Why AI for Coding Won’t Save Your Sprint

[#1]

The other day, a PM said to me, “I want my engineering team to start using AI.”

So, I asked her the 3 WHYs.

Q: Why do you want them to use AI?
A: “To help them be more productive.”

Q: Why do you think they’re not productive today?
A: “They never finish sprint tasks on time.”

Q: Why is that?
A: “They keep finding tasks they didn’t think of at the start.”

Here’s the insight we landed on together:

The core problem wasn’t engineering velocity.
It was missing context.

More upfront thinking, clearer requirements, and better alignment at the start of the sprint would drive far more efficiency than simply adding AI to speed up delivery.

Because no matter how fast engineers code — if they start without the full picture, they will uncover gaps too late.

This reminded me how often Agile teams confuse speed with clarity.
Improving sprint outcomes isn’t just about adding tools.
It’s about creating shared understanding before the work begins.

Curious to hear:
What are your go-to practices for getting teams aligned before a sprint kicks off?

[Nikolaus Varzakakos _Qlerify_]

This is so true and I fully agree! Thanks for a great article. I see this all the time. - dev teams fail to meet sprint objectives regularly and it is not the developers’ fault. In my experience it is insufficient preparation such as insufficient sprint planning (clarity) and insufficient Definition of Ready criteria or discipline. The solution is to provide more context to the developers and stick to the process. One way to do this efficiently is to use AI to add more context and not only rely on vague user stories. Process maps, UI sketches, draft data models, draft architecture designs, test cases, business rules,  acceptance criteria can be added quickly with AI providing all the context developers need. The AI generated content can be refined quickly by the team before the sprint starts. 

[Vishal Biyani]

While full clarity upfront is ideal, waiting too long for it can lead to delays. AI can enhance productivity by handling repetitive tasks, writing or generating unit test cases, and accelerating debugging—saving developers significant time and effort.
However, simply adding AI tools won’t solve the root problem if foundational alignment is missing. Thorough grooming sessions with the development team and client stakeholders help reduce gaps, yet unforeseen scenarios may still arise due to knowledge or understanding limitations. Striking the right balance between preparation and adaptability is key to driving efficiency

[SilkeS]

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