Artificial Intelligence in Business: From "Buzzword" to Strategic Necessity

Practice shows that some entrepreneurs also invoke artificial intelligence in situations where high-quality automation would be sufficient. However, the most important thing is not the name of the technology, but the business benefit. The goals of implementing AI are usually divided into four areas: cost reduction (for example, reducing defects), process acceleration (automatic estimating, data processing), improving customer experience, and increasing sales.

Artificial intelligence is no longer just a futuristic concept or a topic for technology enthusiasts, but an everyday business reality. The wide availability of generative AI tools, such as ChatGPT, has created additional interest, but the most important thing for entrepreneurs is to distinguish short-term hype from real, long-term advantages.

What is understood by artificial intelligence in the business context

It is important to emphasize that artificial intelligence (AI) is not one specific technology, but a set of different technologies. In the business environment, it is often confused with automation. However, there is a significant difference between the two solutions. Automation means the execution of predefined processes according to clear rules, while AI is also able to analyze and interpret data, based on them, make decisions and provide recommendations, as well as learn from its own experience.

Practice shows that some entrepreneurs also invoke artificial intelligence in situations where high-quality automation would be sufficient. However, the most important thing is not the name of the technology, but the business benefit. The goals of AI implementation are usually divided into four areas: cost reduction (e.g., reducing defects), process acceleration (automatic estimation, data processing), improving customer experience, and increasing sales. This does not always require expensive, individual solutions - in many cases, you can start with tools already available on the market and gradually implement more complex systems.

Practical examples of AI use in companies

Interest in AI in Latvia is high - in support programs, approximately a third of project applications are directly related to the integration of AI into company processes. Manufacturing companies are particularly active.

In production, equipment with an AI component is relevant,  for example, machine vision, where AI, using image recognition, performs quality control and identifies defects, thus replacing manual inspection. In sales and customer service, companies are implementing automatic estimation solutions - systems analyze customer specifications and historical data, preparing accurate estimates and thus significantly accelerating service. In e-commerce, AI helps personalize product recommendations and provide 24/7 customer support, while in the financial sector it is used in fraud detection and credit risk assessment.

The importance of data quality and availability

The quality of AI solutions directly depends on the data with which they are trained. Without an organized, complete and reliable database, high-quality results are not possible. In practice, it often turns out that the biggest challenge is not the AI tool itself, but the company's data environment. 

One of the most typical problems is data structuring - information is located in different systems, Excel files or even paper format, and before AI is implemented, it must first be collected and structured in the same way. The second challenge is accuracy: historical data often contains errors or gaps, which can cause AI to "hallucinate" and lead it to incorrect conclusions. It should be noted that organizing data can be more time-consuming and expensive than developing the AI solution itself. 

Risks and responsibilities when using AI solutions

AI solutions are associated with several significant risks. First, the time and financial dimension. Developing special personalized systems can take a year or even longer, and additional time is often required for testing and error correction. The initially planned budget often turns out to be insufficient, as it also needs to be invested in organizing and adapting data. Usually, AI is not a one-time “purchase” – the system must be continuously monitored, adjusted and updated, so the company needs internal competence to work with the implemented solution on a daily basis or a stable cooperation partner that provides support services. 

Choosing reliable partners is particularly important. There is no shortage of loud promises on the market, so before concluding a contract, you should carefully evaluate the supplier's experience, previously implemented projects and ability to provide long-term support.

Finally, issues of ethics, data protection and security are essential. If AI processes personal data, the requirements of the General Data Protection Regulation (GDPR) must be observed, including with regard to automated decision-making and profiling. At the same time, the more digital processes a company implements, the greater the potential risk of cyber threats. Insufficiently tested or poorly managed systems can produce erroneous results, while liability mechanisms that bear the consequences of algorithm errors are only becoming stronger in practice.

Main directions of regulation in the European Union

The European Union is one of the first in the world to create a comprehensive regulation in the field of artificial intelligence, adopting the EU Artificial Intelligence Act in 2024. The regulation is based on a risk-based approach: the greater the potential harm to human rights, health or safety, the stricter the compliance, documentation and monitoring requirements for the AI system. It is planned that by August 2026, the requirements of the act will be fully implemented in the national legislation of the Member States, including Latvia.

The EU regulation is not limited to this act - the use of AI in business should always be assessed in the context of data protection and human rights. The General Data Protection Regulation (GDPR) sets out requirements for automated decision-making and profiling, as well as provides for the right of individuals to know when a decision has been made by an algorithm and to challenge them. 

The Data Governance Act (Data Act) promotes safe data sharing and strengthens the ability of companies to use high-quality data for the development of AI. In turn, cybersecurity requirements, including the NIS2 Directive, oblige companies to ensure a higher level of IT security and risk management. 

In general, the EU approach requires that AI solutions be developed in an innovation-friendly, yet secure and human-centric manner. For an entrepreneur, this means not only technological readiness, but also legal and organizational readiness to work in a strictly regulated digital environment.

AI as a long-term development direction, not a short-term trend

More and more companies understand that artificial intelligence is not a one-time digitalization experiment, but a long-term development direction. The pace of technological development requires a strategic approach to AI, with a multi-year perspective. As experts emphasize, the question is no longer “whether” to use AI, but “how” to do it.

The most rational approach usually turns out to be a “step-by-step” approach – testing ready-made tools available on the market (for example, office software extensions or content generation and data analysis tools) and evaluating their benefits before making decisions about large investments in large-scale, individually developed solutions.

It is important to clearly define the problem that AI will solve and what added value it will create, and to foresee its implementation in the company's long-term digital development. At the same time, employee competence and internal resources must be strengthened. The biggest winners will be those companies that are able to balance technological enthusiasm with a pragmatic assessment of risks and benefits.

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