“Lagom” is a Swedish term that doesn’t have a direct translation in English but roughly means “just the right amount” or “just enough.” It embodies the idea of moderation and balance in various aspects of life. It suggests not too much, not too little, but rather an optimal or suitable amount. 

Let’s talk about the current AI hype that is not directly lagom. Today, generative AI has sparked hype due to its impressive ability to create content like text, images, and music. People are fascinated by its creativity, potential applications across industries, and the possibility of disrupting traditional creative fields. Media coverage amplifies excitement, and while in recent years many people started identifying as AI experts, nowadays we mostly hear about generative AI.

This hype is not new to AI. The history of AI is characterized by alternating waves of hype and skepticism, commonly known as “AI summers” and “AI winters”. Each wave begins with excitement and high expectations for AI’s potential to revolutionize various fields, driven by breakthroughs in technology and promising research findings. However, these periods of optimism are often followed by disappointment in the potential of new technology when progress fails to meet initial promises, leading to lowered interest known as an AI winter. Despite these AI winters, each cycle has contributed to the advancement of the field, as lessons learned during periods of skepticism have led to new approaches, innovations, and renewed enthusiasm in subsequent waves of AI development.

Any hype has pros and cons. On the positive side, hype can generate a lot of awareness and interest, driving investment and innovation in a particular area. It can catalyze research and development efforts, leading to breakthroughs and advancements that may not have been possible otherwise. Additionally, hype triggers excitement, mobilizing communities to explore new possibilities. However, hype can also create unrealistic expectations, leading to disappointment if the promised benefits fail to materialize. 

The introduction of AI in the telecom industry has been gaining speed in recent years. While early experiments and trials show great benefits in improved performance and resilience of the networks, all the way from rollout and operations to deeply embedded features in the heart of the radio access network, the speed of adoption is dependent on observability of systems, underlying AI infrastructures such as data lakes, and MLOps layer making sure the algorithms get refreshed as soon as they start drifting compared to reality. In other words, in the perfect world, where we can observe all the internal workings of the wireless communication system, one can see great benefits of AI, but the path of reaching full observability is not straightforward.

Implementing new technology too quickly can lead to various mistakes that can undermine the success of the initiative. Rushing into implementation without conducting thorough research and analysis can result in choosing a solution that doesn’t align with the organization’s needs or goals. In the worst case, organizations will face the need of making a U-turn, redesigning the whole system from scratch and losing substantial investments. Inadequate training and support for employees can lead to resistance and low adoption rates, hindering the technology’s effectiveness. Neglecting to consider scalability and integration with existing systems can lead to technical issues. Overlooking data security and privacy concerns can expose the organization to significant risks, including data breaches and regulatory violations.

Using AI hype in a smart way involves leveraging the excitement and attention surrounding AI to drive positive outcomes for the telecom industry. It’s important to focus on concrete practical applications of AI that address real business challenges and deliver value. Identifying specific use cases where AI can improve efficiency and resilience of the networks, enhance customer experiences, or drive innovation, is critical for telecom providers to ensure that AI ideas lead to action. In addition, it’s essential to invest in talent and infrastructure to support AI implementation. Building a team with expertise in AI technologies, can ensure that the organization has the capabilities to develop and deploy AI solutions successfully and sustainably. In the times of hype, let’s remember the words of the King: “A little less conversation, a little more action”.

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