Artificial intelligence (AI) is worth using for information extraction and for processing and analysing large amounts of data, as it provides useful guidance for human resources management. For example, AI solutions can determine the suitability of customers for a particular company and assess whether potential employees will be motivated and a good fit for the company's team. Similarly, companies can improve their technical resource planning, including learning how to stock their warehouses properly to avoid overstocking and how to choose the optimal placement of goods. Properly trained MI models can also take care of employees' mental and physical health. For example, by analysing data on our smart devices, in the future employers will be able to monitor the health indicators of their employees, so that work rhythms can be automatically aligned with these indicators. This is the opinion of Jānis Bēniķis, an expert at AI Master Lab.
From a business perspective, the use of AI solutions in everyday life brings a number of valuable benefits, including the ability to reduce operational costs by partially replacing routine work. Large institutions can use it to process incoming message traffic, so staff can focus on improving customer service and experience. For example, a company presentation used to take days to prepare, but today with MI it can be done in minutes. It's worth noting that AI also contributes to overall work efficiency - research shows that 70% of employees who use Microsoft Copilot AI Assistant become more productive, while 68% say it improves the quality of their work.
The rapid development of AI solutions is fundamentally changing the business environment, offering companies a wide and diverse range of options to improve their performance. For example, to automate routine tasks, speed up decision-making and increase efficiency. But for these technologies to help achieve the results expected and for businesses to reap maximum benefits, it is important to tailor AI to individual business needs and to develop the skills that will help employees succeed in the world of technology.
The implementation and use of AI tools is not without its challenges. For example, employees often lack the necessary skills to work effectively with these technologies to get the results they want. And some are still reticent about learning AI tools because of the myth that technology will take over their jobs. To overcome these and other barriers, employers need to lead by example and encourage employees to adapt to change. If they feel supported by their company, AI and its opportunities will no longer be a threat or a burden, but an opportunity to develop new skills, become more productive and gain greater job satisfaction.
One way to do this is to take the time to get additional training from experienced experts in the field. For example, by joining the training programme run by the Latvian Digital Accelerator (LDA), participants can gain an in-depth understanding of what AI is, practical knowledge of how AI solutions can be implemented and used in their company, as well as data analysis and coding skills, according to their level of expertise. To apply for the training and to receive co-funding for the training, you must first fill in the application form on the LDA website. Once this step has been completed, with the support of industry experts, a personalised roadmap and a specialised training offer will be developed for each company to help them integrate their newly acquired knowledge into practical operations as quickly and efficiently as possible.
Overall, we can conclude that AI technologies are only a human support function that can enhance and complement our skills, not replace us. It can be compared to an intelligent assistant whose work always needs to be checked because it too is prone to making mistakes, just like a human being. Moreover, it should be remembered that the possibilities of artificial intelligence are not limitless. There are several areas where AI solutions should be used with particular care. For example, in the medical sector, when diagnosing patients, and in the validation of judgements in justice systems. In both cases, however, the final decision must be made by a human, and this trend will not change any time soon.
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