From PoC to production: how to know if your generative AI proof of concept is ready to scale
Many generative AI PoCs end with a positive result and, even so, never turn into a real project in production. This usually happens because the company doesn’t clearly know which criteria to evaluate when deciding whether it’s worth scaling. This guide shows what to consider before turning a successful proof of concept into a solution used in day-to-day operations.
Did the PoC answer the right question?
Before any other evaluation, you need to confirm that the PoC actually validated the business hypothesis defined at the start, and didn’t just show that the technology works in a generic way. If the success criterion defined at the beginning was met, using the company’s real data, that’s the first sign that it makes sense to move forward.
Does quality hold up outside the controlled environment?
A PoC usually runs with a selected sample of data, in an isolated environment and with few users testing. Before scaling, you need to evaluate whether the quality of the responses holds up when exposed to a larger volume of real data, varied use cases, and users who weren’t trained on how to interact with the system.
Does the cost at scale still make sense?
The cost of running a small PoC for a few weeks is very different from the cost of running the same solution in production, with real usage volume every day. Before scaling, it’s worth projecting the expected monthly cost based on real usage volume and confirming that the expected return still justifies that investment.
Is there a governance and security plan for sensitive data?
In production, the volume and sensitivity of the data processed tend to increase. You need to confirm that there are access controls, data retention policies, and compliance with the regulatory requirements of the company’s industry, something that often isn’t fully defined during the PoC phase.
Does the solution integrate with the systems the operation already uses?
A PoC frequently runs in isolation, without full integration with the company’s internal systems. To become production, it’s usually necessary to connect the solution to systems such as CRM, ERP, or existing support platforms, which requires additional technical planning.
Is there a plan to monitor and improve the model over time?
Unlike traditional software, a generative AI solution in production needs continuous monitoring of the quality of its responses, with defined metrics and a clear process to adjust prompts, update knowledge bases, or review the model used as real usage reveals new patterns.
When the answer isn’t yes yet
If any of these points hasn’t been resolved yet, it doesn’t mean the PoC failed. It means the next step isn’t to scale straight to production, but rather to expand the test in a controlled scope, closing the gaps identified before committing the company’s real operation to a solution that’s still immature.
CloudDog supports companies from the generative AI PoC with Amazon Bedrock all the way to the evolution into a secure production environment with proper governance. Learn about our Generative AI Proof of Concept service and safely plan the next step after your PoC.
