Digital Workplace Innovations

The hardest part of building an AI workflow today is deciding your context strategy, which is how are you going to get the data you need for the task? To help you determine this, I've detailed the 5 context strategies that you can employ in any AI workflow: 1. Local files - The fastest and most reliable way is if your workflow can just read local files. For example, when drafting meeting agendas, I rely on markdown meeting notes that I've downloaded from Granola. This makes it incredibly fast for the AI to look through all my meetings to draft the appropriate next agenda. 2. CLI tools - AI tools are incredibly good at running command-line tools, which are programs that run in the Terminal. CLIs exist for pretty much everything, they are very fast to run, and quite reliable. For example, my workflow for synthesizing customer interviews uses whisper, a command-line tool that can transcribe any video file into text. 3. MCP servers - AI tools make it easy to connect to remote content through easily installed MCP servers. These exist for getting context from Google Docs, Notion, Slack, etc. So my workflow for catching me up on Slack leverages the Slack MCP server to scan the appropriate Slack channels and summarize the context. These generally work well, but if a CLI tool exists for the same data source, I generally prefer it now for speed and reliability. 4. APIs - If there isn't a CLI or MCP for the data source I'm interested in, I check if there is an API for that data source. And then I ask the AI tool to write code to access the API. This makes it so I can get my data from nearly anywhere, but it does take additional work to set this up, since I need to typically download API tools, ensure the AI has access to the latest documentation, and it can be buggy as well. So I only go down this route if I need to. For example, I recently I used the Gamma API to auto-generate a beautiful presentation for my NPS analysis workflow. 5. Browser agent - AI tools can also open and use a browser on your behalf. They can navigate to URLs, click links & buttons, as well as extract information from pages. This gives you ultimate data access even when there are no CLIs, MCPs, or APIs. However, this is the slowest and least reliable method. So I only turn to it when there are literally no other options. For example, I ended up using this to scrape competitor pricing pages to ensure I was getting the most up-to-date information. Next time you are building out an AI workflow, know that you have all five of these strategies at your disposal for getting the data you need.

From Copilot to Pilots: Introducing AFlow and the Evolution from Chat to Agents In the dynamic world of AI, we are witnessing a significant shift from traditional chat experiences to more sophisticated agentic workflows and autonomous agents. An AI agent is a system capable of autonomous action in an environment to meet its designed objectives. Unlike chatbots, which are primarily reactive and follow predefined scripts, AI agents can make decisions, learn from interactions, and adapt to new information. 🤖 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐯𝐬. 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐠𝐞𝐧𝐭𝐬 Agentic workflows completes tasks statically through predefined processes with multiple LLM invocations. Autonomous agents solves problems dynamically through flexible autonomous decision-making. Recent work aims to automate the design of agentic workflows by automated prompt optimization, hyperparameter optimization, and automated workflow optimization. 🆕 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐀𝐅𝐥𝐨𝐰 AFlow is an innovative framework introduced by Jiayi Zhang in an October 2024 paper, “AFlow: Automating Agentic Workflow Generation.” This framework automates the design of agentic workflows using large language models (LLMs), optimizing workflows through iterative refinement. AFlow represents a significant advancement in creating more efficient and adaptable AI systems. Empirical evaluations across six benchmark datasets (HumanEval, MBPP, MATH, GSM8K, HotPotQA, and DROP) demonstrate AFlow’s efficacy, yielding a 5.7% average improvement over state-of-the-art baselines and a 19.5% improvement over existing automated approaches. Additionally, AFlow enables smaller models to outperform GPT-4 on specific tasks at 4.55% of its inference cost. For more details, check out the AFlow paper on arXiv: AFlow: Automating Agentic Workflow Generation The AFlow paper on arXiv: https://lnkd.in/edc7iDmE For more details, watch the explanatory video on AFlow: https://lnkd.in/eDWFPgZZ Philippe Cordier Etienne Grass

The next wave of AI isn’t about prompts - it is about pipelines. Agentic AI is redefining how systems think, learn, and act autonomously. But most teams still treat agents as isolated bots instead of building living pipelines that connect reasoning, feedback, and deployment into one continuous loop. If you are serious about building scalable, self-improving AI systems, you need to master the Agentic AI Pipeline - from foundation to continuous optimization. Here’s how it works step-by-step: 1. Foundation Phase – Define your objective and assemble a multidisciplinary team. Clarify whether your pipeline aims for automation, reasoning, or decision-making. 2. Analysis Phase – Define agent roles (Planner, Executor, Evaluator), ingest and preprocess data with tools like AWS Glue or Azure Data Factory, and choose your cloud platform (AWS, Azure, or GCP). 3. Design Phase – Build conceptual models defining how agents interact. Use services like AWS SageMaker, Azure ML Studio, or GCP Vertex AI to design workflows and manage state for intermediate reasoning. 4. Implementation Phase – Start with low-fidelity prototypes, then scale using CI/CD pipelines. Integrate with Docker, Kubernetes, and apply IAM, key vaults, and data encryption for security. 5. Deployment Phase – Deploy multi-agent pipelines on AWS ECS, EKS, or Azure AKS. Use monitoring tools like CloudWatch and Azure Monitor for optimization. 6. Continuous Optimization – Implement feedback loops using LangGraph, Haystack, or AutoGen to let agents learn from outcomes. Continuously evaluate, scale, and refine models. Goal: Agents that think, adapt, and scale like living systems. Don’t just deploy models, design ecosystems. Because in the agentic era, intelligence isn’t built once - it evolves continuously. Follow Vaibhav Aggarwal For More Such AI Insights !

My Daily AI Workflow as a QA / SDET (Saves 2–3 Hours Daily) A lot of people comment “AI is powerful.” Few know how to actually use it daily. Here’s my practical AI workflow as a QA/SDET 👇 🧠 Step 1 — Requirement Breakdown (15–20 mins saved) Tool: ChatGPT I paste the user story and ask: ✅ Generate positive scenarios ✅ Generate negative scenarios ✅ Suggest edge cases ✅ Identify missing validations Result: Instead of manually brainstorming 25 test cases, I refine AI output. ⚡ Faster thinking, not blind copy-paste. 🧪 Step 2 — Automation Skeleton (30 mins saved) Using: GitHub Copilot Playwright I generate: ✅ Page Object structure ✅ Assertion patterns ✅ Mock test data ✅ Basic reusable methods ✅ Then I clean & optimize it. AI drafts. I architect. 🐞 Step 3 — Debugging Failures (Huge Time Saver) When a test fails: Instead of scanning 400-line logs manually: I paste: ✅ Error stack trace ✅ Relevant code snippet Ask: “What is the likely root cause?” Often it detects: ✅ Timeout misconfiguration ✅ Locator mismatch ✅ Missing await ✅ Wrong test data ⏳ 30–40 mins saved per failure. 🔁 Step 4 — CI/CD Optimization For pipeline improvements: ✅ YAML improvements ✅ Parallel config suggestions ✅ Docker tweaks ✅ AI gives structure → I validate feasibility. This improves release stability. 📊 Step 5 — Documentation & Communication Instead of spending 1 hour writing: ✅ Test summary ✅ Bug explanation ✅ PR comments Technical documentation I generate a structured draft and refine it. Clean. Professional. Fast. ⚠️ Important: What I DO NOT Do ❌ Blind copy-paste AI code ❌ Trust AI without validation ❌ Skip fundamentals ❌ Replace thinking with prompting AI is a multiplier. Not a replacement. 💡 The Real Difference Average QA uses AI occasionally. High-performing QA builds a repeatable AI workflow. That’s where productivity jumps.

𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗳𝗼𝗿 𝗔𝗜: A new INSEAD/Harvard field experiment with 515 startups proves that profitable AI isn't about faster tasks, but about redesigning entire workflows. Both groups got identical tech access. The only difference? One group learned how to remap their whole production process around AI. The results were staggering: 🔍 44% more AI use cases discovered across the value chain ⚡ 12% more internal tasks completed in the same timeframe 💰 𝟵𝟬% 𝗵𝗶𝗴𝗵𝗲𝗿 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 than the equally equipped control group 🏗️ 40% less capital needed to hit milestones, with zero change in headcount The takeaway: speeding up one step in a 10-step workflow barely moves the needle. The real gains come from removing handoffs, parallelizing work, moving humans to exceptions, and adding evaluation loops. AI isn't a technology problem, but a design and strategy problem. The control group had the exact same AI; they just didn't know how to map it onto their operations. 🛑 Stop asking "How can AI do this task faster?" ✅ Start asking "How would we redesign this entire process if AI were native?" Profitable AI = workflow redesign, not task optimization. My full article on how to redesign workflows for AI ���� https://lnkd.in/e_nbd8Gj

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?

I've watched countless AI demos with flashy interfaces fail in the real world. The winners? 𝗕𝗼𝗿𝗶𝗻𝗴 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝘀𝗼𝗹𝘃𝗲 𝗮𝗰𝘁𝘂𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. Take financial data extraction. The 𝗹𝗼𝘀𝗶𝗻𝗴 approach builds another generalized LLM wrapper with a beautiful UI. The 𝘄𝗶𝗻𝗻𝗶𝗻𝗴 approach utilizes small language models, business rules, and robust evaluation frameworks that are embedded directly into existing workflows. The difference is a 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗱𝗿𝗶𝘃𝗲𝗻 focus. Those "𝗯𝗼𝗿𝗶𝗻𝗴" solutions succeed because they involve 𝘀𝘂𝗯𝗷𝗲𝗰𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽. They understand the business rules. They build guardrails that actually work because humans who know the domain helped create them. This is what business-driven AI actually looks like in enterprise settings. It's not about building the most sophisticated model. It's about embedding the people who understand the problem into the solution itself. The most successful AI implementations prioritize workflow integration over technical sophistication. 𝗦𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 matter more than model size when you're solving real problems. The future belongs to AI builders who understand this. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗱𝗼𝗺𝗮𝗶𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗮𝗻𝗱 𝗵𝘂𝗺𝗮𝗻 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 𝗰𝗮𝗻 𝗰𝗿𝗲𝗮𝘁𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗮𝗽𝗽𝗲𝗮𝗿 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗶𝗻 𝗱𝗲𝗺𝗼𝘀 𝗯𝘂𝘁 𝗳𝗮𝗶𝗹 𝘄𝗵𝗲𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱. Business problem-driven builders will define AI's future because they know the secret: the best technology disappears into workflows so seamlessly that users forget they're using AI at all. What boring problem in your workflow needs an AI solution that actually works? #AI #EnterpriseAI #WorkflowAutomation #BusinessDriven #PracticalAI #AIImplementation ✍🏽 I share lessons learned from building AI systems in the field. Follow for more #AIexperiencefromthefield

Our AI workflows just helped Anne Klein Official drive a 50% lift in organic traffic. Here's the story: Anne Klein, a fashion staple since the 60s, came to us with a classic enterprise e-commerce challenge: They knew customers were searching for specific products like "polka dot skirts" or "professional women's blouses," but they couldn't create and optimize collection pages fast enough to capture that traffic. Before AirOps, their reality was: →Hours spent manually creating collection pages →Manual product selection that was error-prone →Inconsistent brand voice across collections →Missing seasonal trends due to lengthy production cycles The interesting thing about fashion e-commerce is that timing is everything. Miss a trend window, and you've left money on the table. Working with their team, we built a custom AI workflow that could: → Automatically score and select the right products for each collection → Generate on-brand descriptions at scale → Create occasion-based collections that aligned with shopper interests → Push everything directly to their Shopify store The results kind of blew us away: →95% faster content creation (days → minutes) →50% increase in collection page traffic →90% increase in indexed pages →Expanded coverage across their entire catalog The most valuable outcome for me was giving their team the ability to be responsive to market trends. As VP of e-commerce Gary Haas put it: "We can now launch collections the moment trends emerge, reaching more customers while maintaining our premium brand." What's even more exciting is that Anne Klein Official is now extending these same workflows to their product detail pages (PDPs). That's the pattern we keep seeing with our most successful customers. Start with one high-impact use case, prove it works, then expand. What could your brand do if content creation was 95% faster? I'd love to hear your thoughts 👇

Turning AI Anxiety into Advantage: A Practical Guide 🎯 The AI revolution isn't abstract—it's already transforming how we work. Here's your concrete roadmap to mastering AI integration: 1️⃣ Build Your AI Testing Lab Create a personal sandbox environment where you can safely experiment. Start with: • Setting up ChatGPT plugins for your specific workflow • Testing GitHub Copilot if you're in development • Using Claude for complex analysis and writing tasks 2️⃣ Map Your AI Leverage Points Audit your weekly schedule and identify: • Tasks that take >2 hours but could be automated • Repetitive processes that drain your creativity • High-value work that could be enhanced with AI assistance 3️⃣ Master AI-Human Collaboration Learn the art of prompt engineering: • Write structured prompts that generate usable outputs • Break complex problems into AI-solvable components • Develop systems to verify AI-generated work efficiently 4️⃣ Create AI-Enhanced Workflows Build processes that combine AI tools: • Use AI for initial research, human insight for synthesis • Implement AI-powered quality checks in your deliverables • Design feedback loops where AI learns from your corrections 5️⃣ Measure and Optimize Impact Track concrete metrics: • Time saved per task • Quality improvements in outputs • New capabilities unlocked 🔍 Reality Check: The goal isn't to use AI everywhere—it's to identify where AI multiplication creates the highest value in your specific role. 📈 Next Step: Choose one process you'll enhance with AI this week. Start small, measure results, and iterate based on real outcomes. #AIStrategy #WorkflowOptimization #ProductivityTech #AITools #ProfessionalGrowth #USAII  United States Artificial Intelligence Institute