Automation In Project Management

Reflecting on Agile Development with DevOps 2.0: A Flexible CI/CD Flow Last year, I shared a CI/CD process flow for Agile Development with DevOps 2.0, and it’s been amazing to see how much it resonated with the community! This framework isn’t about specific tools—it’s about creating a seamless, collaborative process that supports quality and agility at every step. ✅ 𝗣𝗹𝗮𝗻: Building a Strong Foundation with Clear Alignment The journey begins with planning—whether it's user stories, tasks, or broader product goals. Tools like JIRA or Asana (or any project management platform) help capture requirements and align the team with the Product Owner’s vision. This early alignment is essential to avoid misunderstandings and establish a shared understanding of success. Key Insight: Planning thoroughly and involving stakeholders from the start leads to a smoother process. When everyone’s on the same page, the entire pipeline benefits. ✅ 𝗖𝗼𝗱𝗲: Collaborative Development and Real-Time Feedback In the coding phase, developers work together, often pushing code to a version control platform like GitHub or Bitbucket and communicating via real-time collaboration tools like Slack or Teams. Open communication and continuous feedback help catch issues early and keep the team in sync. Key Insight: Real-time feedback is crucial for speed and quality. Regardless of the tools, creating a culture of continuous collaboration makes all the difference. ✅ 𝗕𝘂𝗶𝗹𝗱: Automating Quality and Security Checks As code is committed, it’s essential to automate quality and security checks. Tools like Jenkins, CircleCI, or any CI/CD platform can trigger builds and run automated tests, ensuring that quality checks are consistent and fast. This step helps prevent issues from creeping into production. Key Insight: Automated checks for quality and security are invaluable. Integrating these checks into the build process improves confidence in every deployment. ✅ 𝗧𝗲𝘀𝘁: Structured, Multi-Environment Testing Testing is layered across environments—whether it’s regression, unit, or user acceptance testing (UAT). Using frameworks like Selenium for automated testing or dedicated QA/UAT environments enables rigorous validation before production. Key Insight: Testing across environments is a safeguard for quality. Structured testing helps ensure that code is reliable and ready for release. ✅ 𝗥𝗲𝗹𝗲𝗮𝘀𝗲: Scalable, Reliable Deployments with Infrastructure as Code (IAC) Finally, using Infrastructure as Code (IAC) principles with tools like Terraform, Ansible, or other IAC solutions, deployments are made repeatable and scalable. IAC empowers teams to manage infrastructure more efficiently, ensuring consistent and controlled releases. Thank you to everyone who has engaged with this diagram and shared your insights! I’d love to hear how others approach CI/CD. Are there any tools or strategies that have worked well for you?

Everyone talks about agentic AI. No one shows you how to structure a production AI application from scratch. Here's the 9-layer architecture I'd follow. 1. Data Layer ↳ Ingestion pipeline (extract, clean, deduplicate, store) ↳ Chunking service (strategy depends on your content type) ↳ Embedding pipeline (batch indexing + incremental updates) ↳ Vector database with hybrid search (dense + sparse) 2. Retrieval Layer ↳ Query preprocessing (rewriting, expansion, decomposition) ↳ Hybrid retrieval (semantic + keyword) ↳ Reranking (cross-encoder second pass for precision) ↳ Source filtering (metadata, file-level, domain-level) 3. Memory and State ↳ Conversation memory (sliding window or summary) ↳ Session management ↳ Semantic cache (embed queries, serve cached answers for similar questions) 4. Routing and Classification ↳ Intent classifier (what kind of question is this) ↳ Query router (which retrieval path, which prompt template) ↳ Confidence-based fallback logic 5. Generation ↳ Prompt templates (structured per query type) ↳ Prompt registry (versioned, swappable without redeploy) ↳ Grounding rules (cite sources, handle insufficient context, abstain when needed) ↳ Streaming (real token-by-token SSE, not buffered) 6. Evaluation and Quality ↳ Golden test set (bootstrapped, grown from real failures) ↳ Offline evaluation pipeline (run on every change) ↳ Online monitoring (sampled LLM-as-judge on production traces) ↳ Document grading (system checks retrieval quality before generating) 7. Security ↳ Input validation (prompt injection detection) ↳ Retrieved content filtering (poisoning detection) ↳ Output filtering (PII, credentials, sensitive data) 8. Observability ↳ Per-stage tracing (see where each query spent time and failed) ↳ User feedback capture (linked to traces) ↳ Cost per query tracking 9. Infrastructure ↳ Backend API (async, streaming capable) ↳ Frontend (containerized separately) ↳ Docker Compose for local, cloud configs for deploy ↳ Setup scripts (environment, indexing, dependencies, smoke tests) A production AI app is not an LLM call. It's a system with data, retrieval, memory, routing, generation, evaluation, security, observability and infrastructure all working together. ____ 👋 P.S. If you want to build a system like this from scratch, on your own domain, your own data, with evaluation, security and production infrastructure baked in from the start, the Engineer's RAG Accelerator covers all 9 layers hands-on. 50+ engineers from Microsoft, Adobe, Amazon, Shopify and Visa just did exactly that. The next cohort starts in April -> [Visit my website] to register ♻️ Repost to help someone think beyond the tutorial.

Automation, AI workflow, or AI agent? To always 𝘬𝘯𝘰𝘸 𝘸𝘩𝘪𝘤𝘩 𝘰𝘯𝘦 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥, follow this 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬: Remember when I explained why many "𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴" shared on LinkedIn are actually 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 or 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 in disguise? Turns out: understanding the difference is only partially helpful. The real challenge is knowing 𝘸𝘩𝘪𝘤𝘩 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦. So I built this framework to help you decide. There are 6 key dimensions to consider - working in pairs: 𝐏𝐚𝐢𝐫 #1: 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 ↔️ 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐯𝐨𝐥𝐯𝐞𝐦𝐞𝐧𝐭 aka. how decisions are made - and how much human intervention is required: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: You make ALL decisions upfront when designing your automation, which means that no human intervention is needed after. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: You set boundaries for the AI to operate within; humans occasionally review outputs or intervene when the system encounters edge cases. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: You set high-level goals, and AI determines its own path; this means humans need to provide ongoing feedback to ensure it makes the right decisions. 𝐏𝐚𝐢𝐫 #2: 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ↔️ 𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 a.k.a which type of data the system should process - and how adaptable it has to be: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Requires strictly predefined data formats with no deviation; breaks when encountering unexpected inputs and needs to be re-engineered when processes change. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Handles mostly structured data with some variability allowed; can adjust to parameter variations within defined parameters but needs guidance for significant changes. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Processes diverse unstructured data across multiple sources with varying formats; independently adapts to different inputs and shifting environments without reprogramming. 𝐏𝐚𝐢𝐫 #3: 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 ↔️ 𝐑𝐢𝐬𝐤 𝐓𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞 a.k.a how predictable the outcomes must be - and what level of risk is acceptable: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Delivers highly consistent, predictable results every time; ideal for mission-critical processes where errors cannot be tolerated and predictability is essential. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Produces mostly reliable outcomes with occasional variations in edge cases; balances flexibility with guardrails to prevent major errors while allowing some adaptability. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Creates outcomes that can vary significantly between iterations; optimized for scenarios where discovering novel approaches and adaptability outweigh the need for consistent results. How to use this framework: Always 𝘴𝘵𝘢𝘳𝘵 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘭𝘦𝘧𝘵 and move right only when necessary. 1. Start with automation 2. Move to AI workflows when you need more flexibility within guardrails  3. Only move to agents when you need high adaptability Don’t fall for the AI agent hype - most processes can be automated without agents.

I Built the Ultimate Army of Media Agents in n8n My original “Ultimate Assistant” agent was designed to handle the essentials, emails, calendars, contacts, etc., but this time, I took it much further. Now, the system includes an entire media agent stack: → Creative Agent: Create images, edit them, generate videos, turn images into videos. → Posting Agent: Automatically post content across multiple social platforms. → Social Media Agent: Research content on YouTube, Instagram, and TikTok, then compile insights into a Google Doc and send you the link instantly. → Asset Manager: Organize, rename, share, and send files through Google Drive. This system also come with complete visibility into every action it takes, inputs, outputs, token usage, successes, and errors, so you can see exactly what’s happening and continuously refine it. In my latest YouTube video, I walk through real examples of how it works, how it’s built, and I’m sharing all the workflows, templates, and resources for free so you can set it up in your own n8n instance and start experimenting right away. Link to the full video in the comments 👇

Last quarter, I worked with the MD of a heavy equipment manufacturer who believed AI would make status reports clearer and give leadership better visibility into project progress, but while the dashboards improved and the data looked sharper, the actual profit margins did not improve because delays were still being identified too late to prevent cost overruns. By the time problems appeared in reports, the financial impact had already occurred, and in 2026, with tighter compliance requirements and thinner operating buffers, that delay between issue and action is no longer affordable. What has truly changed is not reporting quality but execution speed, because AI systems can now reallocate resources, adjust schedules, and flag bottlenecks immediately instead of waiting for weekly or monthly review cycles; in plant upgrade programs and supplier transitions, I have seen problems addressed at the point of occurrence rather than after escalation. When corrective action happens closer to where the issue starts, delivery risk declines and cycle times shorten, since decisions are triggered by live data rather than by meetings or manual coordination. The main weakness I continue to see is governance, because many AI agents operate on fragmented data sources without clear ownership of decision rights, which leads teams to override outputs they do not trust and reintroduce manual controls that slow everything down, creating a false sense of stability where dashboards remain green but margin pressure builds quietly underneath. Two mistakes appear repeatedly. The first is treating AI as an advanced reporting layer, because manufacturing projects depend on operational control rather than visibility alone, and insight does not prevent delay unless the system is allowed to act within clearly defined boundaries. The second is deploying AI without defining who owns the decisions it influences, because manufacturing plants rely on accountability structures, and when escalation paths are unclear, agents can create conflicting actions that slow adoption and reduce confidence across teams. If you are beginning this journey, start by mapping a single workflow where approvals consistently delay progress, such as change requests during shutdown planning, and introduce AI only where decision rules are already stable and measurable, while avoiding areas that depend on negotiation or human judgment.  #AIInProjectManagement #AgenticAI #ExecutiveLeadership #FutureOfWork #OperationalExcellence0 #DecisionIntelligence #EnterpriseAI #ProjectGovernance #DigitalTransformation #AIForCEOs #BusinessExecution #AIStrategy

AI is changing the way digitizing is done in GIS 🤖🗺️ The AI Segmentation plugin for QGIS, developed by TerraLab, enables users to segment buildings, vegetation, or other objects directly from raster imagery and export the results as vector polygons in just a few clicks. ✔️ Point-and-click AI segmentation ✔️ Works with drone, aerial, and satellite imagery ✔️ Automatic model setup and fast processing ✔️ Interactive refinement for precise boundaries A practical step toward faster and smarter feature extraction in modern GIS workflows. 🔗 plugin link: https://lnkd.in/dMSZdZ7T 🔗 Tutorial link: https://lnkd.in/d4JGyZwj #GIS #QGIS #RemoteSensing #AI #Geospatial #Cartography #SpatialAnalysis #map #data #freedata #remotesensing #geography #hydrology #automation #process

Most people think having a human approve an AI decision means the decision is safe. It does not. 👀 There is a term for what actually happens when humans rubber stamp AI outputs under time pressure. Automation bias. It is one of the most documented and underreported risks in enterprise AI right now. After 13 years and 200+ deployments, here is what I have learned about building genuine oversight into AI systems. The human reviewing an output needs three things to actually be in the loop. They need to understand what they are reviewing. They need the context to catch what the model gets wrong. And they need to be genuinely empowered to say no without institutional pressure to simply keep moving. Most organisations have none of those three in place. They have a signature process. That is not the same thing. Before any high-stakes AI output reaches a decision point in your organisation, ask these questions. ➡️ Does the person approving this understand the underlying data well enough to catch an error? ➡️ Is there time built in for genuine review or just enough time to click approve? ➡️ What happens if someone says no? Is that genuinely supported? If the answer to any of those is no… you do not have human oversight. You have automation bias with a human signature attached. What does genuine human oversight look like in your organisation right now? #ai #leadership #futureofwork #artificialintelligence #aistrategy #teamhuman #intellectualatrophy #criticalthinking

Humans found 8 units. AI found 16. Same site, same zoning. Here’s what you need to know: Last year I built Asimov Partners as a thought experiment. A real estate development company. Zero employees. Just AI. It's now becoming reality faster than I expected: New companies are automating feasibility analysis. Turning deal flow upside down. And traditional developers are scrambling to keep up. Here's what's really happening in automated development: This isn't about replacing people. It's about reimagining the process: Most folks see: • Cool AI tools • Faster analysis • Nice efficiency gains I see: • Phase one automation • Competitive moats • Deal flow multiplication The secret? Focus on feasibility. Two approaches are winning: Full-stack platforms like Algoma: • Complete feasibility suites • Zoning + financials + design • One integrated workflow Discipline-specific tools like Cove: • AI-first architecture firms • Deep expertise in complex codes • Human verification at scale Pick your approach based on market complexity. The jurisdiction makes or breaks you: Ask these first: • "Is this ministerial or discretionary?" • "How many overlays apply here?" • "What's the real approval process?" Not these: • "What's the fastest tool?" • "Who's cheapest to hire?" • "Can we automate everything?" The winning formula: You need: • Right tool for your market • Human oversight for verification • Local relationships for deal flow Don't think: • One size fits all markets • AI replaces human judgment • Technology solves politics The analyst role is transforming: From Excel jockeys to: • Technical system managers • Automated funnel operators • Qualitative deal investigators When Algoma raises $2.3M and Cove's AI finds 16 units where humans found 8? You know feasibility automation is real. The companies cracking this aren't just building tools. They're rebuilding how development works from the ground up. The question isn't whether AI will transform feasibility analysis. It's whether you'll adapt before your competition does. Drop me a line if you want to chat more about this. Full letter in the comments.

Are you still wasting time collecting site data from 5 different portals? Building footprints from one source. Wind patterns from another. Topography? Probably buried in a PDF somewhere. It’s 2025, and with AI tools around, we shouldn’t be spending hours stitching datasets together just to start a design. I use Aino to cut through the noise and get clean, reliable data fast. Here’s what makes it work so well for site studies: 👉 Building footprints and building use mapped in seconds 👉 Adjustable building heights visualised in a gradient 👉 Real-time wind movement overlays 👉 Street network identified and simplified 👉 Topography with contour clarity 👉 Open spaces sorted into categories My favourite features: ✅ Traffic Heatmaps ↳ See where bottlenecks occur and plan circulation with confidence. ✅ Clip and Export ↳ Crop any area and export in PNG, SVG, PDF, or DXF for design workflows. With Aino, you spend less time on data chaos and more time designing with clarity. Want to see how it works in real projects? I’ve added a short tutorial video below.

AI changed the PM role as we know it. PMs who adapt will be in high demand. The PM role has been about process management. But a new group of project managers is emerging. They're using AI to speed up admin work, and turning their focus to strategic leadership. Most of what project managers do manually today is exactly what AI tools are getting good at: - Status report generation - Project planning and scheduling - Budget tracking and forecasting - Risk monitoring and alerts - Team capacity planning AI can already automate these PM tasks. The technology will only get better. I recently spoke with several executives. They are already moving basic PM tasks to AI. When AI can generate project plans, track budgets, and monitor risks automatically, what happens to the old-school PM role? Simple projects won't need dedicated PMs anymore. AI will handle the basic administrative PM work. We're already seeing this change. But there's a big opportunity here too. AI has a major blind spot. It can't figure out the tricky psychology of team dynamics. It can't handle complex stakeholder politics. It can't connect business goals to what motivates the team. AI can't lead and handle people's problems. → Simple projects: PM roles mix into other roles → Complex enterprises: Strategic PM roles become key Most valuable projects are complex and people-focused. Want to stay relevant? Here's what to think about. Learn how to handle team dynamics ↳ Navigate politics, egos, and conflicting priorities Master stakeholder management and communication ↳ Make sure everyone agrees on what success looks like Study how turn business goals into team motivation ↳ Work with people to get them excited about the project Direct AI with human context ↳ Give AI the right instructions and priorities to work with The PM industry isn't dead. But it is changing. ♻️ Share this to help other project managers. Follow me Alex Barady and my company ENDGAME for pragmatic AI strategies and execution.