Tips for Using Technology to Improve Workflow

𝗗𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗥𝗲𝗮𝗱 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴. 𝗔𝗽𝗽𝗹𝘆 𝗜𝘁. The AI headlines are exciting. But if you're a founder, engineer, or educator in manufacturing, here's the question that actually matters: 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼 𝘵𝘰𝘥𝘢𝘺 𝘁𝗼 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲𝘀𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻? Let’s get tactical. 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Tool to try: Lenovo’s LeForecast A foundation model for time-series forecasting. Trained on manufacturing-specific datasets. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re battling supply chain volatility and need better inventory planning. 👉 Tip: Start by connecting your ERP data. Don’t wait for perfect integration: small wins snowball. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗻𝗲𝘅𝘁 𝗿𝗼𝗯𝗼𝘁 Tools behind the scenes: NVIDIA Omniverse, Microsoft Azure Digital Twins Schaeffler + Accenture used these to simulate humanoid robots (like Agility’s Digit) inside full-scale virtual factories. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re considering automation but can’t afford to mess up your live floor. 👉 Tip: Simulate your current workflows first. Even without a robot, you’ll find inefficiencies you didn’t know existed. 𝟯. 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗤𝗔 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝟮𝟬𝟮𝟬𝘀 Example: GM uses AI to scan weld quality, detect microcracks, and spot battery defects: before they become recalls. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re relying on spot checks or human-only inspections. 👉 Tip: Start with one defect type. Use computer vision (CV) models trained with edge devices like NVIDIA Jetson or AWS Panorama. 𝟰. 𝗘𝗱𝗴𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲 Why it matters: If your AI system reacts in seconds instead of milliseconds, it's too late for safety-critical tasks. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're in high-speed assembly lines, robotics, or anything safety-regulated. 👉 Tip: Evaluate edge-ready AI platforms like Lenovo ThinkEdge or Honeywell’s new containerized UOC systems. 𝟱. 𝗕𝗲 𝗲𝗮𝗿𝗹𝘆 𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 The EU AI Act is live. China is doubling down on "self-reliant AI." The U.S.? Deregulating. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're deploying GenAI, predictive models, or automation tools across borders. 👉 Tip: Start tagging your AI systems by risk level. This will save you time (and fines) later. Here are 5 actionable moves manufacturers can make today to level up with AI: pulled straight from the trenches of Hannover Messe, GM's plant floor, and what we’re building at DigiFab.ai. ✅ Forecast with tools like LeForecast ✅ Simulate before automating with digital twins ✅ Bring AI into your QA pipeline ✅ Push intelligence to the edge ✅ Get ahead of compliance rules (especially if you operate globally) 🧠 Each of these is something you can pilot now: not next quarter. Happy to share what’s worked (and what hasn’t). 👇 Save and repost. #AI #Manufacturing #DigitalTwins #EdgeAI #IndustrialAI #DigiFabAI

Ever feel like your team is stuck in an endless loop of manual data entry? (Automation Tip Tuesday 👇) That’s exactly where one of our clients — an education consulting firm — found themselves. They were juggling a whole tech stack of tools that didn’t “talk”  to each other, creating inefficiencies and double work. We started with a look into their sales workflow. 🔹 Sales data lived in HubSpot, but once a deal closed, someone had to manually update Asana to track project progress. 🔹 Internal teams worked from one Asana board, but clients needed visibility into their own project timelines — cue more manual updates. 🔹 With so much repetitive data entry, valuable time was being wasted on low-impact admin work. Here’s what we did: 🔗 HubSpot → Asana automation: We created an integration that auto-generates project tasks in Asana when a deal reaches a certain stage in HubSpot. No more copy-pasting! 📢 Internal and client boards sync: Internal progress updates in Asana now automatically reflect on client-facing Asana projects, reducing the back-and-forth. Less busywork, more productivity. By eliminating duplicate data entry, the team saved 10+ hours per week — time now spent on strategy and client success. When your tools work together, your team can focus on what really matters. Where is your team losing time? Drop a comment below! ⬇️ -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday  #automation #workflow #efficiency

Many healthcare organizations are trying to optimize their workflows without a clear strategy, and that’s where things can go wrong. While serving as the US Navy's chief medical informatics officer (CMIO), I learned important lessons about workflow optimization, strategy, and technology integration. Here’s the truth: Healthcare workflows are intricate and multifaceted. Without the right approach, there’s a risk of: ⏳ Wasting valuable time on redundant tasks 💸 Incurring unnecessary costs 😟 Compromising patient experiences But it doesn’t have to be this way. 🔍 Here’s what you need to know to streamline and optimize your healthcare workflows with AI: 1️⃣ Identify Bottlenecks. First, not all workflow issues are created equally. Some are more critical than others. → Start by pinpointing the areas where inefficiencies are costing you the most. 2️⃣ Leverage AI for Automation. AI can handle routine tasks like appointment scheduling and data entry. → Free up your staff to focus on patient care and complex decision-making. 3️⃣ Enhance Decision-Making with AI. Insights AI can quickly analyze vast amounts of data, offering insights that improve patient outcomes. → Use AI to support clinical decisions and personalize treatment plans. 4️⃣ Improve Communication Channels. AI-driven tools can streamline communication between departments and with patients. → Ensure everyone is on the same page, reducing errors and enhancing patient satisfaction. 5️⃣ Monitor and Adjust Regularly. AI is powerful, but it is not set and forgotten. Continuous monitoring and adjustments are key. → Regularly review your workflows and tweak AI tools for ongoing optimization. Healthcare is challenging enough. Don’t let outdated workflows add to the stress. With a strategic approach, AI can transform your healthcare operations, making them more efficient, cost-effective, and patient-centered. 👉 Are you ready to explore how AI can elevate your healthcare workflows? Let’s discuss the possibilities.

I made A LOT of mistakes building my accounting firm's tech stack. Here were the worst of them so you don't do the same: 1. Starting With The Wrong Tech Don't start with something shiny. Your practice management system (PM) is the backbone of your firm - everything else should be built around it. While no single tool will solve all your problems, you want your PM to solve as many as possible. 2. Buying Based on Integrations Just because a vendor claims "exclusive integration" (usually a tax software thing) doesn't make their solution superior. I've seen too many firms trapped with subpar tools simply because of promised integrations. Focus on capability first, integrations second. 3. Over-Avoidance of Manual Tasks Here's a controversial take: sometimes manual work is the right answer. I'm a big time automation nerd, but the best tech stacks today still require manual processes. Don't reject an otherwise excellent solution just because it requires manual data syncing. Build systems around the totality of your core workflows, not the one that best integrates. 4. Relying Exclusively on Sales People Salespeople are paid to sell, not to solve your problems. Tap into the wealth of real-world experience in the accounting community. Connect with peers who've already walked your path. Their unbiased feedback is worth its weight in gold. 5. Burning Out Your Team Involve your team early in technology decisions, give them agency in the process to frame it as an opportunity rather than an annoyance. Set clear boundaries around how often you'll make changes to core tech so you aren't in a perpetual state of change. The next time you hire, look for people who are energized by change so you don't have to bring 100% of the energy. 6. Picking The Wrong Tool for the Job Stop trying to make your PM do everything. Three core workflows today require their own tool, your PM isn't good enough: tax intake, proposals, and month-end close automation. I'm all for keeping the stack simple, but in those three cases the upside outweighs the downside. 7. Software Sticker Shock Yes, we're spending more than ever before on software, but there's a silver lining: most core workflow tools are now priced per engagement. A tool that costs the same for a $100 or $10,000 project might seem expensive initially, but will likely accelerate your journey to becoming the $10k firm. 8. Analysis Paralysis Perpetual delay is costly. The best time to implement new tech is after tax season - the second best time is now. Don't let perfect be the enemy of progress. Change now and you'll be asking smarter questions, solving better problems next April. In the spirit of leveraging community to make hard decisions, if you're stuck on a hard tech decisions drop a note below and I bet some smart folks can help you 👇

Recently helped a client cut their AI development time by 40%. Here’s the exact process we followed to streamline their workflows. Step 1: Optimized model selection using a Pareto Frontier. We built a custom Pareto Frontier to balance accuracy and compute costs across multiple models. This allowed us to select models that were not only accurate but also computationally efficient, reducing training times by 25%. Step 2: Implemented data versioning with DVC. By introducing Data Version Control (DVC), we ensured consistent data pipelines and reproducibility. This eliminated data drift issues, enabling faster iteration and minimizing rollback times during model tuning. Step 3: Deployed a microservices architecture with Kubernetes. We containerized AI services and deployed them using Kubernetes, enabling auto-scaling and fault tolerance. This architecture allowed for parallel processing of tasks, significantly reducing the time spent on inference workloads. The result? A 40% reduction in development time, along with a 30% increase in overall model performance. Why does this matter? Because in AI, every second counts. Streamlining workflows isn’t just about speed—it’s about delivering superior results faster. If your AI projects are hitting bottlenecks, ask yourself: Are you leveraging the right tools and architectures to optimize both speed and performance?

Just read #OpenAI’s latest guide on building AI Agents. No fluff. No hype. Just clear, field-tested advice. Here are the 10 takeaways that really stayed with me — not just as a technologist, but as someone helping enterprises build agentic systems that last. 1. Start simple — with one #agent. It’s tempting to jump into multi-agent orchestration, but most use cases don’t need it upfront. In fact, multiple agents often introduce more chaos than value, especially when the basic workflow isn’t stable yet. 2. Choose your problems wisely. Agents shine where there's ambiguity — decision-making, exception handling, and unstructured data. If your task is predictable and rule-based, traditional automation will always be more efficient. 3. Start with the most powerful model. Establish your baseline with #GPT-4 or an equivalent. You need to prove the value first. Once it works, then fine-tune for speed and cost. 4. Your #SOPs are agent instructions waiting to happen. This one hit home. So much enterprise knowledge sits in playbooks and wikis — often ignored. Break them down into steps. Let the agent learn your process as it is, before redesigning it. 5. Tools need boundaries. Don’t make tools up as you go. Define clean interfaces — retrieval, execution, orchestration — and document them well. Reusable tools aren’t just efficient; they reduce technical debt. 6. Guardrails aren't optional. They're layered. There’s no single safety net. Combine prompt checks, rules, APIs, human feedback — whatever it takes to protect privacy, security, and intent. In high-trust environments, this matters more than anything. 7. Don’t over-engineer prompts. Use templates with variables. One solid base prompt that accepts policy or context inputs can scale across workflows. It’s easier to manage and debug. 8. Design for escalation from day one. What happens when an agent hits a blind spot? Or a high-risk situation? There must be a graceful, traceable way to hand off to a human — without friction. 9. Match orchestration to complexity. Some systems need a central ‘manager’ agent. Others are better off with distributed, peer-to-peer tasking. There’s no universal pattern — it’s about choosing what fits your use case. 10. Don’t wait for perfection — deploy early. Real users will always surprise you. The edge cases, the weird inputs, the unexpected outcomes — they show up only after you ship. Your best guardrails will be born from actual failures, not hypothetical ones. This isn’t theory. These are the kinds of lessons we apply every week as we build intelligent systems — where agents augment humans, not replace them. If you’re building in this space: 📌 Start small. 📌 Stay human-centric. 📌 Let trust scale with capability. Because building an agent is easy. Building a system you can trust — at scale, under pressure, and in the wild — is the real challenge. #AIagents #AgenticAI #LLMOps #EnterpriseAI #GauravWrites #BuildingWithTrust

Investing in new tools won't solve your CFO's and FP&A's problems. So instead, do this: 1. Focus on the fundamentals: Automation is about optimizing the core processes and workflows that drive your business. Spend time understanding your current processes, identifying pain points, and exploring ways to streamline them before investing in new technologies. 2. Prioritize user experience: Effective automation should improve the experience of your employees and customers, not create additional complexity. Make sure that any automated solutions you apply are intuitive, user-friendly, and link smoothly with your existing systems. 3. Embrace a continuous improvement mindset: Automation is an ongoing journey, not a one-time project. Always review and refine your automated processes to guarantee they remain efficient and effective as your business grows. 4. Leverage the right tools for the job: While modern tools aren't always necessary, don't be afraid to invest in specialized software or platforms that can truly transform your workflows. Carefully look into your needs and select the tools that will have the greatest impact. 5. Empower your team: Encourage your employees to think critically about their daily tasks and identify opportunities for improvement. Provide them with the training and resources they need to participate in the automation process and contribute their valuable insights. Process efficiency is not about fancy tools. And good automation doesn't mean flashy, high-tech tools. Yes. New technologies can be helpful. But remember, the right automation strategy starts from within.

The #1 reason people don't use AI in their workflows (and how to fix it) In a recent Supra Insider podcast, Jacob Bank from Relay.app shared a powerful playbook for effective AI implementation. His critical insight: "The main reason people don't use AI in practice right now is not because they haven't heard of it, not because they don't think it's cool... just because they can't trust it to do work on their behalves." The solution? Human-in-the-loop design. Instead of viewing AI as "fully automated or not," successful implementations create thoughtful checkpoints where humans remain in control: 1/ Plan transparency Before executing, AI should communicate its approach to the task. This creates confidence by letting users understand what will happen. Without this step, users fear uncontrolled actions like "writing 5,000 emails to every customer individually" or running up costs unnecessarily. Examples: "Here's how I'll tackle this task and where I'll need your input." 2/ Refinement opportunities Create explicit moments where humans can guide the AI's work while it's in progress. These aren't just approval checkpoints but collaborative interactions. These refinement stages are perfect for content creation, telling the AI to "emphasize this part of the conversation more, this part less, go back and try again." Examples: ↳ "This looks good, but emphasize this part more" ↳ "These results need context from last quarter" ↳ "You're missing an important constraint" 3/ Quality assurance gates Establish critical approval points that cannot be bypassed before final output. For successful AI workflows like LinkedIn content creation, never let AI publish directly. For important workflows, multiple QA checkpoints are essential - first reviewing the draft, then refining for polish, and finally a human edit before publishing. Examples: ↳ "Review this draft before sending" ↳ "Confirm these metrics are accurate" ↳ "Approve this selection of priority items" 4/ Outcome verification Close the loop by providing feedback on results to improve future performance. This step makes AI tools progressively more valuable over time. Use this approach to refine content workflows by analyzing which posts perform well and feeding that data back into the system. Examples: ↳ "The approach worked, but next time include X" ↳ "This missed the mark because of Y" ↳ "This exceeded expectations, let's rely on it more" Even with perfect prompts, AI drafts typically only get "80% of the way to the quality bar" needed for publication. The companies winning with AI aren't eliminating humans from the process. They're creating thoughtful collaboration points that leverage the strengths of both. Where are you implementing human-in-the-loop design in your AI workflows? What checkpoints have you found most valuable?

If you’re in the AEC industry, you've heard it countless times: "Digitize or get left behind." Easier said than done, right? Having navigated both sides of this shift, from boots and hardhat in the field to working in AEC tech, I know firsthand that the transition can feel overwhelming. But here’s the secret: digitizing your workflows doesn't have to disrupt your entire operation. Instead, think of it as unlocking new levels of efficiency, accuracy, collaboration and reducing risk. Here are a few practical steps I've found based on my AEC experience and also change management training that can be crucial for successful digital transformation and change management in AEC: Start Small & Scale Up Don't overhaul everything at once. Begin with high-impact, low-disruption areas—like field data collection or site inspections. Prioritize Ease of Use Pick digital tools your team can adopt easily. Remember, the goal isn't complexity; it's clarity. If your tech requires extensive training, reconsider your choice. Clear Communication Wins Your team must understand the "why" behind digitization—not just the "how." Show them tangible benefits: fewer errors, saved hours, improved communication. Make it relatable and practical. Champions & Support Identify internal champions who are excited about tech and can help lead the transition. They’ll be crucial in troubleshooting, encouraging adoption, and providing peer-to-peer support. Integrate & Automate Use digital tools and workflows tools that integrate with existing systems. Integrations with platforms like Autodesk Construction Cloud or Procore not only enhance efficiency but also minimize disruptions to existing workflows. Feedback Loops Regularly check in with your team to understand their experiences and adjust your strategy accordingly. Digitization isn’t a one-and-done; it’s a journey of continuous improvement. Its been said many times before, evolution, not revolution. Embracing digital transformation thoughtfully can boost your team’s productivity and reduce project risks. Change is rarely easy, but with the right approach, it becomes manageable and beneficial.

I turned Claude into my personal workflow automation engine using nothing but slash commands and markdown. The gist: you design complex workflows as custom Claude Code commands that guide you through multi-step processes, pulling data from systems, updating others, and handling tasks that need human judgment - all without tab-switching into oblivion. Here’s how I’m building these: 1 - Sketch the workflow first I use Mermaid diagrams. Not just because I love diagrams, but because I can feed them directly to the agent to help it orchestrate better. Visual structure = better execution. 2 - Break big workflows into Lego blocks Learned this the hard way. Started with one massive workflow file. Total mess, impossible to test. Now I break things down. My ideation workflow? Actually three smaller workflows that call each other: Gather insights and analytics, then prompt for ideas based on real problems Deep dive on the promising ones Design quick tests to de-risk before building Way more flexible. Way less brittle. 3 - Keep steps dead simple Each step does ONE thing. When a step starts doing two things, split it. Makes debugging 10x easier when something inevitably breaks. 4 - Structure everything with markdown & XML Sounds nerdy, but it works. I use XML properties to annotate steps and shift the LLM's behavior for each step. For example, sometimes I want the LLM to act more like a facilitator when executing a step, prompting me for input and guiding me towards a better result. Other times, I just want it to do something like grab data from other systems. 5 - Let the LLM update its own workflows Meta, but practical. Since everything's in Mermaid and structured text, I can ask it to refine its own workflow based on what's working. Saves me tons of time. 6 - Version control everything Git isn't just for code. When you inevitably break a working workflow prompt at 4 pm on a Friday, you'll thank yourself for that commit history. The result? Over the past few weeks, we’ve run several ideation sessions and saved hours pulling data and creating tickets in Vistaly and GitHub. I also started sharing these commands with customers and started to see them run with them and make updates. So cool. Who else is building custom workflows like this? What's the most complex thing you've automated with your LLM/MCP? Drop a comment or DM me if you want to swap workflow files. Building a small library of these things.