Data-Driven Workflow Automation

If you think AI = ChatGPT, you're missing out. 7 tools to automate your work with AI: I've spent 15+ years building large software systems and automation. I've learned that the upfront cost of automating repetitive tasks leads to: - Huge time savings  - Better efficiency  - Fewer costly mistakes Today's AI automation landscape has changed everything. Here are 7 powerful tools that can transform your productivity: Top 7 Workflow Automation Tools ➡️ 1. N8N An open-source workflow automation tool that allows for both no-code and advanced custom coding. Self-hosted for full data control or paid cloud service. • Self hosting option (open source) • Most developer friendly option • Custom JavaScript/Python ➡️ 2. Make A powerful visual automation platform with AI agents and complex multi-step workflows. • Drag-and-drop interface (no-code) • AI agents recently added • Perfect for business process automation ➡️ 3. Zapier The leading no-code automation tool connecting thousands of apps through simple "if this, then that" logic. • Extremely beginner-friendly interface • Massive app ecosystem • Great for everyday business automation ➡️ 4. Relay This one was new to me, but I really like the UI. Collaborative workflow automation platform for team-based multi-step processes without coding. • Create AI agents that work for you • Popular tool integrations • Connect 100+ apps in minutes. ➡️ 5. Gumloop User-friendly platform for building AI-powered workflows without coding knowledge required. • Visual interface • Pre-built AI templates • Built for non-technical users ➡️ 6. FlowiseAI Open-source, low-code platform for building custom LLM applications and AI agents with visual nodes. • 100+ LLMs, Vector DBs • Developer friendly (SDKs) • Integrated traces ➡️ 7. Relevance AI Low-code/no-code platform specialising in AI-powered agents and data intelligence automation. • Complex business process automation • Multi-model AI support with rapid deployment • Best for teams handling large datasets My favourite quote on automation: ❤️ "Automation applied to an efficient operation will magnify the efficiency. Automation applied to an inefficient operation will magnify the inefficiency."- Bill Gates Which automation challenges are you facing in your business right now? --- Enjoy this? ♻️ Repost it to your network and follow Owain Lewis for more.

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?

Many companies are rushing to build AI agents and consolidate data, but here's what I've learned from our journey at FourKites: The key to successful agentic workflows isn't just about connecting data – it's about creating purposeful Digital Workers who can actually execute tasks. While data lakes are important, they're just the foundation. The real transformation happens when your Digital Workers can understand context and take meaningful action. Our early adopters are seeing remarkable results: Within just weeks of deployment, our Digital Workers are autonomously handling over 80% of exception management and driving real-time decisions. This isn't just automation – it's intelligent execution at scale. Three key lessons for making agentic workflows actionable: - Start with specific, high-impact processes rather than trying to boil the ocean - Focus on outcomes over infrastructure – what tasks do you actually need automated? - Ensure your Digital Workers can not only access data but act on it in real-time The winners in this space won't be those who build the biggest data lakes, but those who turn that data into automated action. https://lnkd.in/gZt6jAuc #SupplyChain #DigitalTransformation #AI #Innovation #IntelligentControlTower #DigitalWorkers #ICT FourKites, Inc.

Most data engineers I know aren’t burned out from data. They’re burned out from duct tape. If you've ever spent more time debugging pipeline failures than delivering insights, you're not alone. Modern data stacks promised us agility—but what we got was complexity. “I’ve been OOO for 2 weeks. What’s changed in this pipeline since then?” Normally, that means scrambling through logs, Slack threads, and dashboards. But not this time. With Ascend .io’s Agentic Data Engineering, the platform tells me what changed: ➤ What data has been updated ➤ Which transforms were auto-managed ➤ Whether anything broke—and if so, what was auto-fixed ➤ Where I need to take action (if any) This isn’t just automation. It’s an entirely new category: Agent-assisted, metadata-driven pipelines that evolve on their own—like an intelligent teammate that’s been watching your data while you were gone. Here’s what makes Ascend.io different: ✔️ AI-powered agents help document, debug, and manage pipeline changes ✔️ Dynamic orchestration driven by real-time metadata, not manual DAGs ✔️ Unified control plane across Snowflake, BigQuery, Databricks & more ✔️ Incremental processing — no reprocessing of unchanged data ✔️ Code-first or low-code flexibility with Git-native workflows Real results from teams using Ascend: ✅ 7x increase in productivity ✅ 83% reduction in processing costs ✅ 87% faster delivery This feels like the shift from DevOps to Platform Engineering—but for data teams. Learn more: https://hubs.li/Q03n44B60 What would change for your team if pipelines could explain themselves?

🚀 AI Agents for Data Engineering — the future is here, and it’s automating your pipelines! Just published a deep-dive article on how LangChain agents, paired with tools like dbt Cloud, Airflow, and Snowflake, are revolutionizing modern data workflows. From dynamic SQL generation to triggering DAGs — AI agents are becoming co-pilots for data engineers. Curious how it all fits together? I’ve included architecture diagrams, use cases, and real-world tool integrations 👇 💬 Would love to hear your thoughts or how you're using AI in your data stack! #AI #DataEngineering #LangChain #dbt #Airflow #Snowflake #Automation #DataOps #TechTrends