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.

“What AI skill should my team and I actually learn right now?” I will scream this from the rooftops of NYC. ➡️ Learn agent delegation Target a dedicated workflow or task. Assign an AI agent said role, define the outcome, set constraints, and schedule review gates. Treat it like a junior teammate and give it work, while monitoring so you can review for accuracy. Here’s my do-this-now stack, and how I’d run it with a team ⏬ If you’re a beginner: Start with ChatGPT Agent Mode. Open a new ChatGPT chat and change the dropdown to ‘Agent Mode’. It can plan tasks, execute steps, and return cited outputs for market scans, vendor comparisons, executive briefs, and decision memos. Kick off the job, let it run, WATCH IT RUN, and then review the completion. If you’re more technical or ops-heavy: Use Claude Code when the work requires operating UIs or your computer - clicking through portals, filling forms, wrangling spreadsheets, saving down documents. Expect more upfront setup and ownership, so keep a step-by-step prompt checklist, add automatic reruns for failing steps, and update the checklist only when the site’s labels or paths change. If you’re living in Google Workspace: Turn on Google connectors (Drive, Gmail, Calendar) inside ChatGPT or Claude. Ask the model to find your team’s file, summarize threads, compare document versions, prepare for and schedule meetings, or draft from past emails. This lets your agent pull context and act on it without manual hunting. How to turn this into outcomes in 30 days ⏬ → Twice a week, use Agent Mode to produce a one-page brief with citations and a recommendation on a real business question. Track cycle time and data/citation quality, and, where relevant, use Claude Code to automate in parallel. At the end of the month, you should know where a few agents can tackle real work and have the data to support what to scale. #AIinWork

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.

How we shrank 30-40 hours of weekly manual work into just 2-3 hours 🤯 (Automation Tip Tuesday 👇) This home services company was struggling with their invoice reconciliation process. They received numerous vendor invoices via email (PDF format) and needed to manually match them against jobs in ServiceTitan. Their team was stretched thin, discrepancies and overpaying were daily occurrences, and one day, they had enough. We worked on a three-step automated solution: Step 1: Finding the PDFs Zapier monitors the inbox for invoices. When it detects an invoice with a PDF attachment, it proceeds to Step 2. Step 2: Parsing the Data Nanonets uses AI to extract data from the PDF. Step 3: Data Comparison The extracted data is compared with jobs in ServiceTitan. Any discrepancies are added to a spreadsheet for internal review. 30-40 hours of weekly manual verification time is now just 2-3 hours. With instant discrepancy flagging, their system allows for better vendor management, improved billing accuracy, and more time for the team to pursue higher-value tasks. Which manual task that can be automated is currently taking up too much valuable time? If you’re thinking of one, it’s time we spoke. Book a free call (link in the comments 👇) and let’s see what we can do for your workflow. -- 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

Building an AI-native company means your clients get a portal they never asked for — and it's always up to date. Our clients have a dashboard. They log in. They see their project timeline, strategy documents, ROI tracking, feedback forms, and every deliverable we've ever sent them. We never manually update it. Here's how it works: Every meeting we take gets transcribed automatically. A sync script matches the transcript to the right client using attendee emails and keywords, then drops it into their folder as a dated markdown file. Every deal movement, every note, every action item — it all lives in one database. A nightly script pulls the latest data for each client and regenerates their context page from scratch. Not appending. Full rewrite. Fresh every morning. The client portal reads from that same source of truth. So when a client logs in on Tuesday, they see the meeting notes from Monday's call, the updated timeline, and the three action items we committed to — without anyone on our team copying and pasting a thing. Most agencies send fortnightly update emails that are stale before they hit the inbox. We built a living document that our clients can check whenever they want. The trust impact has been massive. Clients stop asking "where are we at?" because they already know. Total cost: zero. It runs on scripts and free-tier infrastructure. Does anyone else do this for their clients? I've never seen a consultancy or agency give clients a self-serve portal like this, and I can't figure out if we're early or just weird. What does your client communication actually look like?

🔥 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

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?

Automating Autodesk Construction Cloud with Open-Source Low-Code Agents 🤖   Connecting directly to LLMs is powerful, but a dynamic AI agent needs more than just raw intelligence.   It needs to decide how to act and respond based on context and broader goals, without requiring us to explicitly program every step.   n8n is an open-source, low-code workflow automation tool with built in AI features, reducing a lot of the complexity involved in controlling agents.   It also makes it easier to apply Retrieval-Augmented Generation (RAG), which improves AI responses by finding relevant data from external sources before generating text.   RAG uses AI-generated embeddings to classify and search documents based on semantic meaning rather than just keywords, ensuring more relevant and context aware results.   This means: ✅ Smarter AI decisions based on contextual rather than keyword-based searches ✅ More accurate and meaningful interactions with project data ✅ A straightforward way to leverage AI without complex custom development   While hallucinations still happen, tools like n8n are great for quickly prototyping with out of the box AI functionality, with the ability to scale when needed.   With ACC's open APIs, we can easily bring project data into the AI knowledgebase. Here I'm extracting data from ACC Issues and RFIs, querying it using AI, and updating records where needed.   To take this further, n8n could be configured to run scheduled updates to keep the RAG databases in sync with the ACC project.   This excites me because with a solid foundation it's possible to automate entire processes rather than individual tasks. For example drafting an RFI response by reviewing specifications, assigning the correct person to review, flagging critical issues and escalating if not closed out.   🏁 Getting Started n8n is free to run locally. You can follow this guide to set it up: https://lnkd.in/gYwm5sSD   🔗Also check out the ACC APIs: https://lnkd.in/g_TSeUYA   🔗My previous post on Copilot Studio with ACC: https://lnkd.in/g4jWacuK   #Autodesk #n8n #Automation #OpenAI

This is a visual representation of why your team hates Salesforce 😡... Throughout my Salesforce journey, I've seen it all (Insert "Emotional Damage" meme 🫠). One common issue I see often are Flows that "work," but that are not optimized for scale or user experience. They cause ugly error messages, delays on future iteration, & inaccurate data that plague users on a daily basis. Check out the Flow examples below: Version 1 works. It's simple, has only 2 elements, so what's the big deal? To find out, let's look at the #'d boxes in Version 2: 1️⃣ Element Descriptions: Please...for the love of Benioff... document the "Why." Each element allows you to write a description, which explains what it's doing technically and why it's important to the process you're building. This context is essential for future changes and for those that come after you. If another admin can't read your descriptions and understand what it's doing, you haven't documented enough! 2️⃣ Decision Elements after Get Records Elements: In Version 1, the "Get Account Id" element finds a related Account record associated with the triggering Opportunity. What happens if the criteria for the search doesn't find a record? ❌ Flow Error ❌. By checking to see if the Get Records element finds what it's looking for, you can prevent a poor user experience and ensure other automation runs on schedule. 3️⃣ Fault Paths & Error Handling: A fault path is an error handling path that triggers when the element wasn't able to process a change (Update, Create, Delete) in the database. By default, users are presented with red text and a cryptic message without enough readable context to troubleshoot themselves. In Version 2, we've add a fault path for every Create Records element to notify the Salesforce team of new errors. No one likes it when automation fails, but it's a magical experience to reach out to a user and let them know you're already working on it! 🪄🎩🐇 4️⃣ Tracking Performance/Usability: This one is a game changer... What good is an active Flow if you can't measure its performance or usability? Create a custom object called "Automation Saved Time." Any time you add to a Flow, estimate the amount of time the automation saves and add it to a variable. At the end of the Flow, create a new Automation Saved Time record adding the aggregated time for all elements. It'll help answer some amazing questions: a) How much time has your Flow saved users? b) How often has Flow is been run? c) Is this Flow useful? All questions you can only assume the answers to without this data! Build a dashboard and show it to internal stakeholders, so they understand the value you're adding. 5️⃣ Reuse & Recycle: Rather than building a new Flow element each time you need it, connect to an existing element. In this example, we are connecting both fault paths to the same email alert. "In a world full of Version 1s, be a Version 2 💪🏻" #salesforce #salesforceflow #automation #bestpractices #benioff