The Hidden Challenge Ahead for Software Teams in the Age of AI

Software companies are starting to utilize AI to build more stuff faster. But is the rest of the organization ready?

There’s an enormous challenge looming for software companies—and I suspect many aren’t prepared for the wave that’s about to hit. Artificial intelligence is advancing rapidly, and I believe organizations will soon scramble to maintain operational efficiency in response. Here’s why.

I teach a college course on innovation, and as you might expect, we spend a lot of time discussing AI and the transformational era it has ushered in. For the first time, we have the data, computing power, thought leadership, and commercial tooling to truly harness AI as a utility. I often tell my students they’re fortunate: yes, AI will replace some jobs—but it will also create new ones and unlock massive opportunities. Early in my career, I spent countless hours as a business analyst compiling reports for internal teams. Today, an AI agent could likely handle that in minutes.

The same shift is underway in software engineering. Routine, repetitive coding is increasingly being handled by AI, allowing developers to release more code and features at faster speeds. What may be a strategic advantage today will become table stakes in just a few years.

But here’s the real question: are we truly ready?

Yes, I know—I named this blog Build It, Ship It. So how can I possibly argue against shipping more product, faster? I’m not. And I shouldn’t. But many books have been written on the dangers of bloated feature sets and building beyond what’s necessary. For the sake of this post, let’s assume every feature shipped provides real, validated customer value. (Imagine a world where AI agents actually improve existing features rather than just adding new ones. That’s a future I’d like to see.)

Still, in a world where AI accelerates product delivery, what does this mean for the customer?

Let’s consider a simplified version of the software value chain. Today, the bottleneck exists within R&D—features and requests pile up in the backlog, far exceeding what teams can deliver.

If AI speeds up the development side of the equation, does that backlog suddenly disappear?

Not quite.

As “Development” accelerates, we need to examine the rest of the chain. Can your Design team keep up? Can AI effectively test and validate design hypotheses, run customer feedback loops, and maintain a consistent UX across platforms? Perhaps, but it’s not getting nearly enough attention.

And what about Architecture? Can AI consider long-term system health, scalability, and risk in the same way a seasoned architect does? Even if you solve the development bottleneck, architectural decision-making could become the next constraint.

Let’s say we overcome these as well. QA has been using automation for years and is well-positioned to adopt AI-driven testing. While writing test plans and handling edge cases may still need human oversight, this function seems adaptable. But now we hit a different kind of bottleneck.

My favorite definition of product management involves balancing value creation with value capture: we can’t monetize what we don’t build, and we shouldn’t build what we can’t monetize. So, the question becomes: can your go-to-market teams keep pace with accelerated product releases? The logjam has moved.

Will Marketing be ready to effectively launch new features? Can they update messaging and positioning quickly enough? Will Sales be enabled to speak confidently about new capabilities? Can Support teams learn fast enough to guide customers through the latest updates?

Most software leaders might shrug and say, “Bring it on.” Faster development is a good problem to have—one they think can be solved with more resources or, again, AI agents. I don’t disagree. These bottlenecks can be addressed. But I’m not convinced most leaders are even anticipating this issue. They’re focused on speeding up R&D because roadmap delays are the most visible pain point—and their competitors are doing the same.

The real call to action? Strengthen and scale the rest of the value chain. Make sure your organization can absorb, communicate, and support a faster stream of product delivery. If you don’t, customers may never realize what you’ve shipped—or worse, they may feel confused, unsupported, and left behind.

Build it. Ship it. But don’t forget to market it, sell it, support it, and educate around it too.