How to Identify Tasks Suitable for Automation

For PMs who want to use AI agents to be more productive but feel stuck coming up with ideas, I've been experimenting with a prompt: (this works best in a project that already has context on you/your team/product) ❝❝❝ Based on what you know about me and my organization, please brainstorm five ideas for an AI automation I can build using platforms such as Zapier Agents/Lindy AI/Relay app/Cassidy AI/Gumloop/ etc. These should help me as a product manager save time on draining-yet-essential tasks that take me away from more valuable, strategic, and creative use of my attention and energy. Ask yourself: What ongoing repetitive work requires some judgment and writing abilities, but not my full expertise and intuition? # IMPORTANT: these should be event-driven AI automations, not batch tasks Only suggest event-driven automations that process items one-at-a-time as they arrive. Do NOT suggest batch tasks that process multiple items on a schedule (e.g., "every morning scan all..." or "weekly compile..."). Why: AI automations shine in one-at-a-time, repetitive tasks. They do best when designed for immediate responses to individual triggers. ❌ WRONG (Batch Task): "Every morning, scan all new support tickets and summarize them" ✅ RIGHT (Event-Driven): "When a new support ticket arrives, analyze it and alert me if it's urgent" # Examples Below are examples of use cases where product managers have gotten a lot of value from AI agents. 1. Compile fragmented information that would require a lot of clicks “When a new message is posted in the #feature-requests Slack channel, distill the customer request into 2-5 keywords. Search those keywords in recent Slack threads, HubSpot conversations, and Gong snippets, and reply to the thread with what you find.” “Every morning scan my calendar for customer calls, and instead of searching the web, DM me with recent interactions from this customer in Salesforce, Gong, and Zendesk.” “Every Monday morning, prepare a competitor activity digest by scanning recent blog posts, App Store updates, and X announcements.” “When a customer churns, post a message in the #churn-lessons channel with recent support interactions, NPS rating and date, and churn survey response.” 2. Boring, Sisyphean tasks with high upside “Monitor the pricing pages of 5 competitors for changes.” “DM me a weekly report with bugs that are nearing their SLA deadline for the associated customer, and cc each respective CS representative.” 3. Scanning exhausting amounts of data “DM me with support cases where the resolution was around product confusion rather than tech.” “Monitor NPS responses being posted as messages in a Slack channel. If something is clearly a technical issue, create a support ticket in Zendesk.” 4. Drafting updates “Every Friday at 10 a.m., write a summary of progress made across all teams in our project board, across epics, changes made to scope, and highlight any timeline changes.” ❞❞❞

𝗪𝗵𝗲𝗻 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗖𝗮𝗹𝗹 𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁? Not every process needs a full-blown AI agent. Sometimes a simple macro or integration does the trick. But there are clear signs that your workflow is begging for an autonomous assistant. Here’s how to spot them—and why agents succeed where traditional automation stalls: 🔍 𝟭. 𝗖𝗿𝗼𝘀𝘀-𝗦𝘆𝘀𝘁𝗲𝗺 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: You’re juggling data from ERP, CRM, email, and a custom database—and every handoff is a manual export-import. 𝗔𝗴𝗲𝗻𝘁 𝗪𝗶𝗻: An AI agent can ingest records from your ERP API, enrich contacts in your CRM, send templated emails, and log responses. 𝘢𝘭𝘭 in one continuous flow. No more copy-paste handovers. 📚 𝟮. 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱-𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Your team spends hours reading PDFs, extracting key specs, and summarizing them in slides or Jira tickets. 𝗔𝗴𝗲𝗻𝘁 𝗪𝗶𝗻: An agent reads documents, highlights critical passages, generates bullet-point summaries, and files them where you need. slashing review time from hours to minutes. 🔄 𝟯. 𝗕𝗿𝗶𝘁𝘁𝗹𝗲 𝗥𝘂𝗹𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Your decision tree works until a rare edge case pops up, then everything crashes and you scramble for ad-hoc fixes. 𝗔𝗴𝗲𝗻𝘁 𝗪𝗶𝗻: Agents pair a flexible language model with hard constraints (“never quote over X without approval”) so they adapt to new inputs without breaking your guardrails. 📈 𝟰. 𝗦𝗶𝗴𝗻𝗮𝗹-𝗗𝗿𝗶𝘃𝗲𝗻 𝗧𝗿𝗶𝗴𝗴𝗲𝗿𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: You know that building-permit filings or job postings signal capital-investment opportunities. if only you could catch them in real time. 𝗔𝗴𝗲𝗻𝘁 𝗪𝗶𝗻: An agent monitors permit APIs, scrapes relevant job boards, scores leads by fit, and pings reps the moment a trigger appears. 🎯 𝗣𝘂𝘁𝘁𝗶𝗻𝗴 𝗜𝘁 𝗜𝗻𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 1. 𝗠𝗮𝗽 𝗬𝗼𝘂𝗿 𝗦𝘁𝗲𝗽𝘀: Document each tool and data source in your current workflow. 2. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀: Where do handovers break down? Which tasks feel painful or error-prone? 3. 𝗣𝗶𝗹𝗼𝘁 𝗮 𝗠𝗶𝗻𝗶-𝗔𝗴𝗲𝗻𝘁: Start with a single “signal-to-action” flow, say, permit-to-email and measure time saved. 4. 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 & 𝗘𝘅𝗽𝗮𝗻𝗱: Add complexity. Multi-tool flows, conditional logic, and human-in-the-loop checks as you gain confidence. Agents aren’t black boxes. They shine where processes span multiple systems, rely on unstructured inputs, or need continuous vigilance. If your team still wrestles with exports, manual reviews, or brittle scripts, an AI agent could help. 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁 𝗳𝗶𝘁𝘀 𝘆𝗼𝘂𝗿 𝘁𝗼𝘂𝗴𝗵𝗲𝘀𝘁 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄?

I'm not surprised MIT's report shows 95% of AI prototypes fail. This summer, Forerunner automated 2 critical workflows for our research and diligence efforts. The patience and trial & error required far exceeded our expectations. The results? AWESOME. No doubt AI is powerful. After automating the workflows, I would say the power today is one part potential and one part reality. Building an AI workflow is still quite difficult, especially if you are not technical. So how did we do it? Here's an overview of our first step. 1️⃣ Define workflows 2️⃣ Break workflows down into tasks 3️⃣ Define automation evaluation criteria 4️⃣ Evaluate automation potential & impact 5️⃣ Prioritize tasks for automation The first principle thinking required here set us up for success. In fact, the findings of MIT's report supports this approach -- "pick one pain point, execute well, and partner smartly with companies who use their tools." Across all of our research & diligence workflows, we separated our workflows into 120+ tasks and selected five characteristics to evaluate automation potential & impact. 1️⃣ Task requires a unique Forerunner lens 2️⃣ Automating task would save significant time 3️⃣ Task is routine & frequent 4️⃣ AI can create desired output 5️⃣ Task is essential to building expertise Having this framework clarified what tasks made sense to automate first and, as importantly, what tasks did not make sense to automate today (or possibly ever). We believe certain tasks are more important for skill & perspective building even if they could be automated. More to come on what tool we selected, how we automated the workflows, and what we learned along the way -- this is where the patience and perseverance comes in.

The Smart Choice: When to Use AI vs. Automation (And Why It Matters) Here's a conversation I have weekly: "We need AI for everything!" But here's the truth—sometimes the best solution is simpler than you think. The key is matching the right tool to the right problem. AI isn't always the answer, and neither is basic automation. Let me break down when each makes sense. AI vs. Automation: Know the Difference Traditional Automation excels at consistent, rule-based tasks. Think of it as a really smart flowchart that never gets tired. Perfect for invoice processing, data entry, or sending reminder emails based on specific triggers. AI shines when you need judgment, prediction, or pattern recognition. It learns from data and makes decisions in situations that don't follow simple if-then rules. When Simple Automation Wins Sometimes the unglamorous solution is the right one. A manufacturing client was losing orders due to delayed shipping notifications. The fix? A simple automated email triggered when packages left the warehouse. No AI needed—just smart process design that saved thousands in customer churn. For repetitive tasks with clear rules, automation is your friend: Operational Efficiency: Automatically route support tickets based on keywords Compliance Tracking: Flag when documents haven't been updated in 90 days Workflow Management: Move projects through approval stages without manual handoffs When AI Adds Real Value AI becomes powerful when you're dealing with uncertainty or complexity: Predictive Analytics: A retailer uses AI to forecast demand spikes during weather events, adjusting inventory before the storm hits Hyper-Personalization: E-commerce sites that learn your preferences and show products you didn't know you wanted Intelligent Decision-Making: Supply chains that reroute shipments in real-time based on traffic, weather, and capacity constraints The Sweet Spot: Intelligent Automation The magic happens when you combine both. Consider loan processing: automation handles document collection and compliance checks, while AI assesses credit risk and flags unusual patterns for human review. Start With Your Pain Point Before jumping to solutions, ask yourself: Is this problem rule-based or does it require judgment? Do I need to predict outcomes or just execute consistent processes? Where are humans currently adding the most value? The companies getting automation right aren't chasing the latest technology—they're solving real problems with the right tools. What's your biggest operational headache right now? Sometimes the simplest solution is the smartest one.

You want to put AI to work. But how? When I host workshops, the biggest roadblock for most people is figuring out exactly what tasks to delegate. Sure, we all know you can ask Copilot to write an email or have Gemini create an image, but what about all the other tasks on your plate? Identifying which ones are a good fit for AI takes a bit of analysis. I created an easy-to-use framework called STAR to help you out. Here's how it works: S - Summarize We all have too many emails, reports and white papers to read. AI is excellent at ingesting pages of content and summarizing it based on our specifications. T — Transformation AI is incredible at repurposing content. Convert a white paper to a blog or social post. Stop typing notes from scratch. Take a raw meeting transcript and transform it into a clean list of strategic insights, a formatted action list, or a follow-up email to the client. A — Analysis Extract insights, find patterns, or compare data points. Upload three different vendor contracts and ask the AI to table the differences in liability clauses. Or, upload a month's worth of customer support tickets and ask it to categorize the top 5 recurring complaints. R — Research Instead of a simple Google search, use AI to build a comprehensive list of software vendors that meet specific security criteria, or draft a full dossier on a prospect before you hop on a sales call. 💬 How do you identify good candidates for AI automation? Do you use a framework or are you more likely to wing it? If you'd like to explore how to use the STAR Framework (and other ways to put AI to work), head over to my newsletter. Link 👇 🔔  Don't forget to follow so you don't miss my weekly AI insights!

When I first started exploring automation, I had one big question: How do I know if a process is truly ready for automation? The key lies in identifying the right processes that will bring the most value when automated. Here’s a breakdown of what to look for: - Repetitive & Rule-Based Tasks: Processes that are repetitive, time-consuming, and prone to human error are prime candidates for automation. - High Frequency of Execution: The more often a process is performed, the more significant the benefits of automation. - Standardized & Well-Defined Steps: When a process has clear, standardized steps, it’s easier to automate. - Data-Driven: Processes involving large volumes of data or consistent data processing are ideal for automation. - Manual & Time-Consuming: Automating processes that require significant manual effort saves time and resources. - Clear Start & End Points: Processes with well-defined triggers and outcomes are easier to automate. - Measurable Outcomes: When results are quantifiable, it’s easier to track the impact and ROI of automation. - High Impact: Focus on processes that will drive efficiency, cost savings, or customer satisfaction. - Low Complexity: Start with simpler processes for quicker wins and smoother implementation. - Few Exceptions: Processes that follow a consistent path with minimal variations are easier to automate effectively. A useful framework for assessing readiness is the R.E.A.D.Y. approach: - Repeatable: The process is performed regularly. - Executable: The steps are clear and actionable. - Auditable: The results are trackable and measurable. - Dependent: The process relies on specific inputs and produces defined outputs. - Yield: The process will deliver significant ROI when automated. By using this approach, organizations can identify the best candidates for automation and prioritize their efforts for maximum impact. What processes in your organization meet these criteria? It might be time to take them to the next level with automation. #Automation #Efficiency #DigitalTransformation

Automate simple tasks before trying to build complex systems. If you’re new to automation, here’s where to begin: ➡️ Use no-code tools like n8n, Zapier, or Power Automate ➡️ Pick a template workflow like “save email attachments” or “add form responses to a sheet” ➡️ Customize fields, test with sample data, and learn by doing You don’t need to automate everything at once. Here are some beginner-friendly automations that give you quick wins: ➝ Log incoming emails into a sheet to track tasks ➝ Save attachments to Drive and send alerts in Slack/Teams ➝ Route leads from forms into a CRM and send thank-you emails ➝ Create reminders for upcoming events ➝ Monitor website updates and push alerts ➝ Turn RSS feeds into a content idea queue ➝ Trigger alerts from spreadsheets when values change ➝ Sync files or combine datasets to build basic pipelines ➝ Parse structured emails and auto-draft documents ➝ Move client files into folders and notify teams ➝ Generate LinkedIn post drafts from a sheet for faster scheduling Start with one tool. Explore templates. Learn how data moves step-by-step. For content creators and freelance web developers, automations like lead capture, file-handling, and caption drafting are the best places to start. They save time, reduce manual work, and help you focus on what matters. Once you're comfortable, level up by adding filters, branching logic, and transforming data between apps. 👇 Now it’s your turn: ✅ If you’ve already built an automation, share it in the comments - what task did you automate. ✅ If you haven’t built one yet, start today and explain the task you’re automating. I’ll pick the best one and connect with you for a 1:1 call - I’ll guide you if you’re facing roadblocks and help you crack your job or career goals faster. Automation is a powerful skill. Where will you start today? 😊 Repost for others ♻️

Not every repeated task is worth automating. Here's the framework I use to rank automation candidates before I build anything. The 4-condition filter: A task is worth automating only if it passes all four: 1/ Frequency: You do it 3+ times a week 2/ Effort: Each instance takes 5+ minutes 3/ Consistency: The inputs and outputs are predictable 4/ Claude-ready: It's pattern-based, not judgment-based If it fails any one condition, set it aside. The ranking formula: For tasks that pass the filter, rank by: Frequency × Effort × Consistency Score Consistency 1-3 (1 = variable, 2 = mostly consistent, 3 = fully consistent). Most people rank automations by how much time they think they'll save. That's a guess. Frequency × Effort × Consistency is a calculation. 📩 I even built a Claude Skill to find your next automation every week (It saves me 3 hrs/week). Find it here: https://lnkd.in/gNvJhRrr