The biggest problem in AI projects is not the technology

AI is no longer a topic for the future. Companies around the world are investing in data platforms, experimenting with new applications, and developing their first AI use cases.

Yet there is often a major gap between technical feasibility and actual deployment within the company.

Many organizations begin with PoCs (proofs of concept) or pilot projects, but fail to turn them into productive systems. The path from idea to scalable application in day-to-day business is far more complex than it may seem at first glance.

Current studies show that this problem does not just affect individual companies, but represents a structural pattern.

Global developments: innovation, scaling, and regulation

AI is developing internationally with different priorities.

While the US continues to be seen as the innovation leader and many of the leading AI models and platforms are created there, China is focusing heavily on industrial scale. AI is being systematically integrated into economic processes there and deployed broadly across various industries.

Europe, by contrast, is pursuing a different approach. Here, the focus is more on trustworthy and regulated AI, for example through initiatives such as the AI Act. Europe’s strengths lie above all in industrial expertise, quality standards, and governance.

At the same time, however, it is clear that Europe needs to catch up in international competition, especially in terms of speed, scale, and the commercial implementation of AI.

Austria in comparison on AI

And what does the situation in Austria look like?

According to a McKinsey study, Austria is slightly below the European and global average in terms of AI maturity.

The average AIQ score of Austrian companies is 30, compared with an EU average of 34 and a global average of 36. AIQ (Artificial Intelligence Quotient) measures the maturity of companies in their use of AI – in other words, how strategically, technologically, and organizationally AI is already being applied.

The challenge becomes even clearer when looking at the economic impact of AI:

• 61% of companies report no or only minimal efficiency gains from AI
• Only 20% have a clear AI strategy
• 84% operate without defined KPIs for their AI initiatives
• Only 6% manage to integrate AI quickly into their business processes

At the same time, many organizations already have a solid technological foundation. Around two thirds of companies have an AI-ready IT architecture as well as high security standards.

The challenge therefore often lies not in the technology itself, but in the implementation.

From proof of concept to a productive AI system

Many AI projects begin with a PoC (proof of concept). This is an initial experimental implementation used to test whether an idea works technically and whether a specific use case can fundamentally be solved with AI.

A PoC primarily serves to test new technologies quickly and gather initial experience with data, models, and possible use cases. This allows companies to assess relatively early on whether an idea is technically feasible and whether it basically has the potential to create business value.

At this stage, work is usually done with limited amounts of data, simplified architectures, and small project teams. The goal is not to build a stable solution right away, but first to understand whether a particular approach works at all.

This is also the difference compared to later productive use: a productive AI system must deliver far more than a successful prototype. While a PoC primarily validates an idea, a productive solution must function reliably and scalably over the long term. This includes, among other things:

• a robust and continuously available data foundation
• integration into existing IT systems and business processes
• monitoring of model quality and performance
• clear responsibilities for operations and further development
• regular updates and retraining of the models

In practice, an AI model is never trained once and then used unchanged. Data changes, processes evolve, and models need to be continuously adapted.

That is why the step from PoC to productive system is rarely just a technical evolution. Rather, it means integrating an AI solution sustainably into the organization, processes, and systems.

Many projects fail precisely at this transition.

A PoC is often completed successfully, but questions around scaling, integration, or operations are only asked afterward. At that moment, a technical experiment suddenly turns into a complex transformation project.

Based on our experience, it is therefore worth clarifying a few key questions before or during a PoC. Companies should think early about which specific business problem they want to solve with AI, what business impact a solution could have, and what data foundation is required for it. It is equally important to consider from the outset how the success of the solution will later be measured and how it can be meaningfully integrated into existing systems and work processes. When these questions are answered early, the likelihood increases significantly that a successful experiment will also become a productive AI application.

Three success factors for productive AI projects

From our experience, three factors in particular determine whether AI initiatives create real value.

1. Make AI usage measurable

A working model alone does not yet create business value.

Companies should define early on:

• Which metrics indicate the success of the AI?
• How will usage of the solution be measured?
• Which efficiency or quality improvements are expected?

Only when usage and impact become visible can AI be established in the company over the long term.

2. Think about scaling from PoC to productive system early

Many AI projects begin as experiments in which an idea is tested. But even at this early stage, key questions should be taken into account: What data foundation will be available in the long term? How can the model later be integrated into existing systems? Who will take responsibility for operations and monitor the solution’s performance? And how will models be regularly updated or further developed?

If these aspects are only considered after a PoC has been completed, this often creates additional effort and causes projects to stall.

3. Put business value and UX at the center

A third success factor lies in consistently putting business value and the user experience at the center. The biggest mistake in many AI projects is that the focus is placed too heavily on the technology while the actual business problem fades into the background. AI only creates value when it solves a specific problem, can be meaningfully integrated into existing work processes, and is actually used by employees. Usability in particular, as well as integration into existing workflows, is crucial in determining whether a solution will be accepted in everyday work and used sustainably over time.

AI only becomes valuable when it becomes part of daily work

As already noted at the beginning, many companies today are experimenting with AI and developing initial pilot projects. The real value, however, only emerges when AI becomes a productive part of business processes and is used in daily workflows.

The difference between pure experimentation and real value creation usually does not lie in the technology itself. What matters far more is whether companies define clear use cases, create a robust data foundation, and drive implementation forward consistently. Only when these conditions are met can AI unlock its full potential.

Or to put it differently: AI only becomes valuable when it becomes part of daily work.