17
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01
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2025
Source: Rasa Gulbe, Member of the Board, DATI Group SIA
Artificial Intelligence (AI) has for some time been seen as a promising technology that can drive the digital transformation of businesses. So far, we have not yet seen AI revolutionise everyday business. At least not to the extent that has been predicted. One of the reasons is that not all organisations are equally capable of implementing AI and transforming themselves.
AI was already recognised in 2018 as one of the technologies that can drive the digital transformation of businesses. However, it is often confused with digitalisation. It is therefore important to clarify that digitisation is the use of digital technologies to make processes or businesses more efficient. It means streamlining processes and business models - it has the potential to reduce costs and resource consumption to achieve specific results. The positive impact of digitisation is not limited to the company itself - processes are more efficient, so the company is able to earn more. In turn, the positive benefits of digital transformation affect not only the company itself, but also the entire ecosystem. This is where AI can come in to fundamentally transform what a business does, what it offers its customers and how it interacts with the rest of the ecosystem.
AI is not just a technology. It can either be the ability of machines to think like humans or their ability to think rationally—this represents the dimension of thought. The second dimension is the dimension of action, which raises the question of whether a machine is capable of thinking like a human or acting like one. This presents the first challenge: we do not fully understand what it means to act or think like a human. We are nowhere near creating a machine that thinks like a person because we ourselves do not fully understand how we think. As a result, most AI solutions today focus on ensuring that machines can act rationally—meaning they perform activities that achieve the best possible outcome based on a goal defined by the machine's algorithm creator or its user.
Research shows that the adoption of digital technologies, including AI, is progressing slowly in small and medium-sized enterprises (SMEs). What is even more concerning is that the gap between digitally advanced and less digitally developed companies and organizations is widening. This is partly because the implementation of many innovative technologies depends on the solutions that are already in use.
The implementation of AI in an organization is an evolutionary process. AI cannot be introduced without data, and most companies already have it. Data can come from manually recorded inputs by employees, electronic reports, sensor readings, video cameras, or open data sets (such as company registries, meteorological data, etc.). If a company wants to gain the advantages offered by AI, data is the most crucial factor.
Once data is collected, the first step in adopting innovative digital technologies begins—this involves cloud services and their solutions, as data integration mostly takes place in the cloud. Traditional infrastructure lacks scalability and capacity. Over time, real-time data from sensors, cameras, and Internet of Things (IoT) devices can be added to cloud-stored data. All these sources can be interconnected and analyzed together.
Next, it is essential to determine how the data is further integrated to observe various aspects and analyze interactions. A deeper insight cannot be achieved by looking at a narrow or isolated data set.
The following step is big data—this means developing cloud and IoT infrastructure to a level where rapidly changing and diverse data volumes can be processed effectively.
The final stage is advanced analytics, which allows organizations to extract meaningful insights from complex data, enabling smarter decision-making and innovation.
AI implementation projects can be divided into four phases.
Planning Phase – This is where the added value is defined. What problems and challenges does the company face? What specific issues are intended to be solved using AI?
Data Collection and Preparation – AI is often associated with machine learning. In a business context, supervised learning is the most commonly used approach, requiring labeled data. To train algorithms with this data, an initial dataset is necessary, where we define the expected outcomes and the factors influencing them.
AI Model Development, Design, and Testing – This phase may seem less tangible, especially when using generative AI. However, it remains crucial when dealing with data that is not widely available or previously utilized.
Model Deployment and Monitoring – The final step involves integrating the AI model into the system and continuously monitoring its performance.
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Even with a clear understanding of the AI implementation process, getting started can be challenging. Read about obstacles and how to overcome them in our blog: "4 Reasons That Hinder AI Implementation in Companies."
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