Automated Report Generation

I used to spend 3 hours every Friday making marketing reports. Copying data. Formatting slides. Sending endless recap emails. It was the most repetitive part of my week. Until I found a way to automate it without code. Just one platform, one prompt, and a few logins. That’s when I built my Marketing Reporting App inside Replit With Connectors, my data finally started talking to each other. Here is how: Step 1: Log in to Replit → Create your account. → Tell it what you want to build. → Replit sets up your workspace instantly. Step 2: Prompt It → In this case: “Make me an app that reads weekly KPIs from Google Sheets, writes a concise Notion report page with insights and deltas, and emails it to my team via Gmail.” → Clarity > complexity. → Replit starts running the prompt and building the logic. Step 3: Connect Integrations → Log in to Notion, Sheets, and Gmail. → One-time setup, secure by default. → Replit Connectors handle the data flow automatically. Step 4: Review the Plan → Replit shows all app features upfront. → You can edit, simplify, or skip steps. → Choose to build the app or design the UI first. Step 5: Publish It → Watch the automation run live. → Review the workflow on the right-hand side. → Hit publish and your app is live. I didn’t write a single line of code. My reports now pull data, summarize KPIs, and email updates automatically. This is how marketers build tools in 2025! Not by coding, but by connecting. Create your own app here: https://lnkd.in/gcmvWCKW What’s the first thing you’d automate with Replit’s Connectors? ♻️ Share this if you’re tired of manual reporting. P.S. Follow me at Connor Gillivan for more marketing tips and insights.

Case Study: Automating #Sustainability Reporting with #GenerativeAI Automate the sustainability reporting process of a mid-sized manufacturing company to improve efficiency, accuracy, and compliance with global frameworks (#GRI , #TCFD). --- Step-by-Step Process Step 1: Align sustainability reporting with corporate goals and stakeholder needs. Actions: 1. Identify key sustainability metrics (e.g., energy consumption, #GHGemissions, water usage, waste management). 2. Select applicable reporting standards: GRI for general disclosures and material topics. SASB for industry-specific performance metrics. TCFD for climate-related risks and opportunities. 3. Map the company’s #KPIs to these frameworks. Step 2: #DataCollection - Automate the aggregation of sustainability data from various sources. Actions: 1. Identify data sources: Energy bills, waste logs, water consumption records. IoT devices and sensors in factories. Supply chain emissions data from vendors. 2. Deploy a GenAI-powered #ETL (Extract, Transform, Load) pipeline: Extract raw data from multiple systems (e.g., #ERP, IoT platforms). Transform data into a unified format using AI to standardize units, fill gaps, and clean inconsistencies. Load the processed data into a centralized database for analysis. Step 3: Ensure the quality and reliability of the sustainability data. Actions: 1. Use GenAI for data validation: Detect anomalies (e.g., sudden spikes in emissions). Verify data consistency against historical trends. 2. Cross-reference data with third-party sources (e.g., utility providers, industry benchmarks). 3. Automatically flag discrepancies for human review. Step 4: Generate actionable insights from processed data. Actions: 1. Use #GenAI to benchmark performance: Compare current data against industry standards and company targets. 2. Identify trends: For example, a 10% reduction in Scope 2 emissions over two years. 3. Highlight risks and opportunities: Predict potential compliance issues. Suggest operational improvements (e.g., switching to renewable energy sources). Step 5: Automate the creation of narrative content. Actions: 1. Deploy a GenAI language model trained on sustainability reporting templates. 2. Generate drafts for key sections, such as: CEO message and sustainability vision. Performance highlights (e.g., “Our GHG emissions decreased by 15% in 2024). Materiality assessment findings 3. Customize the tone and structure based on the intended audience. Step 6: Ensure the report complies with selected standards. 1. Use GenAI to map data and content to GRI, SASB, or TCFD requirements. 2. Generate automated checklists to ensure all required disclosures are included. Step 7: Internal Review and Iteration 1. Conduct internal audits using GenAI to verify data integrity and narrative accuracy. 2. Enable cross-functional teams to review and provide feedback on the draft report. 3. Report Finalization and Distribution

You spend 80% of your time cleaning data and 20% analyzing it. What if you could flip that ratio tomorrow? I've spent years in the trenches: building financial models, running due diligence, and creating complex operational reports. The story is always the same. You spend 90% of your energy on the mechanics—pulling, cleaning, and formatting data. By the time you're finally ready to do the actual analysis, you're too exhausted to think straight. This is one of the most powerful use cases for AI agents we're implementing for clients. We flip the ratio. Agents do the grunt work. Your team spends 80% of its time on high-value analysis and 20% on fine-tuning. The result? Faster, more consistent reports. But more importantly, your best people are focusing their (fresh) brainpower on strategy and insight, not VLOOKUPs. Here’s a simple 6-step agentic workflow for automating monthly reports: 1. 🗓️ The Trigger: A simple calendar event (e.g., the 1st of every month) kicks off the workflow. 2. 📥 The Data Pull: The agent automatically fetches data from all your sources (HubSpot, QuickBooks, your LMS, etc.). 3. 🧹 The "Clean & Map": It validates the data and maps everything to your master data source. 4. ✍️ The First Draft: An LLM (we've had great results with Claude 3.5 Sonnet) writes the full narrative report. 5. 👨💼 The Human-in-the-Loop: This is the most critical step. The agent Slacks the draft report and the data workbook to the department manager for review. Your new job: Review this draft with the same critical eye you'd use for a new Jr. Analyst's work. This is supervision, not data entry. 6. 🚀 The Delivery: Once approved, the agent sends the final, polished report to all stakeholders. Stop being a data janitor. Start being the expert. What's the one report in your company that "breaks" a team member for three days every month? #AI #Automation #AgenticWorkflows #DataAnalysis #FinancialServices #FinServ #Operations #Productivity #nuDesk

I cut my client reporting time from 3 hours to 15 minutes using Google Sheets and Make.com: Here’s how you can do it too: 1. Map Your Report Process: Write down every step you take to build the report manually. Identify repetitive data entry or copy-pasting tasks. 2. Prepare Your Google Sheet: Create a master sheet where all your data will live. Organize it with clear tabs for raw data, calculations, and the final report. 3. Connect Data Sources in Make.com: Use Make.com to connect where your data lives — whether it’s a CRM, email, or another app — and pull it into your Google Sheet automatically. 4. Set Up Scheduled Triggers: Create a schedule in Make.com so your sheet updates at set times (daily, weekly, monthly) without manual input. 5. Automate Report Generation: Use formulas and formatting in your sheet to create the final report view. Then, set Make.com to export or email the report automatically. 6. Test and Tweak: Run a few tests to make sure the data flows correctly and the report looks right. Adjust as needed. This simple system saves hours, reduces errors, and lets me focus on what matters, not busywork.

My #RadiologyAI takeaway at ICLR 2026: automated report drafting is 𝗰𝗼𝗻𝘃𝗲𝗿𝗴𝗶𝗻𝗴 𝘁𝗼𝘄𝗮𝗿𝗱 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲-𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴, meaning generating findings by anatomical region rather than relying on free-flowing narrative. One paper that caught my attention is "𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗥𝗮𝗱𝗶𝗼𝗹𝗼𝗴𝘆 𝗥𝗲𝗽𝗼𝗿𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: 𝗙𝗿𝗼𝗺 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗙𝗹𝗼𝘄 𝘁𝗼 𝗧𝗼𝗽𝗶𝗰-𝗚𝘂𝗶𝗱𝗲𝗱 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀." What stood out is not the structuring itself, but seeing it pushed further: the model benefits from an anatomy-aware segmentation mask, which forces each anatomical-based finding to be grounded in a specific region of the image. The pipeline is roughly:  • report -> topic (anatomy) splitter  • image -> segmentation model  • then for each topic: (image + region mask + topic prompt) -> one finding sentence  • and finally: concatenate the topic-level findings into a report So the real contribution is not just "better generation," but decomposing reporting into localized, visually grounded sub-problems. Dynamic programming? Not quite, but definitely strong divide-and-conquer energy. This also felt personally validating. I published a paper a little over a year ago entitled "𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗥𝗮𝗱𝗶𝗼𝗹𝗼𝗴𝘆 𝗥𝗲𝗽𝗼𝗿𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻" (ACL 2025), which I believe was the first attempt to explicitly move report generation in this direction. Also, kudos to the authors for citing it in the introduction ❤️ At HOPPR, this is very much the direction we believe in: building structured, anatomy-aware report drafting across anatomies and modalities, from CXR to mammography, MSK, CTH, CTC and many more! Links below 👇

Here’s how AI is quietly revolutionizing UAT, and how you can practically use it 👇 𝟓 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐖𝐚𝐲𝐬 𝐀𝐈 𝐂𝐚𝐧 𝐇𝐞𝐥𝐩 𝐁𝐀𝐬 𝐢𝐧 𝐔𝐀𝐓 1. Auto-Generate UAT Test Cases from User Stories Instead of manually drafting dozens of test cases, use AI to quickly generate them based on the acceptance criteria. 🛠️ Prompt for ChatGPT or Claude: "Generate UAT test scenarios and expected outcomes for a user story where a customer logs into an eCommerce app, adds 2 items to the cart, and completes payment via PayPal." Why It Helps: Saves time, ensures full coverage, reduces human error. 2. AI-Based UAT Checklist Generators Don't reinvent the wheel every time. AI tools can create a checklist based on your domain and system type. 🛠️ Use tools like: ChatGPT + Prompt Templates Notion AI Jasper for structured templates Example: "Create a UAT checklist for a mobile banking application with login, balance check, and fund transfer features." 3. Smart Data Input Generators for Testing Need test data like dummy accounts, fake transactions, or synthetic user profiles? AI tools like Mockaroo, DataGen, or OpenAI + Excel plugins can help you generate realistic, varied data instantly. Why It Matters: Testing boundary conditions, edge cases, and data variations becomes faster and smarter. 4. Summarize UAT Feedback Using AI Tired of going through 100s of comments in Excel or Jira? Use Fireflies.ai, Otter.ai, or ChatGPT to: 👉 Summarize stakeholder feedback 👉 Identify recurring issues 👉 Categorize bugs vs. enhancements Example: Paste the exported UAT comments and prompt: "Summarize key pain points reported by testers, group them by module, and suggest root causes." 5. Auto-Generate UAT Reports & Dashboards Gone are the days of manual report writing. Use ChatGPT + Markdown, Notion AI, or Excel AI to create: 👉 Executive summaries 👉 Defect metrics 👉 Sign-off documentation Bonus Prompt: "Create a UAT sign-off report based on the following test results, defect closure summary, and stakeholder comments." Real-Life Example: On a Loan Origination System project, our UAT cycle had over 60 test cases and 8 stakeholders. By using AI-generated test scenarios, feedback summarization, and report automation, we: ✅ Reduced preparation time by 40% ✅ Got faster stakeholder buy-in ✅ Delivered UAT results 2 days ahead of schedule AI isn’t replacing the Business Analyst — it’s empowering us to focus on the strategic and human side of testing: 🗣️ Stakeholder alignment 📈 Business value validation 🎯 Decision-making UAT is where systems meet business reality. With AI as your co-pilot, you can make it smarter, faster, and more reliable. BA Helpline

I spent $300 on #GenAI tools and every report still sounded like it was written by a corporate bot. The problem wasn't AI. It was my strategy. 🤖 I had to completely rethink how I use AI for writing. I stopped asking one LLM to do everything. Instead, I built a specialist stack where each tool handles what it does best. - ChatGPT builds expansive prompts off my lazy requests. - Perplexity generates detailed research from those prompts. - Claude writes the draft reports using that research The result? Reports that sound like me, not like a bland chatbot trying to be helpful. 👇 Read my article for the complete Specialist Stack approach, including how to use each tool best. Here's what changed: 1️⃣ My last three reports passed the "human test" with colleagues who didn't know I used AI. No one asked if it was AI-generated. They just engaged with the ideas. 2️⃣ The time savings are real. I cut report writing time by 60% while actually improving quality. 3️⃣ Most importantly, I'm not fighting against AI limitations anymore. I'm leveraging what each tool does exceptionally well. This is what happens when you use the right AI for each specific task in your workflow. Effort decreases, usable results increase. 👨💻 Struggling with AI-written content sounding too robotic? What's you solution? 🙋♀️ Maybe Alexis B. Andrew Karlyn Paulo Gomez have better methods?

After working with 300+ healthcare leaders, I’ve noticed something alarming. Most are losing an entire workday every week to tasks that don’t require their expertise. Not just hours… But decision-making capacity. Strategic clarity. And energy that should be reserved for the problems only they can solve. Automation isn’t about “doing less.” It’s about protecting cognitive bandwidth so leaders can actually lead. This week’s infographic breaks down three tasks every healthcare leader should automate immediately — each one removes low-value work and frees up space for high-stakes decisions. 1️⃣ Routine Meeting Management If your calendar runs your day, automate this first. Automate: • Meeting reminders • Agenda distribution • Follow-up task summaries • Cancellations when criteria aren’t met Tools: • AI Assistants: Teams + Copilot, Otter.ai, Fireflies.ai • Calendar Automation: Calendly, Clockwise Why it works: Reclaims hours of prep and follow-up while key decisions are captured automatically. 2️⃣ Inbox Triage & Communication Drafting Perfect if your inbox controls your attention. Automate: • Sorting emails by priority • Drafting routine responses • Flagging decision-required items • Summaries of long threads Tools: • AI Email Assistants: Gmail AI, Outlook + Copilot, Superhuman, Front • Triage Tools: SaneBox, Inbox rules Why it works: Keeps leaders focused on decisions — not inbox housekeeping. (Relief is often immediate.) 3️⃣ Data Reporting & Dashboards A must for leaders drowning in monthly reporting cycles. Automate: • Scorecard updates • Quality/safety metric pulls • Staffing & productivity reports • Operational dashboards Tools: • BI Tools: Power BI, Tableau, Looker Studio • Data Pipelines: Fivetran, Stitch, Airbyte • Scheduled Reporting: Apps Script, BI refreshes Why it works: Real-time insight replaces hours of manual report pulling. Most leaders reclaim 4-6 hours every week by automating just one of these. 🛟 Save this post for quick reference! 👉 If you could automate one recurring task this week, which would create the biggest shift? ------ 👋 Hi, I'm Dr. Megan Carter and I started the Chaos Whisperer movement in healthcare for leaders who are ready to stop proving their worth through exhaustion. 🆓 Want more free tools and insights? Join my email list and our community of Chaos Whisperers (link in comments)

𝗡𝗲𝘄 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱! Radiology reporting is one of the most time‑consuming parts of clinical practice, and spinal MRI is especially challenging: 3D anatomy, multiple sequences, and subtle findings that really matter for patient outcomes. In our latest work, we introduce 𝐒𝐏𝐈𝐍𝐄 – 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧-𝐠𝐮𝐢𝐝𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐒𝐩𝐢𝐧𝐚𝐥 𝐌𝐑𝐈 𝐟𝐨𝐫 𝐍𝐚𝐭𝐮𝐫𝐚𝐥-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐑𝐞𝐩𝐨𝐫𝐭 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧, a 3D vision–language framework that fuses T1, T2, and segmentation maps to generate anatomically aware spine MRI reports. What we built • A 3D-aware VLM pipeline that ingests full lumbar spine volumes (axial and sagittal) instead of isolated 2D slices, better reflecting how radiologists read studies. • Segmentation-guided inputs where vertebrae, discs, canal, and key regions are encoded as additional channels (T1 + Seg, T2 + Seg, and T1 + T2 + Seg). • A report-generation stage that pairs volumetric MRI with LLM-standardized radiology reports, ensuring consistent, structured language for training. Key findings • Adding segmentation consistently improved report quality across BLEU, ROUGE, METEOR, and BERTScore compared to using MRI alone. • The T1 + T2 + Seg configuration delivered the most balanced and robust performance overall, with higher lexical precision and strong semantic alignment. • Models trained on LLM-standardized reports outperformed those trained on raw human-written text, highlighting how linguistic consistency boosts image–text learning. • For synthetic report generation from structured gradings, GPT‑4o produced markedly more fluent and clinically detailed narratives than Grok‑3 across all language metrics. • Expert review of generated reports showed high scores for clarity and terminology, with clinically acceptable accuracy. Why this matters SPINE shows that segmentation-aware, multimodal 3D VLMs can move automated reporting beyond generic image captioning toward anatomically grounded, clinically useful narratives. This opens the door to AI systems that support radiologists with more consistent, interpretable, and scalable spine MRI reporting—particularly valuable in settings with high workload and limited subspecialty expertise. This work was led by Hoda Helmy, with AI Innovation Lab team members Abdullah Hosseini and Ahmed Ibrahim, in collaboration with Mr. Ahmed-Ramadan Sadek and Asfand Baig Mirza from Barking, Havering and Redbridge University Hospitals NHS Trust. 📄 Article: https://lnkd.in/dgxgtnU6 👩💻 Code: https://lnkd.in/du8Xqd4g #AI #MedicalImaging #Radiology #SpineMRI #VisionLanguageModels #Segmentation #ReportGeneration #GenerativeAI #HealthcareAI #AppliedAI #WeillCornellMedicine #Qatar #NHS #Neurosurgery

Most people still aren’t using the best auto-reporting feature in Google Ads. And it’s been out for a while now. Google Ads Report Generation can answer really specific client questions without you having to export, filter, pivot, and build a Looker dash every time. At Understory, we use full dashboards pulling from Google Ads API, CRMs like HubSpot & Salesforce, Clay enrichments, and LinkedIn data to give us a full detailed view of the whole funnel & pipeline breakdown to optimize across the channels. But sometimes I just want a fast answer to a one-off question to optimize. That’s what this report generation tool is great for. Here’s what it’s best for (and how we use it): 🧠 Use Case 1: Location Breakdowns By Campaign Group Your client asks: “What states are our nonbrand campaigns performing best in?” Instead of building a new dashboard or crunching it manually: → Use the report generator. → Prompt it with: “Generate a report breakdown of conversions by location targeting (at the state/territory level) for campaigns containing Nonbrand in the name over the past 6 months. Include conversions, cost/conv., cost, and clicks.” Instant answer. No new filters, manual tables, or custom columns. 🧠 Use Case 2: Keyword (Or Search Term) Breakdowns By Gram Say a client asks: “How are keywords that contain ‘understory’ performing?” Prompt it with: “Generate a report of cost, conversions, and CPA for keywords that contain the term ‘understory’ over the past 60 days.” You can group all metrics by that term, whether it’s in the keyword list or showing up in search terms, and compare across segments like locations, devices, placements, etc. Perfect for both answering client questions and spotting new optimization opportunities. ✍️ Prompt Structure (save this): 1. Start with: “Generate report breakdown of…” 2. Define the metric: conversions, CPA, impressions, etc. 3. Set the segmentation: by location (state/country), keyword, etc. 4. Specify filters: e.g. campaigns that include “non-brand” 5. Include the time range: always do this 6. List your metrics: CPA, cost, conv. rate, etc. (Optional) Add custom columns for it to build for you. (Optional) Add chart type: pie, time series, bar, etc. It builds an editable, interactive table you can tweak, filter, visualize, or download as a CSV, Sheet, or Excel. And if you want to edit it? Edit your prompt. Click regenerate. Done. I’m starting to use this way more often lately for one-off questions and optimization checks without spinning up a whole dashboard or busting out my calculator app while clicking across screens. Give it a shot. It might save you hours of manual work. 🌲 Want help building fast, data-backed answers into your ad ops workflow? Hit the meeting link in my bio and let's chat.