Workflow Automation Solutions

Want to get more time back at work? Use these AI prompts: Productivity isn't about hacks or simply being more efficient. It's about building systems that make everything easier. Here are 21 practical prompts to help: 1. Meeting prep "Act as a strategic operator. Help me prepare for this meeting. Based on the context below, create a short agenda, the key decisions we need to make, the questions I should ask, and the ideal outcome." 2. Decision support "Act as a decision coach. Help me make this decision. Lay out the options, tradeoffs, risks, second-order effects, and the recommendation you would make based on the context below." 3. Clear delegation "Act as a manager known for clear delegation. Rewrite this request so the person receiving it knows exactly what success looks like. Include context, outcome, constraints, deadline, and examples of what good looks like." 4. Action plan "Act as a chief of staff. Take these messy notes and turn them into a clear action plan. Separate decisions, open questions, owners, deadlines, and next steps. Also flag anything that is unclear or missing." 5. Clean handoff "Act as an operations lead. Turn this task into a clean handoff for someone else. Include the goal, context, desired outcome, constraints, examples, common mistakes, and definition of done." 6. Reusable SOP "Act as a process designer. Turn the way I currently do this task into a simple SOP someone else could follow. Include the steps, checkpoints, tools needed, quality standards, and where judgment is required."   7. Executive update "Act as a senior communications advisor. Turn this rough update into a clear executive-style update. Use this structure: what changed, why it matters, what is blocked, what happens next, and what I need from the reader." 8. Bottleneck audit "Act as a workflow consultant. Review this process and identify where work is likely slowing down. For each bottleneck, explain the cause, the impact, and one practical fix." 9. Internal FAQ "Act as a knowledge base editor. Take these repeated questions and turn them into a simple internal FAQ. Make the answers clear, concise, and useful for someone who has no background context." 10. Feedback draft "Act as a strong first-draft partner. Create a draft based on the context below. Do not make it polished yet. Make it directionally useful, easy to critique, and include 3 questions where you need my input." [For the last 11 see the sheet] Getting out of the weeds isn't about caring less or working more. It's about making your work easier to repeat, transfer, and improve. Which of these could you use this week? If you want a PDF that you can copy all 21 prompts from, Sign up for my newsletter here: https://lnkd.in/gjEC_SCG --- ♻️ Repost to help others in your network. And follow me George Stern for more AI prompts.

Workflow Agents in #Oracle_Fusion_AI_Agent_Studio are redefining what “#Enterprise_AI_automation” actually means. Most tools can run steps. Some tools can call an LLM. But Workflow Agents do something much bigger---->> they combine deterministic control flow, reasoning, memory, and multi-agent orchestration directly inside the systems that run the business. Here are 4 patterns that give them some real power: 1. Chaining — Step-by-step intelligence Every step interprets context, transforms data, and feeds the next. Perfect for real enterprise flows with dependencies: onboarding, validation, document-to-decision processes, and month-end close. 2. Parallel — Collective decisioning at speed Multiple branches run at once: diagnostics, policy checks, data lookups, history, extraction. Everything merges into a single, high-quality decision. Faster outcomes with better signal coverage. 3. Switch — Context-aware routing without rule bloat Instead of giant rule trees, the workflow adapts to user, policy, intent, and application state on the fly. Same entry point, personalized paths. Automation that’s flexible, not fragile. 4. Iteration — Goal-seeking refinement Great for scheduling, planning, allocation, cost modeling. The agent loops intelligently until constraints are met. Not “first viable answer” — the right answer. This is only one layer of the bigger story. Fusion supports the full spectrum of AI automation: - Workflows for structure. - Workflow Agents for structure with reasoning. - Agent Teams for autonomous digital workers that pursue outcomes. And because all of this lives inside Oracle Fusion Applications, the automation is grounded in real Fusion data, policies, security, and transactions from the start. Enterprise AI that actually does the work — #built_in_not_bolted_on.

Automate Small Power BI Reports Like a Pro — No Attachments Needed! 😎 👀 Ever had to send small Power BI reports regularly and wished you could just embed the data in an email instead of attaching a file? That’s exactly what I achieved using Power Automate + Power BI semantic model. Recently, I built a Power Automate flow for a client who wanted to receive a Power BI report table embedded directly in their email, not as an attachment. Here’s how I made it happen step-by-step: 1) Run a Query Against a Dataset : Used the Power BI "Run a query against a dataset" action to pull data directly from the semantic model. This allows querying live data using DAX. 2) Parse JSON : This action is crucial because the response from Power BI comes in raw JSON format. Parsing it lets us cleanly extract individual fields and rows because without it, your flow can't understand the structure of the data. 3) Create HTML Table : Why HTML? Because the client didn't want a boring file attachment , they wanted a visually readable table inside the email body. This action transforms your structured data into a clean HTML format. 4) Compose (optional but powerful) : I used Compose after the HTML table to wrap it with styling or headings or gridlines , giving me flexibility to control how the email content looks. Think of it as dressing up your table before presentation. 5) Send Email with Embedded Table : The final touch: embedding the composed HTML table directly into the body of the email using the Send Email (V2) action. 🙄 Why not just send a CSV? Because experience matters. A table inside an email is quicker to read, mobile-friendly, and makes your report look more professional. #PowerAutomate #PowerBI #Automation #EmailReports #NoCode #DataToAction #FlowLogic #LearningByDoing #DataOps

Many factories lose money on problems they can't even see. Tiny defects, machine breakdowns, and small inefficiencies add up quietly. Regular robots and machines can't spot these issues. But AI can see them. The groundbreaking partnership between Intel and LG Innotek tackles this challenge head-on. We are building a smart factory where AI acts as a "superhuman eye" for real-time visual quality control. This system is powered by a suite of Intel technologies, including Intel® Xeon® processors, the OpenVINO toolkit, and Intel® Arc™ Graphics. This is a leap beyond simple robotics. We're now moving into the era of the self-optimizing production line. What does this look like in practice?  - AI vision systems can detect defects invisible to the human eye. Micro-fractures, subtle color variations, minute misalignments prevent flawed products from reaching the next stage. - As the AI analyzes thousands of units, it learns. It begins to identify patterns that predict a future failure, allowing for preemptive adjustments to the manufacturing process itself. - This creates a continuous feedback cycle. The line doesn't just produce widgets; it produces data. That data fuels the AI, which in turn makes the line smarter, more efficient, and more resilient with every shift. I see this as the fundamental shift from automated manufacturing to cognitive manufacturing. The goal is no longer just speed but intelligent adaptation.  Read more here: https://lnkd.in/gz6tURZz #IntelAI #SmartFactories #IntelXeon #IntelArc #AIInManufacturing

LlamaIndex just unveiled a new approach involving AI agents for reliable document processing, from processing invoices to insurance claims and contract reviews. LlamaIndex’s new architecture, Agentic Document Workflows (ADW), goes beyond basic retrieval and extraction to orchestrate end-to-end document processing and decision-making. Imagine a contract review workflow: you don't just parse terms, you identify potential risks, cross-reference regulations, and recommend compliance actions. This level of coordination requires an agentic framework that maintains context, applies business rules, and interacts with multiple system components. Here’s how ADW works at a high level: (1) Document parsing and structuring – using robust tools like LlamaParse to extract relevant fields from contracts, invoices, or medical records. (2) Stateful agents – coordinating each step of the process, maintaining context across multiple documents, and applying logic to generate actionable outputs. (3) Retrieval and reference – tapping into knowledge bases via LlamaCloud to cross-check policies, regulations, or best practices in real-time. (4) Actionable recommendations – delivering insights that help professionals make informed decisions rather than just handing over raw text. ADW provides a path to building truly “intelligent” document systems that augment rather than replace human expertise. From legal contract reviews to patient case summaries, invoice processing, and insurance claims management—ADW supports human decision-making with context-rich workflows rather than one-off extractions. Ready to use notebooks https://lnkd.in/gQbHTTWC More open-source tools for AI agent developers in my recent blog post https://lnkd.in/gCySSuS3

The easiest way to save yourself 3-5 hours a week is to build a personal AI chief-of-staff agent and let it run your daily routine. It reads your mail, calendar, tasks, the web, and the custom sources you add (e.g. CRMs system, Stripe, custom applications), and hands you one prioritized briefing instead of fifteen open tabs. Here is how to build one in Claude Code (best platform to do so): 1 - What the agent does. Start with the architecture, not the code. It is not one agent doing everything, it is several. One sub-agent per source or task (mail, calendar, tasks, web, your custom data), each with a narrow job, running in parallel. Then a single synthesizer agent merges all of their outputs into one clean, prioritized briefing and delivers it to wherever you read (e.g. email, Slack, Notion), automatically. If you cannot draw that split, do not build it yet. 2 - Project structure. The whole thing is build into a small folder: a CLAUDE.md, a constitution.md, your skills under .claude/skills/, and a memory/ folder. CLAUDE.md loads your instructions, so there is nothing to wire up by hand. Boring structure is what makes it reproducible. 3 - Build the core files. Three files do the real work. constitution.md holds your role, priorities, company details and what to ignore. SKILL.md tells the agent how to gather, merge, and deliver. MCP and tools connect Gmail, Calendar, Notion, and the web. Skip this part, and you get generic gibberish instead of useful output. 4 - Run it in Claude Code (the best tool to do so in my view). Open the folder, connect your tools, run /chief-of-staff, and read the draft before anything goes out. The agent can draft replies and queue actions, but you decide, item by item, what it sends/does on its own and what it only prepares for your yes. Most people treat that as a switch, automate it or don't, which is exactly why their agents stay summaries. It is really a dial. Week one, almost everything waits for approval, because you want to see what it would have done. Then you let go, one category at a time. 5 - Automate the workflow. Once the manual runs earn your trust, schedule it: manual while you build, local cron on your machine, or a cloud routine that fires every morning even when your laptop is off. Now the loop runs on its own: briefing, your feedback, a slightly better briefing tomorrow. 6 - Upgrade it over time. This is where it compounds. Give it memory so it remembers what you keep skipping, let it track what you ignore, draft replies, and block time for follow-ups. The version running in month three is noticeably better than the one you launched, not because the model improved, but because it has been watching how you decide. ↓ 𝗜𝗳 𝗔𝗜 𝗶𝘀 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯, 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗶𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂. 𝗜 𝘀𝗵𝗮𝗿𝗲 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝘁𝗼𝗼𝗹𝘀, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁’𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗲𝗹𝗱: https://lnkd.in/dbf74Y9E

Agentic AI just got real for SAP SuccessFactors teams—inside Salesforce 👉 Agentforce + SuccessFactors: Employees can handle HR tasks against SuccessFactors from Salesforce as the front door—no swivel-chairing. Think: a single chat-style interface that understands context, pulls the right data, and takes the next step. 👉 Live employee data, where service happens: Sync key SuccessFactors attributes into Salesforce so cases, leave queries, and approvals run on accurate data—fast. 👉 Governed by your source of truth: Keep SuccessFactors as the system of record while Salesforce becomes the experience layer for agents and employees. (This is part of the current Salesforce release train.) What this really means: Agentic workflows can observe events (e.g., profile changes), reason over HR policies, and act—open/route cases, populate forms with SuccessFactors data, and draft responses for HR—without sending people hunting across tools. Your HR ops get speed; employees get straight answers. Practical use cases to launch first: 1. Unified HR help: employees ask once in Salesforce, the agent checks SuccessFactors data and resolves or routes. 2. Leave queries: agent pulls balances and policy context; escalates only when needed. 3. On/offboarding: trigger checklists from SuccessFactors changes, keep status visible in Salesforce. If you run SuccessFactors + Salesforce, here’s your chance to switch on agentic HR. #SAPSuccessFactors #AgenticAI #Salesforce #HRService #HCM #HRTech #CHRO

🏭 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝗳𝗶𝗻𝗲𝗱 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴: Xiaomi Hyper IMP is a new fully automated AI-augmented factory with their own new software ecosystem, that develops and optimizes its processes autonomously. There are no humans working the new Xiaomi production lines – this new Dark Factory is 100% automated. The company says the system is smart enough to 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝗮𝗻𝗱 𝗳𝗶𝘅 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀, as well as optimizing its own processes to "evolve by itself." "There are 11 production lines, 𝟭𝟬𝟬% 𝗼𝗳 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝗿𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱. We developed our 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 to achieve this." says Xiaomi Founder and CEO Lei Jun. Totally automated dark factories, of course, have been around a little while. Japanese robotics company Fanuc Ltd, for example, opened its first fully automated line back in 2001, and according to CNN Money, by 2003 it had a factory near Mt Fuji in which robots were building other robots, around 50 a day, running totally unsupervised for up to a month at a time. But Xiaomi may have taken things up a notch, 𝗯𝘆 𝗮𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗜 𝗼𝗳 𝘁𝗵𝗲 𝗳𝗮𝗰𝘁𝗼𝗿𝘆 𝘁𝗼 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆 𝗱𝗲𝘃𝗲𝗹𝗼𝗽 𝗮𝗻𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗶𝘁𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲. "What's most impressive," says Lei Jun, "is that this platform can identify and solve issues, while also helping to improve the production process" Key highlights of Xiaomi's Smart Factory: 🔹 The factory's 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 𝗰𝗮𝗻 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝗮𝗻𝗱 𝗳𝗶𝘅 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀, 𝗮𝘀 𝘄𝗲𝗹𝗹 𝗮𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗶𝘁𝘀 𝗼𝘄𝗻 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆. 🔹 Since there are no humans - the facility 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝘀 𝗮 𝗺𝗶𝗰𝗿𝗼𝗻-𝗹𝗲𝘃𝗲𝗹 𝗱𝘂𝘀𝘁-𝗳𝗿𝗲𝗲 environment, ensuring high-quality production. 🔹 The factory operates 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝘃𝗲𝗻𝘁𝗶𝗼𝗻 𝗼𝗻 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗹𝗶𝗻𝗲𝘀, with all key processes 100% automated. 🔹 Capable of producing over 10 million smartphones annually, with a new device completed 𝗲𝘃𝗲𝗿𝘆 𝘁𝗵𝗿𝗲𝗲 𝘀𝗲𝗰𝗼𝗻𝗱𝘀. Xiaomi's achievement raises 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 about the 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴: 👉 How will this level of automation impact global supply chains and manufacturing strategies? 👉 What new skills will be required for workers in these advanced facilities? 👉 How might this technology scale to other industries beyond consumer electronics? AI-assisted manufacturing and product development is here, and we will see a rapid spread of these technologies over the next few years. Christian Erb, Christof Horn, Lin Kayser, Peter Seeberg, Daniel Spiess, Enno Danke #AIinManufacturing #IndustrialAutomation #FutureOfWork #TechInnovation

🧑🏽💻 "I have no idea what I'm doing... but I'll figure it out." This is basically my everyday life working in a seed-stage startup, and I often rely on applying my data best practices to "non-data" business problems to unblock myself. 🚀 Most recently, I've been working on a migration from Hubspot to Salesforce, where I have limited experience in sales and these tools. But by reframing it into a data engineering problem, I all of a sudden have a wealth of knowledge to make this migration happen. 👇🏽 Here's how I approached it: 1. Determine what's the business use case and expectations from my business stakeholders? 2. Create a flow chart that logically maps out the process of going from "lead capture" to "discovery call" and how a lead's "status" changes throughout the workflow. 3. Map the workflow to an underlying architecture of our various tools and integrations to make this process happen, AND determine which data fields are being changed. 4. Determine all the data fields being used in our current system (Hubspot), then map them to the fields in the new system (Salesforce)-- it's unlikely these fields map 1:1, and thus, be sure to document all of your decisions as you are updating business logic. 5. Measure your baseline counts (e.g. lead counts by "lead stage") in your current (Hubspot) and new (Salesforce) systems. 6. Begin unhooking third-party integrations from the old system and move the integration to the new system so new "lead events" are not interrupted. 7. Test the updated integrations with known values-- for me, I went through the entire "sales journey" as if I were a "lead" by filling out our lead form with a test account, scheduling test meetings, etc., and ensuring the expected data shows up in Salesforce. 8. Begin backfilling data and iterating until your expected counts match in the old and new systems. Bonus: Create a doc that details the entire process and your decisions, as well as create a Slack channel to give real-time updates to ensure your business stakeholders are in the loop. 💯 With this reframe, I went from "How do I migrate from Hubspot to Salesforce!?" to instead, "I've done a database migration before, so let's apply it to Hubspot and Salesforce!" 👀 Check the comments below to see the impact already made to for one of my business stakeholders! #data #dataengineering #sales #salesforce

➡️ KPMG journey to build an agentic tax advisory system is a benchmark for technical transformation in consulting. After rigorous risk analysis (including securing sensitive PII), they moved all tax advisory knowledge often scattered across partners’ laptops and documents into a centralized, retrieval-augmented generation (RAG) architecture. Their platform (KPMG Workbench) uses a federated approach, integrating multiple LLMs (OpenAI, Microsoft, Google, Anthropic, Meta) for future-proof model flexibility. To construct “TaxBot,” KPMG’s team engineered an extensive 100-page instruction prompt, refined over months. This prompt defines operational context, intake structure, workflow, compliance guidance, and directs interaction between human experts and the agent. TaxBot ingests four to five key client parameters, then prompts iterative expert input before auto-generating a robust 25-page draft, synthesizing internal tax advice and Australia’s entire tax code. The agent sits behind strict access controls (usable only by accredited tax professionals), maximizing safety and accuracy. 👉🏼 It slashed advisory delivery from two weeks to one day. KPMG’s technical leadership also built agent runtime services, enabling multi-agent workflows writers, editors, and credential managers collaborate in an asynchronous framework to automate document production and knowledge management. Their story is not just about speed, but how a technical prompt engineering discipline, retrieval-augmented architectures, and federated LLM selection can reshape high-impact professional services for resilient innovation. If you’re thinking about agentic automation in highly regulated domains, KPMG’s approach deep prompt engineering, multi-model orchestration, RAG, human-in-the-loop should be your blueprint. #genai #ai #RAG #LLM #KPMG