Twenty-five years ago, seventeen software developers met at a ski resort in Utah and published a manifesto that changed how products get built. They challenged the dominant model of the time: long planning cycles, extensive documentation, rigid specifications, and change control boards. The Manifesto for Agile Software Development argued for something different: short iterations, customer collaboration, and responding to change. For a history of what happened on that day 25 years ago, check out our behind the scenes look at the writing of the Agile Manifesto.
The manifesto didn’t just describe a better way to build software. It predicted which companies would win.
The Original Competitive Split
Over the following decade, a clear pattern emerged. Companies that adopted Agile principles shipped faster, learned faster, and adapted faster. Companies that remained committed to traditional SDLC practices fell behind. Not immediately, but relentlessly.
The advantage wasn’t just speed. Agile companies could test hypotheses, discover what customers actually wanted, and pivot based on real feedback. Waterfall companies could deliver exactly what was specified, often late and over budget. The problem: specifications were also often wrong, and by the time they discovered it, competitors had already shipped three iterations and learned what actually worked.
Traditional organizations optimized for predictability. They measured success by adherence to plans. Agile organizations optimized for learning and flexibility. They measured success by market response. When both approaches competed in the same market, predictability lost to adaptability every time.
Today: The Same Pattern, Accelerated
We’re watching this pattern repeat. AI tools enable teams to build and test features in weeks that previously took months. But the competitive advantage doesn’t go to whoever adopts AI tools first. It goes to organizations that can exploit them.
Here’s where AI reveals organizational dysfunction the same way Scrum does.
In Scrum, the sprint creates transparency. If you cannot deliver working software at the end of the sprint, everyone sees it. You can’t claim to be “90% done” or “on track.” The software either works or it doesn’t. It is either in the customer’s hands or it isn’t it. That transparency exposes the real problems: unclear requirements, technical debt, dependency chains, approval bottlenecks. Scrum doesn’t solve these problems. It makes them impossible to ignore.
AI does the same thing, faster. When AI tools can generate a feature in three days, but that feature takes six weeks to reach customers, the bottleneck becomes obvious. It’s not development capacity. It’s the approval chain. The change control board. The architectural review committee. The release schedule. The organizational structure that requires three teams to coordinate and two managers to sign off.
When development was slow, these bottlenecks were hidden in the overall timeline. When development is fast, they stand out. Organizations can no longer blame slow delivery on development complexity. The code exists. What’s blocking it from reaching customers?
The companies exploiting AI effectively aren’t just the ones with the best tools. They’re organizations that can make strategic decisions about where AI creates value, maintain quality while moving faster, empower teams to execute, and adapt as capabilities evolve. The same capabilities that separated Agile winners from the rest twenty-five years ago.
The pattern compounds faster with AI than it did with Agile. When the Manifesto for Agile Software Development was published, organizations had years to adapt. Companies could observe competitors, see the advantages, and gradually transition. With AI, the gap widens monthly. Companies exploiting AI effectively today are already pulling ahead while others are still setting up pilot programs.
Organizations that genuinely adopted Agile principles already have the ability to adapt quickly to the changes in the market, respond to competitive AI threats, foster AI innovation at all levels, and attract and retain top talent. Organizations that resisted change or superficially adopted Agile now face even larger hurdles in responding to the latest AI market disruptor, only with less time to execute it.
