IT Service Management Platforms

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

When people hear process automation, they immediately think of RPA bots or developers writing scripts. But the reality is—Business Analysts (BAs) are at the core of identifying, mapping, and optimizing these processes before automation even begins. And here’s where AI is becoming a game-changer for BAs 👇 How AI Helps in Process Flow Automation ✅ 1. Auto-detecting Process Steps from Logs Instead of manually interviewing stakeholders for every step, AI can analyze system logs (like transaction trails or audit data) to suggest actual process flows. 👉 Example: In a banking project, AI mapped the “Loan Disbursement” process by analyzing transaction logs and identifying where delays occurred—saving weeks of manual discovery. ✅ 2. Converting Narratives into Flowcharts Stakeholders often explain processes verbally or in emails. AI can now convert these into BPMN diagrams or flowcharts automatically. 👉 Example: During an HR portal project, I uploaded meeting transcripts into an AI tool—it generated swimlane diagrams showing employee, HR, and finance interactions in seconds. ✅ 3. Identifying Redundancies & Bottlenecks AI doesn’t just map flows—it analyzes them. 👉 Example: In an eCommerce order management system, AI flagged multiple approval layers that added no value, helping us recommend an automated 2-step approval process instead of 5. ✅ 4. Automating Workflow Documentation Writing “As-Is” and “To-Be” process documents can take days. AI tools can auto-generate these from captured flows, with embedded decision points. 👉 Example: For a healthcare claim process, AI generated both process flows and a comparative “Gap Analysis” report—reducing documentation effort by 40%. ✅ 5. Testing Process Scenarios AI can simulate process runs to predict exceptions. 👉 Example: In an insurance claim flow, AI tested 1,000 “what-if” scenarios (fraud claim, missing document, duplicate entry) and highlighted rules that needed refinement before automation. 🚀 What This Means for Business Analysts Instead of spending time on manual mapping and documentation, BAs can now: ➡️ Focus on value-driven analysis ➡️ Validate AI-suggested flows with stakeholders ➡️ Recommend automation-ready processes faster AI is not replacing the BA role. It’s amplifying our ability to move from “process mappers” to process strategists. BA Helpline

If you think AI = ChatGPT, you're missing out. 7 tools to automate your work with AI: I've spent 15+ years building large software systems and automation. I've learned that the upfront cost of automating repetitive tasks leads to: - Huge time savings  - Better efficiency  - Fewer costly mistakes Today's AI automation landscape has changed everything. Here are 7 powerful tools that can transform your productivity: Top 7 Workflow Automation Tools ➡️ 1. N8N An open-source workflow automation tool that allows for both no-code and advanced custom coding. Self-hosted for full data control or paid cloud service. • Self hosting option (open source) • Most developer friendly option • Custom JavaScript/Python ➡️ 2. Make A powerful visual automation platform with AI agents and complex multi-step workflows. • Drag-and-drop interface (no-code) • AI agents recently added • Perfect for business process automation ➡️ 3. Zapier The leading no-code automation tool connecting thousands of apps through simple "if this, then that" logic. • Extremely beginner-friendly interface • Massive app ecosystem • Great for everyday business automation ➡️ 4. Relay This one was new to me, but I really like the UI. Collaborative workflow automation platform for team-based multi-step processes without coding. • Create AI agents that work for you • Popular tool integrations • Connect 100+ apps in minutes. ➡️ 5. Gumloop User-friendly platform for building AI-powered workflows without coding knowledge required. • Visual interface • Pre-built AI templates • Built for non-technical users ➡️ 6. FlowiseAI Open-source, low-code platform for building custom LLM applications and AI agents with visual nodes. • 100+ LLMs, Vector DBs • Developer friendly (SDKs) • Integrated traces ➡️ 7. Relevance AI Low-code/no-code platform specialising in AI-powered agents and data intelligence automation. • Complex business process automation • Multi-model AI support with rapid deployment • Best for teams handling large datasets My favourite quote on automation: ❤️ "Automation applied to an efficient operation will magnify the efficiency. Automation applied to an inefficient operation will magnify the inefficiency."- Bill Gates Which automation challenges are you facing in your business right now? --- Enjoy this? ♻️ Repost it to your network and follow Owain Lewis for more.

If you're running automations that handle sensitive data, here's how I'm implementing human-in-the-loop workflows to add a safety layer.   Just integrated Velatir into my n8n workflows, and it works quite differently from n8n's built-in HITL features.   Here's what happening:   I've been building automated workflows for clients, and when you're dealing with sensitive operations - payment processing, customer communications, data modifications - you may need that human verification step.   That's where Velatir comes in. It's a human-in-the-loop platform that adds approval checkpoints to any automation.   Example 1: Payment Processing Automation • Refund request comes in • If above a certain threshold, Velatir pauses the workflow • I get instant notification via email/Slack/Teams • I approve or reject with one click • Workflow continues or stops based on my decision   Example 2: Automated Email Responses • Email arrives from customer • AI drafts response • Velatir shows me the draft before sending • I verify it's appropriate and accurate • Email sends only after approval   What makes this different from basic approval systems:   → Customizable rules, timeouts, and escalation paths → One integration point, no need to duplicate HITL logic across workflows → Full logging and audit trails (exportable, non-proprietary) → Compliance-ready workflows out of the box → Support for external frameworks if you want to standardize HITL beyond n8n   The setup took about 5 minutes - sign up, get API key, add to your n8n workflow.   One interface, one source of truth, no matter where your workflows live.   Question for my network: What's the riskiest automation you're running without human oversight?

Batch scheduling tools are the silent backbone of enterprise IT operations. Whether it’s financial transactions, ETL pipelines, or mission-critical batch jobs, the right scheduler can make or break operational efficiency. 🚀 👉🏻 Here’s a quick comparison of some widely used enterprise schedulers: 🔹 AutoSys (by Broadcom Inc.) Known for its event-driven architecture and strong workload automation capabilities. AutoSys excels in distributed environments and offers robust job dependency handling. However, it may require a steeper learning curve and scripting expertise. 🔹 IBM Workload Scheduler (IWS) (by IBM) A powerful, scalable solution with deep integration into enterprise ecosystems. Ideal for complex workflows across hybrid environments. Strong in mainframe + distributed orchestration, but often considered heavyweight and costly. 🔹 Control-M (by BMC Software) One of the most popular modern schedulers. Known for its user-friendly interface, strong DevOps integration, and cloud readiness. Offers excellent visibility and monitoring, making it a favorite in digital transformation initiatives. 🔹 CA-7 (by Broadcom Inc.) A legacy mainframe scheduler, still widely used in banking and insurance sectors. Extremely stable and reliable for z/OS environments, but less flexible for modern, cloud-native workloads. 🔹 Stonebranch Universal Automation Center (by Stonebranch) A rising modern alternative with API-first architecture. Supports hybrid IT, cloud, containers, and microservices—gaining traction in newer deployments. 🔹 ActiveBatch (by Advanced Systems Concepts) Feature-rich automation platform with low-code capabilities. Strong in Windows/SQL Server ecosystems and widely used for data pipelines and IT process automation. 🔹 Redwood RunMyJobs (by Redwood Software) A SaaS-based scheduler designed for cloud-first organizations. Deep integration with ERP systems like SAP makes it popular in enterprise finance operations. 🔹 Apache Airflow (by Apache Software Foundation) Open-source and highly popular in data engineering. Ideal for orchestrating ETL/ELT pipelines with Python-based workflows. Not a traditional scheduler, but widely adopted in modern data stacks. 💡 So, which one is most popular? 👉 Control-M leads in modern enterprises due to its flexibility, UI, and cloud capabilities 👉 AutoSys & IBM IWS dominate large, complex enterprise environments 👉 CA-7 remains critical in mainframe-heavy industries 👉 Airflow & Redwood are gaining ground in cloud and data-driven ecosystems 📊 Industry Trend: Organizations are shifting toward unified, API-driven workload automation platforms that integrate with DevOps, cloud, and data pipelines. 🚀 Key Takeaway: There’s no one-size-fits-all. The “best” scheduler depends on your ecosystem, scale, and modernization goals. What’s your experience with these tools? Which one do you prefer and why? #WorkloadAutomation #BatchProcessing #DataEngineering #EnterpriseIT #DevOps

I just solved a workflow problem that was eating hours of my time every week - and I want to share how I did it. Like many content creators, I was manually converting my Beehive newsletter drafts into markdown for my website. Copy, paste, reformat, fix images, adjust embeds... you know the drill. It was tedious and error-prone. So I built a custom MCP (Model Context Protocol) server in Java that: • Connects directly to Beehive's API • Pulls draft content automatically • Converts HTML to my specific markdown format • Handles images, YouTube embeds, and Twitter posts • Creates files in the right directory structure The best part? I can just tell Claude: "Grab the latest draft and create the markdown file for my website" - and it handles everything. This isn't just another toy tutorial. It's a real solution to a real problem that saves me hours every week. The MCP server gives Claude the exact tools it needs to automate complex workflows that would be painful to script manually. I've even set up GitHub Actions to build native images for Mac, Windows, and Linux - so you don't need Java installed to use it. The source code is available on GitHub if you want to see how it works or build something similar for your own workflow. What manual tasks in your workflow could benefit from this kind of automation? Sometimes the best solutions come from scratching your own itch. Watch the full demo: https://lnkd.in/e-M2fMZy ##MCP #Java

Over the last few weeks, I’ve been exploring how we can apply AI and workflow automation to improve developer productivity in simple yet impactful ways. As part of a recent hackathon, I built a custom 1:1 productivity tool using n8n — a no-code workflow automation platform. The goal was to reduce the manual overhead developers and managers face after meetings and make knowledge capture seamless. 🛠️ Here’s what the workflow does: ✅ Pulls meeting transcripts from Google Drive 🧠 Uses LLMs to summarize them into Key Wins, Concerns, and Action Items 📊 Updates a structured spreadsheet or Notion table 🔁 Sends a weekly digest — no manual follow-ups needed This simple setup transformed how we track discussions and progress — reducing effort, increasing visibility, and keeping the team aligned without extra docs or meetings. The potential of AI + automation to reshape everyday developer workflows is massive — not just for managers, but for engineers, leads, and entire teams. If you’ve built your own workflows, I’d love to learn from them too. Drop a comment or DM 💬 #AI #DeveloperProductivity #n8n #Automation #LLM #DevTools #NoCode #WorkflowAutomation #EngineeringExcellence

It’s shocking how many hours we waste every week on the same tiny tasks...and no one really notices it happening. We deal with a lot of routine documents:   • Offer letters   • NOC letters   • Experience certificates   • Joining docs   • Contract updates   • Approval notes   • Invoice   • Payment approval   • Promotion letters   • Salary increment letters, etc. Different documents, but the same painful pattern every time: Open the template → replace details → format → export → upload → share → update the tracker It looks simple. It feels harmless. But when we repeat it dozens of times a month, it quietly drains hours without anyone realizing. So I built a workflow using n8n. All you have to do is fill out a small form. That’s it. Behind the scenes, the workflow: ✅ Picks the correct template ✅ Replaces every placeholder (names, dates, positions, anything) ✅ Formats numbers and dates ✅ Creates a clean folder for the output ✅ Drops all final documents inside ✅ Updates the Google Sheet tracker automatically The whole process finishes in about 5 seconds.   • No back-and-forth.   • No mistakes.   • No repetitive steps. And just like that, hours of manual work every month disappeared. Here’s the real point: This isn’t just about one document. Every team has small repetitive tasks like this - Ops, Finance, Admin, Sales, Projects…anyone. People think automations need 𝗯𝗶𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 to be worth doing. But the biggest gains often come from removing the smallest annoyances. One little workflow. One tiny form. A lot more time saved. So, I am curious: What’s one repetitive task you think should already be automated for your business/team? #n8n #WorkflowAutomation

This is my first time building a services company. In the past, my co-founder and I only built product companies. Product companies are infinitely scalable theoretically and VC fund-able, while services companies are not. And being honest, it's more fun to work on a product than a service. However, with AI, this has completely changed. There is a huge demand for services at the moment, so the real challenge is, how do you build an effective IT services company in the age of AI? Delivering services is messy and a lot of unscalable hard work. Think about it like this: You need to bring founder level energy to every project to be effective and deep dive into the details for every new client (and do this over and over and over again). So how do you solve this? The answer is, leveraging AI and LLMs to streamline the service delivery process. What do I mean by this? Implementing LLMs and Agents internally to help go from AI automation idea to SOW all the way to technical tickets being scoped out for engineers to work on. Here is what our current requirements gathering process looks like, using LLMs and AI Agents (this would’ve taken weeks and hours of meetings): 1. Clients interact with our conversational agent to explain their problem, workflow, and goals (Think of this as your technical project manager deep dive) 2. The agent translates this into a draft proposal with potential AI automation plans & options 3. Our team reviews, adjusts for budget + timeline, and turns it into a polished SOW 4. Client reviews the plans → approves the plans → we move to finalized SOW 5. From there, we auto-generate tickets in our project management system so engineers and PMs can execute immediately Clients go from idea to signed proposal + scoped plan in days. We still keep humans in the loop for quality and nuance. To me, this is a small but important breakthrough. One of the best applications of AI / LLMs is improving how IT projects & services are delivered, where the AI does the heavy lifting around requirements gathering, scoping and finalized planning. What scared me away from services before (i.e. the upfront grind) is now the part I actually look forward to. I can see this pattern applying to any services-like business, not just IT services delivery (think Legal / Accounting etc.).

#AutoCon3 From Clicks to Code: Optical Network Automation Journey at GARR Matteo Colantonio, Optical Network Engineer at GARR, shared their journey to automate the optical network at GARR, an Italian research network. They started by looking at widely adopted tools, including Ansible. It worked to help the team update 92 transponders However, they realized Ansible has scaling limitations when things get complex. In the optical layer, some devices don’t support NETCONF so you have to develop a module. If you have simple procedures, such as pushing config, Ansible is fine. But as you get into complex logic to configure services, not just boxes, you may want to reconsider your life choices. They also tried working with vendor controllers. Provisioning optical circuits can take 40 to 50 clicks across 4 GUIs. The vendor controllers sort of worked. It didn’t replace all the manual clicks. They still had to do manual pre-provisioning work, create cross-connections on some cards, and fix non-meaningful names, and add descriptions. They also don’t have a single optical line system, so the controller API only works with one vendor. The Workflow Orchestrator Framwork They discovered Workflow Orchestrator developed by SURF, a Dutch research network. It’s been open-sourced and lets other organizations adopt the framework. workfloworchestrator.org What do you get out of the box? -It’s a framework, not a turnkey solution, but it lets you define your network services or entities, or domain models for your organization -It lets you track instances -It defines clear procedures, or workflows Everything is stored and tracked in a database for object and relational mapping You start by defining building blocks, such an optical fiber. There’s a fiber name, terminiations, OSS ID, etc. You turn these blocks into Products to manage the lifecycle of a Block. Workflows make things happen. It uses Python functions, so you can do whatever you want. It can handle very complex logic. They went from 50 clicks and 15 to 20 minutes to an automated workflow that takes 50 seconds. Was it Easy? No. It’s harder than getting started with Ansible, but it was worth it. From this project they got: -Central service definitions -Consistent execution of service management -They have a consistent architecture -If new hardware comes in, they can modify clients without having to modify workflows Key Take-Aways: 1. If you want to develop a scalable, maintainable solution, the best option is to go with abstract and composable models, and to go with stateful instances of these models. 2. If you want your network to be programmable, use the devices’ programmable interfaces and YANG models, not just CLI 3. Make sure your transformation is sustainable. Automate one service at a time to nudge people out of their comfort zones