Workflow Automation Solutions

🚀 𝗛𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 Microsoft 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 (𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹𝗹𝘆, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲𝗼𝗿𝗲𝘁𝗶𝗰𝗮𝗹𝗹𝘆) Copilot isn’t just a chatbot — it’s embedded across Microsoft 365 to help you write faster, analyze smarter, and present better. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗮𝗱𝗱𝘀 𝘃𝗮𝗹𝘂𝗲 👇 📝 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗶𝗻 𝗪𝗼𝗿𝗱 • Draft documents from short prompts • Rewrite or shorten content for clarity & tone • Summarise long documents into key takeaways 👉 Pro tip:“Summarise this document into 5 bullet points.” 📊 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗶𝗻 𝗘𝘅𝗰𝗲𝗹 • Build charts, formulas & pivot tables from text • Clean, messy data (duplicates, formats, errors) • Instantly surface trends & insights 👉*Pro tip: Select data and ask, “What’s driving this number?” 📽 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿𝗣𝗼𝗶𝗻𝘁 • Create full decks from Word docs • Convert dense text into visuals • Auto-generate speaker notes 👉 Pro tip: “Turn this report into a 7-slide presentation.” 💬 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗖𝗵𝗮𝘁 (𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝟯𝟲𝟱) • Search emails, files & chats instantly • Summarise meetings, docs & action items • Prep for meetings in minutes 👉 Pro tip:“Summarise everything I missed this week.” Copilot works best when you ask specific, outcome-driven prompts — not generic questions. How are you using Microsoft Copilot today — writing, analysis, or presentations? 👇

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

Last quarter, I worked with the MD of a heavy equipment manufacturer who believed AI would make status reports clearer and give leadership better visibility into project progress, but while the dashboards improved and the data looked sharper, the actual profit margins did not improve because delays were still being identified too late to prevent cost overruns. By the time problems appeared in reports, the financial impact had already occurred, and in 2026, with tighter compliance requirements and thinner operating buffers, that delay between issue and action is no longer affordable. What has truly changed is not reporting quality but execution speed, because AI systems can now reallocate resources, adjust schedules, and flag bottlenecks immediately instead of waiting for weekly or monthly review cycles; in plant upgrade programs and supplier transitions, I have seen problems addressed at the point of occurrence rather than after escalation. When corrective action happens closer to where the issue starts, delivery risk declines and cycle times shorten, since decisions are triggered by live data rather than by meetings or manual coordination. The main weakness I continue to see is governance, because many AI agents operate on fragmented data sources without clear ownership of decision rights, which leads teams to override outputs they do not trust and reintroduce manual controls that slow everything down, creating a false sense of stability where dashboards remain green but margin pressure builds quietly underneath. Two mistakes appear repeatedly. The first is treating AI as an advanced reporting layer, because manufacturing projects depend on operational control rather than visibility alone, and insight does not prevent delay unless the system is allowed to act within clearly defined boundaries. The second is deploying AI without defining who owns the decisions it influences, because manufacturing plants rely on accountability structures, and when escalation paths are unclear, agents can create conflicting actions that slow adoption and reduce confidence across teams. If you are beginning this journey, start by mapping a single workflow where approvals consistently delay progress, such as change requests during shutdown planning, and introduce AI only where decision rules are already stable and measurable, while avoiding areas that depend on negotiation or human judgment.  #AIInProjectManagement #AgenticAI #ExecutiveLeadership #FutureOfWork #OperationalExcellence0 #DecisionIntelligence #EnterpriseAI #ProjectGovernance #DigitalTransformation #AIForCEOs #BusinessExecution #AIStrategy

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

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

𝗟𝗟𝗠 𝘃𝘀. 𝗥𝗔𝗚 𝘃𝘀. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 𝘃𝘀. 𝗔𝗴𝗲𝗻𝘁 𝘃𝘀. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 — 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝘄𝗵𝗮𝘁 I keep getting one question from teams building with GenAI: Which approach should we choose? This one-pager visual breaks down the trade-offs. Below is the practical guide I use on real projects. 𝟭) 𝗟𝗟𝗠 What it is: Prompt → model → answer. Use when: General knowledge, ideation, drafting, small utilities. Watch out for: Hallucinations on domain-specific facts; limited to model’s pretraining. 𝟮) 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) What it is: Query → retrieve context from a knowledge base → feed context + query to LLM → grounded answer. Model weights don’t change. Use when: You have proprietary docs, policies, catalogs, tickets, or logs that change frequently. Benefits: Lower cost than training, auditable sources, fast updates. Key tips: Good chunking, embeddings, metadata, and re-ranking determine quality more than the LLM choice. 𝟯) 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 What it is: Train the model on input→output pairs to change its weights (LLM → LLM′). Use when: You need consistent style, domain tone, or task-specific behavior (classification, templated replies, structured outputs). Benefits: Lower prompt complexity, stable behavior, smaller inference tokens. Caveats: Needs clean, labeled data; versioning and evaluation are critical. 𝟰) 𝗔𝗴𝗲𝗻𝘁 What it is: LLM + memory + tools/APIs with a think → act → observe loop. Use when: Tasks require multi-step reasoning, tool use (search, SQL, APIs), or state over time. Examples: Troubleshooting flows, data enrichment, workflow automation. Risks: Loops, tool misuse, latency. Use guardrails, timeouts, and action limits. 𝟱) 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 (𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀) What it is: Coordinated roles (planner, executor, critic) that plan → act → observe → learn from feedback. Use when: Complex processes with decomposition, review, and collaboration across specialized agents. Examples: Customer ops copilots, multi-step ETL with validation, enterprise workflows spanning multiple systems. Challenges: Orchestration, determinism, monitoring, and cost control. Metrics that matter Grounding: citation hit-rate, answer verifiability (RAG) Quality: task accuracy, pass@k, error rate Efficiency: latency, tokens, cost per resolution Safety: hallucination rate, tool misuse, policy violations Reliability: determinism, replayability, test coverage Design Tips: Start with RAG before touching fine-tuning; data beats weights early on. Keep prompts short; push knowledge to the retriever or the dataset. Add evaluation harnesses from day one (gold sets, unit tests for prompts/tools). Log everything: context windows, actions, failures, and human overrides. Treat agents like software: versioning, guardrails, circuit breakers, and audits.

➡️ 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

What if building automation became a driver of production efficiency? At our Phoenix Contact site in Bad Pyrmont, we’re exploring exactly that. During a recent visit, I met with Dr. Hannah Peter to discuss how we’re connecting facility management and manufacturing. The goal is smarter use of energy and resources. Our PLCnext Factory continuously collects data, which is analyzed by AI to provide infrastructure on demand. This leads to up to 50% lower operating costs. Over the past three years, we’ve seen measurable impact: ⬆️ 30% more productivity ⬇️ 30% less energy consumed 💶 Approximately 1.5 million euros saved annually 🌍 Around 200 tons of CO₂ avoided per year Facility systems, production, EV charging infrastructure, and a battery storage unit are all connected and largely powered by our own solar energy. We also collaborate locally, for example via the district heating network, to make use of existing resources. What we test and validate here is shared with customers and partners who are looking to digitize their own operations. This is sector coupling in practice. A step closer to the 1.5°C goal. Do we have all the answers? Not yet. But we’re learning fast and sharing what works. And here’s one more idea: What if we made these systems even more open and scalable with a control solution built specifically for building applications, based on PLCnext Technology?

You must know these 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 as an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. If you are building Agentic Systems in an Enterprise setting you will soon discover that the simplest workflow patterns work the best and bring the most business value. At the end of last year Anthropic did a great job summarising the top patterns for these workflows and they still hold strong. Let’s explore what they are and where each can be useful: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗖𝗵𝗮𝗶𝗻𝗶𝗻𝗴: This pattern decomposes a complex task and tries to solve it in manageable pieces by chaining them together. Output of one LLM call becomes an output to another. ✅ In most cases such decomposition results in higher accuracy with sacrifice for latency. ℹ️ In heavy production use cases Prompt Chaining would be combined with following patterns, a pattern replace an LLM Call node in Prompt Chaining pattern. 𝟮. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: In this pattern, the input is classified into multiple potential paths and the appropriate is taken. ✅ Useful when the workflow is complex and specific topology paths could be more efficiently solved by a specialized workflow. ℹ️ Example: Agentic Chatbot - should I answer the question with RAG or should I perform some actions that a user has prompted for? 𝟯. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Initial input is split into multiple queries to be passed to the LLM, then the answers are aggregated to produce the final answer. ✅ Useful when speed is important and multiple inputs can be processed in parallel without needing to wait for other outputs. Also, when additional accuracy is required. ℹ️ Example 1: Query rewrite in Agentic RAG to produce multiple different queries for majority voting. Improves accuracy. ℹ️ Example 2: Multiple items are extracted from an invoice, all of them can be processed further in parallel for better speed. 𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: An orchestrator LLM dynamically breaks down tasks and delegates to other LLMs or sub-workflows. ✅ Useful when the system is complex and there is no clear hardcoded topology path to achieve the final result. ℹ️ Example: Choice of datasets to be used in Agentic RAG. 𝟱. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: Generator LLM produces a result then Evaluator LLM evaluates it and provides feedback for further improvement if necessary. ✅ Useful for tasks that require continuous refinement. ℹ️ Example: Deep Research Agent workflow when refinement of a report paragraph via continuous web search is required. 𝗧𝗶𝗽𝘀: ❗️ Before going for full fledged Agents you should always try to solve a problem with simpler Workflows described in the article. What are the most complex workflows you have deployed to production? Let me know in the comments 👇 #LLM #AI #MachineLearning