Automating Repetitive Work Tasks

HR pros aren’t scared of AI! they’re just tired of wasting time. Saving hours is key! But how? Okay, here’s the breakdown of how I do it: I make a list of all recurring tasks. Make sure ist a list of tasks, not a list of task bundles. „Selling my online course.“ is a task bundle, not a task. „Identifying my target groups is a task bundle, not a task. „Writing an email template for warm leads based on my lead scoring trigger“ is a task. For each task, I list the tools and documents I use during the task. I also specify the exact process from usual input to desired output of each task. I measure the time each task takes me today with a timer. I estimate how often I do each task per year. I tackle the task with the most time volume per year. I open ChatGPT and describe everything about the task in detail. I ask for a breakdown of how I can use ChatGPT or other AI-tools to automate that task. Here’s an example: Every day at 8:30, I check my mails of the past 24 hours. I structure them with respect to urgency and my tasks related to them. I then answer all urgent emails that do not need pre-work or complex input from my side. All other mails are turned into blockers in my calender corresponding to their urgency. That’s how I stay organized. I now have a scheduled prompt in M365 Copilot, that goes through my mails every morning and delivers a short report on their content, the urgency, and my todos as well as the complexity of the to do. This report has the links to the specific emails. I click the link and answer all emails. I click the other links and turn emails into blockers in my calendar. Soon, the last step will be automatic. This little thing turned a 60-90 minute morning routine into a 30-45 minute routine. 50% efficiency. That’s about 2.5 to 3.75 hours each week. That’s about a third to half of a work day for me. That’s huge. How do you actually save time with AI?

We cut proposal response time by 60% and reactivated 20% of lost deals. How? with just one move: better follow-up. We know that proposal decks get buried in inboxes. Buyers forget what you sent, AEs left chasing the same names for weeks. Not fun! So we looked for a way to rebuild the process. And we came across: AI Deal Rooms. It is basically a centralized room where the buyer can find everything related to the deal: - Proposal or pricing - Demo recap - Case studies - Next steps The AI layer makes the room dynamic: - Pulls in call notes automatically after meetings - Personalizes summaries and intros for each buyer - Tracks engagement (who viewed what, when, and for how long) - Triggers follow-ups when buyers reopen, share, or revisit the content Here’s how we run it now at SalesCaptain 👇 1️⃣ Meeting ends Call notes, key pains, and next steps are summarized automatically (PandaDoc natively, Fathom.ai, HubSpot meeting notes..etc) 2️⃣ A deal room opens instantly in PandaDoc Proposal, demo recap, and case study, all in one link. Each room starts with a short video from the AE (Loom, Tella) 3️⃣ Email goes out fast No “we’ll send it next week.” AEs hit send in minutes. 4️⃣ Signals start tracking We see when buyers open the room, how long they stay, and who else views it (PandaDoc) 5️⃣ Follow-ups trigger on actions - Pricing opened → Slack ping (Zapier). - Proposal revisited → “Still exploring?” check-in. - Old deal reopened → nurture sequence. The results we've seen so far are: → Proposal response rate up 30% → Lost deals re-engaged 20% more → Sales cycles shorter, cleaner, easier to track (the way sales cycles should be) Buyers get one link with everything they need. Reps stop chasing, and focus on closing. I’m seeing this setup change how fast teams follow up and how often buyers come back. If you haven’t tested AI Deal Rooms yet, it’s worth exploring. Shoutout to PandaDoc for this feature. #salesops #revops #salescycle #gtm #pandadoc

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

Every weekday at 7:30 AM, I get a one-paragraph brief for every meeting on my calendar. Last email threads with each participant, open asks, unresolved questions. Claude wrote it while I was asleep. Anthropic shipped three automation tools in four weeks. Two serve you individually. One serves your whole team. The routing decision is simple. Work needs your local files? Cowork Scheduled Tasks. Runs on your machine, reads ~/Documents. Needs to fire while your laptop is closed? Claude Routines. Cloud infrastructure. Competitor checks at 7 AM, sentiment scans on Monday morning, pre-meeting briefs before you wake up. Pro plan gets 5 runs/day. Max gets 15. Needs to serve more than just you? Managed Agents. Every PM queries the same agent, each with their own session and audit trail. Asana, Notion, Rakuten, and Sentry are already running these in production. Rakuten went from quarterly releases to biweekly. The reasoning step is what separates this from Zapier. A Zapier zap chains deterministic actions. A Routine reads a competitor pricing page, decides whether something meaningful changed, and writes a summary in your voice. Different category of work. I set up a competitor pricing monitor in 20 minutes. It visits three competitor pages every morning, compares against yesterday's Notion log, and posts only what changed to Slack. I know about pricing shifts before my sales team hears them on calls. A weekly sentiment scanner does the same thing across Reddit, G2, and Product Hunt. Four weeks of consistent themes tells you what users actually want, not what's loudest internally. I built 7 of these workflows with full prompts, connector setup, failure modes, an engineer handoff brief, and a security doc: https://lnkd.in/gyb4FkHa The PM who walks into Monday planning with automated intelligence will out-prioritize the one going off memory and escalations. That gap compounds every week.

Automating Routine Tasks in Treasury: A Practical Guide   How to get started in automation in treasury…   1. Define Your Task: Start by identifying the specific task you want to automate. Whether it's renaming files, downloading attachments from emails, or extracting data from spreadsheets. Consider the steps involved and the tools you might use. A tool like Copilot, ChatGPT or Claude can help with this if needed.   2. No-Code or Low-Code Tools: If you’re not experienced in coding, tools like Zapier (free to sign up with a google account), Integromat or Power Automate will be a huge help. Check which tools are allowed by your IT function. If you can use them, you can get up and running very quickly. Example -   - Tool: Zapier   - Action: Set up a Zap (automation) that triggers when you receive an email with a specific subject (e.g., "Daily Sales Report"). Configure it to save the attachment to a designated folder in your cloud storage (e.g., Google Drive).   There’s even a dad jokes chatbot – see below!   3. Python Libraries: Python can simply automate a wide variety of tasks and if you’re not sure how to code it, an LLM will be able to help. Just explain what you want to do. Here’s an example:     - Library: Pandas   - Action:     - Write a Python script that reads the downloaded spreadsheet (e.g., in CSV or Excel format).     - Use Pandas to clean up the data (remove empty rows, handle missing values, etc.).     - Extract relevant columns (e.g., product names, quantities, prices).     - Save the cleaned data to a new CSV file.   Python code block - you can run this on a csv file called downloaded_file.csv and it will clean it and save a revised version as output.csv----->    import pandas as pd       # Read the downloaded spreadsheet (assuming it's in CSV format)     input_file = "downloaded_file.csv"     df = pd.read_csv(input_file)       # Clean up data (remove empty rows, handle missing values, etc.)     df_cleaned = df.dropna()       # Extract relevant columns (e.g., product names, quantities, prices)     relevant_columns = ["Product", "Quantity", "Price"]     extracted_data = df_cleaned[relevant_columns]       # Save the cleaned data to a new CSV file     output_file = "cleaned data.csv"     extracted_data.to_csv(output_file, index=False)       print("Data extraction complete. Saved to", output_file)       Remember, start small, and gradually build your automation skills. Soon, you'll be handling routine tasks like a pro!   #ProductivityHacks #AutomationMadeEasy

How tech companies are saving 10+ hours a week (with these 6 simple Salesforce automations): Companies waste hours every week on tasks that should be automated. They lose time in ways no one even notices: • Clicking through screens • Manually updating fields • Logging calls by hand Each task seems small. But together, they slow everything down. Here are 6 Salesforce automations that save tech companies 10+ hours every week: 1) Data entry and lead enrichment Manual data entry slows everyone down. New leads are auto-enriched with: • Company info • Contact details • Other relevant data No typing required. That means sales can sell, marketing gets clean data, and RevOps stops fixing spreadsheets. 2) Lead management and routing Without automation, leads sit in limbo. Sales and marketing waste time figuring out ownership. So we automated lead assignment, marketing handoffs, and customer success escalations. Now everyone knows exactly where a lead belongs. No confusion. No delays. 3) Automated follow-ups, demos, and approvals If teams rely on memory for follow-ups, deals get lost. We trigger automated task reminders when key actions happen. • A new lead comes in • A demo is booked • A proposal goes out Teams get notified automatically. No more missed follow-ups. No bottlenecks. 4) Proposal, contract, and quote generation Teams shouldn’t waste time building proposals, contracts, or quotes manually. We automate it. Pre-built templates pull in Salesforce data: • Proposals are ready in minutes • Contracts auto-route for approval • No chasing down managers Faster contracts = faster deals = faster revenue. 5) Automated email and activity tracking If it’s not logged, it didn’t happen. But teams forget to log emails, calls, and meetings. So we integrate Salesforce with Outreach, Gong, and Slack to log everything automatically. Now leadership gets full visibility into: • Emails sent • Calls made • Customer responses No manual tracking required. 6) Real-time reporting and forecasting Leaders can’t make smart decisions without real-time data. So we build dashboards that track: • Pipeline health • Deal stages • Team activity Better visibility = faster, smarter decisions. The Bottom Line: Manual processes, bad data, and disconnected tools are slowing you down. We help tech companies fix this—fast. If Salesforce feels like more work than it should be, let’s change that. DM me "Salesforce" and let’s talk.

Every Monday used to start the same way. Now the report builds itself before I wake up. The old version was a weekly ritual: Open five tools. Copy the numbers. Rebuild the same report by hand. Half a morning gone before a single decision got made. Salesforce puts the average marketing team at seven data sources to stitch together (2026). Seven was roughly my list. So I stopped stitching by hand. The dashboard now runs on Claude Cowork. A routine fires every morning. It pulls each source through its own connection, compares this week to the last, applies my rules, and writes the read straight into the page. What changed, why, and what to do next. I look at it once a week, not every morning. The daily refresh is not for staring at. It is so the trends are already current when I open it on Monday. High-performing marketers who use AI agents reclaim around eight hours a week (Salesforce, 2026). This is where those hours come from. One thing stays human: The routine recommends. I approve, override, or defer. Nothing acts on its own, because a confident wrong answer at scale is worse than no answer. The build is not the point. The shift is. The machine does the gathering. I do the deciding. If your weekly reporting rebuilt itself tomorrow, what would you actually do with the time? #MarketingAnalytics #AIMarketing #MarketingOps

Years ago, I watched one of the best enterprise salespeople I've ever known lose a million-dollar deal simply because "𝗜 𝗱𝗼𝗻'𝘁 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝗲 𝗽𝘂𝘀𝗵𝘆". This brilliant, capable professional was letting million-dollar opportunities slip away because she was afraid of seeming aggressive. Sound familiar? Here's the reality I've found after analyzing thousands of sales interactions: The average B2B purchase requires 8+ touches before a response, but most salespeople give up after 2-3. 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗳𝗲𝘄𝗲𝗿 𝗳𝗼𝗹𝗹𝗼𝘄-𝘂𝗽𝘀—𝗶𝘁'𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝗼𝗻𝗲𝘀. Working with clients across industries, I've developed what some have called the "Goldilocks Sequence" – not too aggressive, not too passive, but just right for maximizing response rates without alienating prospects. It starts with how we view follow-ups. Stop thinking of them as "checking in" and start seeing them as opportunities to deliver additional value. For each client, we build what I call a "Follow-Up Content Library" with 5-10 genuinely valuable resources for each buyer persona – a mix of their content and third-party research addressing likely challenges. Having this ready means follow-ups can pull the most relevant resource based on the specific situation. The sequence itself has a rhythm designed to respect the prospect's time while staying on their radar: 𝗗𝗮𝘆 𝟭 is the initial value-focused outreach with a specific insight (never generic "I'd like to connect" language). Around 𝗗𝗮𝘆 𝟯, we send a gentle bump, forwarding the original email with: "I wanted to make sure this reached you. Any thoughts on the [specific insight]?" It's brief and assumes positive intent. By 𝗗𝗮𝘆 𝟱, we shift to an alternative channel like LinkedIn, with a personalized note referencing the insight, but still no meeting request. Around 𝗗𝗮𝘆 𝟴 comes the pure value-add – sharing a relevant resource with no ask attached: "Came across this [article/case study] that addresses the [challenge] we discussed. Thought you might find it valuable regardless of our conversation." 𝗗𝗮𝘆 𝟭𝟮 brings what I call the "pattern interrupt" – a brief email with an unexpected subject line and single-question format that's easy to respond to. Then, around Day 18, we send the "permission to close" message: "I'm sensing this might not be a priority right now. If that's the case, could you let me know if I should check back in the future? Happy to remove you from my follow-up list otherwise." This sequence generated a 34% response rate for an enterprise software client compared to their previous 11% using traditional methods. The key difference? Every touch adds legitimate value rather than just asking for time. And because it's systematic, it removes the emotional weight of deciding when and how to follow up. What's your most effective follow-up technique? I'm always collecting new approaches to share with clients. #SalesFollowUp #OutreachStrategy #PipelineGeneration

How far are we from having competent AI co-workers that can perform tasks as varied as software development, project management, administration, and data science? In our new paper, we introduce TheAgentCompany, a benchmark for AI agents on consequential real-world tasks. Why is this benchmark important? Right now it is unclear how effective AI is at accelerating or automating real-world work. We hear statements like: > AI is overhyped, doesn’t reason, and doesn’t generalize to new tasks > AGI will automate all human work in the next few years This question has implications for: - Companies: to understand where to incorporate AI in workflows - Workers: to get a grounded sense of what AI can and cannot do - Policymakers: to understand effects of AI on the labor market How can we begin on it? In TheAgentCompany, we created a simulated software company with tasks inspired by real-world work. We created baseline agents, and evaluated their ability to solve these tasks. This benchmark is first of its kind with respect to versatility, practicality, and realism of tasks. TheAgentCompany features four internal web sites: - GitLab: for storing source code (like GitHub) - Plane: for doing task management (like Jira) - OwnCloud: for storing company docs (like Google Drive) - RocketChat: for chatting with co-workers (like Slack) Based on these sites, we created 175 tasks in the domains of: - Administration - Data science - Software development - Human resources - Project management - Finance We implemented a baseline agent that can web browse and write/execute code to solve these tasks. This was implemented using the open-source OpenHands framework for full reproducibility (https://lnkd.in/g4VhSi9a). Based on this agent, we evaluated many LMs, Claude, Gemini, GPT-4o, Nova, Llama, and Qwen. We evaluated both success metrics and cost. Results are striking: the most successful agent w/ Claude was able to successfully solve 24% of the diverse real-world tasks that it was tasked with. Gemini-2.0-flash is strong at a competitive price point, and the open llama-3.3-70b model is remarkably competent. This paints a nuanced picture of the role of current AI agents in task automation. - Yes, they are powerful, and can perform 24% tasks similar to those in real-world work - No, they can not yet solve all tasks or replace any jobs entirely Further, there are many caveats to our evaluation: - This is all on simulated data - We focused on concrete, easily evaluable tasks - We focused only on tasks from one corner of the digital economy If TheAgentCompany interests you, please: - Read the paper: https://lnkd.in/gyQE-xZG - Visit the site to see the leaderboard or run your own eval: https://lnkd.in/gtBcmq87 And huge thanks to Fangzheng (Frank) Xu, Yufan S., and Boxuan Li for leading the project, and the many many co-authors for their tireless efforts over many months to make this happen.

You need to check out the Agent Leaderboard on Hugging Face! One question that emerges in the midst of AI agents proliferation is “which LLMs actually delivers the most?” You’ve probably asked yourself this as well. That’s because LLMs are not one-size-fits-all. While models thrive in structured environments, others don’t handle the unpredictable real world of tool calling well. The team at Galileo🔭 evaluated 17 leading models in their ability to select, execute, and manage external tools, using 14 highly-curated datasets. Today, AI researchers, ML engineers, and technology leaders can leverage insights from Agent Leaderboard to build the best agentic workflows. Some key insights that you can already benefit from: - A model can rank well but still be inefficient at error handling, adaptability, or cost-effectiveness. Benchmarks matter, but qualitative performance gaps are real. - Some LLMs excel in multi-step workflows, while others dominate single-call efficiency. Picking the right model depends on whether you need precision, speed, or robustness. - While Mistral-Small-2501 leads OSS, closed-source models still dominate tool execution reliability. The gap is closing, but consistency remains a challenge. - Some of the most expensive models barely outperform their cheaper competitors. Model pricing is still opaque, and performance per dollar varies significantly. - Many models fail not in accuracy, but in how they handle missing parameters, ambiguous inputs, or tool misfires. These edge cases separate top-tier AI agents from unreliable ones. Consider the below guidance to get going quickly: 1- For high-stakes automation, choose models with robust error recovery over just high accuracy. 2- For long-context applications, look for LLMs with stable multi-turn consistency, not just a good first response. 3- For cost-sensitive deployments, benchmark price-to-performance ratios carefully. Some “premium” models may not be worth the cost. I expect this to evolve over time to highlight how models improve tool calling effectiveness for real world use case. Explore the Agent Leaderboard here: https://lnkd.in/dzxPMKrv #genai #agents #technology #artificialintelligence