It’s never been easier to build an app. A few prompts, a weekend of vibe coding, and suddenly you’ve got something that looks real. When screens load, buttons click, and data moves, your app feels like a product.
But here’s the uncomfortable truth: looking like a product and being a product are two very different things. And the gap between them is where most AI-built apps quietly fall apart.
Productionization requires product strategy, design systems, architectural rigor, security hardening, and a scale plan. In short, it requires the same discipline that’s always made software succeed, just applied to a radically different starting point.
Summary
Building an app with AI has never been faster, but looking like a product and being one are two very different things. From design consistency and security gaps to scalability risks and missing product strategy, the jump from prototype to production is where most AI-built apps quietly fall apart. Speed is only valuable when it’s paired with strategy, architecture, and experienced judgment. Leaders who understand where AI-generated output ends and real product discipline begins will be the ones who ship software worth trusting.
Speed Creates Value, But Not the Kind You Think
There’s no question that AI has compressed the early stages of product development. What used to take weeks of wireframing, scoping, and initial builds can now happen in hours. That speed is real, and it’s valuable. But the value isn’t where most people assume it is.
“From a design side, it creates a lot of value because we’re able to experiment very quickly and see what will work and what won’t,” said Rachel Foster, Slingshot’s Principal Product Designer. “It helps to be able to iterate and see things working almost immediately.”
Steve Anderson, Principal Developer and AWS Architect at Slingshot, agreed, adding that the speed also opens up room for the things that usually get cut. “It lets you focus on things that typically don’t get a lot of attention: tooling, documentation, testing; things that usually hit the cutting floor.”
Sarah Bhatia, Director of AI Product Innovation at Slingshot, framed it this way: “It compresses the timeline. The things that we were already doing, we can do faster. Specifically, we’ve seen a lot of speed with discovery, iteration, and internal tooling.”
The real value of AI-generated speed isn’t just getting to “done” faster; it’s getting clearer faster. Faster experiments, faster validation, and faster learning. But only if you treat speed as a tool for insight, not a shortcut past the work that still needs to happen.
AI Can’t See the Whole System (Yet)
One of the most deceptive qualities of an AI-built app is that it looks cohesive: screens connect, and flows make sense at first glance. But zoom out even slightly, and the cracks start to show.
“AI is not able to holistically look at a system yet,” Rachel explained. “We’re still seeing that we need to create site and app maps. We have to make sure that what it’s outputting actually makes sense and fits within the system. It does really well screen to screen, but it’s not thinking ‘whole system’ just yet.”
AI excels at generating components in isolation: it can build a beautiful login screen, a functional dashboard, and a clean settings page. But connecting those pieces into a unified, scalable system that makes sense for the user? That’s still a deeply human job.
Sarah put it plainly: “Ultimately, AI doesn’t have taste. It can generate options, but it can’t tell you which one is best. We still have to do a lot of shepherding and guiding of these tools and use human judgment to say what the best option is.”
Steve echoed that from the development side: “AI may make a different judgment than I would. It’s not inherently wrong, necessarily, but it may not be the approach that I think is best for the project.”
The takeaway for technology leaders: don’t mistake functional output for strategic output. An AI-built prototype is a starting point, not a system.
The Product Strategy Gap Is Wider Than Ever
AI has made it possible to build fast. But that speed has also exposed a gap that’s always existed in product development: the gap between having a working thing and having a viable product.
“Some sort of plan for go-to-market is still consistently the gap we’re seeing,” said Sarah. “With how quickly apps can get set up and moved through this iterative development process, you could get to production, and the question becomes: ‘Now what do we do?’”
The irony is sharp. The easier it becomes to build, the more critical it becomes to plan. Product-market fit, competitive differentiation, and positioning: none of these gets generated alongside your code.
“If you’ve got an idea, someone’s probably building it as you speak because new development can happen so fast. Especially if it’s low-hanging fruit,” Sarah added. “From a product perspective, you really have to be looking at product differentiators and product-market fit.”
And the tools to close that gap are more accessible than ever. “You have such a good opportunity to get a robust product plan out the gate now,” Sarah said. “The ability to really go through these business school-type exercises using AI tools is at everybody’s fingertips. There’s really no excuse not to be more holistically prepared as you start that process.”
Speed without strategy is just motion. The organizations that win will be the ones that use AI to accelerate both building and thinking.
Design Consistency Breaks Down at Scale
A vibe-coded prototype can look impressive: The colors match, the typography feels intentional, and the interactions are smooth. But the moment you try to scale that prototype into a real product with 10-plus screens, the illusion starts to unravel.
“When it comes to design consistency, AI can get it pretty close,” Rachel said. “But it’s typically not replicating things perfectly unless it’s hooked into a design system to reference, and even then, it still requires guidance and cross-checking.”
The deeper issue isn’t just visual inconsistency. It’s structural. Without a design system, a clear component library, and defined UX patterns, there’s no source of truth.
“You have these five screens that you just vibe-coded, and those work really well together,” Rachel continued. “But then what happens when you need to scale that to more screens, or there’s a whole new feature that gets added? The tendency is to nut-and-bolt things on, and that’s when user experience drastically plummets.”
Rachel also raised a point that’s easy to overlook but deeply relevant for any product handling sensitive data or transactions: “Users are very perceptive of small inconsistencies, like speed or button inconsistencies. Those small things are what lose trust, especially if you’re handling sensitive data like health information or payment processing.”
For CIOs evaluating an AI-generated prototype, the question shouldn’t focus on ‘does it look good?’ It should be ‘Does it have the structural foundation to stay good as it grows?’
Security Theater vs. Actual Security
Security isn’t usually top of mind during a vibe-coding session. But the moment an app moves toward production, it becomes one of the first things that matters.
“There’s no need to look at security until there’s something of value that you need to protect,” Steve acknowledged. “If you’re prototyping and not touching real data, it doesn’t matter.”
But the moment a product moves toward real users, data, and transactions, the calculus changes entirely.
Steve continued: “A lot of times, the UI will do some validation of data, or hide and show buttons, and it looks like you have security. But on the back end, those same controls aren’t implemented. So you’ve given the appearance of security without actually implementing security.”
That’s the core risk: security theater. The app looks locked down on the surface, but any determined actor can bypass client-side protections with minimal effort. Backend validation, proper access controls, authentication layers, data encryption: these are the things that actually protect users and data, and rarely come out of a vibe-coding session.
Sarah summed up the broader lifecycle consideration: “When you’re vibe coding, it depends on the intention. If you’re just trying to visualize a concept, I don’t think you have to put that much thought into security. But as your app gets through the lifecycle, it’s certainly something you have to consider.”
The message is straightforward: prototypes don’t need security. Products do. And the transition between those two states is where hardening becomes non-negotiable.
Scalability Isn’t a Future Problem. It’s a Now Problem.
One of the more dangerous assumptions about AI-built apps is that you can wait to think about scalability. The app works for you and your team, so it should work for a thousand users, right?
“You have this great idea, and it takes off, and then you find it doesn’t scale,” Steve said. “Now you’ve lost momentum, because the app is slow. You’re running the risk of souring people on your product because of outages.”
The problem isn’t just technical; it’s reputational. Early users are not a forgiving audience. If they encounter slowdowns, crashes, or broken flows, you lose credibility that’s incredibly hard to rebuild.
Sarah offered practical guidance: “Even though you can get an MVP out the door faster than ever, it’s still smart to consider where you’re going. The more possible directions you can think through up front, the better. That way, you’re making smart decisions to ensure scalability as you grow, and not ripping stuff out and rewriting as you go.”
Rachel highlighted the fundamental difference in approach: “Vibe coding is thinking of apps from front to back. Traditional software logic is back to the front: creating the strategy, architecture, UX patterns, and app map before development.”
Production-readiness exists where rapid AI generation meets intentional, experienced team building.
From Prototype to Product: Where Experienced Teams Make the Difference
So, where does all of this leave a technology leader sitting on an AI-built prototype?
The prototype isn’t the problem. The gap between what it is and what it needs to become is the problem. Closing that gap requires evaluating the product through a lens that AI tools simply don’t provide on their own: architectural soundness, UX scalability, security posture, product-market fit, and a roadmap that extends beyond launch day.
The rise of AI-generated apps isn’t slowing down. The tools will only get better, faster, and more capable. But the fundamentals of what makes software production-ready haven’t changed: clear strategy, cohesive design, sound architecture, real security, and a growth plan.
The question worth sitting with isn’t whether AI can build your app. It’s whether your organization is ready to close the gap between what AI generates and what production demands. The teams that answer honestly and early are the ones that will ship products worth trusting.
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Written by: Savannah Cherry
Savannah is our one-woman marketing department. She posts, writes, and creates all things Slingshot. While she may not be making software for you, she does have a minor in Computer Information Systems. We’d call her the opposite of a procrastinator: she can’t rest until all her work is done. She loves playing her switch and meal-prepping.
Expert: Sarah Bhatia
Sarah Bhatia brings people together. In her decade plus of product and product-adjacent experience, her focus has been on cross-functional collaboration, asking lots of questions, and getting big results. She excels at strategy development, and getting the right brains in the room to solve big problems. Sarah would describe herself as a daredevil, because she’s not afraid to ask “dumb“ questions, get smart answers, and take (calculated) risks.
Expert: Steve Anderson
Steve is one of our AWS certified solutions architects. Whether it’s coding, testing, deployment, support, infrastructure, or server set-up, he’s always thinking about the cloud as he builds. Steve is extremely adaptable, and can pick up the project and run with it. He’s flexible and able to fill in where needed. In his spare time, he enjoys family time, the outdoors and reading.
Edited by: Rachel Foster
As UX Lead, Rachel helps guide the design team through strategy, interviews, creation, and testing. Designing software and apps to be both intuitive & beautifully impactful plays into Rachel’s strong desire to connect others. Relying on her Fine Arts background & honed intuition, she gets sudden flashes of ideas and follows them wherever they lead.
Frequently Asked Questions
Operationalizing an AI-built app means taking a prototype or proof of concept built with AI tools and making it production-ready. This includes adding product strategy, design systems, architectural rigor, security hardening, and a scalability plan so the app can reliably serve real users and handle growth.
AI-built prototypes often fail in production because they lack the foundational elements real software requires. Common gaps include missing backend security controls, inconsistent design systems that break at scale, no product-market fit strategy, and architectures that weren't built to handle growing user loads. The app may look and feel functional, but without experienced oversight, critical layers are often missing.
Vibe coding is a powerful way to visualize concepts and iterate quickly, but it is not sufficient for launching a production-ready product. It typically produces front-end experiences without proper backend validation, security hardening, scalable architecture, or cohesive design systems. Turning a vibe-coded prototype into a real product requires experienced teams to close those gaps.
AI-generated applications often create what's known as "security theater," where the interface appears secure through hidden buttons or client-side validation, but the backend lacks real protections. Common risks include missing backend access controls, absent authentication layers, and a lack of data encryption. Any app handling real user data or transactions needs proper security hardening before going to production.
A working prototype demonstrates a concept: screens load, buttons click, and data moves. A production-ready product goes further with cohesive design systems, sound architecture, real security controls, a scalability plan, and a clear product strategy including go-to-market positioning. The gap between the two is where most AI-built apps stall without experienced product, design, and engineering support.
