Benefits of Code Automation

There’s a lot of buzz and real debate about whether AI is helping software engineers or just giving them more noise to clean up. My team has been focused on this. Engineering is at the heart of AI development, and early use cases matter. Tools like Cursor and Windsurf are building for this moment. But the research is still mixed. For instance, a recent study from METR found that in some cases, and under some conditions, AI tools can actually slow developers down. At the same time, an earlier study from Microsoft showed significant gains (links below). That’s why we’ve been learning from the team at Jellyfish, a developer operations platform that works with over 500 companies, representing tens of thousands of engineers. Their data lets us take an early look at how AI tools like OpenAI's Codex are reshaping workflows. While we are working on more conventional research designs, including RCTs, analyzing observational data is a great way to get an early signal on what’s happening. What we found: 📈 Teams that use AI ship more code, faster When teams of any size have a majority of their developers using AI, they show an increase on the order of 1-2 more pull requests (PRs) each week per engineer, compared to a baseline of 1.4 PRs per engineer. These teams also were moving faster, saving ~4 hours per cycle time from initial Jira ticket to the code being merged to production, compared to a baseline of 16.7 hours. Digging deeper into the data, we see that a proportion of PRs go from taking two days to being sped up to same-day resolutions. ⚠️ But code quality raises questions While there were significant gains for team speed and output, we also see a very small increase in the number of PRs that are reverted due to errors. These “revert PRs” increase by about 1 in 50. We also are seeing more bugs being squashed, with an increase of 1 bug fixed for every 10 engineers. But, it’s unclear if AI is creating new bugs or helping teams finally clear their backlog. 👀  AI tools still need human judgment to deliver quality at speed Developers are spending more time reviewing and less time writing code. That’s a shift in task allocation and a reminder that speed doesn’t replace the need for discernment. We’re still early. Observational data like this doesn’t tell the full story. There can be other factors at play that muddy the results, which is why experiments remain a gold standard. However, as we collectively are making sense of this new technology and the shifting nature of work, findings like these add to the growing body of research, experience, and shared intuition that shape our understanding of AI’s impact. METR study: https://lnkd.in/e_m3CDkV  Microsoft study: https://lnkd.in/e2VG38Cz  More from Jellyfish: https://lnkd.in/e7zWipJ3 

As a Sr Principal in Search at Amazon collaborating with product managers, marketers, finance teams and operations specialists across the company, I've witnessed how technical barriers sometimes limit what talented people can accomplish. For years, automation was reserved for "tech-y" people with coding skills. Even simple tasks like analyzing data, creating reports, or automating workflows required knowledge of programming languages, APIs, and system commands. SQL is just one notorious example - a dark art requiring intimate knowledge of database schemas and complex joins. These technical barriers have kept productivity tools locked away from the people who need them most. This is changing dramatically with AI tools (like Amazon Q CLI. and others) The profound shift? Natural languages, like English, are becoming programming languages. These new AI tools aren't just generating text like old-school chatbots and Large Language Models (LLMs). They're agentic systems that execute tasks, interact with systems and applications, and adapt when things fail. What is an AI script? It's a reusable prompt with parameters – a template where you define what you want done once, then change specific values each time. Example: monthly_business_review.qscript with content "Pull updates for OKRs owned by {team_name}, summarize wins/misses, highlight areas for leadership input, suggest questions for the team to prepare for." You can run this script monthly like this: q chat "Run script monthly_business_review.qscript with parameters with team name: Consumer Products" Another example - simplify data access: q chat "Show me conversion rates for landing page visitors last month by traffic source" Behind the scenes, the AI figures out what to do - whether generating SQL, writing and running Python code, pulling data from internal websites, using tools, or processing files. Through conversation, and without programming, marketing managers, product owners, and executives can directly automate tasks. These scripts are: reusable and shareable across team members, self-documenting in plain language, and easily movable across operating systems. To take full advantage of this shift, organizations need proper infrastructure: internal-tool integrations with AI (for example, via Model Context Protocol), SQL data connectors, and authentication frameworks. The AI-driven democratization of automation has profound implications. Domain expertise, not programming skill, becomes the limiting factor in improving productivity. What could your team accomplish if coding wasn't a barrier? What would you automate? The answer might reshape how we all work. #AI #Productivity #FutureOfWork

ai is fundamentally changing how we ship software. think code generation. ai now writes boilerplate, suggests completions, even crafts entire functions. developers become architects, guiding the ai, not just typing every line. think testing and QA. ai can design test cases, identify bugs, and even predict potential failures. this means faster feedback loops and more resilient software. think deployment. ai optimizes release schedules, monitors for issues, and can automate rollbacks. shipping becomes less risky, more frequent. think project management. ai can analyze progress, predict delays, and optimize resource allocation. it brings a new level of clarity to complex projects. the entire software development lifecycle is being infused with intelligence. from idea to production, ai is an active partner. this isn't about replacing developers. it's about empowering them. freeing them from repetitive tasks to focus on complex problem-solving and innovation. teams that integrate ai deeply into their development workflows will ship faster. they'll build more robust products. they'll out-innovate competitors still stuck in manual processes. the future of software development isn't just about better tools. it's about a smarter, ai-assisted way of building.

Exactly a year ago, we embarked on a transformative journey in application modernization, specifically harnessing generative AI to overhaul one of our client’s legacy systems. This initiative was challenging yet crucial for staying competitive: - Migrating outdated codebases - Mitigating high manual coding costs - Integrating legacy systems with cutting-edge platforms - Aligning technological upgrades with strategic business objectives Reflecting on this journey, here are the key lessons and outcomes we achieved through Gen AI in application modernization: [1] Assess Application Portfolio. We started by analyzing which applications were both outdated and critical, identifying those with the highest ROI for modernization.  This targeted approach helped prioritize efforts effectively. [2] Prioritize Practical Use Cases for Generative AI. For instance, automating code conversion from COBOL to Java reduced the overall manual coding time by 60%, significantly decreasing costs and increasing efficiency. [3] Pilot Gen AI Projects. We piloted a well-defined module, leading to a 30% reduction in time-to-market for new features, translating into faster responses to market demands and improved customer satisfaction. [4] Communicate Success and Scale Gradually. Post-pilot, we tracked key metrics such as code review time, deployment bugs, and overall time saved, demonstrating substantial business impacts to stakeholders and securing buy-in for wider implementation. [5] Embrace Change Management. We treated AI integration as a critical change in the operational model, aligning processes and stakeholder expectations with new technological capabilities. [6] Utilize Automation to Drive Innovation. Leveraging AI for routine coding tasks not only freed up developer time for strategic projects but also improved code quality by over 40%, reducing bugs and vulnerabilities significantly. [7] Opt for Managed Services When Appropriate. Managed services for routine maintenance allowed us to reallocate resources towards innovative projects, further driving our strategic objectives. Bonus Point: Establish a Center of Excellence (CoE). We have established CoE within our organization. It spearheaded AI implementations and established governance models, setting a benchmark for best practices that accelerated our learning curve and minimized pitfalls. You could modernize your legacy app by following similar steps! #modernization #appmodernization #legacysystem #genai #simform — PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies.