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

Yesterday at INBOUND, I had the pleasure of interviewing Dario Amodei – CEO of Anthropic and one of the world’s brightest minds in AI. We covered a lot of ground! Here are 5 key takeaways: 1. Balancing hypergrowth and mission-alignment isn’t easy – but it’s essential for building a lasting business. Anthropic is one of the fastest-growing companies of all time. It’s also deeply mission-driven. How do they balance the two? By uniting customers and employees around the things they care about most: safety, security, and trust. For any business, staying grounded in your “why” keeps growth and the mission moving in the same direction. 2. Coding is a phenomenal use case for AI – and the technology is now powerful enough to transform almost any business function Claude Code is transforming how companies build products (including HubSpot). It took off because engineers tend to be early adopters of AI. But the technology is equally capable of transforming sales, marketing, and customer service. The friction lies in adoption – choosing the right use cases, addressing data privacy concerns, and inspiring teams to get started. 3. Using AI internally is a fast track to discovering use cases that deliver customer value Leaders at Anthropic encourage their teams to experiment with AI. That culture of experimentation led to insights that shaped successful products like Claude Code. We’ve taken the same approach at HubSpot – drive AI transformation internally not only to increase productivity, but also to deliver customer value. 4. Human psychology and “street smarts” are critical when using AI in a business context. Anthropic ran a fascinating experiment where Claude managed a vending machine business. The takeaway? AI was good at completing tasks and building a strategy, but it fell short when negotiating with customers. Another reminder that AI augments human qualities, it doesn’t replace them. That’s been a consistent theme at this year’s INBOUND. 5. AI has the potential to help SMBs grow in entirely new ways – the key is knowing where to start. Many of the businesses Anthropic works with have heard the hype about AI but don’t know how it can actually help them grow. When they see a use case that works (like coding for example), that’s the aha moment that sparks wider adoption. At HubSpot, our customers tend to start with proven use cases like customer support, prospecting, and content creation – and scale from there. We’re living through one of the biggest technology shifts in a generation. I left yesterday’s conversation feeling excited about what’s next in AI and grateful for thoughtful leaders like Dario who are helping to lead the way.

Smart manufacturing isn’t just about doing things better; it’s about redefining what ‘better’ means in a digital, sustainable world. What began with Industry 4.0’s ambitious vision—cyber-physical systems, IoT, and connected factories—has evolved into something more grounded, accessible, and human-centric. While Industry 4.0 focused on possibilities, today’s frameworks, like CESMII’s First Principles of Smart Manufacturing, focus on practicality. These principles offer a roadmap to make smart manufacturing achievable for everyone: 1. 𝐅𝐥𝐚𝐭 𝐚𝐧𝐝 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞: Seamless information flow enables fast, decentralized decisions with real-time visibility. 2. 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐭 & 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐞𝐝: Connected ecosystems collaborate to deliver products efficiently and on time. 3. 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞: Systems adapt easily to changing demands, enabling broad adoption across the value chain. 4. 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 & 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭: Optimizes energy use and supports reuse, remanufacturing, and recycling processes. 5. 𝐒𝐞𝐜𝐮𝐫𝐞: Ensures secure connectivity, protecting data, IP, and systems from cyber threats. 6. 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 & 𝐒𝐞𝐦𝐢-𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬: Moves from static reporting to proactive, real-time, semi-autonomous decisions. 7. 𝐈𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐥𝐞 & 𝐎𝐩𝐞𝐧: Empowers seamless communication across systems, devices, and partners. The shift reflects a decade of lessons learned: manufacturers need solutions that are scalable, resilient to disruptions, and environmentally responsible. CESMII doesn’t just ask, “What if?” It answers with, “Here’s how,” bridging the gap between visionary ideas and real-world implementation. 𝐋𝐞𝐚𝐫𝐧 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝟒.𝟎 𝐯𝐬 𝐒𝐦𝐚𝐫𝐭 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐚 𝐜𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧 𝐢𝐧 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: https://lnkd.in/e2BRT5kX ******************************************* • 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!

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

We are witnessing a rapid evolution in cognitive architecture. The terminology gets confusing fast. What’s the real difference between an AI Agent and "Agentic AI"? Is RAG just a feature, or a layer? This breakdown visualizes these layers perfectly, showing not just distinct technologies, but a maturity model for intelligent systems. Here are my key takeaways from this "flower of intelligence": 1. The Foundational "Brain" (LLM - Green) Everything starts here. The LLM provides the core reasoning, language understanding, and internalized knowledge. But as a standalone tool, it’s constrained by its training data cutoff and tendency to hallucinate. It knows a lot, but it doesn't know right now. 2. The Library Card (RAG - Purple) Retrieval-Augmented Generation is the bridge to reality. It solves the grounding problem by giving the LLM access to external, private, or real-time data. We move from "creative writing" to "evidence-based answering." 3. The Hands and Feet (AI Agent - Pink) This is the critical pivot point: the shift from Knowledge to Action. An AI Agent doesn't just retrieve information; it uses tools, calls APIs, executes code, and maintains state memory. It can break down a complex goal into executable steps. 4. The Orchestrated Ecosystem (Agentic AI - Yellow) The frontier isn't a single super-agent; it's a team. Agentic AI is about multi-agent collaboration, where specialized agents (e.g., a coder, a researcher, a critic) are orchestrated to solve highly complex problems autonomously. It involves long-term memory management and self-correction protocols. The magic doesn't happen in the center of these petals; it happens in the overlaps. The most powerful systems today are combining advanced RAG pipelines within agentic frameworks, allowing autonomous agents to access grounded truths before taking action.

𝟓 𝐂𝐨𝐦𝐦𝐨𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐄𝐫𝐫𝐨𝐫𝐬 𝐓𝐡𝐚𝐭 𝐂𝐚𝐧 𝐁𝐫𝐞𝐚𝐤 𝐘𝐨𝐮𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 𝐀𝐧𝐝 𝐇𝐨𝐰 𝐓𝐨 𝐅𝐢𝐱 𝐓𝐡𝐞𝐦 AI agents are transforming how we build, automate, and scale intelligent systems. But even the most powerful agents fail when they encounter real-world complexity. Most failures are not because the models are weak but because the system isn’t designed to handle errors. If you want your AI agents to work reliably in production, you must understand where they break and how to fix them. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝟓 𝐦𝐚𝐣𝐨𝐫 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐞𝐫𝐫𝐨𝐫𝐬 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐤𝐧𝐨𝐰: 𝟏. 𝐈𝐧𝐩𝐮𝐭 𝐄𝐫𝐫𝐨𝐫𝐬 Agents struggle when the input is incomplete, unclear, or incorrectly formatted. This leads to poor decisions and broken workflows. How to fix: Always validate and sanitize inputs before processing. Add checks to prevent invalid data from reaching the agent. 𝟐. 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐆𝐚𝐩𝐬 If you cannot see what is failing, you cannot fix it. Many systems lack proper logging and monitoring. How to fix: Track all errors, retries, and system performance. Create detailed logs to understand when and why failures occur. 𝟑. 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐄𝐫𝐫𝐨𝐫𝐬 Agents can produce incorrect conclusions or hallucinate when reasoning goes wrong. How to fix: Implement self-check loops so the agent reviews its reasoning. If it cannot find information, make it retry retrieval instead of guessing. 𝟒. 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐄𝐫𝐫𝐨𝐫𝐬 Agents can get stuck in loops, forget tasks, or leave workflows incomplete. How to fix: Add execution limits and escalation mechanisms. If a task cannot be completed, escalate it to a human or backup process. 𝟓. 𝐓𝐨𝐨𝐥 𝐨𝐫 𝐀𝐜𝐭𝐢𝐨𝐧 𝐄𝐫𝐫𝐨𝐫𝐬 External APIs or services the agent depends on can fail or return invalid data. How to fix: Always have fallback tools ready. Build redundancy so that if one integration fails, the agent can continue operating. The best AI agents are designed to fail gracefully. Proactively solving these five error types makes your system stable, scalable, and ready for real-world use. Which of these errors do you think most teams overlook when deploying AI agents? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more #AI #AIAgents #AgenticAI

Today’s surprise came from a Swedish AI startup - a “live-code” approach in the enterprise automation space. Most current agent frameworks (LangChain, AutoGPT, etc.) run step by step: → interpret prompt → call tool → wait → return → next step. Reliable, but slow. Similar to a human clicking through a task list. Hard scale well, especially in structured enterprise workflows. Incredible takes a different route, something they describe as a “𝐥𝐢𝐯𝐞-𝐜𝐨𝐝𝐞” execution model. It separates the system into two layers: 1- The LLM handles planning, turning your intent into structured logic 2- Then that logic is packaged and executed in parallel across your connected apps — not step by step, but all at once Think of it like turning a prompt into a full task graph or script, then running it instantly in a controlled, structured runtime. In other words: 𝐋𝐋𝐌 = 𝐩𝐥𝐚𝐧𝐧𝐞𝐫, 𝐫𝐮𝐧𝐭𝐢𝐦𝐞 = 𝐞𝐱𝐞𝐜𝐮𝐭𝐨𝐫 The advantages are clear when it comes to scale, speed, and reliability: ◾Capable of running large numbers of operations across tools in parallel — not one API call at a time ◾Clean separation between intent and execution ◾Designed for structured, context-heavy workflows (In their own examples: processing 2,000+ CRM records or handling multi-gigabyte datasets) 📍Explore/ Try it for free: 🔗https://lnkd.in/gaUDDDvz You’ll see similar execution patterns in dev tools like Replit or Copilot, and in some internal infra. But not much this kind of parallel, execution-first architecture applied to business automation at scale. Worth checking! #generativeai #automation #productivity #swedishtech

Traditional ML completely transformed media and advertising in the last decade; the broad applicability of generative AI will bring about even greater change at a faster pace to every industry and type of work. Here are 7 takeaways from my CNBC AI panel at Davos earlier this year with Emma Crosby, Vladimir Lukic, and Rishi Khosla: • For AI efforts to succeed, it needs to be a CEO/board priority. Leaders need to gain firsthand experience using AI and focus on high-impact use cases that solve real business pain points and opportunities. • The hardest and most important aspect of successful AI deployments is enlisting and upskilling employees. To get buy-in, crowdsource or co-create use cases with frontline employees to address their burning pain points, amplify success stories from peers, and provide employees with a way to learn and experiment with AI securely. • We expect 2024 to be a big year for AI regulation and governance frameworks to emerge globally. Productive dialogue is happening between leaders in business, government, and academia which has resulted in meaningful legislation including the EU AI Act and White House Executive Order on AI. • In the next 12 months, we expect to see enterprise adoption take off and real business impact from AI projects, though the truly transformative effects are likely still 5+ years away. This will be a year of learning what works and defining constraints. • The pace of change is unprecedented. To adapt, software development cycles at companies like Salesforce have accelerated from our traditional three product releases a year to now our AI engineering team shipping every 2-3 weeks. • The major risks of AI include data privacy, data security, bias in training data, concentration of power among a few big tech players, and business model disruption. • To mitigate risks, companies are taking steps like establishing responsible AI teams, building domain-specific models with trusted data lineage, and putting in place enterprise governance spanning technology, acceptable use policies, and employee training. While we are excited about AI's potential, much thoughtful work ahead remains to deploy it responsibly in ways that benefit workers, businesses, and all of society. An empowered workforce and smart regulation will be key enablers. Full recording: https://lnkd.in/g2iT9J6j

Stop pitching automation as a cost cutter. Start proving it’s a profit engine. When calculating ROI for your next automation project, be sure to calculate increased capacity into the return calculation. Assuming you can sell the product on your increased capacity, you can use the gross profit generated from the new revenue in your ROI calculation. This new profit will add up surprisingly quickly and show you how valuable new equipment can be for your organization. Increased capacity can be generated from less downtime (less maintenance or running more frequently through automation), faster cycle time, improved yield. Also be sure to… > Identify all costs associated with the automation project  > Get the exact benefit in cost from your integrator > Make a plan to track costs after installation What gets measured gets managed. So if you can prove that you can achieve a great ROI (<2 years like we provide our customers) through real numbers, your next project has better odds of getting approved.

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