Remote Work Technology Integration

Everyone's building AI agents, but few understand the Agentic frameworks that power them. These two distinct frameworks are the most used frameworks in 2025, and they aren't competitors but complementary approaches to agent development: 𝗻𝟴𝗻 (𝗩𝗶𝘀𝘂𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻) - Creates visual connections between AI agents and business tools - Flow: Trigger → AI Agent → Tools/APIs → Action - Solves integration complexity and enables rapid deployment - Think of it as the visual orchestrator connecting AI to your entire tech stack 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 (𝗚𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻) by LangChain - Enables stateful, cyclical agent workflows with precise control - Flow: State → Agents → Conditional Logic → State (cycles) - Solves complex reasoning and multi-step agent coordination - Think of it as the brain that manages sophisticated agent decision-making Beyond technicality, each framework has its core strengths. 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗻𝟴𝗻: - Integrating AI agents with existing business tools - Building customer support automation - Creating no-code AI workflows for teams - Needing quick deployment with 700+ integrations 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵: - Building complex multi-agent reasoning systems - Creating enterprise-grade AI applications - Developing agents with cyclical workflows - Needing fine-grained state management Both frameworks are gaining significant traction: 𝗻𝟴𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: - Visual workflow builder for non-developers - Self-hostable open-source option - Strong business automation community 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: - Full LangChain ecosystem integration - LangSmith observability and debugging - Advanced state persistence capabilities Top AI solutions integrate both n8n and LangGraph to maximize their potential. - Use n8n for visual orchestration and business tool integration - Use LangGraph for complex agent logic and state management - Think in layers: business automation AND sophisticated reasoning Over to you: What AI agent use case would you build - one that needs visual simplicity (n8n) or complex orchestration (LangGraph)?

Workflow Wednesday: Why Integration Matters More Than Innovation In healthcare, we often get dazzled by shiny new technologies. A new algorithm that detects disease faster. A new model that beats human performance in a study. But here’s the truth I’ve learned as both a radiologist and as Chief Medical Officer at GE HealthCare: the real bottleneck is rarely the algorithm. It’s the workflow. A brilliant #AI tool that sits outside the daily routine of a radiologist won’t move the needle. If it adds clicks, slows reporting, or disrupts communication with clinicians, it risks becoming just another “nice idea” that never scales. This is why integration is everything: AI must live inside the worklist, not in a separate portal. It must prioritize urgent cases automatically, not wait for manual triage. It must speak the language of radiology reports, not produce outputs no one knows how to use. The most successful innovations I’ve seen are not the flashiest. They are the ones that disappear into the workflow so seamlessly that clinicians almost forget they’re using AI at all. As leaders, we need to remember: technology succeeds not when it looks impressive in a demo, but when it quietly improves care at scale. Question for you: In your own practice, what’s the biggest barrier to integrating new tools: technology itself, hospital IT, or cultural resistance? #Radiology #AIinHealthcare #Leadership #gehealthcare #WorkflowWednesday

Whenever I hear about a new startup, I pull out my research playbook. First, I understand the pitch, then find backgrounds of the team, & tally the total raised. Over the weekend, I decided to migrate this workflow to use AI tools, & the process taught me something important about how we’re actually integrating AI into our work. Tools are small programs that expand AI capabilities. ChatGPT might call a web search tool to read a blog post I’d like to summarized. Claude might call the terminal tool to change file permissions in my current directory. Gemini might call a tool to find the latest stock price of the most recent IPO I’ve been following. I replaced each step in my workflow with an AI tool: a web search & summarization tool, LinkedIn research tool, & a capital fundraising history tool. I hadn’t changed the workflow itself—just swapped out the individual components within it. This upgrade revealed something crucial: there are three distinct classes of programs emerging in enterprise software. - Deterministic workflows are my original startup research process—the same steps, in the same order, every time. These excel at mechanization, executing identical processes with small deviations or calculations at each step. - Deterministic workflows with AI components represent my current setup. I still follow the same research sequence, but now Gemini & ChatGPT handle the summarization. The AI makes individual steps smarter while I maintain control over the overall process. - Agentic workflows hand decision-making to the AI entirely. The system decides what to research, in what order, & which tools to call based on the input. Read the full post here → https://lnkd.in/gKe_BcgA

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

AI is no longer just embedded inside enterprise applications as a feature. It is increasingly moving into the execution layer of enterprise systems, where it participates directly in end-to-end workflow completion. This is a fundamental shift from model usage to workflow orchestration and assisted outputs to autonomous process execution. We are now seeing AI integrated into:  • ERP workflows like order-to-cash and procure-to-pay  • CRM systems with automated decision routing  • ITSM platforms with self-resolving tickets  • Data pipelines triggering downstream actions without manual intervention This is not UI-level adoption. This is process-level automation driven by AI orchestration layers (agents + APIs + rules engines). From an enterprise operations standpoint, this introduces a different set of constraints:  𝟭. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗽𝗼𝗶𝗻𝘁𝘀 𝗺𝘂𝘀𝘁 𝗯𝗲 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲𝗱: Not all steps can be autonomous; governance must be embedded in the workflow design.  𝟮. 𝗔𝘂𝗱𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘀𝘆𝘀𝘁𝗲𝗺-𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹: Every AI-driven action must be traceable across systems, not just logged at the application layer.  𝟯. 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗮 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Failures are no longer user-facing; they are workflow breaks across integrated systems.  𝟰. 𝗗𝗮𝘁𝗮 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗮𝗰𝗿𝗼𝘀𝘀 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗮 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗿𝗶𝘀𝗸: AI execution is only as reliable as the underlying master data and integration integrity. The real gap in most enterprises is not 𝗔𝗜 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆; it is process re-engineering for AI-native execution. Most organizations are still layering AI on top of existing workflows. Very few are redesigning workflows assuming AI is part of the execution path. At USM Business Systems, the focus is shifting from AI adoption to governed, execution-ready operating models at scale. Beyond AI adoption, we focus on execution reliability, control design, and system-level integration maturity. How is your organization governing AI-driven execution across workflows? #AI #EnterpriseArchitecture #DigitalTransformation #COOInsights #EnterpriseSystems #Automation #USMBusinessSystems

10 ideas to get more out of AI. Connect to real workflows. Most people experiment with AI in small ways.  But the real value shows up when you connect it directly to your everyday tools. Here are 10 AI integrations worth setting up: 𝟭. 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗿𝗶𝘃𝗲 + 𝗦𝗵𝗲𝗲𝘁𝘀 𝗔𝗻𝗮𝗹𝘆𝘇𝗲𝗿 Summarize docs and research without copy/paste. Grant read access and ask for insights directly. 𝟮. 𝗭𝗮𝗽𝗶𝗲𝗿 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗧𝗿𝗶𝗴𝗴𝗲𝗿𝘀 Turn conversations into automated actions. Ideas become tasks in your project tools automatically. 𝟯. 𝗦𝗹𝗮𝗰𝗸 𝗕𝗼𝘁 𝘄𝗶𝘁𝗵 𝗖𝘂𝘀𝘁𝗼𝗺 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 Answer internal questions at scale. Upload guides and FAQs for faster onboarding. 𝟰. 𝗖𝗮𝗹𝗲𝗻𝗱𝗮𝗿 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 & 𝗣𝗿𝗲𝗽 𝗔𝗴𝗲𝗻𝘁 Daily leadership prep. Get 1-paragraph briefs for each meeting with attendees and topics. 𝟱. 𝗘𝗺𝗮𝗶𝗹 𝗗𝗿𝗮𝗳𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿 𝘄𝗶𝘁𝗵 𝗧𝗼𝗻𝗲 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 Convert notes into polished, professional messages. Faster, clearer, more consistent writing. 𝟲. 𝗝𝗶𝗿𝗮 / 𝗔𝘀𝗮𝗻𝗮 𝗧𝗮𝘀𝗸 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗲𝗿 Focus and prioritization. Get your top 5 tasks by priority with suggested focus order daily. 𝟳. 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗥𝗲𝗽𝘂𝗿𝗽𝗼𝘀𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 One idea into many formats. Turn blogs into LinkedIn posts, tweets, and email previews. 𝟴. 𝗖𝗼𝗱𝗲 𝗥𝗲𝘃𝗶𝗲𝘄 & 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 Faster reviews, better code. Review functions for performance issues and add inline comments. 𝟵. 𝗠𝗲𝗲𝘁𝗶𝗻𝗴 𝗡𝗼𝘁𝗲𝘀 𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻 𝗜𝘁𝗲𝗺𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 Clear decisions and accountability. Extract action items with owners and due dates automatically. 𝟭𝟬. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 (𝗦𝗲𝗰𝗼𝗻𝗱 𝗕𝗿𝗮𝗶𝗻) Build compound knowledge over time. Query what you learned last quarter from saved content. AI agents work best when they eliminate repetitive thinking, not replace strategic thinking. For example, I recently created a app that plugs into a Slack channel. It allows me to enter any thoughts or tasks. Then, Claude categorizes it and inputs it into a Google Sheet. I can then recall anything from the sheet at any time.   Easy input and intelligent output. Start with one workflow that hurts. Automate it. Then expand. Which one are you setting up first? ♻️ Share if this resonates ➕ Follow Jason Moccia for more insights on AI and leadership.

AI Integration and Safeguards How Intelligence Is Applied Responsibly Preo Communications integrates AI as an operational layer that enhances judgment, speed, and accuracy without introducing unnecessary risk. The objective is controlled leverage, not automation for its own sake. Where AI Is Applied AI is used in areas where it meaningfully improves outcomes. Common applications include: Pattern detection in analytics and attribution Forecasting and scenario modeling Audience segmentation and personalization Content optimization and performance analysis Workflow automation and efficiency gains AI supports teams by surfacing insight faster and reducing manual overhead. Human Led Decision Making AI informs decisions, it does not make them. Strategic direction, prioritization, and brand judgment remain human-led. AI outputs are treated as inputs to evaluation rather than instructions to follow without context. This prevents over-optimization and protects brand integrity. Data Quality and Input Control AI performance depends on data discipline. Inputs are carefully selected, cleaned, and structured to avoid bias, leakage, or misleading conclusions. Models are adjusted as data sources change to maintain reliability over time. Guardrails and Testing AI systems are introduced incrementally. Each application is tested in controlled environments before being expanded. Performance thresholds, review checkpoints, and rollback options are defined in advance to limit downside risk. Transparency and Traceability Outputs must be explainable. AI-driven insights are documented and traceable so teams understand why a recommendation exists and how it was generated. This maintains trust and supports better decision-making. Why AI Governance Matters Unstructured AI adoption increases volatility and risk. Governance ensures that efficiency gains do not come at the cost of accuracy, compliance, or strategic clarity. AI becomes valuable when it is embedded into well-designed systems with clear ownership and oversight. By applying AI deliberately and responsibly, Preo Communications enhances performance while preserving control, consistency, and long-term resilience.