About the author
Darko Špoljarić
VP of AI
In AI projects, “rapid prototyping” is often just the engine inside the POC – it’s not something separate. The question is whether to start with a POC vs MVP, and when to choose which?
We learned this first-hand during a recent project where we started out expecting a multi-week POC process. But when we went to test with our existing models, we were able to prove the concept worked with 80% accuracy right away. What could’ve taken several weeks ended up taking just a single afternoon.
If we’d gone the more risk-averse route of going from POC to MVP to product pipeline, we would’ve spent weeks refining that proof. Instead, we moved directly to MVP because the core question was already answered: “Does this work?” Yes. The remaining 20% wasn’t about proving feasibility; it was about optimization, something only real users could tell us.
That’s the insight that changes how teams approach product development in 2025 and beyond. The traditional product pipeline still makes sense. But the economics of validation have shifted dramatically. Today, a POC is best used as targeted risk reduction. Not a default habit, but a tool when neither team nor client is sure about feasibility, or when there’s actual risk that needs managing.
How AI has changed POC’s role and when it matters
Historically, teams would rush straight to MVP – often with little thought for core feasibility, because the technology risks were obvious or minimal, and business owners weren’t focused on them. With the rapid advance of AI, that’s changed. Suddenly, both tech and business teams face new questions like, “Is AI even the right tool for this use case?” Now, POC is newly important – not because it’s obsolete, but because both sides sometimes need to stop and validate.
Of course, in simple or well-understood domains, you can sometimes validate technical feasibility through a few days of hands-on prototyping. This doesn’t mean the POC disappears. Instead, rapid prototyping is often the mechanism inside your MVP. The distinction is less about naming, and more about risk: if the risk is low, iterate quickly and move forward; if risk is high or the domain is unfamiliar, formalize your POC and document it.
It’s not about eliminating the POC phase altogether, but about introducing it smartly – using rapid prototyping when risk is low, and structured POC when risk is significant or the domain is unfamiliar.
Understanding when you need which is the difference between moving fast and moving wastefully.
The POC approach: When complexity demands understanding
But not every project works this way.
Take a financial assistant system – one designed to analyze income and expenses, predict scenarios, and recommend adjustments for goals like early retirement. This isn’t a simple language problem; it’s a complex, multi-faceted domain where understanding matters enormously.
The validation questions multiply:
- How accurately does the system classify financial transactions?
- How reliable are predictions based on incomplete data?
- What happens at the edges – irregular income, unexpected expenses, market changes?
- Which functions should be AI-driven and which should remain deterministic (calculator, Excel, established financial formulas)?
- What compliance constraints apply?
These questions can’t be answered with rapid prototyping alone. They require structured discovery – understanding the domain deeply before building toward a solution.
This is where a formal POC approach makes sense. Not building a prototype and testing it, but rather conducting a POC Discovery workshop: a structured exploration where you map the domain, identify where AI genuinely adds value versus creates risk, and develop specific hypotheses to test.
The output is clarity – not just “can this work?” but “what’s the optimal way to build this? Where should the slider between AI and traditional code sit?”
The slider concept: Where AI meets traditional code
Here’s what most teams get wrong: they assume every problem should be solved AI-first.
In language-heavy domains like the data enrichment project mentioned earlier, the answer is close to 100% AI-driven. These systems are what modern language models do best. But in number-heavy or mixed domains, the answer is fundamentally different.
Think of it as a slider. On one end, pure AI. On the other, traditional deterministic code and databases.
For the data enriching tool, the slider sits almost entirely on the AI side. But for financial systems, for example, it’s somewhere in the middle. AI handles pattern detection and recommendations, while deterministic code handles calculations, compliance, and core logic.
You wouldn’t use ChatGPT to sum a list of numbers, right? That’s what we have calculators for. Excel spreadsheets are the golden standard for that kind of work. Don’t abandon what already works just because AI is shiny and new.
Knowing where your slider sits determines your entire approach:
- Language-heavy domains (content, copywriting, summarization): Slider far right (AI-heavy). Rapid prototyping works. Move to MVP quickly.
- Mixed domains (finance, operations, recommendations): Slider in the middle. POC Discovery is essential. You need to understand the domain before building.
- Deterministic domains (compliance, regulations, calculations): Slider far left. Traditional development. POC doesn’t add much value.
Skip this analysis, and you’ll either waste weeks validating something that never needed validation, or you’ll waste months building the wrong solution in a complex domain.
The cost of mistiming
There are two ways to get this wrong:
Mistake 1: Extending POCs
You’ve already proven capability at 80%. You know your slider positions, understand the domain, and the use cases. But you continue refining, testing, validating (under AI engineering influence), and delaying the moment you can get real feedback from real users.
Every day spent perfecting a prototype in a controlled environment is a day not spent learning what actual users will do with your system. Users create edge cases. They have mental models you didn’t anticipate. They use features in unexpected combinations.
None of this emerges until real deployment. If you already have 80% confidence, deploying an MVP will teach you more than additional POC work.
Mistake 2: Skipping discovery in complex domains
Conversely, teams working in complex domains who skip formal POC discovery often discover critical problems midway through MVP development. And then they’re forced into expensive pivots or very steep learning curves, because they didn’t understand the domain well enough.
A proper POC Discovery workshop answers questions that can’t be answered through rapid prototyping alone. It’s insurance against building the wrong thing.
How to tell which is right – rapid prototyping or POC?
Here’s how to decide which approach makes sense for your next project:
What rapid prototyping actually looks like
It’s not chaos. It’s structured speed:
- Identify your core assumption – What one thing must be true for this to work?
- Design the minimal test – What’s the absolute smallest thing you can build or test to answer that question?
- Build/test it – Days, not weeks
- Observe the results – Does it validate your assumption at 80%+?
- If yes: plan MVP – What will real users teach you?
- If no: pivot or abandon – Better now than after months of development
The goal isn’t perfection. It’s fast learning.
We’ve seen this play out with our clients. One project started with what we thought would be a multi-week POC phase. Within a week, we’d already validated the core hypothesis. If we were starting that same project today with what we know now, we’d probably skip the POC entirely and go straight to MVP.
Why? Because we already know it works. The question now is how to make it better, and that can only be answered by real users in real scenarios.
The modern development cycle
AI has compressed timelines for certain types of problems while making others more complex. The teams that move fastest are those who understand which category their problem falls into.
For language-based problems, rapid prototyping is your friend. For complex domains, a proper discovery process isn’t a luxury—it’s essential planning.
The future of efficient product development isn’t about moving faster through every phase. It’s about moving intelligently: compressing the phases that can be compressed, protecting the time for phases that genuinely reduce risk, and getting real feedback as quickly as possible.
Because here’s the thing; you can spend 18 months bouncing from POC to POC, getting incrementally smarter about each domain. Or you can move fast where it makes sense, slow down where it matters, and ship products that people actually use.
We’ve done both. Trust me—the second approach ages you a lot less.
Conclusion
As you evaluate your next project:
- Classify your domain – Is it language, numbers, mixed, or deterministic?
- Assess complexity – How well do you understand the problem space?
- Test your core assumption – What’s the minimal validation possible?
- Know your 80% milestone – When is capability proven enough to move on?
- Plan for real learning – What questions can only users answer?
Don’t get stuck in analysis paralysis. Don’t ship something untested. Find the middle ground: use rapid prototyping inside the MVP when risk is low, and structured POC phase when risk is high or the domain is unfamiliar.
The question isn’t whether to eliminate the POC, but how to use it wisely: Does this project truly require structured risk reduction, or is confidence already high enough to go to MVP?
Answer that based on real risk, and you’ll move from POC (with rapid prototyping where possible) to MVP to Product far more efficiently than teams still following yesterday’s playbook.
For further guidance and insights, contact us and talk to our experts.
