5 risks companies are facing when implementing Artificial Intelligence

The digital transformation has brought with it a new consumer that is characterized by being much more demanding and requires digitized, more personalized, and agile services.

This has created the need for companies to automate their processes, and this is where Artificial Intelligence steps in. Although Artificial Intelligence is currently used by only 11.8% of Spanish companies with more than 10 employees, according to the latest Ontsi report, more companies are taking the decision to use it and, in fact, in 2022 this percentage was 3.5 points higher than in the previous year.

The surge of this new technology, among other benefits, allows companies to offer better services adapted to the new consumer, improve their efficiency in decision-making, increase their productivity, and save costs.

In fact, Frost & Sullivan predictions suggest an annual growth of 25.2% of this market in Europe, rising to 9.8 billion euros by 2027. However, while Artificial Intelligence brings many advantages to companies, it also entails some risks and barriers, which must be considered. In this regard, knowmad mood, a technology consulting firm providing digital transformation services, has gathered some of the dangers that companies may face when it comes to implementing this new technology.

Lack of knowledge

One of the main obstacles that companies have to overcome to implement solutions based on Artificial Intelligence is the lack of knowledge and expertise in the field. Being a highly specialized field and due to its high level of sophistication, it is not easy for companies to acquire the required talent to perform demanding projects. In fact, as the latest study by the Industrial Association for the Promotion of the Data Economy and AI (IndesIA) states, the lack of qualified professionals in Spanish companies will leave more than 6,500 job offers in Data and Artificial Intelligence unfulfilled in 2023.

Confidence in its decisions

Artificial intelligence-based systems are fed with data that are used for learning and training algorithms to build models that solve certain tasks. If the input data is biased, the systems can generate imbalanced decisions in terms of race, gender, or unfair inequalities.

Furthermore, the more complex the algorithms the less transparent they become, and it may be difficult or even impossible to understand how decisions are made. Preserving the “explainability” of models is essential to preserve trust in decisions and to enable more responsible and safer use of AI systems.

Security & Privacy

Although in 2021 the European Commission presented a draft regulation on the use and development of artificial intelligence, it is still pending approval by the European Parliament and Council. However, the use of AI is already associated with the obligation to comply with laws on the protection of confidential and sensitive data of both customers and employees of the company itself. Thus, the creation and subsequent maintenance of AI-based solutions require the collection of a large amount of data that is used to design and evolve their models, so companies must ensure that these data protection rules and regulations are complied with in their projects in two different ways: on the one hand, the data used for the actual building and evolution of the models and, on the other hand, the data used by the models when they are in a real productive environment, since the AI system works as a superuser that has almost unlimited access to a gigantic volume of data for processing and decision making.

Data availability

Most AI-based systems rely on huge amounts of data to build and maintain. If data is available in limited quantities, it will not be possible to find the patterns and relationships that enable the systems to make accurate decisions. Therefore, companies depend on the availability of such data of good quality and stored correctly so that it is not damaged and remains useful, but also so that they know how to extract useful information from it. That is why it is important to know what the data will be needed for, how they will be exploited, and how will they be related to each other so that the information they provide improves the efficiency and competitiveness of companies.

The right infrastructure

Not only is the availability and quality of data critical to the performance of AI systems, but so is the infrastructure required for its storage and processing. Building intelligent systems relies heavily on a powerful infrastructure that is capable of processing large volumes of data and thanks to advances in the fields of big data and dedicated computing hardware, the entry barrier that companies had a few years ago is being breached. The increased adoption of the cloud reduces the cost required for both storage and processing, and there are more and more manufacturers offering pre-built cloud-based AI system products and solutions. This way companies have access to its benefits and can use it without having the necessary infrastructure on their own servers and without worrying too much about the security and vulnerabilities of that infrastructure.

Our experts comment

“Artificial intelligence is revolutionizing the way we interact with the world. Its potential to improve people’s quality of life is practically unlimited, making enormous advances in a variety of disciplines, from medicine to self-driving cars and applications that involve human interaction, such as ChatGPT. For companies, AI has led to a considerable increase in their productive capacity and has enabled them to build a promising future. However, it also poses ethical and social challenges that highlight the need to find a proper balance between its evolution and respect for human values and rights. Many of these risks can be mitigated with proper governance of AI projects, managing the data lifecycle ethically and responsibly.” says Roberto Fuentes, M&A and Markets Director of knowmad mood.