Workflow Optimization Reviews

“𝘖𝘶𝘳 𝘯𝘦𝘸 𝘵𝘪𝘤𝘬𝘦𝘵 𝘳𝘰𝘶𝘵𝘪𝘯𝘨 𝘪𝘴 𝘴𝘮𝘢𝘳𝘵𝘦𝘳 𝘵𝘩𝘢𝘯 𝘦𝘷𝘦𝘳.” That’s what this support operations lead told me this morning. 🙈 Her team had spent weeks optimizing their support workflows. 📍 𝐒𝐤𝐢𝐥𝐥𝐬-𝐛𝐚𝐬𝐞𝐝 𝐫𝐨𝐮𝐭𝐢𝐧𝐠—to send complex tickets to the right agents. 📍 𝐋𝐨𝐚𝐝 𝐛𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠—to make sure no one got overwhelmed. 📍 𝐑𝐨𝐮𝐧𝐝-𝐫𝐨𝐛𝐢𝐧 𝐚𝐬𝐬𝐢𝐠𝐧𝐦𝐞𝐧𝐭𝐬—so every agent had an equal share of work. It looked perfect in theory. But then she noticed something strange. 🔹 Response times went UP, not down. 🔹 Agents were spending more time reassigning tickets than solving them. 🔹 Customers were getting stuck waiting for the “perfect” agent instead of the available one. “What the hell is going on?” she asked me. The answer? 𝐎𝐯𝐞𝐫-𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧. When ticket routing is too smart for its own good, it actually slows everything down. 𝐇𝐨𝐰 𝐎𝐯𝐞𝐫-𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐞𝐝 𝐑𝐨𝐮𝐭𝐢𝐧𝐠 𝐇𝐮𝐫𝐭𝐬 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 🚨 𝐓𝐡𝐞 ‘𝐏𝐞𝐫𝐟𝐞𝐜𝐭 𝐀𝐠𝐞𝐧𝐭’ 𝐅𝐚𝐥𝐥𝐚𝐜𝐲 – her team was waiting for the most qualified agent… but they were always busy. Other agents? Sitting idle. 🚨 𝐎𝐯𝐞𝐫-𝐑𝐞𝐥𝐢𝐚𝐧𝐜𝐞 𝐨𝐧 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 – The system followed the rules too rigidly, leaving high-priority tickets stuck in limbo. 🚨 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐒𝐰𝐢𝐭𝐜𝐡𝐢𝐧𝐠 𝐊𝐢𝐥𝐥𝐬 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 – Agents weren’t focusing on solving problems. They were figuring out where tickets should go. So, what did she do? 𝐒𝐡𝐞 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐭𝐡𝐞 𝐬𝐲𝐬𝐭𝐞𝐦. ✅ For fast-moving teams: She removed unnecessary filters—the first available agent is often the best agent. ✅ For high-volume queues: She focused routing on just two or three key factors, instead of overcomplicating it. ✅ For urgent issues: She gave agents the ability to pull tickets instead of waiting for the system to decide. The result? 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐭𝐢𝐦𝐞𝐬 𝐝𝐫𝐨𝐩𝐩𝐞𝐝, 𝐭𝐢𝐜𝐤𝐞𝐭𝐬 𝐦𝐨𝐯𝐞𝐝 𝐟𝐚𝐬𝐭𝐞𝐫, 𝐚𝐧𝐝 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐠𝐨𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 𝐬𝐨𝐨𝐧𝐞𝐫. 𝐓𝐀𝐊𝐄𝐀𝐖𝐀𝐘: If your support team is drowning in complexity, 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐬𝐧’𝐭 𝐲𝐨𝐮𝐫 𝐚𝐠𝐞𝐧𝐭𝐬—𝐢𝐭’𝐬 𝐲𝐨𝐮𝐫 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬. 𝐓𝐡𝐞 𝐬𝐦𝐚𝐫𝐭𝐞𝐬𝐭 𝐭𝐢𝐜𝐤𝐞𝐭 𝐫𝐨𝐮𝐭𝐢𝐧𝐠 𝐢𝐬 𝐨𝐟𝐭𝐞𝐧 𝐭𝐡𝐞 𝐬𝐢𝐦𝐩𝐥𝐞𝐬𝐭. 🔥 P.S. Have you ever seen a routing system backfire? 🔥 P.P.S. Sometimes the best workflow isn’t a new automation—it’s just letting an agent get to work.

Yet another reason estimates are ridiculous. One of the silliest things about time estimates is that the vast majority of time it takes for a team to finish something is spent waiting. For the average development team to create something of value, only 10-20% of the total start-to-finish completion time is spent actively working on the item. The majority of the time is spent waiting. 🔵 Waiting for Reviews 🔵 Waiting for team member hand-offs 🔵 Waiting on other teams or departments So much time is spent waiting… instead of asking, “How much time will it take WORKING to complete this?” You’d be better off asking, “How much time will it take WAITING to complete this?” This, of course, is impossible to answer since most teams have zero control (or even awareness) of waiting time. You’re far, far better off ditching time estimates entirely and focusing on reducing wait states instead. But how? 1] Use Flow Efficiency ↳ Few teams are even aware of the most critical flow metric: Flow Efficiency. ↳ Flow Efficiency tells you how much time is spent actively working on increments of value (features, assets, stories, etc.). ↳ Flow Efficiency (%) = Active Time / Total Time X 100 ↳ Any good workflow tool will calculate your Total Time (Cycle Time). 2] Determine Active Time ↳ To figure out Active Time, you need to track your wait states by adding a “Done” state to every existing stage in your workflow. ↳ For Example: Development -> Development Done -> Testing -> Testing Done -> Review -> Review Done -> Released ↳ The “Done” columns are your wait states.  ↳ Now, you can effectively determine Active Time for each item in your flow vs. Wait Time. 3] Improve Flow Efficiency ↳ Once you can visualize and track wait times, you can focus on fixing the worst offenders. ↳ Add team members, reduce work in progress, remove dependencies… there are many ways to minimize wait states. ↳ Any reduction made to any of your wait states will improve Flow Efficiency An average team will have a Flow Efficiency of 20%. Your team should achieve a Flow Efficiency of 40% or greater to be considered high-performing. Will this take some effort? Of course! But far less effort and total team time (and annoyance) than asking for estimates. Plus, the increase in productivity will far outweigh any loss in imagined predictability.

Over a year ago, Church Media Squad hit a wall with Dropbox. 300+ TB of files. 4.5+ million files. 60+ people. One teamspace. Every upload, whether from a designer or our automations, locked the folder for a few seconds. Multiply that by hundreds of concurrent operations and you get the picture. It was a 2-lane road with hundreds of cars trying to use it at the same time. Constant traffic jams. Write operation errors. Automations failing. Designers stuck waiting. So we rebuilt everything from the ground up. v1 Rewrote all our Dropbox automations from scratch in n8n. v2 Moved from one shared teamspace to individual teamspaces per designer. Every designer gets their own 2-lane road. v3 Spent months working through every edge case in n8n. Folder conflicts, human-created folders, subtask dependencies, rate limits, name sanitization. The stuff that only shows up at scale. But the workflows had become so complex from patching edge case after edge case that we were still drowning in errors. It was time for a complete rebuild with everything we'd learned. v4 We took all that hard-won knowledge, handed our n8n workflows to Anthropic Claude Code, and had it write a Python orchestrator that runs on Windmill. One entry point. Give it a task, and it figures out where the folder needs to be. Creates it, moves it, archives it. All automated. Clean pipeline: fetch data, classify the action, execute, log everything. It handles moves, template creation, folder pickup (auto-detecting folders designers create manually), linked subtasks, client asset syncing, and self-healing when things get out of sync. I've spent the last 3 weeks refining it, and the numbers speak for themselves: 44,000+ automated folder operations across 15,000 tasks. 19,000 folder moves at 99.5% success. Template creation and folder pickup running at 100%. Peak day hit nearly 10,000 operations. No more locks. No more traffic jams. No more 60 people fighting over one teamspace. Really proud of Brian Watson for the fight he has fought with this one. More than anything, it frees up my team to focus on things other than file management. If you're managing large-scale file operations in Dropbox and hitting the same walls we did, teamspaces per user + a webhook-driven orchestrator changed everything for us.

Clay is not just a tool. It’s the engine behind multi channel outbound that actually works. Most teams treat Clay like a fancy spreadsheet. Run a list, enrich some data, hope for the best. That’s why they get average results. After building hundreds of Clay tables for client campaigns, I found there are 5 workflows that actually move the needle. Not theory-real systems that drive pipeline. Here’s what we use in the field: Workflow 1: The Waterfall Email Finder Stop settling for 40% email coverage. Stack your sources. → Start with Apollo email finder → If empty, try Prospeo.io → If still empty, LeadMagic → Then Findymail → Last, Clay native patterns (first.last@domain) End result: 85-90% email coverage. One source alone never gets you there. Workflow 2: Signal-Based List Builder Find prospects when they’re most ready to buy. → Pull target companies from #Ocean.io or #Apollo → Clay scrapes recent funding news → LinkedIn enrichment for hiring spikes (3+ new roles) → BuiltWith for tech stack changes → Score: 3 signals = contact now, 2 = nurture, 1 = long-term We see 22% reply rates vs 8% with generic outreach. Workflow 3: Website Visitor → Email Sequence Turn anonymous website traffic into warm leads. → RB2B identifies website visitors → Push to Clay for enrichment → Find decision-makers at those companies → Run the waterfall email finder → Auto-push to Instantly.ai for email, or to lemlist for LinkedIn DM if email is unverified Result: 28% reply rate. These leads already know you. Workflow 4: LinkedIn Engagement → Outreach Turn post engagement into pipeline. → Trigify.io tracks who engages with your posts → Push all commenters/likers to Clay → Enrich with company and role data → Score for ICP fit → Trigger a personalized sequence Conversion jumps to 4x vs cold lists. Workflow 5: Job Change Champion Reactivation Re-engage people who already trust you. → Export past customers/champions from your CRM → UserGems 💎 or Clay tracks job changes → Enrich new company details → Find decision-maker contact info → Trigger: “Congrats on [NewCo]. Remember when we helped you at [OldCo]?” Result: 35-40% reply rates. Warmest outreach you can do. Here’s the kicker: Most teams only run Workflow 1. Cold prospecting. That’s why reply rates flatline. We layer all 5. Each audience gets a different temperature, a different play. Here’s what happens when you stack them: → Cold prospects: 6-8% reply rate → Signal-based: 18-22% → Website visitors: 25-30% → Engagement-based: 30-35% → Champion reactivation: 35-40% Clay is not just an enrichment tool. It’s your entire outbound orchestration engine. If you’re still running cold lists and hoping for the best, you’re missing the compounding effect. Want the exact Clay workflows, setups, and SOPs we use for clients? Comment below and I’ll DM you the full playbook.

🤖 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀? 𝗛𝗲𝗿𝗲'𝘀 𝗮 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘁𝗵𝗮𝘁 𝗰𝘂𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗵𝗮𝗹𝗳. After months of wrangling ChatGPT to help me write real, production-grade code, I landed on something that actually works: 𝗧𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁/𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗿 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 It splits AI sessions into two distinct roles: 👷Architect: handles brainstorming, design, and planning. 🛠️ Implementer: executes the plan step-by-step, in a clean session. Each feature flows through four structured phases: 𝗗𝗲𝘀𝗶𝗴𝗻 (𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁) 🔹Use a Q&A prompt to refine the idea 🔹Approve design in 200–300 word chunks 🔹Write a detailed plan: bite-sized tasks, file paths, test strategy, commit messages 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 (𝗡𝗲𝘄 𝗔𝗜 𝗦𝗲𝘀𝘀𝗶𝗼𝗻) 🔹Load plan, implement 3–4 tasks at a time 🔹Use /compact & /clear to manage context 🔹Stick to the script, no surprise deviations 𝗥𝗲𝘃𝗶𝗲𝘄 𝗟𝗼𝗼𝗽 🔹Architect reviews each chunk of work 🔹PM (me/you) relays questions & feedback between sessions 𝗙𝗶𝗻𝗮𝗹𝗶𝘇𝗲 🔹Push to GitHub 🔹Open PR 🔹(Optional) AI code review with CodeRabbit 𝗪𝗵𝘆 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 🔹50%+ faster code reviews 🔹2–3 features in parallel (thanks to Git worktrees) 🔹No more “why did it do that?” questions 📂 Full breakdown + prompts + examples: link in the comments #AI #SoftwareEngineering #PromptEngineering #Productivity #DeveloperTools

I was asked to optimize UAE-home-grown famous burger brand central kitchen. The day I walked in the kitchen was a shocker, just one word can describe it - 𝗖𝗛𝗔𝗢𝗦. 𝗣𝗥𝗢𝗕𝗟𝗘𝗠 70 staff members. 3 different brands. Z̲e̲r̲o̲ ̲w̲o̲r̲k̲f̲l̲o̲w̲ ̲s̲y̲n̲c̲h̲r̲o̲n̲i̲z̲a̲t̲i̲o̲n̲.̲ Orders backing up. Quality was inconsistent. Staff were fed up and burning out. The issue wasn't the people or the recipes. It was the invisible enemy every kitchen faces: 𝗽𝗼𝗼𝗿 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗱𝗲𝘀𝗶𝗴𝗻. Here's what I did and also learned from redesigning that operation: → 𝗖𝗿𝗼𝘀𝘀-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 eliminated bottlenecks. When your grill cook calls in sick, your prep team should be able to cover his shift seamlessly. - TRAINING MATTERS → Station positioning is more important than equipment. I moved the sauce station 3 feet closer to assembly. Result? 15% faster ticket times (yup, I got a stop watch for that) → Communication beat shouting. We installed simple visual cues that reduced order errors by 40%. (2 kds installation helped too) RESULT? Staff absentism stopped Transformation took 6 weeks. Customer complaints disappeared. And sweet Profit margins improved 8%. Workflow optimization isn't about working harder. It's about designing systems that work smarter. What's the biggest workflow challenge in your kitchen right now?

A single welcome flow optimization generated $82k in just 72 hours for a client. During a client audit, we discovered their welcome sequence was treating all subscribers identically. Regardless of their their intent, they were all receiving the exact same email journey. This was leaving serious money on the table. So our team implemented zero-party data capture at the point of signup. Then we built distinct email paths - each tailored to specific customer behaviors and purchase intent. Every segment received messaging that actually matched where they were in their buying journey. We also refined the timing to hit inboxes when engagement peaks. The results after 72 hours: Original flow: $44k Optimized flow: $82k That's an 86% lift in revenue. The lesson? Generic email sequences leave money on the table. When you personalize the experience based on actual customer data, people respond. They engage more. They trust faster. They actually buy. Treat different customers differently. The data tells you what they need - use it.

If you're spending ten minutes to find your HubSpot workflows, you're doing it wrong. Unfortunately, it's more common than you think. Out of the last ten HubSpot portals I've reviewed, zero had any cohesiveness to their workflow structure and taxonomy. No naming conventions. No folder structure. Not a big deal, right? Wrong. Unfortunately, the reason you have... • Three workflows sending the same type of alert to the same Slack channel • Workflows sending Covid-specific emails to an event list from 2021 • Workflow routing leads to employees who haven't been with the company for two years ...It's because you have no idea what is going on with your workflows (and yes, these are real examples). Creating a folder structure for your workflows gives you: 1. Find workflows 10x faster when troubleshooting issues 2. New team members can navigate workflows without training 3. Easily identify outdated or duplicate workflows 4. Quickly isolate issues to specific business functions 5. See at a glance what areas are over-/under-automated So what does a good structure look like? HINT – it's not what I tell you it is. It's what makes most sense for your business, what your team will use, and what makes sense. A guide I like to use is below, and then have our clients customize for themselves: 𝟭. 𝗟𝗲𝗮𝗱 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 • Lead Scoring • Lead Routing • Lead Qualification 𝟮. 𝗡𝘂𝗿𝘁𝘂𝗿𝗲 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻𝘀 • Top of Funnel (TOFU) Nurture • Middle of Funnel (MOFU) Nurture • Bottom of Funnel (BOFU) Nurture 𝟯. 𝗗𝗮𝘁𝗮 𝗛𝘆𝗴𝗶𝗲𝗻𝗲 • Field Normalization • Duplicate Management • Data Validation 𝟰. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 • Onboarding • Upsell/Cross-sell • Retention/Churn Prevention 𝟱. 𝗘𝘃𝗲𝗻𝘁-𝗧𝗿𝗶𝗴𝗴𝗲𝗿𝗲𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 • Webinar/Event Registrants • Product Launches • Survey Responses 𝟲. 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗡𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 • Sales Alerts • Task Automation • Team Notifications 𝟳. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 & 𝗦𝘆𝗻𝗰 • Salesforce Sync • Third-Party Tools • API Workflows 𝟴. 𝗗𝗲𝗽𝗿𝗲𝗰𝗮𝘁𝗲 As the example in the image shows, it can be customized for flexibility and what's being automated the most, and it is a starting point. And yes, you should have a proper naming convention, but that's another post ;) Are you using a folder structure for your workflows? Would love to hear it. #hubspot #automation #businessprocess  

ServiceNow is full of power features, but this just may be my favorite. It’s called Process Mining. Fine, call for a nerd alert. 🤓 BUT, listen - if you're already a ServiceNow customer using workflows, you likely have access to this gem. The ServiceNow toolset not just about building a workflow, then hoping for the best. Process mining lets you deep dive into your existing workflows. It helps you see: → Where is it working? → Where is it NOT working? Then you can go in, fix things, and relaunch! Wait - it gets even better - AI powered recommendations are available to summarize findings and suggest how to remediate issues. ✨ This is how you keep up with today's fast-moving market. It's how you differentiate and find new ways to serve your customers. True ServiceNow optimization isn't about the platform. It's about continuously understanding, refining, and connecting your processes to deliver the best possible experience. Have you explored process mining within ServiceNow? What insights did you uncover? ⚒️ #servicenow #processmining #optimization #digitaltransformation #foreverhumanai