Workflow Automation Hacks

I built an AI agent that handles my entire inbound system. (And I used to be against automation). Here's how I did it: I used two tools: --> Make: For automation workflows --> Relevance: For AI agents Here's what my AI agent handles: When someone fills our form, it- --> Analyzes their LinkedIn profile --> Reviews their website --> Checks if they match our criteria --> Makes a decision in seconds For qualified leads: --> Sends personalized pitch deck --> Books discovery calls --> Handles initial questions For non-qualified leads: --> Sends a thoughtful rejection --> Explains why we're not the right fit --> Keeps the door open for future The best part? My team and I can focus on what matters - strategy and client success - instead of spending hours on admin work. No more: -Manual lead checking -Back-and-forth emails -Calendar scheduling headaches -Just high-quality conversations with pre-qualified founders. Want to know the biggest lesson? Automation isn't about replacing the human touch. It's about creating more time for it.

If you’re learning AI automation without a roadmap, you’re guaranteed to get overwhelmed. People usually “learn AI automation” by jumping straight into tools… and then wonder why nothing works consistently. Real automation requires structure - thinking, logic, testing, and a gradual build-up of skills. This 18-day roadmap breaks down the exact sequence to go from zero → confidently building automations with AI, APIs, tools, and no-code platforms. Here’s the full breakdown, day by day: Day 1 - AI Automation Fundamentals Learn what automation really means, how it differs from AI and agents, and see real examples. Day 2 - Automation Thinking Break work into steps, triggers, and outcomes - the mindset behind every good automation. Day 3 - APIs & Webhooks Basics Understand how apps communicate and how events trigger workflows. Day 4 - No-Code Automation Platforms Explore Zapier, Make, n8n - and how no-code tools actually run workflows. Day 5 - Build Your First Automation Create a simple trigger-action workflow and connect two apps. Day 6 - Data Handling Pass data between steps, map fields, and work with text, numbers, and dates. Day 7 - Logic & Error Handling Add filters, conditional logic, retries, and fallbacks to keep automations reliable. Day 8 - AI Model Basics Learn prompts vs system instructions, tokens, limits, and LLM behavior. Day 9 - Using AI Inside Automations Insert AI steps into workflows and parse structured AI outputs. Day 10 - Prompt Design for Automation Write consistent prompts and reduce hallucinations with JSON outputs. Day 11 - Text-Based Task Automation Automate email replies, summaries, CRM updates, and document tasks. Day 12 - Knowledge Automation (RAG Basics) Connect AI to internal documents and fetch accurate answers from real data. Day 13 - AI Agents Basics Understand agent planning, tools, and identify use cases for agents. Day 14 - Business Use Case Automation Automate lead qualification, ticket routing, and internal processes. Day 15 - Sales & Marketing Automation Personalize outreach, repurpose content, and automate follow-ups. Day 16 - Operations Automation Manage approvals, notifications, and repetitive operational tasks. Day 17 - Monitoring & Optimization Track workflow success, cut costs, and improve performance. Day 18 - Build & Ship Your System Design, test, document, and finalize a complete end-to-end automation. You don’t master AI automation by learning tools, you master it by learning systems thinking, data flow, and structured execution. Follow this roadmap, and you’ll build automations that are reliable, scalable, and business-ready.

KEY 5S AUDIT POINTS AND AUDIT SHEET 1. Sort (Seiri) Identify Unnecessary Items: Separate items that are not required for current tasks. Red-tagging: Use red tags to mark and remove unnecessary items. Free Up Space: Clear clutter and create a clean workspace. Minimize Waste: Reduce excess inventory and non-essential materials. Simplify Work Areas: Ensure only essential tools and equipment are present. 2. Set in Order (Seiton) Organize Tools and Materials: Arrange items in a logical order based on usage frequency. Label Items Clearly: Use labels or color codes to make identification easier. Create Storage Locations: Assign specific places for each item to reduce searching. Visual Controls: Implement visual cues like shadow boards to guide proper storage. Optimize Workflow: Design the workspace for maximum efficiency and minimal movement. 3. Shine (Seiso) Regular Cleaning: Perform daily cleaning of the work environment, machines, and equipment. Inspect Equipment: Look for signs of wear, damage, or malfunction during cleaning. Maintain Cleanliness: Keep floors, tools, and surfaces tidy to avoid contamination. Eliminate Dirt and Debris: Ensure all work areas are free from dust and waste materials. Preventive Maintenance: Develop a routine for maintaining and cleaning machinery to avoid breakdowns. 4. Standardize (Seiketsu) Create SOPs (Standard Operating Procedures): Develop written procedures to standardize tasks. Implement Visual Cues: Use color codes, labels, and signs for consistency. Ensure Consistency: Make sure practices are uniform across shifts and teams. Documentation: Keep records of standards to track adherence. Training and Awareness: Ensure all employees are trained on standardized procedures. 5. Sustain (Shitsuke) Develop Discipline: Foster a culture of self-discipline to maintain 5S practices. Regular Audits: Conduct routine audits to ensure 5S principles are followed. Continuous Improvement: Encourage feedback and constant updates to the 5S system. Management Commitment: Ensure leadership supports and promotes 5S initiatives. Employee Engagement: Involve employees in maintaining and improving 5S practices.

I haven't typed a full paragraph in months. Most founders are still grinding out content the hard way. Meanwhile, I'm creating more efficiently using voice and AI systems. Content creation shouldn't come with burnout. Here's how I create 30 pieces instead: 1. Voice-First Creation I speak my ideas instead of typing them. Voice is faster than fingers and captures natural conversational flow. AI transcription tools turn my thoughts into polished content instantly. 2. The 3-Tool Content Engine Tool 1: Voice recorder for raw idea capture during walks or commutes. Tool 2: AI transcription that turns speech into structured drafts. Tool 3: Content optimization AI that adapts one idea across multiple platforms. 3. The Secret Content Checklist Before any content goes live, it passes through 5 systematic checks. Hook strength, value delivery, platform optimization, engagement triggers, and call-to-action clarity. Quality control happens through systems, not hope. 4. Content Multiplication System One 10-minute voice recording becomes 30+ pieces of content. LinkedIn posts, Twitter threads, YouTube scripts, newsletter sections. Each optimized for its platform while maintaining core message integrity. 5. Batch Production Days I record all content in focused 2-hour sessions. Then AI handles the heavy lifting of adaptation and optimization. Creation becomes systematic instead of reactive. The result: Content creation that scales without burning out the creator. Most founders create content. I systematize content production. Your voice is your competitive advantage. AI can optimize and multiply, but it can't replace your unique perspective and experience. Stop typing yourself into burnout. Start speaking your content into existence. __ Enjoy this? ♻️ Repost it to your network and follow Matt Gray for more. Want to learn how to create content the easy way? Get my free AI course that shows you the systems that helped me scale my businesses with AI automation. Join here: https://lnkd.in/eVfUj42h 

Here's another way we're using AI at Reformed IT to improve our client experience without replacing the human touch 👇🏻 Every time a client emails us about an issue, we use AI to analyse the tone of their email and the likely level of satisfaction. 📩 Their tone could be: 🤬 Angry 😠 Frustrated 🤔 Confused 😟 Concerned 😐 Neutral 😊 Polite 😁 Happy Which would in turn lead to a likely satisfaction score between 1 - 10. If we detect that a client is Angry or frustrated with us based on their emails, we'll flag this ticket automatically with our head of service, Dan, to review. ✅ As you'll have seen recently, we track a lot of stats/data around customer service and satisfaction. 📊 However, we will only get feedback after we've completed a task. But we're picking up sentiment from the client during the entire interaction. By looking at the signs of frustration early on, we're more likely to be able to deal with the root cause of these frustrations and ensure that we turn it around to have a happy client by the time we've done the work. 😁 I've talked a lot about AI recently and the fact it will have an impact on jobs, but I also think, when used in the best way, it can really empower your business and people to do the best they can. 🤖 + 👨🏻💼 Are you using AI and Automation to improve your client experience? If so, how?

My take on what's actually happening in automation right now: The gold rush is over. The reality check is here. What I'm seeing with clients: 👉 Less "we need AI!", more "we need this specific problem solved." 👉 Less shiny tools, more lean, strategic workflows.  👉 Less automation everywhere, more automation that matters. The shift is subtle but significant: Companies aren't asking for fancy tech. They're asking for smarter thinking about their processes. My prediction for 2025? The winners won't be the ones with the MOST automation. They'll be the ones with the most thoughtful automation. Smart companies are asking: 1️⃣ Which processes actually need automation? 2️⃣ Where are we automating around problems we should eliminate? 3️⃣ How do we measure automation success beyond time saved? We're finally moving past the "automate everything" hype cycle to something more valuable: Intentional automation. And that’s definitely reason to celebrate. What’s a shift you’re seeing in your industry? -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ At Flow Digital, we help business owners like you unlock the power of automation with customized solutions so you can run your business better, faster, and smarter. #automation #workflow #strategy #2025 #trends

This is why AI agents are exploding in adoption—they deliver real business value by turning LLM intelligence into automated action. They are becoming the backbone of automation in customer support, operations, sales, and internal workflows, replacing repetitive tasks that humans perform by clicking buttons and following rules. Instead of just generating text, AI agents orchestrate actions, making them far more valuable in real business environments. A perfect example is customer-support order-tracking. Every day, support teams receive hundreds of emails asking, “Where is my order?” A human agent reads the message, extracts the order number, searches in the backend system, checks the shipment status in the carrier portal, decides what’s wrong, and finally replies or creates a follow-up ticket. This manual process takes 2–3 minutes per email—highly repetitive and expensive at scale. An AI agent can now automate this entire workflow end-to-end. It first extracts the order ID from the customer’s message, then calls the lookup_order tool to fetch order details, and the check_tracking_status tool to get carrier updates. Next, it analyzes the status and determines whether delivery is delayed, lost, or on track. Based on the result, it triggers the right action, such as create_internal_ticket, initiate_carrier_trace, or reschedule_delivery. Finally, the agent generates a personalized reply to the customer with the latest status—without any human involvement. With memory, it can even handle future follow-ups intelligently. Read more on the internal architecture of an AI Agent in detail: https://lnkd.in/gEhVX5cY Build Your First AI Agent in 10 Minutes! (No Code Needed): https://lnkd.in/gjNf5yyr

Did you ever need to have a Power Automate flow trigger on a new/updated item in one table, but only when certain conditions are met in a related table? I've been asked about this during my session at the #NordicSummit recently. And I've needed it myself in the past, too. So, imagine that you need to process new tasks when they appear under a project, but only if the project is active (let's say, identified via a Boolean field or a Date field on the Projects table). Or, in my case, I had Projects and Submissions, where the Projects table had a Category field which was a global choice. And since submissions under two projects needed to be processed automatically when they appeared, but in different ways, I wanted to build separate flows, that would trigger on new submissions but only for the relevant project. My case was slightly easier, because I would still need to fire the trigger on every submission, and could just split the processing logic across child flows. But there are definitely scenarios where we would not even want the trigger to fire at all if the conditions on the related table are not met. However, there is no way to expand the trigger conditions to related tables natively, as values from related tables are not a part of the trigger outputs in Power Automate. So, the seemingly only option would be to have the flows fire too frequently and then have a condition in the very beginning to terminate the flow if it is irrelevant based on the related table. Not very efficient, if you ask me. So, a possible solution to that could be adding a calculated field to the target table that would fetch a value from the related table. We used to do that previously quite a bit. But when I tried doing it now, it said that Calculated columns are being deprecated and we should use a new type called Formula now. Funnily enough, the info on "Formula" tables states that it allows making calculations based on the fields *within the same table*, which is a bit misleading. I thought this was a limitation and I will no longer be able to fetch data from related tables this way. However, it actually works perfectly fine and the syntax is so simple, I'm more than happy to stop using Calculated columns now. The limitation, obviously, is that it needs a N:1 relationship where the target table has a lookup to the related table. When we have that, we can simply use {RelatedTableName}.{ColumnNameInRelatedTable}. And it comes back with suggestions and auto-fill, so it really is extremely easy to use. May not work in all scenarios if you need those conditional triggers on tables you cannot edit, but if you can, this could really save you lots of work and lots of irrelevant flow runs.

SMBs are facing a critical challenge: how to maximize efficiency, connectivity, and communication without massive resources. The answer? Strategic AI implementation. Many small business owners tell me they're intimidated by AI. But the truth is you don't need to overhaul your entire operation overnight. The most successful AI adoptions I've seen follow these six straightforward steps: 1️⃣ Identify Immediate Needs: Look for quick wins where AI can make an immediate impact. Customer response automation is often the perfect starting point because it delivers instant value while freeing your team for higher-value work. 2️⃣ Choose User-Friendly Tools: The best AI solutions integrate seamlessly with your existing technology stack. Don't force your team to learn entirely new systems. Find tools that enhance what you're already using. 3️⃣ Start Small, Scale Gradually: Begin with focused implementations in 1-2 key areas. This builds confidence, demonstrates value, and creates organizational momentum before expanding. 4️⃣ Measure and Adjust Continuously: Set clear KPIs from the start. Monitor performance religiously and be ready to refine your AI configurations to optimize results. 5️⃣ Invest in Team Education: The most overlooked success factor? Proper training. When your team understands both the "how" and "why" behind AI tools, adoption rates soar. 6️⃣ Look Beyond Automation: While efficiency gains are valuable, the real competitive advantage comes from AI-driven insights. Let the technology reveal patterns in your business processes and customer behaviors that inform better strategic decisions. The bottom line: AI adoption doesn't require disruption. The most effective approaches complement your existing workflows, enabling incremental improvements that compound over time. What's been your experience implementing AI in your business? I'd love to hear what's working (or not) for you in the comments below. #SmallBusiness #AI #BusinessStrategy #DigitalTransformation

The AI workflow produced great results, yet people did not feel safe relying on the output. ⛔ That was the situation I encountered in a client workshop in Brussels last week, and it is far more common than most organisations like to admit. The team had invested time and effort into designing an AI-supported workflow. The use case was clear, the technical setup was sound, the data quality was acceptable, and the people involved had already received training on how to use AI. Despite all of this, the workflow was barely used in practice. People ran the AI step, reviewed the output, and then quietly redid the work themselves. During the workshop, we mapped the real workflow together, step by step, focusing not on how the process was documented but on how the work actually happened on a normal working day. At one point, a participant looked at the whiteboard and said: “I only trust the result after I have checked it myself anyway.” That sentence shifted the entire conversation. As we continued mapping the process, a pattern became visible: Everyone validated AI outputs differently.  Some checked everything, even low-risk drafts.  Others barely checked high-risk decisions. Accountability was assumed but never explicitly defined. Human validation was happening constantly, but it was invisible, inconsistent, and highly personal. We redesigned the workflow and introduced a simple checklist for built-in human validation. 💡 This checklist replaced individual safety habits with a shared, explicit process. ✅ Define the risk level of the output. Clarify whether the AI output is a draft, a recommendation, or a decision with external impact. ✅ Decide if validation is required. Make it explicit which outputs require human review and which can flow through without intervention. ✅ Specify the validation moment. Define when validation happens in the workflow and before which downstream step. ✅ Assign clear responsibility. Name the role that validates the output and the role that makes the final decision. ✅ Separate generation from judgment. Ensure the AI prepares content or options, while humans remain accountable for approval and outcomes. ✅ Remove unnecessary checks. Regularly review the workflow to eliminate validation steps that add friction without reducing risk. Once this checklist was applied, people felt much more confident about the AI output because they knew when human judgment was required. 👉 Is human validation in your AI workflows clearly designed, or is it still improvised? Let’s discuss.