🚀 How does enhancing developer experience impact business growth? Industry experts Kaushik Chaubal from BlackRock, Karen Bullen from Capgemini Invent, and Hardeep Bath from Harness discuss the core elements of developer experience that should matter to every business: ⚡ Experimentation → sparks creativity and new ideas 😃 User Experience → content engineers lead to productive teams 📈 Outcome-Based Culture → focus on results, not just actions 🤖 Automation → remove delays and increase speed The key takeaway is that prioritizing developer experience is essential for business success. 👉 Check out insights from leaders at BlackRock, Capgemini, and Harness on the evolving landscape of DevOps. https://lnkd.in/enAPg7nA #DeveloperExperience #Innovation #DevOps #Automation #Harness #CloudTransformation What are you doing to improve your developer experience?

🚀 Why automate your DevOps pipeline? Back in the 2000s, manual deployments meant developers spent long weekends fixing broken configurations and chasing errors in production. Fast forward to today — there’s a far better way. ✅ Speed: Automated pipelines cut deployment time from days to minutes. According to DORA research, teams with automation release 208x more frequently and 106x faster than low performers. ✅ Quality: Automated tests run with every release, catching bugs early. Gartner reports that 90% of companies see improved product quality thanks to deployment automation. ✅ Cost efficiency: Instead of wasting expensive engineering time on repetitive manual tasks, automation frees developers to focus on building features. Deloitte notes automation can cut spend by up to 30% while boosting productivity by 65%. In 2025, the difference is clear: Companies with automated DevOps pipelines ship features daily. Those without are still planning releases. 👉 Curious how to make deployment headaches a thing of the past? Read our new blog: https://lnkd.in/dY5AEevk #DevOps #Automation #CICD #CloudComputing #SoftwareEngineering #DigitalTransformation #SoftwareDelivery #TechTrends #EngineeringExcellence

Last week’s #IBMTechXchange conversations made one thing clear — DevOps is evolving faster than ever. From labs to hallway chats, teams are rethinking how they deliver software in an AI-augmented world. Here are our top 3 takeaways: 1. Intelligent automation is the new baseline. It’s not just about faster pipelines — it’s about smarter ones. The sessions on AI-assisted DevSecOps showed how intelligent gating and anomaly detection are becoming core to delivery. 2. Visibility across the value stream is the real differentiator. Without a clear view from idea → code → release → feedback, even the best automation hits bottlenecks. Connecting insights across hybrid environments helps teams deliver with confidence. 3. Policy-based governance keeps scale sustainable. As teams grow, security and compliance need to move with them — baked into workflows, not bolted on. The best pipelines now blend speed and control seamlessly. These themes came to life in the sessions around DevOps Loop — showing how integrated AI, end-to-end visibility, and policy automation can unlock higher velocity without sacrificing trust. What was your biggest takeaway from TechXchange? Explore how DevOps Loop helps teams turn those insights into action — with AI-driven automation, built-in governance, and clear visibility across the delivery loop. https://lnkd.in/ecYRdKUY #DevOps #AIAutomation #SoftwareDelivery #Productivity James Hunter Imran Hashmi Chris Haggan Randy Langehennig Mark Garcia Samruddhi Pandav

I had the opportunity to attend Harness Unscripted, their annual flagship event. While I joined virtually to avoid travel, it turned out to be an incredibly insightful day-long experience. I’m sharing some key takeaways that stood out and are worth reflecting on for our positioning and thought leadership. Key Themes - Staying true to this year’s theme of the event - "The Future of AI-Powered Software Delivery", the conversations moved beyond the now-common “AI for writing code” and focused on how AI-driven platforms and AI-powered SDLC cycles are redefining the way software is developed, tested, and delivered. Highlights: 🔹 Nick Durkin (CTO, Harness) & Jyoti Bansal (CEO, Harness) kicked off the event and showcased the new next-gen features in Harness: SEI 2.0, AI/ML Ops, AI for Testing, AI for RCA & Fixes, AI for Security Scans, AI for IDP, AI for Pipeline Development, and Harness DB. 🔹 The event continued as Amit Zavery (CPO & COO, ServiceNow) & Jyoti Bansal sat for a fireside chat and the key highlights of their discussion was around - 1.Agentic AI as a driver of efficiency, modernization, and infrastructure optimization. 2.AI’s role in dependency management, unit testing, and documentation. 3.Emphasis that enterprise platforms infused with AI will define the future, while fragmented “spare parts” AI tools will lose relevance. 4.Software engineering roles won’t disappear but will evolve with new skill sets and focus areas. As the day progressed, Leaders from Capgemini Invent, BlackRock, Regions Bank, Capital One, Deluxe, and others also highlighted how they are leveraging AI to reduce developer cognitive load and amplify productivity in meaningful ways. My Reflection The event reinforced a pivotal shift: AI is no longer just a coding assistant – it’s becoming the backbone of end-to-end software delivery, operations, and enterprise modernization. This strongly resonates with our own narrative around AI-infused SDLC and platform-led transformation. It also validates a belief I held back in 2021 – that Harness was on a trajectory to be a leader in SDLC platforms. Today, I can clearly see that vision becoming reality. Thank you for inviting me to the event Nick Davidson and Adeeb Valiulla . You can view the sessions online at - https://lnkd.in/gEqziHBK #harness #aisdlc #Artificialintelligence #opustechnologies

Engineering teams deliver on time. Budgets stay intact. Stakeholders still aren't happy. The problem isn't speed—it's something deeper. The real issue? Organizations treat engineering teams as order takers, not value creators. Even with AI tools like Atos's Polaris AI accelerating development, the core problem persists. Teams focus on outputs instead of outcomes. The solution is shifting from project-centric to product-centric thinking: 🎯 Transform teams from service providers to problem solvers 🎯 Empower product managers as domain experts 🎯 Define success through business outcomes, not task completion 🎯 Foster collaboration between product and delivery teams Companies like Infosys and the U.S. Department of Defense have proven this works. They report faster innovation cycles and improved operational agility in AI-driven environments. The transformation starts small: •Secure executive buy-in •Define value in business terms •Coach stakeholders on product centricity •Start with one AI or cloud product •Scale success across the enterprise The key insight? Give teams the autonomy to solve problems, not just complete tasks. This shift requires cultural change. But organizations that embrace product thinking deliver meaningful outcomes faster. What's holding your team back from focusing on value over velocity? #ProductCentric #AgileTransformation #DevOps 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/eXrPjyQp

For many DevOps engineers, tracing a pipeline error looks like this: 🔹 10+ tabs open 🔹 Jumping between dashboards 🔹 Searching for logs across different tools That’s not productivity — that’s chaos. Wistx AI brings everything together into one intelligent workspace. Less tab-hopping, more deploying. 🚀 #WistxAI #DevOps #CICD #Automation #Productivity

𝗪𝗲 𝗔𝗹𝗹 𝗟𝗼𝘃𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗨𝗻𝘁𝗶𝗹 𝗜𝘁 𝗕𝗿𝗲𝗮𝗸𝘀 𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 Automation is often sold as the answer to everything: ✅ Faster workflows ✅ Fewer manual tasks ✅ More “efficiency” But here’s the reality we see inside many organizations: 🚫 Bad automation creates fragile systems that break silently… until the damage is already done. 🚫 Real Problems We’re Seeing: 🚫 Over-automated DevOps pipelines, one config change triggers a full deploy… to the wrong environment 🚫 RPA bots that silently fail when a UI changes, leaving work unfinished 🚫 Misaligned alerts or workflows that create noise, not value 🚫 No human-in-the-loop — so when things go wrong, no one knows until it’s too late 𝗦𝗼 𝗵𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗳𝗶𝘅 𝗶𝘁? Here’s what we recommend to clients at 𝗔𝘅𝗶𝗼𝗻𝗹𝗼𝗴𝗶𝗰𝘀: 👉 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗳𝗮𝗶𝗹𝘂𝗿𝗲, 𝗻𝗼𝘁 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗶𝗼𝗻: Build observability and exception handling into the automation from the start. 👉 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁: Create checkpoints where humans can review, override, or intervene when needed. 👉 𝗦𝘁𝗮𝗿𝘁 𝘀𝗺𝗮𝗹𝗹, 𝗶𝘁𝗲𝗿𝗮𝘁𝗲 𝗳𝗮𝘀𝘁: Pilot automation in low-risk areas first, and refine based on feedback, not assumptions. 👉 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗽𝗲𝗲𝗱 𝘄𝗶𝘁𝗵 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲: Efficiency isn’t worth it if you’re constantly cleaning up avoidable messes. 📣 The Big Question: Is your automation actually saving time, or just hiding problems better? If you’ve seen automation go sideways in your org (or want to make sure it doesn’t), let’s talk. There is a smarter way to do this. 💬contact@axionlogics.com 👉view our portfolio: https://axionlogics.com #Automation #DevOps #DigitalTransformation #EnterpriseIT #RPA #ITOperations #Observability #Axionlogics #TechLeadership #HumanInTheLoop

Behind the Scenes: Lessons Learned Building a Scalable Automation SaaS Building an automation SaaS that truly scales isn't just about writing code; it's about anticipating growth, managing complexity, and making architectural choices that stand the test of time. Our journey building a platform from the ground up offered invaluable insights into the realities of designing for high performance and reliability. Here are some core lessons from the trenches: 1️⃣ Architectural Foundation is Key. We quickly learned that a monolithic approach, while simple initially, becomes a bottleneck. Shifting towards an event-driven microservices architecture provided the necessary agility. It allowed us to scale individual components independently and reduce blast radius. The key was defining clear service boundaries early and embracing asynchronous communication patterns from the start. 2️⃣ Taming Data Complexity in Distributed Systems. Automation often means managing persistent state across multiple services. Ensuring eventual consistency and reliable data integrity across a distributed system was a significant challenge. We relied heavily on idempotent operations and explored patterns like the transactional outbox to bridge the gap between service actions and data updates, minimizing race conditions and data loss. 3️⃣ Observability Isn't Optional; It's a Lifeline. When your system has dozens of interconnected services, "where did it go wrong?" becomes a daily question without robust observability. Implementing comprehensive logging, metrics (Prometheus/Grafana), and distributed tracing (OpenTelemetry) from day one was non-negotiable. This allowed our SRE and development teams to quickly diagnose issues, understand performance bottlenecks, and maintain system health proactively. 4️⃣ Empowering the Engineers: Developer Experience (DX) Matters for Scalability. True scalability extends beyond infrastructure – it encompasses the engineering team's ability to operate efficiently. Investing in a robust CI/CD pipeline, automated testing, and developer-friendly deployment tools proved crucial. Reducing friction for developers to build, test, and deploy features directly correlates to the platform's ability to evolve and scale. Building a scalable SaaS is a continuous learning process, a balance between innovation and operational resilience. Every architectural decision and engineering practice contributes to the overall ability to meet growing demands. #ScalableSystems #Microservices #Automation #SaaSDevelopment #EngineeringLessons #DistributedSystems #DevOps #Observability

The DevOps automation tools market is spiraling upwards 📈 Let's cut through the noise: the DevOps tools market was pegged at an eye-popping $12.11 billion in 2024, and brace yourselves—it's anticipated to escalate to between $14.44 billion and $15.06 billion in 2025. If that doesn't make you sit up, projections for 2029 shoot up to $35.68 billion, and by 2032, we’re talking a staggering $72.81 billion! What's fueling this meteoric rise? 🟠 IT environments are getting tangled up in their own complexity. Enter advanced automation tools that slice through the chaos. 🟠 AI and ML are not just buzzwords. AIOps and MLOps are the new frontier demanding automation and predictability in software delivery. 🟠 The pressure is on—deliver faster, deliver smoother. Organizations are in a mad dash to deploy software at warp speed, driving the hunt for better DevOps tools. 🟠 Digital transformation isn’t just a fad. It's a hard-hitting overhaul happening across industries, requiring tools that ensure rapid and secure software release. But here's the hitch: 🟠 A skill shortage crisis looms. We might have the tools, but where are the pros to wield them? 🟠 Ventures face integration nightmares. Juggling tools from multiple vendors can transform any project into a delicate balancing act. 🟠 Yet, there's a treasure trove in trends like microservices architecture, chaos engineering, and multi-cloud strategies. These are the golden geese that could redefine the market landscape. The market isn't homogeneous either. North America might be the maestro now, but keep an eye on Asia Pacific—it’s the dark horse gaining speed. The market is a patchwork quilt of segments: think configuration management, CI/CD, container management, and infrastructure automation tools. Are you ready to ride this wave or watch from the shore? 🌊 #DevOps #Automation #AI #ML #DigitalTransformation #ITInnovation #MarketTrends #BusinessGrowth #TechOpportunities 🧐 🔗https://lnkd.in/dSyTnaEv 👉 Post of the day: https://lnkd.in/dACBEQnZ 👈

Many teams deliver great work but struggle to track productivity, effort, and outcomes effectively. Without visibility, improvement is guesswork. With integrated dashboards, productivity becomes measurable, traceable, and actionable.   📊 What gets measured, gets improved.   How are you currently measuring productivity across your DevOps lifecycle?  .  #a-devops #automotiveinnovation #aiworkforce #aspice #softwaredevelopment #techinnovation #realtimemonitoring #software #automotivetechnician #techdriven #devops #aifordevelopment