How AI Can Reduce Developer Workload

I spend a lot of my time now speaking to companies about AI strategy. It's exciting but I find it challenging sometimes because while discussing the amazing potential we can't ignore the societal risks. I'll start a dialog with you today with one example involving the state of software coding: A recent research paper (https://lnkd.in/emyyQsZw) examined how AI tools can enhance developer productivity, focusing on the use of GitHub Copilot at ANZ Bank, a large organization employing over 5000 engineers. The study found that GitHub Copilot led to a significant increase in developer productivity and job satisfaction, helping engineers code up to 55% faster on average. Additionally: - 46% of code is now being written with the help of GitHub Copilot across all programming languages, and up to 61% for Java code specifically. - 90% of developers reported completing tasks faster with GitHub Copilot. - 73% said it allowed them to better stay in flow and conserve mental energy. - Up to 75% of developers felt more fulfilled and able to focus on satisfying work. The authors conclude that AI will likely transform software engineering practices and the developer experience in the coming years. This raises the question, will AI continue to be primarily an effective assistant, or will more advanced tools begin to change the nature of what it means to be a software engineer? An example of a more ambitious AI coding tool is Devin from Cognition Labs (https://lnkd.in/ewAgg-We), described as an engineering "buddy" that can build alongside developers or independently complete tasks for review. While still early, this six-month-old company has generated significant interest and is valued at $2 billion dollars. We can also see open-source projects exploring similar ideas, such as the combination of Wasp and Aider (https://lnkd.in/ehz3UkdZ), which aims to provide an AI-driven development workflow. As AI continues to advance, it's interesting to consider how the role of these tools may evolve in software development. Could we see a progression from AI "buddies" to "mentors" or even "managers"? While the trajectory from narrow AI to more general or "Super AI" is still largely theoretical, it's a fascinating area of speculation. Personally, I find these developments both exciting and thought-provoking. The potential for AI to augment and enhance human capabilities in software development is significant. However, it's also important to consider the potential risks and disruptions these advancements could bring. What about you? Are you more apprehensive or excited about the future of AI in software development? What potential benefits or concerns come to mind? #AI #SoftwareEngineering #DeveloperProductivity #GitHubCopilot #Devin #CognitionLabs #WaspAider #NarrowAI #GeneralAI #SuperAI

AI saved 280,000 dev hours and protected jobs. But the real story is how it reshaped what those jobs looked like. Morgan Stanley had a problem: a massive legacy codebase, slowing down innovation. Updating it manually would take years. Instead of offloading the work, or the people, they built DevGen, an internal AI tool that: → Translates outdated code → Auto-generates documentation → Aligns everything with modern standards The result? ✅ 280,000+ hours freed ✅ Developers stayed in the loop ✅ No one replaced—just refocused This is a blueprint for what responsible AI enablement looks like: Empower people with the tool, not against it. 🧠 Train your team to lead the change, not fear it. 📩 Want more examples like this? Follow The AI Report for weekly stories on how AI is transforming work from the inside out. How are you helping your team grow with AI instead of around it?

𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝟳 𝘁𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝘁𝗮𝗸𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 (𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗮𝗱𝗮𝗽𝘁): Fortune 500 deployment data shows these categories are transforming work faster than leaders realize. I studied it and did a full breakdown for you. 𝟭. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗧𝗮𝘀𝗸 𝗔𝗴𝗲𝗻𝘁𝘀 → Function: Invoice processing, data entry, scheduling → Tools: UiPath, Power Automate, Zapier + AI → Impact: 70% cost reduction, 24/7 operations → Your move: Design workflows they execute 𝟮. 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗴𝗲𝗻𝘁𝘀 → Function: Customer support, HR queries, IT tickets → Tools: Dialogflow, Dynamics 365, Agentforce → Impact: 60% faster resolution, handles 80% Tier 1 → Your move: Focus on complex relationships 𝟯. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁𝘀 → Function: Analyze literature, validate data → Tools: OpenAI Deep Research, Perplexity Pro → Impact: Weeks of research → hours → Your move: Interpret findings strategically 𝟰. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗔𝗴𝗲𝗻𝘁𝘀 → Function: Data → dashboards, reports, insights → Tools: Power BI Copilot, ThoughtSpot → Impact: Real-time insights, no manual reports → Your move: Master data storytelling 𝟱. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗔𝗴𝗲𝗻𝘁𝘀 → Function: Code, debug, site reliability → Tools: GitHub Copilot, Cursor, Claude Code → Impact: 7+ hours autonomous work, 4x productivity → Your move: Focus on architecture design 𝟲. 𝗗𝗼𝗺𝗮𝗶𝗻-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗴𝗲𝗻𝘁𝘀 → Function: Law, healthcare, finance specialization → Tools: Harvey, Hippocratic AI, Hebbia → Impact: Expert-level regulated industry analysis → Your move: Develop irreplaceable domain expertise 𝟳. 𝗕𝗿𝗼𝘄𝘀𝗲𝗿-𝗨𝘀𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀 → Function: Control websites, fill forms, automate workflows → Tools: OpenAI Operator, Computer Use → Impact: End-to-end task automation → Your move: Strategic oversight and quality control Want more on this? 📺 I just published a whole video about it. Link in comments. Key Stats: • OpenAI agent revenue: $3B → $29B by 2029 • Model Context Protocol (MCP) connects agents to thousands of apps in revolutionary ways • Companies save “hundreds of thousands annually” on staffing Strategic Response: 1. Audit which categories overlap your role 2. Shift toward strategy/creativity/relationships 3. Learn agent management vs. competing with AI 4. Help your company deploy before competitors ….. What’s your company’s agent strategy? This isn’t about IF agents transform your industry—it’s whether you lead the transformation or get transformed by it. Share in the comments. ….. Julia McCoy is the founder of First Movers, and one of the world’s leading content marketers (former CEO & founder of a 100-person content writing agency), helping companies navigate the AI transformation. Follow to stay ahead of the automation curve. #AIAgents #FutureOfWork

I  recently joined the "Decoding GenAI" program for a fascinating discussion with Eugina Jordan and Mariana Saddakni about the transformative effects of Generative AI (GenAI) on software engineering. The episode explored how GenAI is impacting developers across three categories: 1️⃣GenAI Engine and Large Language Model (LLM) Developers: These researchers at companies like OpenAI, Meta (Llama3), and Google (Gemini) are building the core foundations of GenAI technology. 2️⃣AI Application Developers: These engineers leverage tools like the LLMs we mentioned before, TensorFlow, and others to create groundbreaking AI applications in areas like image recognition, recommendation engines, scientific computing, and many more. 3️⃣Software Engineers (Non-AI Focused): This is the largest group and is being significantly impacted by GenAI in different ways than the second group. Their work is evolving as GenAI automates repetitive tasks and introduces innovative approaches to software development. We talked about  the rise of the AI assistant: GenAI-powered tools like Microsoft Github Copilot, Amazon Q, IBM WatsonX, and Tabnine are becoming invaluable allies for software engineers. These AI assistants handle tasks like: ✅ Developing code snippets: They can generate boilerplate code, suggest function calls, and complete code based on context. ✅ Automating tests: They help write unit tests and integration tests, streamlining the testing process. ✅ Debugging: They can help identify and fix bugs in code. ✅ Performing code reviews: They can analyze code for potential issues and suggest improvements. ✅ Creating documentation: They can automatically generate documentation from code, saving developers valuable time. We talked about the challenges and solutions. One of the key takeaways is that this is just the beginning. We can expect even more powerful automation capabilities as GenAI technology continues to develop. Link to the full episode in the comments. I'd love to hear your thoughts on how GenAI is impacting your work. Share your experiences and predictions about the AI-powered future in the comments below! #aiforleaders #ai #artificialintelligence   _______________ ➡️ About Me: I'm Talila Millman a management advisor, keynote speaker, and executive coach. I help CEOs and C-suites create a growth strategy, increase profitability, optimize product portfolios, and create an operating system for excellence. 📘 Get My Book: "The TRIUMPH Framework: 7 Steps to Leading Organizational Transformation" launched as the Top New Release on Organizational Change 🎤 Invite me to Speak at your Event about Leadership, Change Leadership, Innovation, and AI Strategy

Given knowledge workers' discomfort with AI, I find this survey fascinating: it shows that generative AI makes software developers happy 😮 Check out the provocative findings from Begum Karaci Deniz, Chandra Gnanasambandam, Martin Harrysson, Alharith Hussin, and Shivam S. at McKinsey & Company here: https://lnkd.in/ejCbcn_i My take is that by overcoming fear and embracing tools such as ChatGPT from OpenAI, Claude from Anthropic, or GitHub's copilot, knowledge workers can boost productivity and free up brainspace to do better work. That improves mental focus, performance, and job satisfaction. These numbers apply to developers but IMO have implications about the future for knowledge workers across the board. Data and AI leaders, what do you think? What anecdotal results have you seen with your teams so far? Report excerpts: "Our latest empirical research finds generative AI–based tools delivering impressive speed gains for many common developer tasks. "Documenting code functionality for maintainability (which considers how easily code can be improved) can be completed in half the time, writing new code in nearly half the time, and optimizing existing code (called code refactoring) in nearly two-thirds the time. "With the right upskilling and enterprise enablers, these speed gains can be translated into an increase in productivity that outperforms past advances in engineering productivity, driven by both new tooling and processes." "The research finds that equipping developers to be their most productive also significantly improves the developer experience, which in turn can help companies retain and excite their best talent. "Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow. "They attributed this to the tools’ ability to automate grunt work that kept them from more satisfying tasks and to put information at their fingertips faster than a search for solutions across different online platforms." And yet... software development needs humans for tricky tasks: "Generative AI technology can do a lot, but our research suggests that the tools are only as good as the skills of the engineers using them. Participant feedback signaled three areas where human oversight and involvement were crucial... > "Examining code for bugs and errors" > "Contributing organizational context" > "Navigating tricky coding requirements" What do you think? Chime in here. Wayne Eckerson Eckerson Group Jay Piscioneri Jeff Smith #artificialintelligence #ai #generativeai Bill Schmarzo

AI is taking all our jobs. Here's how I'm updating my resume. I'm only partly joking. I'm on the record: if you're in tech, AI isn't replacing you. From firsthand experience using AI, it's been a massive productivity boost. I wish I could clone myself to use even more AI. Nothing drove this home quite like teaching my kids to use AI. "Wow dad, you used to write all this code by hand? I just built a multiplayer game in 30 minutes and now my friends and I are playing it. Your job must have sucked." Ouch. But that's exactly what brings joy to software development. Sure, there are parts of coding that are a complete bore, but the art of creating software has always been awesome. It's never been more awesome than today, thanks to AI. I recently had the privilege of discussing AI's impact on tech with Scott Hanselman (check out his recent TEDxPortland), and it got me reflecting on what's actually changing. More AI: AI will get faster, cheaper, and gobble up more capabilities. As it becomes more efficient and accessible, we’ll apply it to more tasks, driving even greater demand for AI. It’s a modern echo of Jevons’ Paradox. More jobs (but different): Some roles will disappear, just as toll booth operators gave way to automation. But we’re not headed toward fewer opportunities, just different ones. As AI reshapes workflows, it shifts what needs doing and who does it. AI is fast, but also frustratingly slow: That first "this is amazing" moment quickly becomes "I could've just done this by now." Working with AI can feel like trying to get a child to follow instructions. Your clarity and context matter, and you’ll pay for being lazy with your prompts. AI won’t teach you what you don't know you don't know: Software was already a liability. Software built with AI? An even bigger liability. If you don’t understand the system, AI won’t save you, but it might help you ship broken things faster. The visual advantage is real: Using AI to generate applications you can see and interact with is a game-changer for builders. The visual feedback lets you verify correctness quickly. But backend code? That’s a different beast; harder to inspect, harder to trust. This is why we’re building Postman Flows: a visual, low-code, AI-native tool that lets builders see and verify how their applications work on the backend. We’ve used Flows internally to deploy dozens of AI-powered applications (agents), from turning Slack threads into Jira action plans, to handling product feedback. More and more, applications are just AI orchestrating APIs. Lots of APIs. Flows makes that explicit and buildable. Using AI-ready APIs (which Postman helps you define, test, and structure) is key to making this work reliably. These agents have already saved us hundreds of hours. We’re sharing the agents soon, so you can use them too. So here's how I'm updating my resume: spending all my time learning and building new things with AI. The future belongs to those who learn to work with AI.

AI's hype is everywhere, but its practical application is what truly matters. !! Unlike the self-driving car hype of a decade ago, AI's implementation in the real world is uniquely different. Over the past year, I've witnessed firsthand how AI can augment our capabilities at SJ Innovation. It may not replace our jobs, but it does serve as a powerful assistant, handling numerous tasks efficiently. Since OpenAI introduced the "OpenAI Assistant," we've created over 250 specialized assistants within our organization. Upon reviewing these AI assistants, I've come to realize they haven't replaced any jobs. Instead, they're akin to having a team of interns, each adept at performing specific tasks, saving us 10-15 minutes each time. If you're leveraging 5-10 such assistants, that's a savings of 1-2 hours per day — a significant boost to productivity that will only improve over time. Here are some unusual and small assistant example: 1) Attendance Analysis: Develop AI solutions to analyze attendance data across multiple files, generating comprehensive reports to identify patterns and optimize team schedules. Create and Used by: Admin/Hr department 2) Quality Assurance Report Review: Assist QA teams Assistant manager by tracking project hours versus contracted hours to prevent burnout and ensure optimal productivity. 3) QA/Test cases for Client Project: Upload client project data, past test cases and input new requirements. Result new cases 4) Convert my code to old Version of Cakephp: Client running an application with old version, write code and it convert to old version of cakephp 5) RFP helper: Upload All document about project and old RFP document and now it can help write based on client requirements and our past RFP My advice? Get involved. Sign up for ChatGPT premium, create your own GPT, or if you're leading a team, develop your own assistants using the API. These digital helpers could become your next competitive edge, much like an diligent interns, ready to streamline your daily tasks and workflows. #AIAssistants #ProductivityTools #Innovation #OpenAI #Teamwork #SJInnovation

Exciting times ahead with Gen AI poised to revolutionize software development productivity! While the potential is undeniable, focusing on simpler use cases initially can be a strategic approach to nurture talent and instill confidence in this emerging technology. Interesting article on 'Unleashing Developer Productivity with Generative AI,' provides a good overview of how Gen AI can enhance developer efficiency in key areas like code documentation, generation, and refactoring. The article highlights noteworthy gains in simpler tasks, such as expediting manual work and facilitating the initial stages of code creation and modification. However, when it comes to more intricate activities, such as bug detection, consideration of organizational context, and addressing complex coding requirements, the article suggests that the impact of Generative AI is less pronounced. Particularly intriguing is a developer’s observation ‘Generative AI is least helpful when the problem becomes more complicated, and the big picture needs to be taken under consideration’ caught my attention’. Internally, we are actively conducting A/B tests on medium to complex software development tasks to offer our clients informed guidance on integrating Gen AI effectively. It's clear that to tackle complex development scenarios with Generative AI, the supporting ecosystem, encompassing training, tooling, security, and processes, must evolve concurrently keeping pace with the fervor surrounding this promising technology. Excited about the journey ahead as we navigate the evolving landscape of Gen AI in software development! #generativeai #softwaredevelopment #innovationinprogress