Workflow Automation Hacks

𝐑𝐞𝐚𝐥 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐟𝐨𝐫 𝐀𝐈 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 I have been meeting with many enterprise CXOs and AI advisory firms about AI adoption over the last few months. Almost all of them start the same way: 1. Map the current workflows. 2. Identify the manual steps. 3. Find where people are spending time. 4. Layer AI on top to automate or accelerate the work. This is the default playbook. And it is not wrong. It is the safe, best way to test and show quick results. A great entry point for AI. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨w 1. Customer calls in. 2. L1 agent picks up, follows a script. 3. Cannot resolve. Escalates to L2. L2 reads the notes, asks the customer to repeat the problem, checks the knowledge base. Maybe escalates to L3. 4. Resolution happens 3 handoffs and 48 hours later. Most enterprise AI deployments in customer support follow the same default playbook: 1. Automating L1 with a voicebot 2. L2 with AI-assisted responses 3. Giving L3 a copilot. Same tiers, same structure, just faster and cheaper. 𝐖𝐡𝐲 𝐝𝐨 𝐭𝐡𝐞𝐬𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐞𝐱𝐢𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐩𝐥𝐚𝐜𝐞? Most processes were designed around human limitations — quality, consistency, onboarding, training, error containment. 𝑩𝒖𝒕 𝒘𝒐𝒓𝒌𝒇𝒍𝒐𝒘𝒔 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒕𝒉𝒆 𝒈𝒐𝒂𝒍. 𝑻𝒉𝒆𝒚 𝒂𝒓𝒆 𝒂 𝒎𝒆𝒂𝒏𝒔 𝒕𝒐 𝒕𝒉𝒆 𝒈𝒐𝒂𝒍. The goal was never "route through 3 tiers." If AI can access the full knowledge base, understand context, and maintain quality — why not give the customer or a single agent an AI tool that resolves it directly? Three tiers collapse into one. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 is to return to the original objective and move from multi-step process to single-step outcome as confidence builds. This is also where the biggest opening exists for new AI startups — not workflow automation, but outcome-based automation. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: Before you automate your current workflows, ask why they exist. The enterprises that will get the biggest AI wins are the ones redesigning toward outcomes — not just making existing steps faster.

AI in Customer Support isn’t new. I’ve been rethinking how we actually use it. Customer Support is moving past basic "faster replies" and learning to implement Claude as a core part of our workflow. The goal? Shifting from reactive firefighting to structured, scalable systems. It’s a work in progress, but here is the blueprint we’re using to turn Claude into a true CX reasoning engine: 1️⃣ It’s not about speed. It’s about structure. Yes, you can draft replies faster. But the real value comes from setting it up properly: → align it with your tone and guidelines → connect it to your knowledge base → define clear boundaries (what it can and can’t say) → train it to understand context, not just keywords That’s how you get consistent, reliable output across the team. 2️⃣ It helps move Support from reactive → proactive Used well, it’s not just answering tickets. It’s helping you: → detect sentiment and urgency → identify recurring friction points → surface gaps in self-service → spot early churn signals That’s where Support starts influencing the whole customer experience. 3️⃣ It fits into your existing workflows (not replaces them) The most effective setups I’ve seen are simple: → Claude + Zendesk → ticket analysis → Claude + Zapier → automate workflows → Claude + Gong→ review calls → Claude + Intercom → inbox support → Claude + n8n → workflow automation → Claude + Notion → knowledge management No complex rebuilds. Just better use of what you already have. 4️⃣ The quality of output = quality of input Small things make a big difference: → assign a role (support agent, CX lead, analyst) → provide context (customer, goal, constraints) → iterate with examples (good vs bad responses) Without this, you get generic answers. With it, you get something your team can actually use. From a leadership perspective, this isn’t about “adding AI.” It’s about designing how your Support team operates at scale. Because the goal isn’t to answer more tickets. It’s to build a system where fewer things break, and when they do, the experience still feels consistent. If you’re already using AI in Support, what’s actually working for you? 👇

Salesforce just launched Headless for AI Agents today. We've already been living it for 6 months. Benioff's framing: "No browser required. Our API is the UI." For anyone actually running agents in production, this isn't a product announcement. It's a description of what's already happening. Our AI VP of Marketing, our AI VP of Customer Success, and every one of our AI sales agents work directly at the API level on top of Salesforce. We never actually log in as humans. Literally as I was typing this post, our AI VP of Marketing "10K" did something new: → Logged into Salesforce (headless) → Pulled 4,000 people who said they want to come to SaaStr AI Annual but haven't bought tickets → Generated custom emails with their specific promotion codes → Sent them Autonomously. Nobody told it to. It saw the gap and acted. That's not a dashboard. That's an executive. The rest of the stack running headless on Salesforce: → Qualified: 700K+ sessions, $4M+ closed, 71% of closed-won sponsorship deals → Agentforce win-backs on 1,000 ghosted leads: 72% open rates → Artisan: 15,000+ messages sent, 5-7% response rates → Momentum: every call auto-transcribed into Salesforce → Monaco: follows up on every prospect. Our human SDRs were too "busy" to chase. → 10K: runs our Monday standup, assigns humans tasks, posts daily to Slack → Qbee: manages 100+ sponsors, 70% fewer human hours vs last year Nobody on the team starts their day by logging into Salesforce. They start it by reading what 10K and Qbee posted into Slack overnight. Slack is the surface. Salesforce is the brain. The agents are the translators. We spend 5x more on AI agents (including Agentforce) than on Salesforce itself. That ratio is going to grow, not shrink. Benioff is right. The API is the UI. The only question worth asking is whether you've already started building like it is, or you're still waiting for someone to tell you it's okay.

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.

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.

If chatbots talk, AI agents execute. What’s an AI agent? An AI agent is autonomous software that understands your goal, plans the steps, uses tools/APIs, and learns from feedback to finish the job with minimal supervision. Think proactive operator, not just a chatbot. 🧠🛠️ Why it’s a game-changer 🚀 - From replies to results: Books meetings, files tickets, reconciles data, triggers deployments, and verifies outcomes. - From tasks to outcomes: Orchestrates multi-step workflows and collaborates with other agents to hit KPIs. - From scripts to learning: Adapts to edge cases, remembers context, and improves every run. Real wins you can copy today ✅ - Customer Support: Auto‑triage tickets, search KBs, summarize history, propose fixes, and escalate only when needed. - Sales Ops: Prospect → qualify → personalize → schedule → update CRM without nudges. - Content Engine: Research → outline → draft → fact-check → repurpose for LinkedIn/IG/X → analyze and iterate. - IT/DevOps: Watch logs, detect anomalies, run playbooks, verify recovery, and post‑mortems—fewer 3 a.m. alerts. - Finance Ops: Reconcile transactions, flag anomalies, prep monthly close, draft stakeholder updates. How it works (simple loop) 🔁 Perceive → Reason → Act → Learn. Inputs in, plans made, tools called, results improved—on repeat. Start this week (no fluff) 🗂️ - Pick one repeatable workflow with clear success criteria. - List required tools/APIs (docs, CRM, ticketing, calendar, storage). - Set guardrails for autonomy vs. human approval. - Log everything; review weekly to tighten prompts, memory, and policies. Scroll-stopping openers 🎯 - “Chatbots answer. Agents deliver.” - “Outcomes > outputs. Meet AI agents.” - “One agent > five manual workflows.” 💬 Comment “AGENT” for a plug‑and‑play blueprint to automate your most annoying workflow this week. #AIAgents #AgenticAI #Automation #GenAI #LLM #ToolUse #Workflows #Productivity #CustomerSupport #SalesOps #DevOps #MLOps #AIinBusiness #Growth #Startups #APIs #Operations #Engineering #TechLeadershipa

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?

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?