Agile in the AI era

- [Presenter] When Agile was born at the turn of the 21st century, access to the internet and using email was just starting to become a workplace norm. At the time, offices mostly functioned through paper pushing and phone calls. Fast forward to today, and the world looks very different. The rise of remote working means teams often don't work in the same office. AI tools like ChatGPT, Claude, Gemini, Copilot and more allow us to generate output faster than ever. Tasks that once took hours of thoughtful back and forth, drafting a test plan, generating user stories, refactoring code, these can be done in seconds with a well-engineered prompt. Even ideation, once considered the domain of uniquely human insight can now begin with machine-generated options. Looking at these differences, it's easy to write Agile off as a relic of a bygone era, but Agile was never about the standups and the sprints. It has always been about responsiveness, allowing teams to be nimble and, well, agile in environments where there is significant change, complexity, and volatility. When we understand this fundamental truth and recognize that new technology is only accelerating the pace of change, knowing how to implement Agile effectively is more important than ever. The promise of AI is compelling: fewer repetitive tasks, faster iterations, and insights we may have otherwise missed, but these gains come with trade-offs. When AI takes over the doing, we risk skipping the thinking. When it becomes easier to generate options, we can lose the rigor of evaluating them. When tools produce polished artifacts, we may mistake visual clarity for actual alignment. This is the paradox of AI-powered agility. We can move faster than ever before and yet miss more than ever, too. So what do we do? We remember what Agile was always meant to be: a response to change and complexity, a framework for making sense of ambiguity, a culture that values learning over illusion, collaboration over control, and outcomes over vanity metrics. In this new era, the values of Agile remain not just relevant, they're essential, but the way we live them must evolve. That means recognizing Agile was never a mere collection of ceremonies. It's a cultural DNA that emphasizes collaboration, communication, empathy, and learning, reinforcing shared understanding even when AI tools provide tempting shortcuts, making space for disagreement and exploration even when suggestions seem good enough, designing decision-making practices that account for human judgment, ethical nuance, and context, especially where AI can't. Most of all, it means treating AI not as a teammate but as a variable in the system, one that requires continuous reflection, integration, and calibration. Agile doesn't need to be discarded, it needs to be re-understood. AI isn't replacing the need for agility, it's raising the stakes. And that brings us to the most important shift of all. In the age of AI, Agile can't just be about delivering faster. It must be about learning faster together not just iterating on products but on meaning; not just managing tasks, but deepening trust; not just building software but shaping systems of collaboration that are resilient, reflective, and ready to adapt no matter how the tools evolve because ultimately, agility was never about certainty and ceremony. It's always been about our capacity and capability to change. In the age of AI, remembering that core tenet is more important than ever.