Workflow Automation Case Studies

I recently saw the release of Anthropic integration with Webflow. At first, I thought it was just another AI announcement. Then I decided to actually test it. The result? It saved us over 6 hours of work. Here’s our use case: We’re currently migrating a website from WordPress to Webflow. One of the most time-consuming parts of a migration is moving over SEO metadata, things like: - meta titles - meta descriptions - and making sure they match the right pages That gets even more time-consuming when the site is large. In this case, we’re working with 200+ pages. To speed this up, I used Claude to create a simple scraping workflow that pulled the meta titles and descriptions from a list of URLs I provided in a Google Sheet. Then the Webflow + Anthropic integration did the part that made this really useful: It matched those old WordPress URLs to the corresponding pages in the new Webflow build, and applied the correct meta titles and descriptions directly to those pages. After reviewing the output, there were a few small errors here and there (expected), but nothing we couldn’t clean up quickly. The result: ~6 hours saved on a repetitive migration task, while our team stayed focused on QA and the rest of the build. That’s the kind of AI use case I care about! Not replacing expertise, but removing manual work so teams like ours can move faster. This partnership is gold! Allan Leinwand 🙏 #webflow #ai

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

#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

I finally built what I've wanted to automate for years: win/loss analysis. One of the most critical yet manual workflows in B2B GTM to understand the drivers and detractors of winning deals. Most companies only run win/loss every quarter because of how cumbersome it is. But by the time you spot a pattern, you've already lost 15 deals to the same issue. Automating this enables companies to proactively catch patterns in real-time and act on them immediately - a necessary foundation in today's fast moving world of AI. Here's the flow: 1. Brainstorm with Claude (or your LLM of choice) on the best approach and get detailed setup instructions at each stage. 2. Use Zapier to automatically sync relevant Salesforce fields to Google Sheets - no more manual downloads. 3. Build a live dashboard in Lovable to analyze themes over time and cut the data any way you want. Key lessons from building this: 🔍 Sense-check your work. Claude originally told me to spend API credits analyzing each deal individually, but the real value is in analyzing aggregate patterns. Always question if a solution is actually the most relevant. 📝 Plan upfront. Like any project, a good plan and assessing your options saves time in the long run. 🤖 Pick workflows worth automating. Anything you do weekly or monthly is a good candidate for an agent. 🛑 Expect roadblocks. You'll hit issues and need to do a good amount of debugging, but quick wins will keep you motivated. ⏰ Invest the time. Dedicate at least as much time as you'd spend manually. The learnings compound fast. Feeling relieved and excited about what's possible. Next up: layering in Gong transcripts, customer interviews, product feature requests, and more. What workflow are you automating next? Drop a comment - I'd love to compare notes and get ideas! 

Yesterday I posted a case study on how we reduced a client's time to contract and invoice by 30% and saved them 5-7 hours per week. Here's exactly how: After posting this yesterday, I'm receiving a lot of messages asking how we did it. I thought I'd make a post about this. Here's exactly how we did it: First, we mapped out the process. Before working with us, the company relied on a fragmented and unreliable system. Their order-taking, contracting, and invoicing processes lacked automation, leading to delays, errors, and a poor experience for both their team and clients. Then we optimized it. We designed a fully integrated workflow that begins with a Typeform order form, which feeds directly into Monday and Airtable to manage requests, generate contracts, and track invoices with a Softr interface for easy access to order updates and relevant documents. Then we implemented. The new system helped the sales team save approximately 5-7 hours per week by streamlining client intake and ensuring name cohesion across tools. It also reduced the time it took to send invoices and contracts by about 30%. Finally, we optimized again after implementation. Key features include automated contract and invoice generation, real-time order tracking, and a client-facing portal built with Softr. All of which improved efficiency, accuracy, and the overall client experience. The result? A centralized, user-friendly experience that eliminated manual steps and improved operational efficiency. The takeaway: Don't just automate. Optimize first, then implement, then optimize again based on real usage. Follow me Luke Pierce for more automation case studies like this.

What’s working in the land of AI workflows? (Real examples of how companies are building time-saving AI workflows below) I’ve been seeing a lot of companies buy AI tools without thinking through the whole workflow and then saying it’s not driving ROI. But what teams should be doing is to: -Start with workflows, not tools. The best teams figure out what eats up time first, then plug in the right AI where it fits -Think in systems, not tool collections. Instead of managing 20+ disconnected tools, build intelligent workflows with 7-8 foundational pieces that work together In catching up with Boz Vitanova, founder of TeamLift, a platform helps teams level up by embedding AI into real workflows, we were talking through the fact that once you've mapped the workflow, it's about building your system: core automation (Zapier/Make/N8N) + intelligence layer (ChatGPT, Claude, Copilot) + specialized connectors for your specific needs. She gave me hard data on how much time her clients are saving so sharing real examples from teams she's working with using this approach: Sales: Meeting → Content → Follow-up Pipeline >Captures sales meetings, creates concise summaries with next-step checklists, drafts personalized follow-up, and saves highlights into CRM. >>Foundation stack: Make.com (automation) + ChatGPT (intelligence) + Fathom (recording) + Gmail (email) + HubSpot (CRM) >>>Outcome: Saves up to 2 hours a day in follow-ups by removing manual write-up. Ops: Automated KPI Reporting >Pulls metrics from spreadsheets and tools to produce readable weekly status digests with trends and prioritized recommendations. >>Foundation stack: Zapier (automation) + Copilot (intelligence) + Excel (data) + SharePoint (storage) + Teams (distribution) >>>Outcome: Reclaim 7-8 hours per week previously spent compiling reports. HR: One-Click Talent Profiles >Takes a LinkedIn profile URL and automatically creates structured candidate profiles with AI summaries, rubric scores, and outreach messages. >>Foundation stack: Zapier (automation) + ChatGPT (intelligence) + PhantomBuster (LinkedIn data) + Clearbit (enrichment) + Affinity (ATS/CRM) + Slack (notifications) >>>Outcome: Save 6-7 hours per week with consistent evaluation standards. The pattern: automation backbone + AI intelligence + specialized tools = workflows that run themselves. Next time you're tempted to try the latest AI tool, ask: "How does this connect to my existing workflow?" If the answer isn't clear, you probably don't need it yet. TeamLift is great; if you need help figuring out which workflow to start with, schedule a call: https://lnkd.in/e9Q6vJyx

🚀 Built Two AI Agents to Automate My QA Workflow using n8n and Jira I’ve been experimenting with AI agents and workflow automation and recently built two AI agents that work together inside my QA process: ✅ AI Agent #1—Bug Ticket Creator Automatically creates structured Jira bug tickets from issue descriptions, ensuring: Clear summaries Proper formatting Required technical details Consistent reporting ✅ AI Agent #2—Test Case Generator Reads Jira tickets and automatically generates relevant test cases, helping speed up test preparation and improving coverage and convert them into CSV file template for RTM Jira. ⚙️ Tech Stack: n8n (workflow automation) AI Agents / LLMs Jira API Prompt engineering 💡 Result: Reduced manual documentation work Faster bug reporting Instant test case generation More consistent QA workflow This is just the beginning — exploring how AI agents can become real teammates in software development and QA. Would love to hear how others are using AI agents in their workflows! #AI #AIAgents #Automation #n8n #QA #SoftwareTesting #Jira #GenerativeAI

📄 Thousands of patient intake forms. ⏳ Endless hours of manual data extraction. 🧠 Brilliant humans doing painfully boring work. Until AI stepped in — quietly. On Lead with AI, John Fitzpatrick (former Apple AI engineer, now CTO of Nitro Software) shares a jaw-dropping real-world moment from pharma that shows where AI delivers its real power. The old workflow: ➡️ Flat Word docs & PDFs ➡️ Patients fill forms manually ➡️ Teams extract every field by hand ➡️ Data cleaned, structured, analyzed — slowly The AI-powered shift: ✨ Flat documents instantly converted into structured forms ✨ Fields like DOB, name, address auto-identified ✨ Thousands of completed forms processed together ✨ Clean CSV or Excel output — in seconds No reformatting. No human data entry. No waiting. Even better? This breakthrough didn’t come from a polished roadmap — it came from a hack week. Engineers saw wasted human time, built a prototype, and shipped it into the product. 💡 “Boring AI applications are actually the best use cases.” This is enterprise AI done right: invisible, practical, and radically time-saving. 🎧 Watch the full episode of Lead with AI to see how AI is quietly transforming real workflows — link in comments. Watch Now: https://lnkd.in/ek2_BNhB #LeadWithAI #EnterpriseAI #DocumentAI #WorkflowAutomation #AIProductivity #HealthcareTech #PharmaCompliance #DataExtraction #FutureOfWork #AIInAction #BoringAI #AutomationTools #DigitalTransformation #TechLeadership #AIEfficiency #SaaSTools

Ever seen an ad drop from Maximum Effort (Ryan Reynolds’ agency) within days of a cultural moment? Here’s the secret: speed wasn’t the problem. The system was. Before we stepped in, their “workflow” looked like this: 1. Slack threads everywhere, no central source of truth 2. Project setup done manually (channels, job numbers, Frame.io accounts) 3. Clients couldn’t see the process, it felt like smoke and mirrors 4. Emergency turnarounds were possible… but chaotic and draining 5. The talent was world-class. The operations? Holding them back. So we rebuilt it. One centralized workflow “brain” that everything plugs into, automated setup across Slack + Frame (i.e. no more manual grunt work) and dashboards that pull clients into the process with full visibility. A roadmap to scale without slowing their creative agility. The result? - Every team member now saves ~0.5 days/week - Clients trust the process instead of questioning it - The team gets to stay in their creative zone instead of chasing files Maximum Effort kept their chaos → but now it’s the good kind. If your ops can’t keep up with your agency's creativity, drop “ops” in the comments and I'll reach out to set up a call. P.S - link to the full case study below. #Marketing #Advertising #AdAgency

⏰ Case Study Time! Here's a quick overview of a project I recently completed with my client Elemeno Health to integrate three previously disconnected platforms into an orchestrated production, review, and approval workflow. Read on to learn more about the steps we followed to: ✅ 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 Elemeno's content production pipeline for planning, authoring, editorial review, clinical review, and client approval ✅ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 connections between Sanity (content production), ClickUp (task management), and Elemeno's client-facing Admin interface ✅ 𝗥𝗲𝗱𝘂𝗰𝗲 bottlenecks, process variation, redundant data entry, manual workarounds, and email black holes 🕳️ https://lnkd.in/gRpv3Mid #contentoperations #headless #workflow #contentengineering