Johnny Mullaney’s Post

As we travel through time we see advances in Technology: 1970s: Personal computers gave individuals computing power 1990s: Internet connected those computers to the world 2020s: AI gives you a digital labor on demand In 30 years, people will look back at this exact moment as "the great agile unlocking". One of the key benefits of AI is: Agile Transformation. Not to create a new shiny product that uses AI, but to in-Source to AI at speed and do more with less. The real meaning and transformative capability of the word we have been trying to fit into scrum teams, agile now has a new direction and meaning for technical teams. Agile insource to AI: Outsource providers in cheap labour markets will see lower demands as AI steps in and becomes the place to go to for low value work fulfilment. The IT Industry trend to outsource to other countries, we will see demand drop as AI is used to fill this gap also. We're already seeing this with AI automation and also AI coding apps so you don't need a whole scrum team. This trend is only really just started and when you look at building internal products, you're gonna have to ask yourself: "Do we really need a full scrum team with all the different types of roles that we've traditionally had; business analysis, scrum master, developer, devops, product owner, architect, QA tester? Or can we manage with a smaller team and use AI in the build process, with 2 to 3 people max? Can we use AI agility?". If this the new trend for internal software development? Why would we not look at it for our customers product and services? Forward thinking companies will become more nimble, faster to market and be able to create pilots and MVP's in a quick start-up style way. Fail fast, maybe this is what agile was meant to be and has been waiting for AI to come along to give it a new meaning. My final thought: The days of planning a big product may be over for most. The time to listen to what your customer want, and get that prototype to them next month is here in real terms. The future is bright, the future is better for our customers with AI Agile Transformation.

Can AI help make Agile, well, more agile? That's the hope. AI is reshaping Agile development. Here's how teams can boost productivity and quality while avoiding the biggest risks. (My latest in ZDNET)

🚀 AI + Agile: The Future of Software Development is Here Artificial Intelligence isn’t replacing Agile — it’s redefining it. At i-Qode Digital Solutions, we explore how this powerful fusion is reshaping software delivery — making development smarter, faster, and more data-driven. 🔹 92% of companies are increasing AI investments 🔹 Developers are working 56% faster 🔹 Test execution time has dropped by 50% Our latest blog dives deep into: ✅ How AI enhances Agile through automation and analytics ✅ Key challenges and best-practice frameworks ✅ The evolving human roles in AI-driven sprints ✅ Why this shift is a true competitive advantage for IT services companies Read the full article to discover how to harness AI-Agile synergy for smarter delivery and stronger market impact. Read here: https://lnkd.in/dG23c4vM Let’s talk: info@i-Qode.com www.i-Qode.com #AI #Agile #AITools #DigitalTransformation #SoftwareDevelopment #iQode #AIinAgile #AgileTransformation #Automation #FutureOfWork #Leadership #Innovation

Agile methodology is not just a choice but a necessity when it comes to Machine Learning (ML) products—it's as essential as oxygen. ML projects inherently involve continuous iterations: data changes, hypotheses evolve, and models degrade over time. Agile practices provide ML teams with the rhythm, governance, and teamwork needed to iterate quickly and deploy securely. Here's why Agile is crucial for ML success: - **Hypothesis-driven cadence**: Short iterations transform concepts into measurable outcomes. Define a hypothesis, conduct experiments (offline/online), analyze results, and adjust the roadmap swiftly. Say goodbye to long, opaque model development cycles. - **Safe, staged delivery**: Safely move from initial ideas to production using techniques like feature flags, shadow deployments, and canary rollouts to minimize risks. - **Data and model readiness/done criteria**: Establish clear entry/exit standards (data quality checks, reproducible pipelines, model fairness/robustness assessments) to ensure alignment on project progress. - **Continuous feedback loops**: Connect experiments to business results (e.g., customer acquisition cost, customer satisfaction) rather than just model performance metrics. Regular reviews lead to enhancements in models and workflows. - **Efficient governance**: Incorporate safeguards like data agreements, lineage tracking, approval workflows, and rollback strategies for speed and accountability. - **Addressing drift proactively**: Agile practices help in maintaining production readiness by monitoring model drift, setting service level objectives for model health, and planning for retraining and feature enhancements. - **Cross-functional collaboration**: Product, Data Science, Machine Learning Engineering, Data Engineering, Operations, and Compliance teams collaborate on outcomes rather than roles, ensuring shared visibility and faster value delivery. In summary, Agile methodology shifts ML from a theoretical exercise to reliable product delivery—enabling faster learning, secure launches, and measurable impact. Share with us: What Agile practice has had the most significant impact on your team's ML projects—cadence, control, or collaboration? #Agile #MLOps #MachineLearning #ProductManagement #DataScience #AI #LeanProduct #SAFe #AIOps #Experimentation

🚀 AI is Making Waterfall Obsolete — and Turbocharging Agile in Indiana’s IT Projects Over the last two decades, the State of Indiana has led the nation in government technology modernization — from hardware consolidation to shared services and cloud adoption. The next frontier is software development transformation powered by Artificial Intelligence (AI). Traditional Waterfall methodologies, with their long requirement and coding phases, are rapidly losing relevance. In a world where AI can translate business needs into functional prototypes within hours, spending months gathering requirements, designing, and developing is no longer efficient or necessary. In the AI-driven future, we’re seeing: 🤖 Automated requirement analysis – AI tools interpret policy and business documents into user stories or functional flows. ⚡ Instant prototyping – Generative AI can build working mockups or even testable modules in days, not months. 🔁 Continuous feedback loops – Agile and Scrum teams can now iterate faster with AI-assisted sprint planning, code reviews, and testing automation. For Indiana’s state agencies, this shift offers a unique opportunity: * Faster delivery of citizen services – from permits and licenses to public portals, applications can be built and improved in near real time. * Reduced IT costs – less manual coding, faster validation, and fewer redundant systems mean a leaner, more efficient IT budget. * Cross-agency collaboration – with AI accelerating shared code and design patterns, multiple departments can benefit from common platforms and reusable digital assets. Indiana’s success in hardware consolidation proved what’s possible when leadership embraces change. Now, AI-driven software consolidation and agile acceleration can redefine how state IT delivers value to Hoosiers — faster, smarter, and at a fraction of the cost. It’s not just about writing code anymore. It’s about teaching AI to build the future of digital government. #AIinGovernment #DigitalTransformation #AgileDevelopment #IndianaIT #PublicSectorInnovation #StateofIndiana

What Agile was to the 2001, AI-DLC could be to the 2026. The AI-powered software development lifecycle (AI-DLC) proposed by Amazon isn’t a tweak to Agile or a plugin for SDLC. It's a complete systems-level rethink of how modern teams build with AI. And no, this isn’t about retrofitting AI into your Jira board. Here’s what makes it different: It’s not linear: The AI-DLC is a loop, not a sprint, nor a waterfall and enables for hourly or daily iteration. It integrates human oversight throughout, not just at the end. It starts with Intent: we’re moving away from a backlog and spec docs. Instead, a one-paragraph statement of purpose that captures goals, constraints, and success criteria. It’s the north star that steers both humans and AI. AI orchestrates the plan. From Intent, AI agents expand into tasks, propose architectures, surface risks, and outline experiments. This is a move away from sprint planning to AI-augmented design. Code, tests, edge cases, and docs are auto-suggested. Scaffolds and diffs are generated quickly. Humans stay in control with validation gates at critical stages. Live telemetry feeds the next iteration. Deployed features are monitored for anomalies, user signals, and incidents. AI synthesises the feedback and proposes next-sprint actions. It’s human + AI. Not AI-only. Humans own key judgment calls - security, ethics, architectural invariants - while AI takes on the coordination and heavy lifting. It’s a new system that can: Adapt faster Scale more cleanly And preserve human judgment where it matters most. Unlike the Agile Manifesto, there’s no playbook. No 12-step guide to copy-paste. And that’s probably a good thing. It means every team has the chance (and responsibility) to design how AI fits their workflow - intentionally, not reactively to AI.

AI is stepping into every corner of the software development process, including Agile practices. But can it actually take on the role of a Scrum Master? 💡 - It can track sprint progress.  - It can suggest backlog priorities.  - It can even generate summaries from daily stand-ups. But let’s be clear:  -  It still can’t read the room.  - It can’t mediate team tension.  - It doesn’t build trust. As AI gets smarter, the human role evolves.  Not by being replaced, but by being augmented. Use AI to optimize.  Keep humans to lead. Have you seen AI tools helping with Agile ceremonies or sprint planning? 💭 #Software #SoftwareDevelopment #Nearshore #Outsourcing #TechLeadership #RemoteTeams #GilaSoftware #YourTeam #Scrum #Agile #AI 

Heard this story a couple times now: Big consulting firm gets hired to "improve efficiency." They roll out textbook Scrum. Two-week sprints, story points, daily standups, the works. They collect their fee and leave. Six months later, employees are drowning in process overhead but efficiency hasn't budged. The problem: These weren't software companies. One was a research-driven hardware firm. The other, a large heavy-industry behemoth. At the hardware company, a researcher told me: "I can't even read a paper anymore. I have to write a user story about reading it, estimate how long it'll take, then report my progress every morning at standup." What's agile about that!? The industrial company? "Our work happens at massive scale with complex dependencies. Nothing here fits into two-week increments or neat user-stories." Now I'm watching companies make the exact same mistake with AI. Top-down mandates. One-size-fits-all playbooks from the same consulting firms. Going wide before going deep. Remember Gall's Law: Complex systems that work evolved from simple systems that worked. You can't jump straight to complex. Whether you're adopting agile or integrating AI, the approach is the same. Start with a small pilot. Learn what actually works for your organization. Build from there. Off-the-shelf transformations rarely transform anything except your budget. --- 📧 Daily newsletter on building AI/software projects that deliver. Link in profile.

“At the hardware company, a researcher told me: "I can't even read a paper anymore. I have to write a user story about reading it, estimate how long it'll take, then report my progress every morning at standup." What's agile about that!?” This applies to software too. You shouldn’t be spending more time speaking about the widget you’re building than what it takes to actually build it!

The funny thing about this is that 'Agile' means different things to different people. At the top it means 'reacting fast to changing market conditions' and on the floor it means Scrum/Kanban and all the other BS that consultants love to sell. So we have this disconnect since companies (that do not sell software) need to convince investors that they are Agile in the 1st sense of the word. And they think the implementation of the 2nd sense will achieve the 1st. Small tip: it doesn't.