From PoC to Production: Many Proofs of Concept, but Few in Production.

We have spent more than two very intense years with Artificial Intelligence everywhere and at every level. This has triggered and driven an enormous number of proof of concepts, applying different technologies to different use cases to demonstrate potential returns for businesses and customers.

All of this has worked relatively well. Beyond the constant change and evolution across the entire technological landscape related to AI, there are no major problems until the moment comes to make it production-ready.

The reality of AI in production

As we already mentioned in the first post of this three-part series, the AI Platform is no longer an option but a necessity. But why is this need so urgent? In my opinion, it’s because we are losing money, time, and opportunities every single day.

Most companies are trapped in a discouraging “Groundhog Day” loop: proofs of concept tend to succeed, but moving them into production is painful. The reality is that Data Scientists perform magic in their notebooks and the results are promising, but when trying to move those models into the real world, they hit a wall of bureaucracy, tickets, lack of standardized tools, missing automation, and security team bottlenecks.

It becomes a nightmare. And that nightmare translates into very concrete bottlenecks and problems that the AI Platform must eliminate.

Problem 1: self-service and the pain of TicketOps

Let’s put ourselves in the shoes of the AI team. They have just trained a model that could save millions, and all they need is surprisingly to deploy it.

What happens next? In 90% of cases, they need to contact an infrastructure team, open a ticket, wait three days to get a namespace assigned, request network permissions, and hope for access to a feature database. This is what we call "TicketOps", and it is a major problem for Time-to-Market.

⚠️ Important: this process is necessary. We should not underestimate the potential security or regulatory compliance issues that could arise.

However, the situation is becoming even worse. With the rise of LLMs, the problem has grown significantly. Deploying an LLM is very different from deploying a simple regression model. It may require GPUs, specialized storage, and APIs that manage the complexity of prompting and memory usage. Without a self-service tool that allows this to be deployed in minutes, it simply does not happen. The democratization of LLMs ends right there.

Previously, a Data Scientist could request a server and that solved everything. Today, it is necessary to deploy a microservice that consumes an LLM, uses a vector database for long-term memory, and also requires dedicated access to a GPU node in the cluster. If that process cannot be reduced to a single command in our IDP, we are creating a technical barrier for every new AI idea — directly impacting the business and, therefore, our customers.

Problem 2: compliance and regulatory requirements

When we talk about AI, the risk is enormous. A model that makes biased decisions or operates with outdated (or private) data can cost a fortune in fines or even destroy a company’s reputation.

Here, the problem is twofold:

1. Quality

The AI Platform must guarantee that training data is consistent. If we do not standardize how data is ingested and versioned, the production model will inevitably drift. It is Garbage In, Garbage Out taken to the extreme.

The consistency problem is caused by the absence of a Feature Store. Data Science teams often calculate the mean or standard deviation of a data column during training, but the code that calculates that feature in real-time in production is different.

A Feature Store, managed by the platform, guarantees that the code used to compute a feature is the same during training and serving (production). It is the only way to ensure mathematical consistency.

2. Security

Who has access to the model? How are changes in code and datasets tracked? Without traceability and security policies by design (integrated into the Golden Paths), auditing becomes impossible.

And honestly, the idea of a Data Science team manually configuring firewall rules gives me chills. It’s a disaster waiting to happen.

Problem 3: Time-to-Market (TTM) pressure

This is the point that matters most to the business. What is the value of having an innovative model if it takes six months to reach the customer? Competitors are not waiting, and Time-to-Market becomes the most critical business metric.

Today, platform teams face enormous pressure to operationalize AI (that is, to make it reliable and fast). They must move at the speed of innovation, not at the speed of infrastructure.

The solution — and this is where Platform Engineering starts to feel almost like science fiction, is moving from TicketOps to Intent-to-Infrastructure.

In other words, giving a Data Scientist the ability to tell the platform: "I want an environment to train a classification model with a specific dataset."

The platform automatically translates that intent into all the required infrastructure (Kubernetes, networking, secure storage) in a matter of minutes.

This removes the biggest bottleneck: friction and dependency between teams. It allows us to move from idea to production at the speed the business demands.

Conclusion

If we combine bureaucracy and rigid processes (often tied to ticket-based workflows), the importance of data quality, and relentless Time-to-Market pressure, we quickly understand that AI without a platform becomes an expensive and frustrating research project, not a competitive advantage.

AI platforms are the only tool that allows organizations to maintain control without sacrificing speed.

We have seen the real problems and bottlenecks that slow down the ability of AI to deliver value to businesses and customers. In the final post of this series, we will stop talking about pain and start focusing on solutions.

We will explore what the industry is doing, with concrete examples and use cases, and define the first steps of a practical roadmap so your team can start building that model factory today.