Stermedia

An AI demo can look impressive.

But production is where the real test begins.

A production AI system has to work with messy data, existing tools, security requirements, user habits and business exceptions.

That is why many AI projects stop after the pilot stage.

The problem is not always the model.

Often, the missing part is deployment: connecting AI with the real workflow.

This is exactly where Forward Deployed Engineering becomes important.

We explain this in our article:
https://stermedia.ai/forward-deployed-engineering-why-enterprise-ai-needs-more-than-a-model/

Healthcare AI needs more than a model.

It needs a trustworthy process.

In one of our projects, the challenge was to transform complex clinical trial documents into structured data ready for further analysis.

The solution combined OCR, AI, structured outputs, validation and human review.

This is a good example of Forward Deployed Engineering in practice.

The goal was not only to use AI.

The goal was to make AI work inside a real expert workflow.

Read the case study:
https://stermedia.ai/ai-for-clinical-trial-planning-at-scale/

Is Warsaw becoming one of the most important AI hubs in Central and Eastern Europe?

AI growth is not driven by models alone. It needs business demand, engineering talent, data, funding, infrastructure, and implementation partners that can turn ideas into working products.

In our new article, “Artificial Intelligence in Warsaw: Why the City Matters,” we look at why Poland’s capital is becoming a serious location for AI development, automation, and data-driven products.

We dive into:
🔹 Why Warsaw’s startup, fintech, and enterprise ecosystem creates strong demand for AI solutions
🔹 How the city combines engineering talent, business buyers, investors, and public-sector momentum
🔹 Why production-ready AI requires more than generic tools — it needs software architecture, integration, testing, and governance
🔹 Where AI demand in Warsaw is likely to grow, from fintech and healthcare to cybersecurity, logistics, and public services

As we wrote in the article:

“AI does not scale through models alone. It needs business problems, data, technical talent, infrastructure, capital and implementation partners.”

Read the full article to see why Warsaw is becoming a practical environment for building AI products that solve real operational problems.

👇 Link in the comments!

Is your business dependent on a legacy system that nobody wants to touch — but nobody can afford to break?

Taking over an existing product is rarely just a technical handover. In most cases, it means stepping into a live business environment where stability, delivery, and customer trust all depend on decisions made under pressure. In our new article, "Legacy System Takeover Without Downtime," we look at how to take over a legacy platform without putting business continuity at risk.

We dive into:
🔹 Why a legacy system takeover should start with stability, visibility, and operational control — not a full rewrite
🔹 How to modernize incrementally using seams, phased rollout, and fallback mechanisms
🔹 What companies should prioritize first when taking over a system from another vendor or internal team

As we wrote in the article:

"A successful legacy system takeover protects continuity first and transforms architecture second."

Read the full article to see how companies can regain control over inherited systems without disrupting day-to-day operations.

👇 Link in the comments!