Artificial intelligence solutions used in the work of the organisation include AI-based technologies that support daily operations and processes in companies, institutions, and organisations. They can include task automation, data analysis, and decision support as well as the management of human resources, finance, marketing, and production. These solutions are key to optimising organizational efficiency, reducing costs, and supporting innovative business processes.
AI Solutions Used in Organizational Work
Type of technology
Description of the technology
Basic elements
- Artificial intelligence algorithms: Technologies that automate processes, such as scheduling, data analysis, and personalisation of offers.
- Resource management systems: Tools to support the management of human resources, finance, production, and marketing.
- Machine learning: Technology that enables automatic learning from data to adapt to changing conditions.
- Data visualisation: Tools to transform AI analysis results into user-understandable reports and charts.
- Human-machine interfaces: Tools that enable direct user interaction with AI systems.
Industry usage
- Human resource management: Recruitment automation, employee performance analysis, and career path planning.
- Finance: Accounting automation, financial trend forecasting, and risk management.
- Production: Supply chain management, production optimisation, and forecasting of raw material requirements.
- Marketing: Personalising marketing campaigns, analysing customer behaviour, and predicting campaign effectiveness.
- Project management: Optimising resource allocation, monitoring progress, and automating administrative tasks.
Importance for the economy
AI solutions used in the work of the organisation have a major impact on increasing the efficiency of enterprises and institutions. Automating processes, analysing large data sets, and optimising decisions help reduce operating costs and improve service quality. Organisations that use Artificial intelligence can react faster to changing market conditions, better manage resources, and innovate, which contributes to their competitiveness.
Related technologies
Cloud Computing
AI solutions in organisations can be deployed in the cloud, which increases their scalability and flexibility.
Big Data
AI analyses huge amounts of data from various organisational processes, enabling more accurate decision-making.
Robotisation
AI supports the automation of processes such as production, human resources, and sales management.
Automation
AI supports the automation of processes such as production, human resources, and sales management.
Mechanism of action
- AI solutions used in organisations collect data from various sources, analyse it with advanced algorithms, and generate recommendations or automate specific processes. These systems can monitor operations, analyse trends and patterns in data, and support decision-making processes, e.g. in human resource management, sales, or production. By using machine learning techniques, AI systems in organisations can adapt to new challenges and optimise their performance as new data arrives.
Advantages
- Process optimisation: Automation of routine tasks saves time and resources.
- Better decision-making: AI supports organisations in analysing data and making more accurate decisions.
- Improving efficiency: Automated processes are faster and more precise than manual operations.
- Personalisation: AI solutions enable customisation of offers and services to meet individual customer needs.
- Cost reduction: Automation of processes and optimisation of resource management reduce operating costs.
Disadvantages
- Risk of errors: Bad input data or inadequately designed algorithms can lead to wrong decisions.
- Implementation costs: Implementing advanced AI solutions in organisations can be costly.
- Lack of flexibility: AI systems may not be flexible enough to respond quickly to sudden changes.
- Technological complexity: Implementing AI requires specialised knowledge and technical infrastructure.
- Threats to jobs: Automation can lead to a reduction in demand for certain jobs.
Implementation of the technology
Required resources
- Data sets: Organisational data that can be analysed and used by AI systems.
- IT infrastructure: Powerful servers and cloud systems that support the deployment and operation of AI solutions.
- Software: AI tools as well as ERP and CRM systems that are integrated with Artificial intelligence solutions.
- Technical team: Experts in Artificial intelligence, data analytics, and IT management.
- Computing environment: Computing infrastructure to process large amounts of data in real time.
Required competences
- Machine learning: Knowledge of techniques used in AI systems that support organisational processes.
- Data analysis: Ability to interpret organisational data and optimise processes based on analysis results.
- Programming: Knowledge of tools for integrating AI with organisational systems, such as ERP and CRM.
- Project management: Competence in implementing and monitoring the performance of AI solutions in organisations.
- Data security: Ability to secure organisational data processed by AI.
Environmental aspects
- Energy consumption: The operation of AI systems and the processing of large data sets require considerable energy resources.
- Emissions of pollutants: The development of data centres that support AI solutions may contribute to CO2 emissions.
- Raw material consumption: The IT infrastructure needed to support AI requires advanced materials, such as rare earth metals.
- Recycling: Computing equipment upgrades and replacements generate electronic waste.
- Water consumption: Cooling data centres that process AI data can contribute to water consumption.
Legal conditions
- Legislation governing the implementation of solutions, such as AI Act (example: regulations on accountability for decisions made by AI systems in organisations).
- Safety standards: Regulations for the protection of organisational data processed by AI systems (example: ISO/IEC 27001 regarding information security management).
- Intellectual property: Protection of AI algorithms and data processing results in organisations (example: patent law on AI algorithms used in resource management).
- Data security: Regulations for the protection of personal data and sensitive information processed by AI systems in organisations (example: GDPR).
- Export regulations: Restrictions on the export of advanced AI systems and organisational technologies to sanctioned countries (example: AI technology export regulations).