Cross-Departmental Workflow Automation

3 Workflows I've Automated for in-house teams. ① Ask Legal ② Procurement ③ Contract Review (not just the review!) 1. Ask Legal [or any department for that matter 🤷🏼♀️] You've heard me talk about legal teams and knowledge management. Long story short, your legal team is answering the same 20 questions over and over 😵💫 A simple way to save a CHUNK of time answering questions from the business (enabling them to go faster) ALL while having complete control & keeping a human in the loop? ↪️ Set up an 'Ask Legal' bot in your comms platform. ↪️ Sync it with your knowledge base (e.g GDrive/Notion/Sharepoint). ↪️ Set up your custom instructions (Want it to tag Bob on privacy questions only, specifically on a Tuesday? No problem).  ↪️ Don't want the answer to go straight out to the business without reviewing it first? Cool, turn on co-pilot mode. The result? 60-80% fewer repetitive queries. Your team focuses on the high value things that need a human lawyer. 2. Procurement Businesses have 100's of tools, but when departments don't speak to each other you end up with duplicate tools & subscriptions 😭 💵 🚽.  What if there was a way for the business to find out in <1 minute if there was a tool available that covered their needs, before needing to spend some hard secured department budget? Moreover, what if I told you, they could kick off the internal procurement process from the comfort of your comms platform? Team member : “Do we already have a tool for X?” in Slack/Teams ✅ Bot checks knowledge base (policies, procurement tool). ✅ If a match is found, it shares the approved tool & owner to contact. ✅ If not, the bot can ask the user for more info and direct them with next steps to kick off the procurement process from inside Slack/Teams. Ensuring your users ACTUALLY follow the process, without adding friction. Did I just see your CFO cry tears of joy? 3. Third Party Vendor Contract Review & Project Management Getting AI to redline a contract (as a first pass) is a huge win, but there's still the other pieces of the process missing, like: 🤷🏼♀️ The business figuring out IF legal review is even needed (according to company policy). 📨 The business actually submitting the contract to legal. 😩 Managing review capacity within the legal team. 🖥️ Getting the legal team to log & update the PM tool. The list never ends. Legal reviews only what actually needs their eyes, turnaround times improve, and the business stops pinging the team for “update pls?” in Slack : ) TLDR; Most legal teams are drowning in admin work that could be automated. I've built all of these using simple processes and tools (that I've found most businesses have). You also know I love a good Figma flow. So I’ve built them for all three of the above (see a sneak peak below). Want the entire thing? Comment "FLOWS" and I'll send them over. Also, tell me what you want to see - more of the above or step-by-step how-to build videos?

This week I improved a process that impacts every single person in our company. Every Monday we share a weekly agenda that keeps all teams aligned. It is how instructors know what classes are happening, how departments see what projects are moving forward, and how we talk through needs or wins from the previous week. It worked, but building it manually each time took more effort than it should. So I updated the automation behind it. We already track every class and project in a SharePoint list. Past, present, future. All the information was there. It just needed to be organized in a way that created clarity. Here is what the flow does now: → It runs automatically every week without anyone having to remember. → It pulls all classes and projects from our SharePoint list. → It separates them into three clean tables that are easy for everyone to scan. • Classes that happened this week • Classes happening next week • Projects that are still in progress → It formats all three tables into a simple OneNote agenda with consistent styling. → It sends that agenda to all employees so everyone starts the week on the same page. The outcome is exactly what we needed. ★ Teams walk into Monday already aligned. ★ Instructors can prepare for the week ahead with zero guesswork. ★ Discussions about wins, problems, and priorities happen with shared context. No one has to build a document from scratch. No one has to remember to gather the information. The agenda just shows up, every week. This is the kind of automation I love! → Just something that removes repetitive work and makes collaboration easier. If your team has information scattered across lists, spreadsheets, or multiple hands, there is usually a simple flow waiting to be built. Let’s start building!

The UK's Department for Business and Trade just released a 48-page evaluation of MS Copilot. Their conclusion? A generic, off-the-shelf AI chatbot isn't producing significant efficiency gains. Shocker… Here's what they found; 🔹 72% user satisfaction with basic writing and summarizing tasks 🔹 Modest time savings: ~1 hour saved on document drafting, negative time impact on scheduling and presentations 🔹 22% of users encountered hallucinations requiring fact-checking 🔹 Biggest benefits for neurodiverse users and non-native English speakers 🔹 No evidence of broader organizational productivity improvements Basically, it's a decent writing assistant. If we're expecting off-the-shelf LLMs to transform work, we're missing the point. LLMs aren't about optimizing existing workflows - they're about making work conversational. Imagine telling your procurement system: "Flag vendors with unusual pricing patterns from last 18 months" or "Generate an audit response comparing our data practices against our policy frameworks." That requires domain-specific training, system integration, and task-specific capabilities, none of which exist in off-the-shelf LLM driven copilot. Most companies are making the same mistake as the UK government. They're licensing generic AI tools and expecting productivity gains on individual tasks, when the real opportunity is building conversational interfaces to their actual business logic. To hit the nail on productivity gains with AI? 1️⃣ Start with the problem → Look for workflows where people navigate multiple systems, coordinate across functional areas, pass data back and forth, analyze it, and perform well-defined repetitive tasks. 2️⃣ Identify 1-2 specific processes and break them into testable components → Pick process you can decompose into individual tasks. Don't attempt to automate entire workflows until you've proven AI can reliably handle each component. 3️⃣ Invest in clean data, metadata, and integrations → Ensure you have the data infrastructure and system connections needed for AI to execute tasks rather than just generate text. 4️⃣ Measure each task against your hypothesis → Does it help? If all individual tasks were combined, would it provide enough gains to be worth the investment? 4️⃣ Be smart about expectations → This is emerging technology that will improve. Don't expect 100% accuracy out of the gate. The hard truth? Transforming your organization with AI requires an innovation mindset, not digital transformation. It's not about buying a tool, implementing it and seeing immediate ROI. Real transformation requires engineering investment and domain expertise. And that won't come from MS Copilot alone. The organizations that figure this out first won't be asking "Does AI save time on emails?" They'll be asking "What can we make possible when our systems can take orders in plain English?"

No workflow is sacred. No process is safe. No part of your business should be shielded from evolution via AI. It's precisely the things you've done the same way a thousand times that you can mistake for being good enough already. Whether you shave a few seconds off a frequent task or eliminate a whole manual function, it all adds up to a more competitive, successful company. Real AI-driven efficiencies make it possible to scale without compromising quality, maybe even increasing quality… Launch a company-wide initiative to analyze all the work you do, and find where AI can help. Even better, make it a friendly competition to see which department can deliver the most improvements. Anything you do can benefit. The key is to have an open mind to identify the opportunities, the discipline to evaluate the potential impact and value, and the ability to execute. Socialize the findings to inspire other teams to find new places to look. Create a baseline by measuring how long your workflows take, how many people and how much money to complete. Don't forget to include time wasted on cross-team bottlenecks and feedback cycles that could be slimmed from days or weeks to just seconds of waiting for AI's next output. Once you have a clear list of hypotheses for where you could save time and energy, it's time to go find solutions and put them to the test. But remember this isn't some one-time overhaul. Assessing where AI could help should become a constant motion as the technology advances to solve more problems. It's a new muscle your team has to develop. There’s natural resistance because sometimes it’s easier to stick to old habits. But this will change once everyone understands the manual way is no longer acceptable. Additional headcount won’t be assigned to problems that could be done with AI. Welcome to the new status quo.

September 2023: Sole data engineer leaves. Operations paused. Major crisis. January 2024: Automated pipelines. self-serve reports. 25+ hours p/w saved. Here's how we achieved this for a fast-scaling telehealth firm: When their only data engineer handed in his notice, every department - from operations to sales, finance to the C-suite - suddenly found themselves scrambling. Their entire reporting stack relied on brittle SQL scripts and manually generated CSVs. Without that one engineer to babysit the workflows, key processes ground to a halt. This wasn’t just an inconvenience; it became an immediate operational and HIPAA compliance risk. So, what did we do? 𝟏. 𝐑𝐞𝐟𝐚𝐜𝐭𝐨𝐫𝐞𝐝 𝐜𝐨𝐫𝐞 𝐒𝐐𝐋 → Rewrote over 30 core queries. → Slashed execution times by 60% → Set the foundation for scalable, repeatable workflows. 𝟐. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 → Built a suite of Python scripts that automatically handle validation, transformation, and reformatting. → Brought manual errors down to 0 → Delivered a reusable codebase for future use cases 𝟑. 𝐒𝐞𝐜𝐮𝐫𝐞𝐝 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 → Implemented a scheduled, audited email automation system → Sends appropriate files to the right people → Saved 8–10 admin hours per week → Created a full audit trail for compliance 𝟒. 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐬𝐚𝐥𝐞𝐬 → Built one-click EMR exports that gave them instant access to the data they needed. → Prep time dropped by 90%, → Made client follow-ups seamless 𝟓. 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐡𝐢𝐫𝐞 → Documented every pipeline, SQL convention, and Python script → Spent several weeks training the incoming engineer 𝐓𝐡𝐢𝐬 𝐫𝐞𝐬𝐮𝐥𝐭𝐞𝐝 𝐢𝐧: 25+ hours p/w saved across teams through automation Tightened HIPAA compliance posture Real-time insights for decision-makers A sustainable system that outlives any one person 𝐓𝐋;𝐃𝐑: If your healthcare org still runs on patched-together scripts and one data engineer, you’re one departure away from disaster. Modernize with automation Secure distribution Do intentional training ... and watch your risk, costs, and bottlenecks vanish. ♻️ Share this to help someone in your network Follow me for more on data modernization in healthcare.

Most companies don't have an automation problem. They have an automation clarity problem. 🧵 We kicked off our automation journey with so much excitement. 6 months later? ❌ 10 bots running in the background ❌ 15 workflows nobody fully understands ❌ 3 apps built in a rush ❌ And somehow… the same exact problems still exist Sound familiar? Here's what I've learned after going through this myself: Automation doesn't fail because of the tools. It fails because nobody stopped to ask "What TYPE of problem are we actually solving?" Once I started thinking in 4 clear buckets, everything clicked. 👇 🔵 1. Task Automation (RPA, Scripts & Macros) Best for repetitive, rule-based human steps. If a human is doing the same thing copy-pasting data every single day automate it here first. 🟢 2. Process Automation (Workflows & BPM) Best for approvals, routings and handoffs between teams. If something keeps "falling through the cracks" between departments this is your answer. 🟠 3. Decision Automation (Rules, Logic & AI) Best for eligibility checks, prioritisation, recommendations and smart triage. If your team is making the same judgment calls 50 times a day teach the machine to decide. 🟣 4. Platform Automation (Low-Code Apps & Integrations) Best for end-to-end operational systems. If your entire process lives across 6 disconnected tools this is where you modernise. Most failed automation projects I've seen tried to solve a Process problem with a Task tool. Or built a Platform when all they needed was a simple Decision rule. Wrong bucket = wasted budget, frustrated teams, and zero ROI. Map the problem first. Pick the tool second. Always. 💬 Which type of automation have you found hardest to get right in your organisation? Drop your experience below would love to hear what's worked (and what hasn't). 👇 #Automation #RPA #ProcessAutomation #LowCode #AI #Workflows #PowerPlatform #Creatio #DigitalTransformation #OperationalExcellence

The real power of AI isn't in isolated tools—it's in breaking down the walls between teams. When your design team experiments with AI in isolation, you're capturing only a fraction of its potential. The same happens when engineering or product teams keep their new solutions to themselves. The most transformative AI implementations don't just optimize individual tasks—they reimagine entire workflows across departments. Next time you're showcasing the ROI of your AI initiatives, think bigger: ➡️ How might this workflow extend beyond your immediate team? ➡️ What if that design automation tool could seamlessly feed into engineering processes? ➡️ Or what if your AI-powered user research insights could directly inform product strategy? This isn't just about efficiency—it's about creating compound value. When you can bridge departmental gaps, the benefits multiply exponentially, creating a network effect that elevates everyone's work. Break free from task-oriented thinking. Embrace service design principles that connect the dots across your organization. The most valuable AI implementations don't just make individual jobs easier—they transform how entire teams collaborate. Is your team thinking about AI as a departmental tool or as an organizational bridge? The difference could determine whether you're getting incremental or exponential returns. #AIStrategy #CrossFunctionalCollaboration

Microsoft is redefining enterprise automation with the latest updates in Microsoft Copilot Studio, bringing together AI agents and workflows into a single intelligent automation experience. The biggest shift? Workflows are no longer limited to rigid rule-based automation. Organizations can now combine deterministic workflows with AI-powered agents that reason, adapt, and make decisions dynamically. Key highlights from the announcement: • A new visual workflow designer for building automations faster with low-code simplicity • AI agents embedded directly into workflows for intelligent task execution • Support for MCP-enabled tools and enterprise integrations • Improved testing, debugging, and monitoring capabilities before deployment • Seamless orchestration between workflows, copilots, and enterprise systems • Faster automation of complex business processes across departments This marks a major evolution from traditional BPM automation toward truly agentic enterprise systems. Instead of static automations that break when conditions change, businesses can now create adaptive workflows powered by AI reasoning and contextual decision-making. Microsoft is clearly positioning Copilot Studio as the foundation for the next generation of enterprise AI operations. The future of automation is not just workflow automation anymore. It is intelligent orchestration between humans, agents, and systems. Read more: https://lnkd.in/g7npnH6Q #Microsoft #MicrosoftCopilot #CopilotStudio #AI #GenerativeAI #EnterpriseAI #Automation #WorkflowAutomation #AIAgents #AgenticAI #DigitalTransformation #BusinessAutomation #LowCode #FutureOfWork #AIInnovation