Use of AI in companies
The business world is on the cusp of a major transformation from the increasingly incredible capabilities of artificial intelligence. More and more companies are using this revolutionary technology to improve their operations, workflows and workloads, data-driven decisions and ultimately their profitability and future viability. In this article, we look at the history, uses and future prospects of AI. In addition, we explore how AI and LXPs (Learning Experience Platform) are merging and the groundbreaking role that platforms such as U2D Aprenia are playing in this.
The rapid development of AI in the past decades and years is at the same time a retrospective of human creativity and human progress. This retrospective begins with Alan Turing in the 1950s. Up to advances in machine learning and neural networks today, AI has come a long way. Its disruptive impact is particularly evident in the business world, as companies rely on it to automate processes, analyse patterns of data and make "reasoned" decisions based on it.
History of AI
The beginnings of AI logically start at the same time as the first attempts at advanced computer development - especially in the 1950s. The aforementioned mathematician and physicist Alan Turing developed the Turing Test, named after him, to investigate whether it is possible for a machine to imitate human-like thinking. This idea laid the foundation for the development of AI systems.
In the post-war period, research into the potential of AI enjoyed great enthusiasm. Already at that time, this enthusiasm led to the development of programmes that could play chess or understand simple language. Because of set limits in hardware and algorithms, the further development of the systems was slowed down and it led to a decline in expectations. In the 1970s, there was therefore talk of an "AI winter".
The comeback was not long in coming, however, and expert systems became popular in the 1980s. These were software that mimicked human-like knowledge in certain areas such as medicine and finance. However, they again ran up against limited technical resources. The main problem was that the programmes could not operate outside their context.
In the last decade of the old millennium and the "noughties", science increasingly focused on machine learning and neural networks. The aim was to counteract the limitations of the previous decades. It was now possible for programmes to train complex algorithms and recognise patterns based on them. This gave rise to decision trees, support vector machines and later deep learning. This culminated in an AI developed by Google beating the world's best human "Go player".
Today, AI is at a peak of social and economic interest. Deep Learning, Natural Language Processing and Image Processing have become an integral part of our society. Chat GPT took the hearts of all tech nerds and anyone else interested in innovative developments by storm, and more and more AI tools are being used in social media for image processing. For companies, AI processes become interesting when they simplify or improve processes, resources are thereby conserved and profitability increases - in other words, almost always!
In this brief overview of AI history, it becomes clear that the success of this technology depends on the staying power and innovative capacity of the human brain. The road to fully autonomous artificial intelligence is still long - even Chat GPT wants to be fed by humans - but the developments of the past form an ever-widening foundation for exploration in the future.
Current possibilities of use
The use of AI has become indispensable in many areas of business today. In most cases, companies rely on a variety of AI tools. The AI-based solutions are versatile and their spectrum ranges from automation to personalised customer targeting. One of the best-known and most popular uses is robotic process automation. This uses AI to automate recurring tasks. This results in two advantages for companies. On the one hand, this immensely increases the effectiveness of work processes and on the other hand, the occurrence of human errors is reduced. Companies can free up resources based on this and employees can focus on more demanding tasks.
In customer service, AI tools have been used for quite some time. Chatbots and virtual assistants use artificial intelligence to mimic human-like interactions and answer customer queries. The advantage over human resources is, firstly, time - the bots are on duty around the clock and without a break - and provide precise answers within seconds. Their tasks include guiding customers through processes, providing information, receiving and processing complaints and forwarding them.
AI also plays a role in data analysis. For example, companies use it to collect large amounts of data that would be difficult to process without AI. Machine learning can recognise patterns in the data to draw important "conclusions" about consumer behaviour, market trends and operational performance. Predictive analytics uses these patterns to predict future operations and lay the groundwork for informed decisions. Following on from the ability to analyse market trends and consumer values, AI also provides a basis for marketing. Marketing strategies can be designed or derived that specifically address the wishes and concerns of customers and interested parties. This leads to higher customer loyalty and an improved return on investment (ROI).
The use of AI is also increasing in the education sector. The field of e-learning in particular is benefiting from the findings of this research direction. Learning Experience Platforms (LXP), for example, use AI to provide personalised learning paths that meet the individual needs of learners. This is where the U2D Aprenia platform comes in, using cutting-edge AI technologies to deliver personalised learning experiences. From identifying knowledge gaps to recommending customised learning content, AI is transforming the way organisations train employees.
In summary, the current use of AI in business shows a wide range of potential applications. From process optimisation to improving customer loyalty, AI offers an ever-growing portfolio of solutions that are transforming the business world. In times of digitalisation, it will therefore become increasingly important for companies to use AI technical "know-how" in order to be able to hold their own in their economic sector.
Future development opportunities
The necessity of using AI in companies has already been described in detail. The last thing we will now look at is the future developments and possible uses that will arise as AI continues to evolve.
The further development of AI will lead to companies offering even more precise and effective solutions. Predictive analytics will become an even more important tool for decision-makers, as it is about predicting future trends and events on the basis of empirical data and adjusting to them or acting on them as quickly as possible. In this way, it will also be possible to react more quickly to market changes. Another area for future development is the automation of increasingly complex tasks.
The merging of AI and Learning Experience Platforms (LXP) is at the heart of an educational technology revolution. LXPs use AI to design personalised learning paths that meet the needs of all users. This is where U2D Aprenia takes the stage - a pioneering platform that uses cutting-edge AI algorithms to recommend personalised learning content, identify knowledge gaps and create a tailored learning experience. By combining AI and LXP, learners are empowered to learn at their own pace while gaining deeper insight into their progress.
With each innovation that AI brings to businesses and educational institutions, it is clear that we are at the beginning of an exciting era where technology and learning go hand in hand. It is time to move away from traditional approaches and pave the way for a new era of knowledge acquisition that focuses on personalised, relevant and effective learning methods. U2D Aprenia and similar platforms are pioneers of this movement and show how AI can change the educational landscape for good.