Der Einfluss von Künstlicher Intelligenz auf das IT-Asset Management


The influence of artificial intelligence on IT asset management

Artificial intelligence and machine learning are on the rise and are becoming increasingly important. The new technologies are revolutionizing the IT world of companies and are on everyone's lips. The field of IT asset management is also affected by the rapid development of artificial intelligence. But what exactly is artificial intelligence?

The term "artificial intelligence" - or AI for short - is a generic term that encompasses various technologies for the use of data and algorithms. These allow computers to use complex algorithms to perform functions that already come very close to human intelligence. So-called GenAI (Generative Artificial Intelligence) - as a further development of traditional AI models - also makes it possible to recognize patterns in large amounts of data. Based on these patterns, texts, images, videos or other content can be generated independently and thus support the execution of routine tasks.

The rapid development of AI makes it difficult to maintain an overview

It seems as if there are new reports of further breakthroughs in the field of artificial intelligence every day. According to a Gartner forecast on the "Top Strategic Technology Trends 2024", over 80% of all companies are expected to use GenAI models or GenAI-enabled applications in the production environment by 2026. This represents a rapid increase compared to 5% at the beginning of 2023.

However, many companies still lack the necessary expertise to successfully master this technological development. In addition, the existing governance structures in most companies do not adequately reflect the topic of artificial intelligence. As a result, both management and operations are uncertain about how to deal with this topic and how to make sensible use of investments. Manufacturers and service providers are constantly advertising new products and services on the market. In most cases, their promise is to support companies in their day-to-day business and transition them into the new era of artificial intelligence. The product portfolio is constantly growing for both providers and users, making it increasingly difficult to ensure that the market is up to date and that the company is transparent.

The use of artificial intelligence in IT asset management harbors opportunities and risks

In the rapidly changing infrastructure environments of companies, IT asset managers are confronted with compliance risks, a lack of transparency in the asset portfolio and constantly rising costs. The mix of on-premise and cloud solutions and the increase in software-as-a-service (SaaS) resources make work even more difficult and increase complexity. The use of artificial intelligence can be both a blessing and a curse.

The use of AI can pose major challenges for IT asset management in terms of maintaining transparency regarding the software and devices used, correct licensing and costs incurred. The large number of AI products used and their often complex licensing with the help of new license metrics and the simultaneous lack of expertise often pose a major challenge here. Furthermore, in addition to the high initial investment required for implementation, the costs and complexity of the infrastructure can also increase, as additional resources are required to process large volumes of data. Processing large volumes of sensitive or personal data also entails considerable compliance and data protection risks. Companies therefore have their hands full building up the necessary expertise, establishing internal processes and doing their homework in the areas of governance and compliance.

However, AI also offers opportunities to support companies in this transformation. Not only providers of IT asset management tools, but also software manufacturers and service providers offer individualized solutions based on artificial intelligence to meet the needs of companies in IT asset management, such as simplified, transparent inventory or reporting. According to the manufacturers, many AI-supported tools should integrate seamlessly into the existing infrastructure and can be scaled to all company sizes as required. Artificial intelligence should facilitate the identification and inventory of assets, create forecasts on usage and utilization in real time, significantly reduce manual effort and support data-based decision-making. As a result, it should be possible to identify optimization opportunities in infrastructure and licensing as well as compliance or security risks, thus contributing value to IT asset management and reducing resources and costs.      

As is so often the case, good preparation is half the battle

The advance of AI is progressing, companies are afraid of being left behind and therefore want to prepare for the upcoming changes. As a time-consuming and labor-intensive activity, IT asset management can benefit significantly from the opportunities. Companies often lack resources and expertise in IT asset management, so AI-supported tools can help to reduce resource requirements by automating analyses, processes and tasks. In addition, the tools' advanced analysis capabilities can help less experienced IT asset managers to identify optimization needs and risks and to develop reports with suitable action plans. However, the quality of the underlying data is also essential for effective and efficient IT asset management when using AI. AI can help to identify deviations in existing data, but completeness and high data quality form the basis for drawing reliable findings from analyses and forecasts from AI-supported tools.

In addition, it is important to lay the foundations in governance, compliance and processes for the use of AI. It is crucial to identify requirements, priorities and existing gaps in order to optimally prepare for implementation. On this basis, the future strategy and required expertise can be derived and gaps can be closed at an early stage.  Once the security and data protection requirements and objectives are known, options can be evaluated in a targeted manner.

Ultimately, the individual requirements of the company in question and the expertise available are at least as important as the financial investment that can be made. Careful preparation helps to avoid bad investments as far as possible and to ensure that the selected options meet the company's requirements and that compatibility with existing systems is guaranteed.

Author: Jessica Müller