Scrum Alliance Sponsors AI in Software Engineering Conference

AI fails don’t feel like experiments for businesses. They feel like accidents. In large companies, an AI failure often becomes an internal postmortem.  For growing businesses, it is usually a customer, revenue, or trust issue. And it happens immediately.  A World Bank study shows that SMBs operate with far thinner buffers than large enterprises, often without dedicated risk or governance teams. This makes AI failures more visible and more costly when they happen.  That’s why AI in businesses cannot be treated as a playground for tools or prompts.  System-level decisions like data boundaries, human oversight, and escalation paths determine whether AI becomes a leverage or a liability.  If this resonates, I would love for you to join our discussion on February 19. 

AI-powered software development has crossed the line from experiment to strategic imperative. Leaders who treat AI as a simple coding add-on risk amplifying broken processes instead of creating business value. The following article outlines a pragmatic, five-step path: benchmark and set value-based targets, run a holistic SDLC diagnostic, analyze each lifecycle step, redesign org and skills, and scale AI through iterative pilots tied to outcomes. For business leaders, the takeaway is clear: it’s time to move from isolated AI tools to an operating-model level transformation. Read the full article here: https://ow.ly/QuRf50Yc3Ar

AI-powered software development has crossed the line from experiment to strategic imperative. Leaders who treat AI as a simple coding add-on risk amplifying broken processes instead of creating business value. The following article outlines a pragmatic, five-step path: benchmark and set value-based targets, run a holistic SDLC diagnostic, analyze each lifecycle step, redesign org and skills, and scale AI through iterative pilots tied to outcomes. For business leaders, the takeaway is clear: it’s time to move from isolated AI tools to an operating-model level transformation. Read the full article here: https://ow.ly/x09n50YhQA8

AI-powered software development has crossed the line from experiment to strategic imperative. Leaders who treat AI as a simple coding add-on risk amplifying broken processes instead of creating business value. The following article outlines a pragmatic, five-step path: benchmark and set value-based targets, run a holistic SDLC diagnostic, analyze each lifecycle step, redesign org and skills, and scale AI through iterative pilots tied to outcomes. For business leaders, the takeaway is clear: it’s time to move from isolated AI tools to an operating-model level transformation. Read the full article here: https://ow.ly/awrP50Y6GiP

AI-powered software development has crossed the line from experiment to strategic imperative. Leaders who treat AI as a simple coding add-on risk amplifying broken processes instead of creating business value. The following article outlines a pragmatic, five-step path: benchmark and set value-based targets, run a holistic SDLC diagnostic, analyze each lifecycle step, redesign org and skills, and scale AI through iterative pilots tied to outcomes. For business leaders, the takeaway is clear: it’s time to move from isolated AI tools to an operating-model level transformation. Read the full article here: https://ow.ly/xzJ850Y8oeI

AI-powered software development has crossed the line from experiment to strategic imperative. Leaders who treat AI as a simple coding add-on risk amplifying broken processes instead of creating business value. The following article outlines a pragmatic, five-step path: benchmark and set value-based targets, run a holistic SDLC diagnostic, analyze each lifecycle step, redesign org and skills, and scale AI through iterative pilots tied to outcomes. For business leaders, the takeaway is clear: it’s time to move from isolated AI tools to an operating-model level transformation. Read the full article here: https://ow.ly/FLZ850Y8tt6

AI-powered software development has crossed the line from experiment to strategic imperative. Leaders who treat AI as a simple coding add-on risk amplifying broken processes instead of creating business value. The following article outlines a pragmatic, five-step path: benchmark and set value-based targets, run a holistic SDLC diagnostic, analyze each lifecycle step, redesign org and skills, and scale AI through iterative pilots tied to outcomes. For business leaders, the takeaway is clear: it’s time to move from isolated AI tools to an operating-model level transformation. Read the full article here: https://ow.ly/ERIl50YaCGc

Reinventing the #SDLC The traditional software development lifecycle (SDLC) is under pressure with fragmented tools, constant maintenance, and increasing demands for speed and quality. While Agile and DevOps have been beneficial, they also come with their own set of limitations, underscoring the need for a more advanced solution. It’s time for a new model: Agentic Software Engineering. Capgemini Engineering’s RAISE for Software Product X transforms the SDLC into a living, intelligent system. AI agents work together across the lifecycle, from requirements to deployment, learning and improving with every iteration. 📈 Empower your teams to focus on innovation, not coordination, and explore the future of autonomous software development with us. ��� https://bit.ly/4pM8Sci #SoftwareEngineering #AIAgents #AgenticFramework #AI #SoftwareDevelopment #SoftwareDevelopmentLifecycle #SoftwareProductX

Estimating is hard, AI can make that harder. All software engineering teams go through some kind of maturity curve with estimating. Whilst.. Executives and customers just want to know when “something will be done”. But those of us on the coal face understand the nuance of the details here. That the scope might be so big we have to iterate and split deliverables. That the user may need to validate and respond with each iteration, and that we may have criteria that shift for what is considered “done” with those iterations. Especially in machine learning, there are often long discovery phases. Agile starts to look like waterfall delivery. Estimating accurately demands deterministic outcomes and assumes you have done something before. Rinse and repeat. Evolving new machine learning models for specific use cases is very often frontier territory. It’s more important than ever then to ask the right question in the first place. To understand the context of what the team will deliver on. Questions that abstract away from that only deserve a corresponding answer. Rather than ask.. “when will this be done”, it’s better to frame this as what will achieve the user/customer outcome? And what are the steps to get there. Working through that will put shape around your delivery timeline. Estimating is hard. But asking the wrong questions in the first place won’t make it easier. #softwareengineering #cto

I work across software development, AI, analytics, and DevOps. This cross-functional approach helps me see products holistically — from code to data to deployment to business impact. The future belongs to professionals who can connect technology with purpose. Continuous learning is non-negotiable. #FullStackDeveloper #TechSkills #ContinuousLearning #FutureOfTech