In the last two years, we have experienced an era of exploration in IT. Innovation labs have experimented with large language models and created some impressive prototypes. This phase of “play” was essential: it reduced fears of contact, spread technical expertise and cemented the strategic relevance of AI.
But while the joy of experimentation is flourishing, a look at the hard facts paints a sobering picture. According to a study by IDC (2025), around 70% of AI projects still fail due to the hurdle of scalability. We see a massive “adoption gap”: many experiments, but almost no comprehensive integration into productive live operation.
The strategic turning point: benefits through operationalization
It would be wrong to claim that prototypes are fundamentally worthless. They are the humus for digital transformation and important for internal learning. But there is one crucial point: while proof of concepts (PoCs) are valuable for exploration, they only develop their full economic benefit through reliable operationalization.
AI is only ever as useful to a company as it can be safely controlled, monitored and scaled. Without this framework, AI remains an “island talent” – impressive in the demo, but risky in everyday operations.
The hidden trap: when PoCs become an end in themselves
However, one critical aspect that we repeatedly observe at Parashift is the meaningfulness of many PoCs themselves. Valuable engineering time is often wasted on building solutions from scratch that already exist as standard features.
It is a paradox of the current AI wave: companies invest months of work and large budgets to develop complex extraction logic or validation workflows “custom”. In doing so, they overlook the fact that specialized platforms already offer these functionalities “out-of-the-box” and industrially proven.
The result? You put a lot of effort into building a bicycle, only to realize at the end that you actually need an airplane for productive live operation. A PoC should show whether a technology solves a business problem – it should not be used to painstakingly recreate standard infrastructure.
Why adoption is stalling in live operations: three key barriers
Despite existing solutions, companies are often reluctant to go live. The barriers are usually of a structural nature:
1. the trust and transparency dilemma (explainability)
Errors are often tolerated in a laboratory context. In live operation, for example in credit checks, they can threaten the existence of the company. Although advances in Explainable AI (XAI) today enable a better understanding of model decisions, their integration into productive processes requires in-depth expert knowledge.
2. scalability and infrastructure
A system for 50 documents does not need orchestration. A system for 50,000 documents does. This requires an architecture that offers monitoring, drift detection and automated retraining cycles – components that are usually missing in a simple PoC.
3. industry-specific compliance requirements
In the financial sector, liability and regulation (e.g. EU AI Act) dominate, while process stability is the main focus in logistics. A generic PoC often does not cover these specific requirements.
The market for solutions: Between generalists and specialists
If you want to operationalize AI, you have a choice:
- Horizontal platforms: Providers such as Microsoft Power Automate offer broad automation suites. They are good all-rounders, but often reach their limits when it comes to specialized document logic.
- Legacy specialists: Legacy OCR providers have experience, but sometimes struggle with the agility of modern cloud-native approaches.
- Focused infrastructure (The Parashift approach): We focus on bridging the gap between cutting-edge AI and the hard requirements of Intelligent Document Processing (IDP). Our goal is to ensure that companies no longer have to reinvent the wheel. We already offer the features that make the difference between success and failure in live operation – such as clear confidence values and integrated human-in-the-loop workflows – as standard. The PoC solutions can be integrated. We ensure operability.
Conclusion: agility needs an efficient path
Keep experimenting, because standing still is not an option. But do it with a clear path to operationalization and a critical eye on your resources.
Ask yourself with every new project: are we building real added value here or are we just reinventing a feature that we could simply source elsewhere? The competitive advantage of the future lies with those who put their energy into solving their business problems instead of laboriously replicating basic technology.
