Agile methodologies have changed the way software is developed. They brought speed, short cycles, continuous delivery, and a culture of rapid adaptation. For IT directors, this model is almost mandatory today. According to VersionOne, 95% of organizations have already adopted agile practices at some level.
The challenge is that validation, when done in a traditional way, can take up too much time. Manual testing, lengthy reviews, and extensive approvals end up delaying cycles that should be short. In an agile environment, every sprint needs to move fast, and when validation can’t keep up with that pace, the benefits of agility are lost. This is where artificial intelligence makes a difference, accelerating validation without sacrificing quality.
And here’s the key question: in an agile cycle, where does validation begin, and where does it end?
How Does Project Validation Work in the Agile Cycle?
Validating deliveries isn’t just about running tests to make sure the system doesn’t break. It’s about ensuring that what has been developed actually meets the real needs of the user or the business area.
That means validation goes beyond code. It involves confirming value, usability, and alignment with the project’s objectives. In practice, it starts very early, right in the backlog refinement, when user stories are defined. If a story doesn’t make sense, you already have a validation problem.
And when does it end? It doesn’t stop at technical approval. Validation is only complete when the delivery reaches the user and generates a positive impact. In other words, it’s a continuous process that permeates the entire agile cycle.
The Role of Agile Methodologies in Validation
Frameworks like Scrum and Kanban already include checkpoints. The sprint review, for example, is a clear opportunity for validation: the team demonstrates what was built, and the client assesses whether it meets expectations.
The problem is that, when validation happens in a traditional way, it tends to be slow and unable to keep up with agile cadence. Manual tests and long approval stages delay cycles that are supposed to be fast. On top of that, business teams often take part in validation without having enough technical knowledge to evaluate thoroughly, which leads to mistakes or superficial approvals. The result is a process that consumes time and still leaves quality gaps.
This is where AI naturally connects with agile: automating tests, anticipating failures, and providing continuous feedback. Validation then becomes a seamless part of each sprint, ensuring fast and reliable deliveries.
The Challenge of Validation in Traditional IT Flows
For IT directors and managers, validating deliveries is a recurring pain point. Some common obstacles include:
- Difficult alignment between technology and business: Many times, what is delivered works from a technical standpoint but not from a strategic one, the opposite scenario also happens.
- Pressure for speed: The focus on releasing quickly can be disrupted by an inefficient validation process.
- Lack of objective criteria: Without clear metrics, validation becomes a matter of perception.
The impact is significant. McKinsey reports that 45% of IT projects exceed their original budget, and much of this is due to flaws in requirements definition and validation. In other words, skipping validation early on leads to higher costs later.
How AI Transforms Delivery Validation
This is the turning point: artificial intelligence. When integrated into the agile cycle, it changes the validation game in three main ways:
- Test automation: AI can generate, run, and fix tests continuously. This speeds up the process and reduces human errors.
- Scenario simulation: AI models analyze data and simulate behaviors to predict how the system will perform under different conditions.
- Smart feedback: Algorithms cross-check business requirements against technical deliveries, pointing out possible gaps during development.
Where Does Validation Begin and End in the Agile Cycle?
The short answer: it starts in planning and only ends in the impact analysis once in production.
In the traditional model, validation is seen as something that happens at the end. In agile, it needs to be considered from backlog creation and tracked sprint by sprint. With AI, this validation becomes continuous, because every increment is already tested, adjusted, and aligned with the expected value.
The outcome is a cycle where the team doesn’t just deliver fast, but delivers right. It’s not just about “running software,” but about running software that generates real business impact.
Why is NextAge your Ideal Partner?
At the end of the day, without validation, agility becomes a risk. Validating deliveries in the agile cycle is what ensures that speed doesn’t turn into rework.
NextAge understands this and goes even further: it offers an innovative service that integrates artificial intelligence into the entire development cycle, with a focus on validating projects in an agile, intelligent, and reliable way. Learn more here.
If you are an IT decision-maker looking to reduce risks, gain speed, and have clarity on the real value of your deliveries, the answer is simple: talk to NextAge. We’ll help your company turn validation into a competitive advantage.
