Barriers to AI Adoption: Why Slovak Companies Face a Steep Climb

While Artificial Intelligence is hailed as a primary driver of productivity, the path to successful implementation is fraught with challenges. Recent research identifies key barriers through the lens of the TOE model, revealing that in the Slovak context, digital literacy may be the ultimate bottleneck.

To understand why companies struggle with AI, researchers utilize the TOE model, which categorizes obstacles into technological, organizational, and environmental factors. Addressing these isn’t just a matter of IT; it’s a strategic necessity for staying competitive in a rapidly evolving market.

High Costs and Infrastructure Gaps

The first hurdle is often financial and technical. Multiple studies emphasize the high costs associated with AI implementation and daily operation. Beyond the price tag, companies face significant “under-the-hood” issues:

• Integration Complexity: Merging AI with existing workflows is rarely seamless.
• Unsuitable IT Infrastructure: Older systems often lack the power to support modern AI.
• Data Scarcity: Without high-quality data, AI models cannot deliver meaningful results.

The Human Capital Crisis in Slovakia

Organizational barriers often prove more stubborn than technical ones. A primary constraint is the profound lack of knowledge and qualified human capital. In Slovakia, this challenge is particularly acute.

According to Eurostat, only 21.7% of the Slovak population possesses digital skills above a basic level, falling short of the EU average of 27.32%. This gap in literacy creates a “distrust ripple effect”:

• It leads to low trust in AI-generated decisions.
• It fuels fears of losing control over core business processes.
• It results in limited awareness of the actual benefits AI can provide.

Ultimately, these factors strengthen managerial resistance, making organizations far less ready to adopt new technologies.

The Role of the Environment

Even a prepared company can be held back by its surroundings. Environmental factors, such as market conditions and customer distrust toward AI-based products, play a critical role.

Public support—or the lack thereof—is a deciding factor. To move from “hype” to practice, the research highlights three essential forms of government intervention:

1. Financial support to offset high initial costs.
2. Technical support to assist with infrastructure.
3. Workforce training to bridge the digital skills gap.

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