Zsolt Tövis - Strategic Master Architect
Zsolt TövisStrategic Master Architect
What is Artificial Intelligence
What is Artificial Intelligence

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is the most defining technological trend of our time, fundamentally reshaping corporate operations and decision-making. The following is a business-focused analysis that emphasizes strategic implementation, ROI, and risks rather than technical details.

The Essence of the Technology

Artificial Intelligence (AI) is a collective term for advanced software solutions capable of processing large amounts of data, recognizing patterns, and independently preparing or executing decisions based on them. It is not a single piece of software but a capability. The system learns from available data and becomes more accurate over time without every step being pre-programmed. In a business sense, this means automating manual, cognitive (intellectual) work and supporting decision-making with data.

Business Benefits

Implementing AI offers a measurable competitive advantage and significant efficiency gains. The technology can automate routine administrative tasks, potentially achieving up to a 25% reduction in operating costs. Through predictive analytics, business planning becomes more accurate, whether for inventory management or financial forecasting. Additionally, the speed and quality of decision-making improve, as the system can uncover correlations in data that would remain invisible to the human eye.

Drawbacks and Risks

The most significant risk is not the technology itself, but the quality of the company's data assets. With disorganized or incomplete data, AI may reach incorrect conclusions ("garbage in, garbage out"). From a legal and ethical perspective, data protection (GDPR) and copyright compliance require special attention, particularly with generative AI solutions. A common pitfall in implementation is setting unrealistic expectations and lacking clear business goals, which leads to a significant portion of projects failing to deliver real business value.

Practical Application

AI is no longer experimental technology but an integral part of large-scale corporate operations. In the financial sector, it is used for fraud prevention and risk analysis; in manufacturing, for predicting machine maintenance needs (predictive maintenance); and in retail, for creating personalized offers and dynamic pricing. Global companies such as JPMorgan (finance), Siemens (industry), and Netflix (content recommendation) have incorporated it into their core processes.

Executive Summary

The introduction of AI is a strategic necessity to maintain market competitiveness, but it cannot be treated as a simple IT procurement. The key to successful implementation is not buying the most expensive software, but organizing the corporate data strategy and digitizing processes. A phased introduction of the technology is recommended. Start with a well-defined pilot project promising measurable return on investment, while simultaneously building the necessary internal competencies and data protection framework.

Frequently Asked Questions

The cost structure is typically consumption-based (pay-as-you-go). For cloud-based AI services, you pay based on the amount of data processed or the number of transactions, which means a low entry barrier but harder-to-plan operational expenses (OPEX). Open-source models also exist, their license fee is zero, but operating them involves high internal infrastructure costs.

There is currently extreme excess demand for AI experts and data scientists in the labor market, so their compensation is exceptionally high, potentially multiples of the market average. As an alternative, it may be worth starting with external consulting firms or "AI-as-a-Service" platforms until internal competency is built.

This is the most critical question. Using public AI models (e.g., the free version of ChatGPT) with corporate data is strictly prohibited because data can leak outside the company's control. In the case of closed models running in a corporate environment (Private Cloud or On-premise), the security level matches that of traditional banking systems.

AI rarely requires replacing old systems, it typically integrates via a data layer. The main risk is "Vendor lock-in" (dependency on one cloud provider), but this can be managed by using multi-cloud or hybrid architectures to avoid relying on a single ecosystem.

Training models requires massive computing capacity, necessitating expensive hardware (Industrial-grade AI GPUs), so this is best done in the cloud. Running already trained models (inference) requires fewer resources, but a stable, high-bandwidth data connection is essential.

AI is not a passing trend but the foundational technology of the next decade. Although specific software becomes obsolete quickly, the data assets, data structure, and organizational knowledge built during implementation represent lasting value upon which future systems can be built.

ROI comes from two sources. Cost reduction (automation, fewer errors) and revenue increase (better customer experience, new products). However, it is important to know that the ROI of AI projects usually does not appear immediately but becomes significant over a 12–24 month horizon.

The EU AI Act introduces strict rules for high-risk AI applications (e.g., HR decisions, biometric identification). Non-compliance can result in severe fines, so involving the legal department from the very first step of planning is mandatory.

The biggest mistake is treating the technology as a "magic bullet" without defining a specific business problem. If it is not precisely defined which process we want to improve and how we measure the result, the project remains just an expensive experiment without results.

Traditional software is "rigid", it only does what was pre-programmed into it. AI systems are "flexible", they adapt to changing market conditions and new data, making them capable of solving complex problems that traditional algorithms are unsuited for.

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