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.

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.

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

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

Standardizing tools ≠ Driving Standardization The typical approach: pick a single CI system, mandate one IaC framework, roll out a common portal…and then declare the job done. But tool sameness isn’t delivery consistency. What actually happens? Each team still builds their own ecosystem within the “standard” tool.  • Team A has 47 Jenkins plugins   • Team B creates pipeline templates nobody understands  • Team C finds workarounds because the chosen tools don’t fit their needs. What actually drives standardization:  • Golden Paths over mandated tools - Opinionated templates and reference architectures that teams want to use because they’re faster and safer  • Automated guardrails - Security, compliance, and cost checks built into workflows, not relying on tribal knowledge  • Connected workflows - Linking infra, deploy, and runtime data for better decisions (human and AI)  • Outcome-focused feedback - Scorecards and SLOs that align teams on results, not tool usage  • Evolution by contribution - Let teams improve standards instead of bypassing them The anti-pattern? Replacing tool sprawl with tool monoculture and calling it progress. Real standardization = Consistent patterns and governance, powered by tools and not limited by them. I’ve seen teams with different tools achieve better consistency than teams sharing identical platforms. Why? Because, they standardized how they work first. How do you balance alignment with team autonomy?

Ownership gaps kill momentum. And they happen more often than most teams realize. Here’s how it shows up: → A form fill sits in the CRM with no owner assigned. → A prospect asks for a call-back on Tuesday… and no one follows up. → A support ticket gets routed to the wrong queue and disappears. → A deal moves stages, but the next step isn’t clear—so nothing happens. No one wakes up saying, “I’m going to let revenue leak today.” It just happens when handoffs aren’t owned and everyone assumes “someone else has it.” That’s where automation becomes a safety net. At Flow Digital, we help clients close those gaps by building guardrails that keep work moving even when humans are stretched thin: → Auto-assign and escalate if no owner is set within minutes. → Trigger reminders when a promised follow-up time arrives. → Enrich data automatically so the next step isn’t blocked. → Highlight orphaned tasks in daily reports so nothing dies quietly. This isn’t about replacing people. It’s about making sure every customer touchpoint actually happens—even when calendars explode or roles blur. Because lost deals don’t come from big disasters. They come from a thousand tiny “no owner” moments. Automation doesn’t remove accountability. It makes sure you never lose a customer because the baton was dropped. — 🔔 Follow Nathan Weill for no-fluff posts on automation, GTM systems, and the workflows that keep revenue from slipping through the cracks. #Automation #RevOps #GTM #Operations #SignalBasedWorkflows #BusinessOps #FlowDigital

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

I’ve been thinking a lot about how much time we waste just getting a website started. Not the design polish. Not the copy. Just… the setup: templates, builders, hosting, and endless tweaking before you even know if the idea is worth it. So I asked myself a simple question: What if a website could start the same way an AI chat starts… with one prompt? I ended up building an AI-powered workflow in n8n that generates and deploys a complete website automatically. Here’s the flow: → Chat trigger captures the website description → AI Agent turns it into a clean website brief → Google Gemini generates a full HTML/CSS file → GitHub Pages publishes it live in seconds And the wild part? It’s not a “demo” that stops at a mockup. It actually ships a live site. The first time I watched it deploy a full website in seconds from a single sentence… I realized this isn’t just “cool automation.” This is a new way to prototype. Because now: • Founders can validate ideas faster • Designers can get instant mockups • You can skip monthly website builder fees • Agencies can scale delivery with repeatable automation I break down the entire build step-by-step in my latest tutorial and you can download the workflow template for free when you join my Skool community here - https://lnkd.in/gtAExXGv If you’re experimenting with AI automation, this is one of the best “start here” projects. Drop a “WEBSITE” in the comments and I’ll send you the link to the full tutorial.

Four years ago, I worked on a technical automation project for a client via Fiverr. This week, he reached out again, same client, new challenge. He needed a system where AI-powered agents could make personalized inbound & outbound calls to leads and then automatically handle all the follow-up tasks without human intervention. So, I built a connected automation using Retell AI, GoHighLevel (GHL), Make.com, and Chatdash that: ➡️ For inbound calls: Looks up the lead in GHL in real-time, sends the details back to the agent, and allows live appointment booking while on the call. ➡️ For outbound calls: Triggers from actions in GHL, sends lead info to the agent for a personalised approach, waits for the call to finish, gathers transcript + sentiment, and stores it in GHL. ➡️ Across both: Retell AI checks calendar availability, and Make.com books meetings based on the lead’s preferred time, no manual follow-up needed. Impact: ✅ Personalized conversations every time ✅ Automated note-taking and sentiment logging ✅ Faster appointment scheduling with zero back-and-forth Automation isn’t just about replacing task; it’s about enhancing human interactions so agents can focus on building relationships, not juggling tabs and CRMs. If your sales or support team still spends time searching for client info during calls or manually scheduling follow-ups, this is the type of automation that changes the game. PS: What’s one repetitive client interaction in your business you’d love to automate?