Management Systems Consulting

Building useful Knowledge Graphs will long be a Humans + AI endeavor. A recent paper lays out how best to implement automation, the specific human roles, and how these are combined. The paper, "From human experts to machines: An LLM supported approach to ontology and knowledge graph construction", provides clear lessons. These include: 🔍 Automate KG construction with targeted human oversight: Use LLMs to automate repetitive tasks like entity extraction and relationship mapping. Human experts should step in at two key points: early, to define scope and competency questions (CQs), and later, to review and fine-tune LLM outputs, focusing on complex areas where LLMs may misinterpret data. Combining automation with human-in-the-loop ensures accuracy while saving time. ❓ Guide ontology development with well-crafted Competency Questions (CQs): CQs define what the Knowledge Graph (KG) must answer, like "What preprocessing techniques were used?" Experts should create CQs to ensure domain relevance, and review LLM-generated CQs for completeness. Once validated, these CQs guide the ontology’s structure, reducing errors in later stages. 🧑⚖️ Use LLMs to evaluate outputs, with humans as quality gatekeepers: LLMs can assess KG accuracy by comparing answers to ground truth data, with humans reviewing outputs that score below a set threshold (e.g., 6/10). This setup allows LLMs to handle initial quality control while humans focus only on edge cases, improving efficiency and ensuring quality. 🌱 Leverage reusable ontologies and refine with human expertise: Start by using pre-built ontologies like PROV-O to structure the KG, then refine it with domain-specific details. Humans should guide this refinement process, ensuring that the KG remains accurate and relevant to the domain’s nuances, particularly in specialized terms and relationships. ⚙️ Optimize prompt engineering with iterative feedback: Prompts for LLMs should be carefully structured, starting simple and iterating based on feedback. Use in-context examples to reduce variability and improve consistency. Human experts should refine these prompts to ensure they lead to accurate entity and relationship extraction, combining automation with expert oversight for best results. These provide solid foundations to optimally applying human and machine capabilities to the very-important task of building robust and useful ontologies.

Does Operational Excellence mean much in an AI-First world? If Michael Hammer were alive today, he would say YES. Hammer remains my favourite Operational Excellence scholar of all time. His books modernised the topic and changed it forever. In over 30 years, I have seen many fads come and go. But Hammer’s principles remain the gold standard. His best books - Reengineering the Corporation The Agenda Faster Cheaper Better. He believed in blowing things up and starting over. Reengineering. 1. Reengineering the Corporation (with James Champy) Don't just "fix" a process. Ask "Why do we do this at all?" If the answer is weak, delete the process. Incremental improvement (10%) is the enemy of reengineering. Aim for quantum leaps (100% or 10x improvement). This book in 1993 came along with Six Sigma but has lasted longer and is more relevant today than ever. 2. The Agenda Be Easy to Do Business With (ETDBW): Your internal complexity is not the customer's problem. Hide your mess. The customer interface should be seamless and simple. The End-to-End Process: Customers don't care about your "Sales Dept" or "Shipping Dept." They care about "Order to Delivery." Manage that flow, not the silos. This book reads like one is reading a modern day J M Juran. And there can't be higher praise than that. 3. Faster Cheaper Better (with Lisa Hershman) The 9 Levers: Improvement isn't just about changing steps. You need to pull 9 levers: 5 for the process (design, metrics, performers, owner, infrastructure) and 4 for the company (leadership, culture, expertise, governance). Process is the DNA: Processes are not just "things you do." They are the assets of the company, just like people or cash. They need "Owners" who are senior executives. The PEMM Framework: The "Process and Enterprise Maturity Model." You can't skip stages. You must mature your processes and your management capability together. Sadly, Hammer could not complete this book and passed away in 2008. 5 enduring lessons from his pathbreaking work. 1. Don't pave the cow path. Most companies take a bad process and add software to it. That is a mistake. Technology shouldn't just speed up old ways. If the process is bad, don't automate it. Delete it. 2. Start with a clean sheet. Incremental change is safe. It is also slow. Hammer taught us to aim for 10x improvement, not 10%. Stop asking "How do I fix this?" Start asking "Why do we do this at all?" 3. Kill the hand-offs. Delays happen when work moves from one department to another. The customer doesn't care about your internal silos. Organize around the outcome, not the task. 4. Be Easy To Do Business With. Your internal complexity is not the customer's problem. Hide the mess. If it is hard for them to buy, you are failing. 5. Process is an asset. Treat your workflows like you treat your cash or your people. They are assets. They need owners. They need maintenance. They need respect. If you got this far, thanks.

Knowledge Management is hands down the most important factor for scalable GenAI adoption. Here’s a breakdown of the key components: 𝗖𝗲𝗻𝘁𝗿𝗮𝗹 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: The knowledge lifecycle spans the entire knowledge management process, interacting with all other components. It acts as the main decision-making and routing mechanism. 𝗖𝗿𝗲𝗮𝘁𝗲: Documenting knowledge and guiding users on how to capture their experiences with knowledge (both positive and negative). 𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗲: Structuring content and organising it in a way that ensures ease of access and effective retrieval. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲: Knowledge management relies on systems thinking. As systems evolve, knowledge must be continually improved. 𝗦𝗵𝗮𝗿𝗲: The way existing and new knowledge is presented to users determines its effectiveness. Every business must understand its knowledge-sharing practices—at its core, this is change management. 𝗥𝗲𝘂𝘀𝗲: Reducing redundant work is fundamental to knowledge management. Creating reusable knowledge leads to faster time-to-value for an organisation. For every instance of unsuccessful scaling of an AI solution, there is often a story of poor knowledge management. The more projects we complete at Insurgence, the clearer it becomes that effective and automated knowledge management is at the heart of successful AI adoption at scale. Yes, it’s not glamorous, but it drives progress for the initiatives that do capture attention. Step 1: Find great ideas for AI. Step 2: Build a mechanism to enable them to thrive throughout your organisation at scale. Mandatory component of Step 2: Knowledge Management. At Insurgence we're doing both. Feel free to reach out for a yarn on where AI could help out your team!

Buzzwords like Agile, Lean, and Six Sigma often dominate conversations about process improvement. They all have their place, but you don’t need a certification to start driving effective transformation. Here’s a simple, framework-agnostic way to approach process optimization: 1️⃣ Understand the as-is state – Map the current process, including tasks, ownership, time taken, and any delays. Visualizing the flow makes inefficiencies clear. 2️⃣ Assess value at each step – Ask: does this add value for the client, the business, or is it non-value add (NVA)? NVA steps are where bottlenecks and waste usually hide. 3️⃣ Validate and collaborate – Confirm your findings with stakeholders, then go further: involve them in shaping the “to-be” state. Co-creation builds stronger buy-in and better solutions. Processes are either sources of value and competitive advantage or the reason performance stalls. By stripping out non-value activities and collaborating on improvements, you can build efficiency at pace without overcomplicating things. 📬 If you want more practical insights like this, subscribe to my newsletter, Make Change Happen, and get actionable tips straight to your inbox. 👉 https://lnkd.in/gvax5faC

Continuing with the GenAI series, I am excited to share how we revolutionised the knowledge management system (KMS) for a leading client in the manufacturing industry. R&D teams in manufacturing often face the tedious task of manually sifting through complex engineering documents and standard operating procedures to ensure compliance, uphold safety standards, and drive innovation. This manual process is not only time-consuming but also prone to errors. To address this, we collaborated with our client to automate their R&D function’s KMS using Generative AI (GenAI). By allowing precise querying of specific sections of documents, our solution sped up access to critical information, reducing search time from hours to mere seconds. Our Generative AI team processed over 110 R&D-related documents, leveraging Large Language Models (LLMs) to generate accurate responses to complex queries. Hosted on a leading cloud platform with an Angular-based UI, the solution delivered remarkable benefits, including: - Significant accuracy in generated answers - Faster and more accurate data search and summarisation - Enhanced decision-making with easier access to critical R&D information - Improved overall employee productivity By implementing GenAI for knowledge management, the client's R&D function was also able to improve its competitive edge by tracking and responding quickly to market trends and consumer behavior. With plans to scale the solution to process over 1,500 documents across multiple departments, the client is creating a centralised hub for all their information needs. Taking advantage of GenAI can revolutionize knowledge management by delivering the right information to the right person on demand and enabling strategic impact. #GenAI #ManufacturingInnovation #KnowledgeManagement #GenAIseries #GenAIcasestudy #Innovation #R&D #DigitalTransformation #AI #Deloitte

For enterprises Knowledge as a Service (KaaS) is getting crucial for AI readiness. The knowledge layer needs to sit on top of existing enterprise systems, making organizational knowledge accessible, maintainable, and AI-ready while preserving existing operational capabilities and governance. Let me try to bring clarity to KaaS Knowledge Discovery and Mapping Map all operational databases and their relationships Identify data warehouses and their current analytical models Document unstructured data sources (documents, emails, process documentation, pictures, videos etc.) Catalog existing business intelligence reports and dashboards Knowledge Flow Analysis Map how data flows between different systems Identify key business processes and their data dependencies Document decision points that require knowledge access Knowledge Structure Development Categorize data based on business context and usage Identify critical knowledge areas and their relationships Create taxonomy for organizing enterprise knowledge Establish metadata framework for knowledge assets Knowledge Model Creation Design knowledge graphs connecting different data sources Create semantic relationships between business concepts Develop ontology for business domain knowledge Map data lineage across systems Technical Implementation Deploy knowledge management platform Implement connectors to operational databases and data warehouses Set up real-time data synchronization mechanisms Create APIs for knowledge access and retrieval Processing Pipeline Develop ETL processes for knowledge extraction Implement AI-powered categorization systems Create automated tagging and classification workflows Set up validation and quality control mechanisms Knowledge Transformation Enrich operational data with business context Create relationships between different knowledge components Implement version control and lifecycle management Integration Layer Connect knowledge platform with existing BI tools Enable knowledge discovery through search interfaces Implement role-based access control Create audit trails for knowledge usage AI Readiness Knowledge Componentization Break down complex information into AI-digestible components Create training datasets for AI models Implement RAG (Retrieval Augmented Generation) capabilities Develop knowledge validation workflows AI Integration Set up AI models for knowledge processing Implement machine learning for continuous improvement Create feedback loops for knowledge refinement Enable automated knowledge updates Operational Excellence Monitoring Setup Implement usage tracking and analytics Create performance dashboards Set up alerting for knowledge quality issues Monitor system performance and utilization Governance Implementation Establish knowledge management policies Define roles and responsibilities Create maintenance procedures Implement compliance controls #GenerativeAI #EnterpriseAI #LLMIntegration #AIImplementation #Innovation

🟢🔍 LOSS IDENTIFICATION: Are Your Efforts Leaking Away? 💸💧 Imagine pouring your time, money, and energy into operations… …only to watch it drip away through unseen inefficiencies. 📉 Just like a leaky bucket, even the best-run processes can lose value if we don’t identify and plug the holes. 👇 Let’s break down the 7 common operational “leaks” – and how to fix them! 🔧 1. Equipment Failure 🛠️ Loss: Downtime, repair costs, missed deadlines. 🔒 Plug the Leak: ✅ Preventive Maintenance (PM) ✅ Predictive Technologies (PdM) ✅ Operator Training & Inspections 📉 2. Quality Issues ❌ Loss: Scrap, rework, warranty claims, lost trust. 🔒 Plug the Leak: ✅ Rigorous Quality Control (QC) ✅ Six Sigma & Root Cause Analysis ✅ Team Ownership of Quality ♻️ 3. Material Waste 💸 Loss: Rising costs, waste disposal, environmental harm. 🔒 Plug the Leak: ✅ Lean Practices & 5S ✅ Accurate Forecasting ✅ Smart Inventory Control ⏱️ 4. Setup Loss (Changeover Time) 🛠️ Loss: Downtime, lost capacity, frustration. 🔒 Plug the Leak: ✅ SMED (Single-Minute Exchange of Die) ✅ Quick-Change Tooling ✅ Setup Standardization 🛋️ 5. Idle Time 😴 Loss: Underused labor & machines, high overhead. 🔒 Plug the Leak: ✅ Better Scheduling ✅ Cross-Training ✅ Workflow Optimization 🔄 6. Process Variation 🎢 Loss: Inconsistent quality, planning chaos. 🔒 Plug the Leak: ✅ Standardized Work Instructions (SWI) ✅ Statistical Process Control (SPC) ✅ Continuous Process Optimization 🐌 7. Slow Cycle Time 🐢 Loss: Bottlenecks, reduced throughput, missed demand. 🔒 Plug the Leak: ✅ Time & Motion Studies ✅ Process Streamlining ✅ Optimize Machine Speeds 🎯 Your investment deserves a tight, efficient system. Start with identifying your losses — that’s the first step to Operational Excellence. 💼✨ 📣 What hidden “leaks” have YOU found in your processes? How did you plug them? 💬 👇 Share your insights below – let’s learn from each other! #LossIdentification #LeanManufacturing #OperationalExcellence #ContinuousImprovement #SixSigma #Productivity #QualityFirst #BusinessEfficiency #Kaizen #ManufacturingMatters #surenderjhagta

As a Business Analyst who’s worked across multiple domains, I kept asking: "How can we analyze and improve processes while ensuring alignment with customer experience, automation opportunities, and real-world execution constraints?" So 𝐈 𝐜𝐫𝐞𝐚𝐭𝐞𝐝 𝐚 𝐧𝐞𝐰 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 & 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 called 𝐓𝐑𝐀𝐂𝐄—designed for Business Analysts, by a Business Analyst. 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: 𝐓𝐡𝐞 𝐓𝐑𝐀𝐂𝐄 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 A structured 5-step approach to analyze, redesign, and implement better business processes. ✅ T - Touchpoint Mapping Map every customer, system, and employee interaction throughout the process. ⏩ Why? Because pain points often lie hidden between handoffs and touchpoints. 🔸 Example: While improving a claims process in insurance, we mapped the customer journey and discovered that 4 out of 7 delays occurred during internal handoffs—not external approvals. ✅ R - Root Cause Discovery Go beyond symptoms. Use tools like 5 Whys, Fishbone diagrams, or even process mining to get to the bottom of inefficiencies. 🔸 Example: A healthcare provider noticed repeated data entry errors. Root cause? The patient registration interface required double entry into two systems due to poor integration. ✅ A - Automation & Adaptability Assessment Assess which parts of the process can be automated (RPA, AI, workflow engines), and how adaptable the process is to scalability, policy changes, or compliance. 🔸 Example: In a telecom project, we flagged a manual SIM activation step as a bottleneck. After RPA automation, processing time dropped by 85%. ✅ C - Change Impact Analysis Evaluate how proposed changes will impact stakeholders, systems, SLAs, and compliance. Build readiness through a Change Impact Matrix. 🔸 Example: In a bank’s loan onboarding process, changing document verification impacted 4 systems and 3 departments. Early impact analysis helped us prep all affected users and avoid go-live delays. ✅ E - Execution Blueprint Create a visual and documented blueprint of the improved process: • Swimlane diagrams • RACI matrix • System handoffs • Success metrics 🔸 Example: For a logistics firm, we redesigned the inventory return workflow. The execution blueprint became the training, UAT, and SOP foundation, saving 2 weeks of rollout effort. 𝐖𝐡𝐲 𝐓𝐑𝐀𝐂𝐄 𝐖𝐨𝐫𝐤𝐬: ✔️ Human-centric (starts at touchpoints) ✔️ Analytical (root cause and impact driven) ✔️ Future-ready (focus on automation and adaptability) ✔️ Grounded in BA tools (flows, matrices, UAT, change analysis) ✔️ Outcome-focused (delivers real, implementable blueprints) 𝐎𝐯𝐞𝐫 𝐭𝐨 𝐘𝐨𝐮: Would you try TRACE in your next process improvement initiative? 𝐋𝐞𝐚𝐫𝐧 𝐁𝐏𝐌𝐍 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐟𝐫𝐨𝐦 𝐦𝐞: https://lnkd.in/eYHriqm3 BA Helpline

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻/𝗥𝗲-𝗶𝗺𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻: 𝗕𝗲𝘆𝗼𝗻𝗱 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 In today's age of instant gratification and FOMO, there is a tendency to implement AI solutions without fundamentally re-imagining processes. This rush reflects a common misconception: that business process redesign must align with technology implementation. The reality? Process re-imagination should be a pre-cursor to technology adoption. 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲: When ERP systems were first introduced in the 1990s, they came with "best practices" positioned as a shortcut to process redesign. Organizations adopted these standardized processes believing they represented industry excellence. However, these so-called best practices didn't sustain long-term – they were too rigid and failed to account for unique organizational needs. Fast forward to today's AI implementation wave, and we're seeing history repeat itself. Organizations are rushing to implement AI solutions without fundamental process re-imagination, hoping technology alone will solve their inefficiencies. Like the ERP era, this approach risks embedding existing problems into new digital workflows. 𝗞𝗲𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀: 𝟭. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝗻 𝗶𝘁𝘀 𝗼𝘄𝗻 𝗺𝗲𝗿𝗶𝘁 • Process improvement can be achieved without technology • Focus on eliminating non-value-adding activities first • Challenge existing assumptions about how work is done 𝟮. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 • Don't automate inefficient processes • Technology should augment well-designed processes • Automation without process re-imagination perpetuates waste 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: Activity operation cost value (acceptable? Yes/No) Customer satisfaction level (1-5 scale) Does it create a bottleneck? (Yes/No) Do we have the right resources in place? Is it digitalized? (Yes/No) Is automation genuinely beneficial here? (Yes/No) Is it an audit compliance step? (Yes/No) Activity value to the process (1-10)? If less than three, should it be eliminated? Does this activity directly support our process goals? Are we adding value for the customer here?(Yes/No) Is it an internal activity or does it require external input? Stakeholders impacted Is this B2B or B2C, and are we treating it appropriately? 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: The goal isn't to automate everything possible, but to first ensure each process step adds value. Technology should enhance already streamlined processes, not mask inefficiencies. The lesson from both ERP and AI implementations is clear: sustainable process improvement comes from thoughtful redesign and re-imagination. #BusinessTransformation #ProcessImprovement #AI #Technology #BusinessProcessRedesign #DigitalTransformation #Innovation #Leadership

Focus on Knowledge Management NOW I have been working on the ServiceNow platform for over six years, and one common mistake organizations make is neglecting to mature their knowledge bases and articles. A poor ServiceNow knowledge base can make your entire platform feel bleak. It can be very frustrating when you want to introduce new capabilities, like the virtual agent, or improve your service catalog, but your knowledge bases lack sufficient articles. Organizations need to invest time in building a strong knowledge base before they can successfully develop more comprehensive IT Service Management workflows. Here are several ways organizations can build a strong knowledge base: 1. Conduct an audit of existing knowledge articles to identify which articles should be retired or updated. 2. Hire a dedicated Knowledge Manager responsible for updating existing knowledge articles and creating new ones. 3. Develop a knowledge management governance process for creating new articles to ensure consistency in formatting, a clear content strategy, and proper meta tagging. Create a knowledge article template for this purpose. 4. Establish a review and approval process involving the Knowledge Manager, subject matter experts, and key stakeholders. 5. Ensure that knowledge articles are appropriately linked within service catalog items, virtual agents, and other relevant ServiceNow portals.  6. Gather valuable feedback from end users to ensure that knowledge articles are useful and effectively address their requests and incidents. 7. Review the knowledge management data to identify which articles are viewed the most. This will help you understand how to improve other ITSM workflows related to your service catalog items and request forms. 8. Knowledge Management is not a one-time task; become comfortable with making continuous improvements. Listen to your end users, as they can help you make your knowledge bases better. How do you improve your knowledge articles? Comment below #ITSM #ServiceNow #KnowledgeManagement #ITIL