Scheduling with AI and Machine Learning

I spend a huge part of my week just managing my calendar — finding free slots, rescheduling meetings, dealing with recurring events, and juggling multiple time zones. It’s tedious and eats into real work. That’s why I decided to build my own solution: a Google Calendar AI agent powered by Google’s Agent Development Kit. This agent can: 👉 Understand plain English commands like “Schedule a 1-hour call with Alex next Tuesday morning”. 👉 Suggest free time slots based on my existing calendar. 👉 Handle recurring events, cancellations, and attendees automatically. 👉 Work across time zones without any manual conversion. While building this, I learned something crucial: AI isn’t just about generating text — it can actually perform actions that solve real problems. Designing this agent taught me how to bridge natural language understanding with real-world API actions. I wrote a detailed step-by-step blog, including code snippets and logic, so anyone can replicate this setup or build their own AI productivity assistant: https://lnkd.in/dsDhtcMr #AIAgents #AgentDevelopmentKit Google Cloud #GoogleAI #GoogleCalendar #CalendarManagement #AgenticAI

Time is the most expensive line item we never budget. So I gave AI 90 days of my calendar. I asked, “Where am I wasting my time and payroll?” It found the leaks. I closed them. These 9 shifts returned 15 hours/week and sharper decisions: 1. Meeting Transcription + Action Mining Old way: 45-minute meeting, 30 minutes writing notes after New way: AI captures everything, extracts actions in 2 minutes Tools I use: → Otter.ai records and transcribes → Claude analyzes for decisions and commitments 💡 You need actions, not archives. 2. Email Triage on Autopilot Old way: Start day with 47 emails, lose 90 minutes New way: AI pre-sorts, drafts responses, flags only what needs me My setup: → Superhuman AI categorizes by urgency → ChatGPT drafts routine responses 💡 80% of emails don't require leadership thinking. 3. Calendar Defense System Old way: Back-to-back meetings, zero thinking time New way: AI blocks focus time based on energy patterns What changed: → Reclaim.ai analyzes my productivity patterns → Automatically blocks deep work when I'm sharpest 💡 Your calendar reflects your priorities. Let AI be the bouncer. 4. Decision Documentation Old way: Decisions made, context lost, repeated discussions New way: AI creates decision logs with full context The system: → Every decision recorded with why, who, when → Searchable knowledge base 💡 Leaders waste 5 hours/week revisiting old decisions. 5. Prep Work Automation Old way: 30 minutes prep per meeting New way: AI briefs me in 3 minutes AI handles: → Participant background summaries → Related past decisions 💡Most prep work is information gathering, not strategic thinking. 6. Real-Time Coaching Notes Old way: Try to remember feedback for 1:1s New way: AI captures moments as they happen My process: → Voice note immediate observations → 1:1s have rich, specific examples 💡 The best feedback happens in the moment. 7. Strategic Thinking Amplification Old way: Brainstorm alone or in groups New way: AI as thought partner How I use it: → Feed challenge into Claude/ChatGPT → Get 10 perspectives I hadn't considered 💡 AI doesn't replace thinking. It accelerates it. 8. Delegation Optimization Old way: Guess who's best for what New way: AI matches tasks to skills/capacity The system tracks: → Team member strengths → Development opportunities 💡 Bad delegation costs 3x more time than doing it yourself. 9. Energy Management Old way: Push through energy dips New way: AI optimizes task-energy matching What AI revealed: → My peak decision hours: 9-11 AM → Creative energy: 2-4 PM 💡 Working against your energy patterns is counterproductive. The result? I stopped being busy and started being strategic. Most leaders are drowning in tasks AI could handle in seconds. They're so busy being busy, they can't lead. ♻️ Repost if a leader needs to see this. Follow Carolyn Healey for more AI insights.

10^30000 scheduling combinations. 50 hours per week in Excel. If you've lived inside traditional WFM tools, you know this headache. Assembled's new AI-powered Schedule Generation does it in minutes. Here's the breakdown: 1,000 agents. 5 shifts each. 8 hours per shift. That's 5,000 shifts to schedule. Each shift needs: One productive event (chat, email, or phone). Two breaks. One lunch. One meeting. Discretize 8 hours into 15-minute blocks and you get 32 options. For non-productive events alone: 32 × 31 × 30 × 29 / 2 = 431,520 combinations per shift. Multiply by 3 productive event options. 1,294,560 combinations per shift. Now do that for 5,000 shifts. (10^6)^5000 = 10^30000. That's a number with 30,000 digits. At 2,000 digits per page, it takes 15 pages just to write it out. The “nurse scheduling” problem is a classic NP-hard problem. This is what workforce managers are solving with spreadsheets. Assembled's AI-powered Schedule Generation feature handles this in minutes. Agent needs Thursday off for a doctor's appointment? Old way: Submit request. Wait for approval. Hope it doesn't conflict. Assembled's way: Integer linear programming for coverage optimization. Constraint programming for breaks, lunches, and labor law compliance. Decomposition to break 34,000 weekly shifts into 50 parallel subproblems. 2 hours becomes 10 minutes. Agents can also browse available swaps directly in the system. AI ensures swaps follow your rules: Matching skills Queue compatibility Channel requirements. Our schedule Layers prevent coverage gaps entirely. It has three intelligent layers: Productive work Meetings/breaks Time off. When a training cancels, productive work surfaces automatically underneath. One global payments company told us: "This replaces our hideous spreadsheet where we export schedules just to flag compliance issues. Programming rules directly in is chef's kiss." AI handles 10^30000 combinations. Managers can now handle strategy. Kudos to the team on this big, NP-hard launch. Antony Phillips, Claire D., Jack Gleeson, Malfy Das, Nicole Pan, Zach Clark, Chancie(Qianshi) Zheng, Charlie Rotholtz, David Patou, Devon Berger, Todd Bergman, Dan Hertz

Appointment scheduling is an unglamorous, under-discussed yet prime area for harnessing AI. Think of it as a matching problem. How do we schedule patients with the right clinicians, at the right time, at the right place, and with the right concurrent services for their specific needs? However, assigning patients to the right pathway can be painstaking. Sometimes, non-clinical staff (or sometimes nurses) sift through long records to find the information needed to make scheduling decisions. Other times, patients are simply scheduled haphazardly. This is especially challenging in the UK, where patients are routinely placed on very long waiting lists, and some deteriorate while waiting for their appointment. Here, The Times explains how C2-Ai’s system reviews waitlists to identify patients to prioritize for sooner care and/or who need coaching before surgery. The company reports impressive results on its website (e.g., 99% clinician agreement, 8% reduction in emergency admissions, 125 saved bed-days per 1,000 patients, and five minutes saved per patient triage). This is a very pragmatic, valuable AI use case. I see clear opportunities to apply AI both earlier and later in referral processing and scheduling workflows. First applying a blend of AI to process referrals and guide scheduling decisions (avoiding wait lists when possible + necessary). Later, applying Gen AI to create referral/patient summaries for clinicians to quickly learn about who they are about to see. Though this may not be as exciting as AI for diagnosis or as widely discussed as AI for tasks like note writing, it’s quite practical, attainable, and impactful.

Scheduling in Kubernetes happens in various ways. Depending on the workload, you might need different algorithms like 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴. Volcano, a CNCF project, supports this and can optimize complex workflows such as AI training, inference pipelines, and distributed data processing.  🚀 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴? Gang scheduling ensures all pods in a group ("gang") start simultaneously or none do. This prevents partial execution, which is critical for interdependent tasks like distributed training or multi-stage AI pipelines. Without it, a single delayed pod could stall an entire workflow, wasting resources. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: In distributed AI training, if three worker pods are needed, Volcano’s gang scheduler waits until all 3 are available. If even one fails to schedule, the scheduler releases reserved resources to avoid cluster deadlocks. ⚡ 𝗪𝗵𝘆 𝗩𝗼𝗹𝗰𝗮𝗻𝗼? Volcano extends Kubernetes’ default scheduler to handle batch workloads and multi-pod dependencies. It’s ideal for: → AI/ML workflows (e.g., TensorFlow/PyTorch jobs). → Big Data processing (Spark, Flink). → High-performance computing (HPC). Key features: ✅ PodGroup orchestration: Treats multiple pods as a single schedulable unit. ✅ Fair-share resource allocation: Balances cluster resources across teams. ✅ Preemption/Reclaim: Prioritizes critical workloads without manual intervention. 🌟 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Imagine training a large language model (LLM) across 3 GPUs. With gang scheduling: → Volcano groups all worker pods into a PodGroup. → The scheduler reserves resources only when all 3 GPUs are available. → If a node fails, Volcano retries or releases resources instantly, avoiding idle clusters. This eliminates "resource hoarding" and ensures cost-efficient scaling for AI teams. #Kubernetes #mlops

Hi #Planners ,, I'm a Project Controls Engineer Who Just Entered the Data Science Field. Here's What Actually Works. I’ve spent the last 6 months living a double life: building P6 schedules during the day, training ML models at night. Not because of some career shift — I simply entered this field through my university study. I’ve always loved numbers, and I enjoy looking behind the scene to understand why things really happen in a project. Everyone in construction talks about AI. Nobody shows you how to apply it to your P6 Schedule. That's what this series is about. I'm not going to bore you with "the future of construction" fluff. I'm going to show you exactly what I learned — the hard way — about making machine learning predict schedule slippage, flag risky change orders, and find patterns in your historical data that you didn't know existed. First lesson: Linear regression is your best friend. It's not sexy, but it's the foundation of everything else. I'll show you how I used it to prove that procurement activities with >10 days float are basically schedule poison (78% late finish rate). What I got wrong early on: I thought I needed fancy algorithms. Turns out, 90% of the value comes from cleaning your P6 export. Garbage in, garbage out. My goal here: give you a foundation to start using ML on your own schedules. No PhD required. Just Python, P6, and the willingness to question your baseline logic. Next post: Linear Regression on P6 data — the one algorithm that will make your project team take your warnings seriously. #MachineLearningForPlanners #AIinConstruction #ProjectControls #PrimaveraP6 #ConstructionAnalytics #DataScienceForConstruction #PredictiveScheduling #AIAutomation #PlanningEngineer

After years of working in both machine learning and optimization, especially in large-scale automotive and supply chain applications, I’ve come to appreciate their differences, and more importantly, how they complement each other. Machine learning is about predicting what’s likely to happen. It shines when you need to estimate demand, classify outcomes, or forecast patterns. It learns from data and captures complexity in ways that are hard to hand-code. Operations research, on the other hand, is about deciding what to do. It helps you find the best plan under constraints. Whether it’s allocating inventory, routing trucks, scheduling labor, or selecting vehicle builds, optimization focuses on making the best possible decision given what you know. The confusion happens when teams try to solve a decision problem with only ML or treat optimization like it can predict behavior. In reality, the power comes from using them together. Here’s how I think about it: 👉 Use machine learning to estimate unknowns: customer preferences, demand forecasts, delay probabilities, etc. 👉 Feed those predictions into an optimization model to make constrained, tradeoff-aware decisions 👉 If you’re building a recommender, for example, ML helps rank items based on learned behavior, while optimization ensures feasibility, budget alignment, and product compatibility ML answers: What will likely happen? Optimization answers: Given what I know, what should I do? Used together, they form a decision intelligence system: informed, adaptable, and grounded in real-world constraints. The future of business isn’t just AI or OR. It’s the thoughtful combination of both. #OperationsResearch #Optimization #MachineLearning #DataScience

AI May Not Steal Your Job—But It Might Save Your Sanity Microsoft’s 2024 Work Trend Report found 64% of developers using AI assistants like GitHub Copilot reported lower stress levels. Even better? GitHub’s internal data showed AI tools cut after-hours work by 11 hours/month. The narrative that AI fuels burnout is outdated—if you wield it right. Real Teams, Real Wins: -- Atlassian’s "Focus Mode" uses AI to auto-block Slack/email notifications during deep work blocks. Engineers regained 9 hours/week. -- Shopify’s AI scrum master analyzes sprint velocity to recommend meeting cuts. Teams slashed standups by 40%. -- Google’s Project Ellmann answers "Why was this code written?" via commit history, cutting onboarding time by half. Actionable Steps (No Code Required): 1. Automate the Mundane Use tools like reclaim.ai to auto-schedule focus time, buffer meetings, and block burnout triggers (e.g., 8 AM meetings after late-night deploys). 2. Let AI Say "No" For You Train ChatGPT to draft polite pushbacks on low-priority requests. Example prompt: "Write a response deferring this task without damaging the relationship. Tone: collaborative but firm." 3. Run a "Stress Audit" Plug your calendar/email into Microsoft Viva Insights. If >25% of your time is spent in reactive mode (putting out fires), AI isn’t the problem—your workflow is. #AI #Productivity #WorkplaceWellbeing #Tech #FutureOfWork

The AI-RAN Taking Shape I'm thrilled to announce our latest research contribution that fundamentally transforms how we design, deploy, and test key functionalities of cellular networks. Our new paper "ALLSTaR - Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN" represents three major industry firsts: ⚡ First-Ever Automated Scheduler Generation: We've developed LLM agents that automatically convert research papers into functional code, generating 18 different scheduling algorithms directly from academic literature using OCR and AI. No more months of manual implementation in ns-3 or Matlab! Automatically generated schedulers are automatically deployed in a live network as dApps through a CI/CD pipeline - without the need to change a single line of code in the gNodeB implementation (CU or DU);  ⚡ Intent-Based Scheduling: Network operators can now express high-level requirements in natural language ("prioritize users with bursty traffic") and ALLSTaR automatically translates these into optimized scheduling policies according to operator’s intent. ⚡ World's First O-RAN Compliant AI-RAN Testbed: All validation conducted on X5G with AutoRAN, production-grade, multi-vendor 5G infrastructure with GPU acceleration, AI-for-RAN and AI-and-RAN capabilities, demonstrating real-world viability at scale. This work also introduces a methodological paradigm shift: instead of implementing one algorithm at a time, we can now systematically evaluate a vast body of scheduling literature in production-like environments. We're moving from manual, months-long integration processes to automated, intent-driven networks that adapt in real-time. This is the Open RAN and the AI-RAN vision - and a pathway toward 6G that builds on our national strengths and open ecosystem. Full paper: https://lnkd.in/eTNWPNRR Open6G www.open6g.us #ORAN #AIRan #OpenRAN #5G #WirelessResearch #AI #MachineLearning #Telecommunications #Research Our brilliant team: Maxime Elkael Michele Polese Reshma Prasad Stefano Maxenti Office of the Under Secretary of Defense for Research and Engineering NSF AI-EDGE Institute National Telecommunications and Information Administration (NTIA) Qualcomm

I used AI to build an app to predict what classes I'd get from Wharton's course scheduling system, and it's now helping over 1000 of my classmates. The Wharton School has a unique course algorithm called Course Match. Every semester, each student expresses their class preferences on a 0-100 scale. The system then balances the supply and demand for each class and attempts to build the best schedule for each student. It's incredibly powerful... but there's a catch: students have no way to predict what their preference inputs will actually get them. As a result, students often struggle to express their preferences and many are disappointed by the schedules they receive. I heard this from hundreds and hundreds of students. I decided to solve this problem, first for myself and then for everyone else. CourseCast was born! With limited coding experience, I did what every good MBA does: I recruited a team to build it for me. Except the team I recruited is a little unconventional: ChatGPT as my data scientist, Claude as my system architect, and Cursor as my lead developer. Together, they allowed me to think deeply about the problem rather than learning each of these skills from scratch. The project evolved from a simple Excel model to a full web application. Here's how it works: → Predicts class prices based on historical data using machine learning → Solves an optimal schedules with mixed integer programming → Incorporates uncertainty by simulating schedules many times The ultimate output is the probability of receiving specific classes and the likelihood of receiving entire schedules, given your preferences and uncertainty. If you change your preferences, you get immediate intuition about how this impacts your likely schedules! In a little under one week after launch, over 1000 students (around 65% of Wharton) used CourseCast to plan their spring schedules. The feedback has been incredible, with many students saying they finally received schedules they are happy with. We're living in a time where you have incredible agency to solve problems you care about using AI. And chances are, if something frustrates you, it frustrates others too. Take action — you might just help thousands of people along the way! The 1 Minute MBA 🎓