Why AI without a Digital Factory is just a prototype
Our Approach to Digitalization
Today we would like to discuss with you AI at a larger scale – how we approach it while using it for acceleration in the end-to-end digitalization process. Seeing the bigger picture is crucial for real business impact.
AI implementation is continuing to speed up – copilots, chatbots, internal tools are everywhere. Companies are experimenting and speeding up processes. AI can produce some impressive-looking results, but don’t get too hyped about them before taking a closer look.
When examined in detail, most results bump into a problem with maintainability. They stay stuck somewhere between a demo and a pilot.
The illusion of progress
The prompts are usually good. The results from the LLMs are also impressively good. The engineers are great at doing their job and checking the quality of code. However, the system that combines all together is missing.
This phase of AI is about easiness. Everyone is currently vibe coding, accelerating processes, experimenting with the automation of tasks. Then reality hits and the solution is not integrated into core systems and there’s no clear leadership or ownership of how the results show be approached.
For companies, being able to scale with reliable systems is key for creating measurable business impact. So, “creators” using LLMs should focus more on operating all models they’ve created according to the needs of their business.
What comes after the prototype
Moving from prototype to production comes with a whole new set of problems:
- Data is scattered, fragmented, and often not ready for real use.
- There are no reliable pipelines to feed and maintain the system.
- Ownership is unclear - is it product, engineering, or data?
- Quality assurance is not adapted to probabilistic systems.
- There is no monitoring, no feedback loop, no continuous improvement.
- Security, privacy, and compliance concerns slow everything down.
- AI doesn’t create the problems itself, but these come as a software delivery problem.
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Enter the Digital Factory
We’re noticing this pattern and what’s helping us to get past it is the concept of a Digital Factory.
It follows the idea of managed processes and delivery management, but works more like a system, designed to turn ideas into actual effective and reliable digital products.
It combines:
- cross-functional teams with clear ownership
- structured delivery processes
- a solid engineering and platform foundation
- integrated data and AI capabilities
- governance through KPIs and feedback loops
Most importantly, it focuses on outcomes, and the ownership stays in place.
How it gives context to the AI era
Traditional software is deterministic - you build it, test it, deploy it, and it behaves as expected. AI on the other side depends on data – it evolves over time, behaves probabilistically, requires continuous tuning and validation. It is deeply connected to multiple systems and workflows.
In other words, we should treat AI not as a feature, but as a living system.
And like any living system, it needs an environment to survive and improve.
Without that environment:
models degrade
outputs become unreliable
trust disappears
the solution gets abandoned
And we have to pay close attention to these factors to try and get the best from the hype cycle.
The mindset shift you need to make
To move beyond prototypes, companies need to rethink how they build and operate software.
Plan thoroughly to move your focus:
- from experiments -> to systems
- from isolated tools -> to integrated capabilities
- from one-off projects -> to continuous delivery
- from vendors -> engineering partners
Having such groundbreaking technology as artificial intelligence leads to more than a technology shift – it is a full-scale operational shift.
The new milestones with software development
An AI-ready Digital Factory is putting the biggest focus on how everything works together. Make sure you include these milestones:
Teams are organized around products and outcomes.
Data is treated as a first-class asset, with ownership and pipelines.
AI is embedded into the delivery lifecycle.
Quality assurance evolves to include evaluation, edge cases, and real human behavior.
There are continuous feedback loops - from users to models to improvements.
Thus, AI is implemented as a system, part of the whole engineering process, giving it space to develop as a capability.
Final thoughts
In 2024, AI created the “wow” effect.
In 2026, the winners will be the companies that can operate AI at scale -with reliability, long-term quality, and real business impact.
So, from our decades of experience with software development, we stand behind the idea of a Digital Factory, it leads to measurable results with our project and it helps us adapt, accelerate and be efficient in the era of artificial intelligence.
If you have interest in the topic, continue with the topic of AI implementation with 4 of our projects that we turned to reality with full delivery management and system ownership: read the article.
