Workflow Automation Hacks

Many of you reached out about creating a clear, visual guide to CI/CD on Azure. Well, here it is – hot off the press! First off, let's talk about what makes this Azure setup unique: 1. Azure Boards Integration: Unlike traditional CI/CD, we're tightly coupling project management with our pipeline. This means better traceability from idea to production. 2. Built-in Security Scans: Azure DevOps doesn't just build; it bakes in security at every step. This is crucial in today's threat landscape. 3. Environment Specificity: Notice how we have distinct pipelines for Dev, QA, and Prod? This isn't just about separation; it's about tailoring the deployment process for each environment's unique needs. 𝗡𝗼𝘄, 𝗹𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: • Developer Experience: We start with Azure Boards for task management, feeding directly into your favorite IDE. Commit to Azure Repos, and the magic begins. • Build Pipeline: Here's where it gets interesting. We're not just compiling; we're running unit tests, code analysis, and those crucial security scans. It's like having a whole QA team in your pipeline! • Release Stages: Dev → QA → Prod. Each stage is a mini-adventure, with its own deployment steps and validation processes. It's like your code is leveling up! • App Service: The final destination. Your app, scale-ready and eager to serve. Pro Tip: Leverage Azure's Deployment Slots for zero-downtime deployments. It's a game-changer for high-availability apps. What's your take on this setup? Have you tried something similar?

🚀 While building a scalable data platform for consumers, infrastructure deployment and resource management are critical aspects. Over the past few months, I’ve had the opportunity to optimize Terraform workflows and Azure DevOps pipelines extensively, and I wanted to share some key lessons (while there are many) that significantly improved performance and reliability. 🚅 Concurrency in Plan and Apply Leveraging concurrency during the plan and apply stages speeds up execution, especially when dealing with large and complex environments. Setting appropriate parallelism levels can drastically cut down deployment time. 📬 Using Plan Output as Input for Apply A simple but effective optimization is passing the plan output directly as input for the apply stage. This avoids surprises caused by runtime drifts and ensures the changes being applied match exactly what was planned. 🎯 Targeted Resource Updates with --target When we already know the impacted resources and the Terraform state is consistent, using the "--target" flag allows us to apply changes to specific resources instead of the entire stack. This has been a game-changer for quick updates and fixes without unnecessary redeployments. 📢 Publishing Plan Results in CI/CD Pipelines We adopted "publishPlanResults" in TerraformCLI to display plan results directly in the Azure DevOps pipeline execution tab. This added transparency, making it easier to review changes before applying them. 🧱 Modularization and Dependencies Management Organizing configurations into modules helped us maintain reusable, scalable, and manageable code. Adding "depends_on" where needed preserved resource dependencies and eliminated race conditions during deployment. ✅️ Staying Updated with Terraform Versions Finally, upgrading to the latest Terraform versions ensured we benefited from new features, performance improvements, and security enhancements. Keeping dependencies up-to-date is often overlooked but makes a big difference in the long run. ⛳️ These practices not only improved the performance and stability of our Terraform and Azure DevOps pipelines but also made the overall process more transparent and predictable. #Terraform #InfrastructureAsCode #AzureDevOps #DataPlatforms #Automation

About 12-18 months ago I posted about how AI will be a layer on top of your data stack and core systems. It feels like this trend is picking up and becoming a quick reality as the next evolution on this journey. I recently read about Sweep’s $22.5 million Series B raise (in case you're wondering, no, this isn't a paid ad for them). If you're not familiar with them, they drop an agentic layer straight onto Salesforce and Slack; no extra dashboards and no new logins. The bot watches your deals, tickets, or renewal triggers and opens the right task the moment the signal fires, pings the right channel with context, and follows the loop to “done,” logging every step again in your CRM. That distinction matters for CX leaders because a real bottleneck isn’t “more data,” it’s persuading frontline teams to actually act on signals at the moment they surface. Depending on your culture and how strong of a remit there is around closing the loop, this is a serious problem to tackle. You see, when an AI layer lives within the system of record, every trigger, whether that is a sentiment drop, renewal milestone, or escalation flag, can move straight to resolution without jumping between dashboards or exporting spreadsheets. The workflow stays visible, auditable, and familiar, so adoption happens almost by default. Embedding this level of automation also keeps governance simple. Permissions, field histories, and compliance checks are already defined in the CRM; the agent just follows the same rules. That means leaders don’t have to reconcile shadow tools or duplicate logs when regulators, or your internal Risk & Compliance teams, ask for proof of how a case was handled. Most important, an in-platform agent shifts the role of human reps. Instead of triaging queues, they focus on complex conversations and relationship building while the repetitive orchestration becomes ambient. This means that key metrics like handle time shrink, your data quality improves, and ultimately customer trust grows because follow-ups and close-outs are both faster and more consistent. The one thing you will need to consider is which signals are okay for agentic AI to act on and which will definitely require a human to jump on. Not all signals and loops are created equal, just like not all customers are either. Are you looking at similar solutions? I'd be interested to hear more about it if you are. #customerexperience #agenticai #crm #innovation

I built Crew Agent Generator, a Streamlit app that turns plain English instructions into fully configured CrewAI agent teams. No need for complex scripting—just describe your needs, and it figures out the rest. For example, I simply typed: "I need a travel planning team for a 7-day Japan trip, including itinerary creation, budget management, and local culture insights." ✨ The tool instantly generated specialized agents for itinerary planning, budgeting, and cultural research, each with the right tools. What I Learned: ✔ Simplicity is key—Streamlit makes AI automation easier. ✔ Smart prompt analysis ensures relevant agent configurations. ✔ Flexibility matters—users can tweak results as needed. Link to blog - https://lnkd.in/gWuESZ6d If this sounds interesting, let's connect & collaborate! 🚀 #ai #automation #openai #llm #agents #crewai #machinelearning

When an Account owner changes in Salesforce, business users often expect all related records (Contacts, Cases, Opportunities, Orders, Invoices, etc.) to follow the new owner. But this is not standard behaviour for custom objects, and even some standard ones too. There are common ways to approach this — multiple Flows, object-specific triggers, or scheduled jobs. Each works, but they tend to be either hard to maintain, fragmented, or not real-time. I wanted a design that was scalable, maintainable, and declarative where possible. Here’s what I built: 1 - A record-triggered Flow, which detects the Account ownership change. 2 - The Flow invokes a single Apex method that performs the ownership cascade. 3 - A Custom Metadata Type defines which objects are included, and which lookup field ties them to the Account. - The Apex dynamically queries and updates the related records in a bulk-safe way. This approach isn’t the only valid one. You could use separate triggers on each child object, or even solve access concerns with Territory Management or sharing rules. But in this case, explicit ownership needed to change, and I wanted to avoid scattering logic across multiple places. What makes this design valuable is how it balances trade-offs: • Configurable: adding or removing objects is a metadata update, not a code change. • Bulk-safe: it can handle a single update or a large batch without hitting limits. • Separation of concerns: Flow handles orchestration, Apex handles logic. • Hybrid approach: declarative where possible, programmatic where necessary. Lesson learned: the best Salesforce solutions often come from combining declarative tools with programmatic techniques, rather than forcing one approach. By using metadata to control Apex behaviour and letting Flow handle orchestration, you get something that is scalable, flexible, and still admin-friendly. #Salesforce #SalesforceArchitect #SalesforceFlow #Apex #CustomMetadata #SolutionArchitecture #Automation #ClicksNotCode #LowCode #ProCode #SalesforceConsultant #SystemDesign

How I Automated Travel Management and Made Life Easier for a Busy Executive Managing travel logistics for a high-profile executive is no small task. As a Virtual Assistant, I was juggling flight bookings, hotel reservations, itineraries, and last-minute changes. It was overwhelming—until I automated the entire process. The Problem: Handling all aspects of business travel manually became increasingly difficult as the executive’s travel frequency ramped up. From booking flights and hotels to managing itineraries and tracking expenses, it was a struggle to keep everything organized and error-free. My Solution: I built a comprehensive travel management system using Make.com, integrating tools like Google Calendar, Kayak, Booking.com, Gmail, and Expensify to automate every step of the process. Here’s how I did it: 1. Automated Flight and Hotel Bookings     - I connected Kayak, Booking.com, and Google Calendar to automate the search and booking process based on travel dates and preferences. Once approved, bookings were confirmed and added to the calendar without any manual input. 2. Effortless Itinerary Creation    - I used Google Drive and Gmail to automatically compile and share detailed travel itineraries. Any changes in bookings were instantly reflected in the itinerary, ensuring everyone was always up-to-date. 3. Proactive Travel Alerts     - By integrating Kayak and Slack, I set up real-time travel alerts for any delays or cancellations. This allowed me to react quickly and make alternative arrangements, keeping the executive on schedule. 4. Streamlined Expense Tracking   - I automated expense tracking using Expensify and Google Drive. Receipts were automatically organized, categorized, and compiled into a report for easy review and approval. 5. Pre- and Post-Travel Checklists    - I created automated checklists to ensure all tasks were completed before and after each trip. These checklists were shared via Slack and updated in Google Calendar, making sure nothing was missed. The Results? - 50% Time Saved: Automation cut down the time spent on travel arrangements by half, allowing me to manage more trips efficiently. - Enhanced Accuracy: Real-time updates and alerts reduced errors, ensuring a seamless travel experience. - Improved Communication: Automated sharing of itineraries, alerts, and expense reports kept everyone in the loop. - Organized Expense Management: All travel costs were tracked and reported effortlessly, simplifying the reimbursement process. I’m Munirat Asubiaro, a Virtual/Executive Assistant and Business Process Automation Specialist. I help busy professionals streamline their business and workflows. Which workflow would you like me to automate next? Questions about this setup? I’m here to help! If you found this helpful, share it with your network!

✅ USE CASE: Automated Proposal Delivery Using Make ➕ Salesforce ➕ Google Workspace 🧩 Business Need: A client needed to automatically send personalized business proposals to every new lead they received through Google Forms (stored in Google Sheets). The proposals had to be generated dynamically based on existing Salesforce data and delivered through Gmail as part of their sales workflow. They were already managing customer records in Salesforce, but they didn’t want their sales team spending time checking, copying, pasting, and emailing manually. ⚙️ Solution: We designed a simple and fast automation flow using Make, that integrates natively with Google Workspace and Salesforce. 🔁 End-to-end process: Lead Entry via Google Sheets: New leads are collected through a Google Form and saved into a spreadsheet. Check if Lead Exists in Salesforce: Make searches Salesforce using the lead’s email address to see if it already exists. Routing: ✅ If the lead exists: → We fetch all lead details from Salesforce → Auto-generate a business proposal using a Google Docs template → Create a Gmail draft with the proposal link and a personalized message ❌ If the lead does not exist: → We create a new Lead record in Salesforce → Update the Google Sheet to reflect that the lead has been inserted 💡 Although this could be built using native Salesforce Flows or Apex, it would take more time and involve more complex setup and testing. We chose Make because: It has native connectors for Gmail, Google Docs, Sheets, and Salesforce. It allows us to build and test fast. It’s ideal for prototypes, MVPs, or teams that want to move quickly without developer resources. 🔎 What we considered during implementation: ✅ Error handling: We know Salesforce can fail silently if required fields are missing. We made sure to build conditional checks and fallback steps (e.g. logging status in Google Sheets). ✅ API limits and quotas: Google Sheets has daily limits on write operations. We added row limits and included logic to avoid overload (e.g., processing only unprocessed leads marked as Processed = FALSE). ✅ Data validation: Each field passed to Salesforce or Gmail is validated and formatted to reduce rejection or formatting issues. 🎯 Impact: Business proposals are generated and sent in seconds, not hours. No more copy-pasting or manual entry. Sales team focuses on real conversations, not admin work. The process is scalable, trackable, and easily editable. 📣 This is just one of many ways to combine Make + Salesforce + Google Workspace to automate real business problems. #Salesforce #Automation #Make #CRM #GoogleSheets #LeadManagement #WorkflowAutomation #Gmail #Proposals #Productivity

🔧 New Blog: Advanced Azure DevOps & Microsoft Dataverse Integration via Power BI Dataflows 🔧 Just released a detailed exploration into bridging Azure DevOps with Microsoft Dataverse through Power BI Dataflows. This isn't just a how-to guide; it's a journey through the maze of technical challenges and innovative solutions: - Mastered authentication with Personal Access Tokens where basic authentication falls short. - Navigated Azure DevOps API's `POST` request limitations using the M language in Power BI. - Creatively leveraged Power BI and Dataverse dataflows to bridge connectivity gaps caused by missing direct connectors and authentication types. - Demonstrated how to effectively chunk data for API calls within Azure DevOps' limits, ensuring smooth data transfer without hitting those ceilings. Targeted at developers, data engineers, and anyone passionate about tech, this blog dives deep into the subtleties of integrating these powerful platforms to streamline project management and data analysis. Enhance your integration skills and explore sophisticated solutions to complex problems: https://lnkd.in/gj_R43jj #AzureDevOps #MicrosoftDataverse #PowerBI #DataIntegration #TechnicalSolution