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.

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 

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.

I’ve been practically living in Claude Code lately. If you build with AI daily, you know that your orchestration workflow is just as important as the model itself. Recently, Boris Cherny (the creator of Claude Code at Anthropic) shared the internal best practices and workflows his team actually uses on a daily basis. Someone brilliantly distilled those threads into a structured CLAUDE.md file that you can drop straight into the root of any project. It essentially acts as a system prompt that turns Claude into a much more autonomous, rigorous engineering partner. Here is what the file enforces: 1/ Workflow Orchestration: Mandates a "Plan Node Default" for any task over 3 steps and uses subagents liberally to keep the main context window clean. 2/ The Self-Improvement Loop: This is the real magic. After ANY correction, it updates a tasks/lessons.md file. You are building a compounding system where the mistake rate drops over time because it actively learns from your feedback. 3/ Verification Before Done: It cannot mark a task complete without proving it works (diffing behavior, running tests, checking logs). "Would a staff engineer approve this?" 4/ Autonomous Bug Fixing: Zero hand-holding. You point it at failing CI tests or error logs, and it goes to work without requiring constant context switching from you. 5/ Strict Task Management: Forces a "Plan First" approach written to a todo.md with checkable items before implementation even starts. It forces the AI to prioritize simplicity, find root causes instead of temporary fixes, and minimize the blast radius of its changes. If you are spending hours a day in the terminal with AI, setting up a strong .md instruction file like this will save you a massive amount of time and frustration. It takes time and effort to set this up. If you do, you are ahead of everyone else.

🔥 Six months ago, I had no idea what low-code tools could actually do. Today, I've built chatbots that onboard vendors, prioritize sales leads, and verify claims. All automated. Here's what I experimented with: Low-code exploration: 🔥Power Automate + Copilot Studio to build intelligent agents ✨Vendor onboarding agent: validates details, stores them in DB via cloud flows 🌍Sales lead targeting agent: checks and prioritizes leads automatically 🤠Claim verification agent: validates user claims before approval Backend and AI development: 🔥Built a "Chat with PDF" prototype using Python, LangChain, and Streamlit ❤️Implemented JWT authentication with Pydantic in a FastAPI backend ✨Got hands-on with real-world API design and security patterns The biggest lesson? You don't need to master everything at once. Pick one thing, build something small, break it, fix it, repeat. Low-code taught me automation logic. LangChain taught me how AI agents think. FastAPI taught me how to structure backends properly. All of it together? That's what makes you job-ready. What's one skill you've been experimenting with lately? #AI #BackendDevelopment #LearningInPublic

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 automated my entire team's workflow—and then THIS happened. Ever wonder what would happen if your team could complete a week's worth of work in a single day? Sounds like a dream, right? Well, that's exactly what we achieved. A few months back, I noticed my team was bogged down with repetitive tasks. Brilliant minds were spending hours on mundane activities. So, I decided to take a bold step. We invested in automating these tasks. The initial push was challenging: - Learning new tools - Changing long-standing processes - Overcoming resistance to change But the payoff was incredible. Results: - Productivity skyrocketed: We accomplished more in less time. - Stress levels dropped: The team felt less overwhelmed. - Innovation flourished: Free time led to creative solutions. - Employee satisfaction increased: Work became more fulfilling. The most surprising outcome? Our team cohesion strengthened. With less time on grunt work, we collaborated more on strategic projects. The takeaway? Automation isn't about replacing people. It's about freeing them to do what they do best. Embrace technology to unlock your team's true potential. Have you implemented automation in your work?

In my day-to-day work, I recently came across a simple scenario that had a big impact. I needed to regularly execute a SQL stored procedure, and doing it manually just wasn’t cutting it. To make things easier and more efficient, I decided to automate the whole process using Azure Data Factory (ADF). Here’s how I set it up: 🎯 The Situation: I had a stored procedure that was responsible for updating data in a SQL table, and I needed it to run automatically at specific intervals, without me having to trigger it manually every time. My goal was to set up a process that would handle this reliably in the background. 🛠️ How I Solved It: 1. Creating a Linked Service: The first thing I did was create a Linked Service in ADF to securely connect to my SQL database. This is basically how I gave ADF access to my database, and I used Managed Identity to keep the connection secure without having to worry about storing credentials. 2. Setting Up the Pipeline: Next, I built a pipeline in ADF using the Stored Procedure Activity. This allowed me to select the stored procedure I wanted to run and pass in parameters if needed. One thing I really liked was how easy it was to set dynamic values for those parameters — like using the current date to make the process flexible and adaptable. 3. Adding a Trigger: To make sure the process ran automatically, I added a Schedule Trigger that would execute the pipeline daily. This meant no more manual execution, and I could trust that the data would be updated consistently, even if I wasn’t around to check on it. 4. Testing & Monitoring: Of course, before fully deploying it, I ran some tests to make sure everything was working smoothly. ADF’s Trigger Now feature came in handy for that. Once I was happy with it, I monitored the pipeline using ADF’s built-in Monitor feature to track the runs and check the logs in case anything went wrong. ⚙️ What I Learned: This automation ended up being a real time-saver. Not only did it take away the need for manual intervention, but it also reduced the chance of human error. Plus, now that I’ve got this process running smoothly, I can easily scale it up to handle more stored procedures or even more complex workflows in the future. 💡 Takeaway: If you’re dealing with repetitive tasks like this, automating them with a tool like Azure Data Factory is a game-changer. It’s secure, scalable, and lets you focus on more important work. I’m excited to continue exploring more ways to automate and improve my workflows — and I’d love to hear how others are using ADF or similar tools in their projects! Feel free to share your thoughts or tips. 😊 LIKE 👍 COMMENT💬 RESHARE ♻ Follow more for Priyanka SG #DataAnalyst #SQLServer #Excel #PowerBI #Python #DataVisualization #AzureDataFactory #Automation

Ever feel like your team is stuck in an endless loop of manual data entry? (Automation Tip Tuesday 👇) That’s exactly where one of our clients — an education consulting firm — found themselves. They were juggling a whole tech stack of tools that didn’t “talk”  to each other, creating inefficiencies and double work. We started with a look into their sales workflow. 🔹 Sales data lived in HubSpot, but once a deal closed, someone had to manually update Asana to track project progress. 🔹 Internal teams worked from one Asana board, but clients needed visibility into their own project timelines — cue more manual updates. 🔹 With so much repetitive data entry, valuable time was being wasted on low-impact admin work. Here’s what we did: 🔗 HubSpot → Asana automation: We created an integration that auto-generates project tasks in Asana when a deal reaches a certain stage in HubSpot. No more copy-pasting! 📢 Internal and client boards sync: Internal progress updates in Asana now automatically reflect on client-facing Asana projects, reducing the back-and-forth. Less busywork, more productivity. By eliminating duplicate data entry, the team saved 10+ hours per week — time now spent on strategy and client success. When your tools work together, your team can focus on what really matters. Where is your team losing time? Drop a comment below! ⬇️ -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday  #automation #workflow #efficiency