝗦𝗲𝘁 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀 - 𝗶𝘁'𝘀 𝗻𝗼𝘁 𝗴𝗼𝗶𝗻𝗴 𝘁𝗼 𝗯𝗲 𝗾𝘂𝗶𝗰𝗸 𝗶𝗳 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲 Don’t rush an implementation. ERP migrations are complex and require time to integrate properly. A phased approach allows for troubleshooting and mitigates a risk for failure. ❓Any other "get rights" i missed and you would add from your experience. #erp #businesstransformation #migration #sap4hana