IT Service Management Platforms

What are 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗦𝘆𝘀𝘁𝗲𝗺𝘀? Here is an example of a simple (naive) RAG based Chatbot to query your Private Knowledge Base. (Advanced concepts covered in following weeks). First step is to store the knowledge of your internal documents in a format that is suitable for querying. We do so by embedding it using an embedding model: 𝟭: Split text corpus of the entire knowledge base into chunks - a chunk will represent a single piece of context available to be queried. Data of interest can be from multiple sources, e.g. Documentation in Confluence supplemented by PDF reports. 𝟮: Use the Embedding Model to transform each of the chunks into a vector embedding. 𝟯: Store all vector embeddings in a Vector Database. 𝟰: Save text that represents each of the embeddings separately together with the pointer to the embedding (we will need this later). Next we can start constructing the answer to a question/query of interest: 𝟱: Embed a question/query you want to ask using the same Embedding Model that was used to embed the knowledge base itself. 𝟲: Use the resulting Vector Embedding to run a query against the index in the Vector Database. Choose how many vectors you want to retrieve from the Vector Database - it will equal the amount of context you will be retrieving and eventually using for answering the query question. 𝟳: Vector DB performs an Approximate Nearest Neighbour (ANN) search for the provided vector embedding against the index and returns previously chosen amount of context vectors. The procedure returns vectors that are most similar in a given Embedding/Latent space. 𝟴: Map the returned Vector Embeddings to the text chunks that represent them. 𝟵: Pass a question together with the retrieved context text chunks to the LLM via prompt. Instruct the LLM to only use the provided context to answer the given question. This does not mean that no Prompt Engineering will be needed - you will want to ensure that the answers returned by LLM fall into expected boundaries, e.g. if there is no data in the retrieved context that could be used make sure that no made up answer is provided. To make it a real Chatbot - face the entire application with a Web UI that exposes a text input box to act as a chat interface. After running the provided question through steps 1. to 9. - return and display the generated answer. This is how most of the chatbots that are based on a single or multiple internal knowledge base sources are actually built nowadays. As described, the system is really just a naive RAG that is usually not fit for production grade applications. What are we missing? - Advanced Chunking/Retrieval. - Guardrails/Firewalls. - Evals. - Observability/Governance. - Scalability. More on this in the upcoming posts, so stay tuned in! #LLM #GenAI #LLMOps #MachineLearning

Instead of asking "what should I automate?" Focus on WHY you should automate and HOW it solves the data problem. Most data engineers automate the wrong things at the wrong time. Here's the framework I use after 8 years of building production systems: ✅ AUTOMATE WHEN: → Task runs daily/weekly → Human errors cause outages → Work blocks other priorities → Team growth = more manual work Examples: Reports, schema checks, alerts ❌ DON'T AUTOMATE WHEN: → Task happens quarterly → Requirements change weekly → Process isn't understood yet → Manual steps reveal insights My rule: If it’s done 3+ times, script it; 10+ times, automate it; fails 5+ times, redesign it. Automate what matters, when it matters—not everything! Here's how Airflow makes data automation ridiculously easy: 🎯 The Magic Triangle: → Scheduler: Triggers workflows on time → Executor: Distributes work to available workers → Workers: Actually run your Python code 💾 Smart State Management: → Metadata DB: Tracks every task run → Queue: Manages task priorities → Web UI: Visual monitoring & debugging 🔄 Why It Works: → Write Python DAGs once → Airflow handles the rest → Automatic retries & error handling → Parallel task execution → Visual dependency tracking Real Example: Instead of: ❌ Cron jobs that fail silently ❌ Manual dependency management ❌ No visibility into failures You get: ✅ Visual workflow monitoring ✅ Automatic failure notifications ✅ Smart task scheduling ✅ Easy debugging & restarting Image Credits: lakeFS The Bottom Line: Apache Airflow turns complex data workflows into manageable Python scripts. What's your biggest pipeline automation challenge? #data #engineering

How we turned a 5-minute task into 30 seconds. You’ll wonder why you didn’t do this sooner: “We don’t need Salesforce.” That’s what one client told me. They were running their customer service department through Asana. And it worked—kind of. The problem? Their internal ticketing process was slowing them down. Every sales rep had to create multiple tickets a day. Each one took five minutes. With 10 reps, that’s hours wasted every week. Here’s what we did: - We built a custom integration so they could create tickets directly in Salesforce. - No more switching tools. - No more wasted time. Now? Each ticket takes 30 seconds. Think about the impact: - A process that once burned hours is now done in minutes. The result? - A faster, more efficient customer service department. - A team that’s happy to use Salesforce because it works for them—not against them. Sometimes it’s not about convincing someone to trust Salesforce. It’s about showing them what’s possible. P.S. What’s one process in your business that’s taking too long right now? Thanks for reading. Enjoyed this post? Follow Jordan Nelson And share it with your network.

Business value in IT is never abstract; it emerges when technology choices are read through stakeholder needs, measured in outcomes that matter, aligned with business services, and communicated in a language that speaks to shared objectives. The key is not in technology alone but in the way it reflects what stakeholders truly value. Numbers confirm this perspective: according to Gartner, companies that translate IT impact into business terms increase executive confidence in digital investments by over 30 percent. This means that alignment between IT costs and business services is not a technical exercise, it is a way to ensure that every decision resonates with objectives that matter at the organizational level. Building value narratives that address both change and continuity strengthens this approach, because it provides leaders with a clear picture of how technology supports evolution while sustaining daily operations. Communicating this value in the language of stakeholders is essential. If those who fund IT understand the link between investment and strategic outcomes, the conversation moves from cost to shared value creation. #DigitalTransformation #ITStrategy #BusinessValue #Stakeholders #TechnologyLeadership

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!

🚀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 + 𝗛𝘆𝗽𝗲𝗿𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: 𝗛𝗼𝘄 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗡𝗼𝘄 𝗶𝘀 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 2025 marks a tipping point where #agenticAI is no longer experimental—it’s enterprise-ready. Organizations are moving from pilots to enterprise-wide rollouts, backed by frameworks for integration, governance, and scalability. 𝗪𝗵𝘆 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗡𝗼𝘄 𝗦𝘁𝗮𝗻𝗱𝘀 𝗢𝘂𝘁: While major players like Amazon Web Services (AWS) (AgentCore, Strands), Salesforce (Agentforce), Google, and Microsoft are making big strides, ServiceNow is becoming the backbone of enterprise #hyperautomation. Here’s how ServiceNow is shaping the future:  • 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: ServiceNow integrates AI-powered agents into the Now Platform, orchestrating complex IT, HR, and customer workflows with minimal manual intervention.  • 𝗛𝘆𝗽𝗲𝗿𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲: Combining ServiceNow’s process mining, low-code automation, and GenAI allows enterprises to unify siloed systems and scale automation enterprise-wide.  • 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: ServiceNow’s AI governance capabilities ensure traceable, secure, and compliant agentic AI deployments, which is becoming a board-level priority.  • 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗢𝗽𝘀: The platform’s AI Agent Builder (released this year) allows organizations to design specialized agents for IT service management, reducing resolution times and enabling 24/7 digital workforce augmentation. 𝗧𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝗿 𝗧𝗿𝗲𝗻𝗱: Gartner and Deloitte predict that agentic AI will drive the next era of #hyperautomation by:  • Streamlining end-to-end workflows.  • Enabling cross-functional "𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗰𝗼-𝘄𝗼𝗿𝗸𝗲𝗿𝘀" that act autonomously.  • Accelerating time-to-value for enterprise transformations. 💡 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: ServiceNow isn’t just adopting #agenticAI; it’s building the infrastructure for the AI-driven enterprise of tomorrow. The #NowPlatform is increasingly where strategy, execution, and automation converge. 𝙃𝙤𝙬 𝙙𝙤 𝙮𝙤𝙪 𝙨𝙚𝙚 𝙎𝙚𝙧𝙫𝙞𝙘𝙚𝙉𝙤𝙬’𝙨 𝘼𝙄-𝙙𝙧𝙞𝙫𝙚𝙣 𝙝𝙮𝙥𝙚𝙧𝙖𝙪𝙩𝙤𝙢𝙖𝙩𝙞𝙤𝙣 𝙩𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢𝙞𝙣𝙜 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙞𝙣 2025? Let's discuss in the comment 👇 Onboarding. ✅ Self-service. ✅ Personalized support. ✅ Agentic AI is taking employee experience to the next level—and the results speak for themselves. See how companies like Mondelēz International and Danone are getting it right: https://lnkd.in/gW9T4dxP #AgenticAI #Hyperautomation #EnterpriseAI #AIFirst #AITransformation #GenAI #ServiceNow #NowPlatform #DigitalWorkflows #ITAutomation #ITSM #CustomerExperience #FutureOfWork #TechTrends2025 #AIInnovation #DigitalTransformation #EnterpriseAutomation #CIOInsights

𝐈𝐓 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐬 (𝐈𝐓𝐆𝐂) 𝐂𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭 𝐀𝐜𝐜𝐞𝐬𝐬 𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐬 -User access provisioning and de-provisioning processes are established. -Access rights are assigned based on job responsibilities. -Segregation of duties (SoD) controls are in place. -Regular access reviews are conducted. -Strong password policies are enforced. 𝐂𝐡𝐚𝐧𝐠𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 -Formal change management processes exist for all system changes. -Changes are documented, approved, and tested before implementation. -Segregation of duties between development, testing, and production environments. -Regular reviews of change management are conducted. 𝐁𝐚𝐜𝐤𝐮𝐩 & 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 -Regular backups of critical systems and data are performed. -Backup integrity checks are regularly conducted. -Backup and recovery procedures are documented and tested. -Off-site storage of backups is maintained for disaster mitigation. 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 -Formal incident response plans are in place. -Procedures for reporting and documenting incidents are established. -Incident response teams are trained and ready. -Post-incident reviews are conducted for improvement. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 -Intrusion detection/prevention and antivirus are deployed. -Network segmentation minimizes breaches. -Regular vulnerability assessments and penetration testing are conducted. -Wireless network security controls prevent unauthorized access. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 -Policies protect sensitive data. -Data encryption is used in transit and at rest. -Data classification policies categorize data by sensitivity. -Regular data privacy training for employees. 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 -Logging mechanisms record security-related events. -Regular review and analysis of logs for security incidents. -Monitoring of system performance and availability. -Intrusion detection systems monitor suspicious activity. 𝐕𝐞𝐧𝐝𝐨𝐫 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 -Vendor risk assessments before engaging third parties. -Vendor contracts include security and compliance provisions. -Ongoing monitoring and oversight of vendor activities. -Procedures for terminating vendor access. 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 & 𝐀𝐮𝐝𝐢𝐭 -Regular compliance assessments and audits. -Documentation of IT policies, procedures, and controls is maintained. -Remediation of control deficiencies or non-compliance issues. #kpmg #periodicreviews #cybersecurity #itgc #technology #learning

Why most ITSM efforts fall flat❓ Most teams focus on the wrong things. They set up tools. They define rules. They check the boxes. 🤔 But they miss what really matters. Gartner reports that 50% of ITSM efforts fail to deliver value. Why? Because they focus on tools, not real outcomes. The result? Wasted time. Rising costs. Frustrated users. ⚡ Here’s the truth: Tools don’t solve problems. People and processes do. Where should you start? Fix these 8 areas: 1️⃣ Incident Management 🛑 Hidden Challenge: Teams focus too much on speed, not learning. 💡 Action: Track recurring patterns, not just ticket closures — use repeated incidents to fix upstream issues. 2️⃣ Change Management 🛑 Hidden Challenge: The same people review and approve changes, creating blind spots. 💡 Action: Add an outside observer to review high-risk changes. 3️⃣ Problem Management 🛑 Hidden Challenge: Tech teams miss the user view of recurring issues. 💡 Action: Invite customer-facing teams to problem reviews. 4️⃣ Configuration Management (CMDB) 🛑 Hidden Challenge: Trying to add everything upfront. 💡 Action: Focus only on critical services — map what matters most to keep the business running. 5️⃣ Service Request Management 🛑 Hidden Challenge: Teams buried under repetitive manual work. 💡 Action: Identify top 5 frequent requests and automate them (e.g., password resets, access requests). 6️⃣ Service Level Management 🛑 Hidden Challenge: SLAs that don’t reflect real business needs. 💡 Action: Involve business stakeholders when defining SLAs — ensure they align with actual expectations and outcomes, not just IT targets. 7️⃣ Knowledge Management 🛑 Hidden Challenge: Outdated, unused articles pile up. 💡 Action: Assign owners to key articles and set a quarterly review cycle to keep knowledge fresh. 8️⃣ Continual Service Improvement 🛑 Hidden Challenge: Waiting for “big” improvement projects. 💡 Action: Embed small improvement actions into weekly meetings — even tiny, regular wins add up over time. Tools are just part of the puzzle. People carry the real weight. Teams create the results that last. Which of these is your team missing? Share your thoughts below. ♻️ Repost this to help others in your network. ➕ Follow me (Maxime Saporta) for more.

Still confused by CMDB vs CSDM in ServiceNow? You’re not alone! Here’s a simple breakdown using a relatable analogy 👇 Think of your CMDB like a giant Excel file for IT. And CSDM? It’s the pivot table and playbook that gives it structure, meaning, and enterprise reach. 🗂️ CMDB = Your IT Inventory + Relationship Map + Operational Backbone ➕ Like a master spreadsheet of IT assets → Tracks everything from physical devices (servers, routers) to logical services (apps, databases) as Configuration Items (CIs) ➕ Like using cell references and formulas → Maps relationships and dependencies between items, enabling impact analysis and service mapping ➕ Like combining spreadsheets with real-time dashboards → Supports Change, Incident, and Problem Management for faster root cause analysis ➕ Like a live data model with integrity checks → Includes CMDB Health, Identification & Reconciliation, and Data Certification to keep data clean ➕ Like the foundation for automation and digital workflows → Powers Service Mapping, AIOps, ITSM, Risk, ITAM, and more 🧠 CSDM 5 = The Enterprise & Operational Service Model ➕ Like a pivot table on top of your spreadsheet → Organizes CMDB data into service-aligned, enterprise-ready structures ➕ Like a playbook for modeling services → Prescribes how to structure data and relationships so workflows across IT, business, and ops connect ➕ Like built-in standards across tabs → Ensures consistency across all ServiceNow products and better-together use cases ➕ Like grouping rows by category → Connects infrastructure to business services, capabilities, and outcomes ➕ Like laying the foundation for AI → Provides structured, trustworthy data for digital transformation and value networks 💡 Why CMDB Matters ✔️ Visibility into your IT environment ✔️ A unified view of configuration items and their connections ✔️ Better incident, change, and problem management ✔️ A baseline for automation, compliance, and audits 💡 Why CSDM 5 Matters ✔️ A common language for IT, business, and ops ✔️ Alignment between technical components and enterprise goals ✔️ A foundation for AI-enabled transformation ✔️ A model that scales beyond IT to the whole business 📈 The Impact? ✔️ Faster incident resolution ✔️ Smarter decision-making ✔️ More effective service mapping ✔️ Stronger alignment with business outcomes ✔️ Readiness for AI-driven insights 📌 Without CSDM, your CMDB is just rows and columns. With CSDM 5, it becomes a connected, decision-ready system for the entire enterprise. 🤔 What’s been the hardest part of your CMDB and CSDM journey? Let’s share tips and lessons learned 👇 ♻️ Repost this with your thoughts to help others mature their ServiceNow CMDB with CSDM 5. ➕ Follow me, Nicole Hoyle with AJUVO, for ServiceNow success. #AJUVODeliversNow #NicoleOnNow #CSDM5 #CMDB

AI that resolves requests before a ticket exists. Most “AI for IT” just routes faster. Resolve’s agents actually remove the ticket, shifting from automation to autonomy. What I saw Resolve’s service desk agent, RITA, handles real requests right where they start (Slack/Teams), then verifies policy, talks to your tools, and posts proof when it’s done. The architect agent, Jarvis, turns a plain-English description into a production-ready workflow, with guardrails and approvals baked in. Zero Ticket™ IT This isn’t “faster triage.” It’s no routing at all for the common stuff—requests get verified, executed, and closed at the edge. Fewer handoffs, less SLA ping-pong, more proactive fixes. That’s the real productivity unlock. Scale + ecosystem fit It drops into the stack you already run—ITSM, IdP, MDM, CMDB, observability—with no-code when you want speed and full-code when you need control. And yes, there’s a deep integration library so you can orchestrate end-to-end instead of stitching scripts together. For one of my enterprise clients, we started with the noisy “quick wins”: access requests and device fixes. RITA now handles them in chat with policy checks and audit trails; Jarvis converted a manual new-hire runbook into an automated flow (groups, apps, channels, MDM baseline, manager approval, rollback plan). Within weeks, ops was spending more time on real incidents and fewer cycles on copy-paste tickets. The vibe shift was obvious: less queue, more resolution. If you’re chasing real AI ROI, look for platforms that replace repetitive human intervention. That’s the architecture shift to autonomy. https://resolve.io