Institutions dependent on IT services and resources acknowledge the crucial significance of an IT help desk system, that act as a centralized hub connecting IT staff and users for service requests. Employing various Machine Learning models, these IT help desk systems allow access to corrective actions used in the past, but each model has different performance when applied to different datasets. This work compares eleven Information Retrieval techniques in a dataset of IT support tickets, with the goal of implementing a software that facilitates the work of Information Technology support analysts. The best results were obtained with the Sentence-BERT technique, in its multi-language variation distilluse-base-multilingual-cased-v1, where 78.7% of the recommendations made by the model were considered relevant. TF-IDF (69.0%), Word2vec (68.7%) and LDA (66.3%) techniques also had consistent results. Furthermore, the used datasets and essential parts of coding have been published and made open source. It also demonstrated the practicality of a support ticket recovery system by implementing a minimal viable prototype, and described in detail the implementation of the system. Finally, this work proposed a novel metric for comparing the techniques, whose aim is to closely reflect the perception of the IT analysts about the retrieval quality.

One of today's primary priorities of companies is to improve the Customer Experience (CX) to increase customer satisfaction and reduce churn. However, "just 2 percent of organizations reached the top stage of CX maturity [and] most organizations are in early stages of CX maturity" (Dorsey et al., 2022). According to a recent study by Qualtrics (2022), 47 percent of customers ranked support as the second most important area of improvement in CX. One major factor of customer satisfaction identified in recent research (e.g., Service Excellence Research Group, 2021) is the speed at which customer support answers customer inquiries. Demand for customer support is rising and often exceeds the supply of available support agents. Especially missing knowledge and multiple re-routings between support agents are major factors for delays in resolution time. Further research suggests that due to information overload, the quality of decisions decreases with the number of decisions (Hemp, 2009; Viegas et al., 2015). In most recent studies, lack of time and resources are mentioned as the main issues in customer support, which harm the performance and, ultimately, the customer experience (HubSpot, 2022; Serrano et al., 2021).

Intelligent solutions enable self-service for both customers and support agents, allowing them to ask questions using their own words as if they were speaking to a person. This shift started with digital devices and the multiple customer engagement channels those devices have enabled. Organizations are required to provide timely, meaningful customer communications and responses to increasingly complicated customer questions. Technology will continue to change how financial organizations operate and engage with increasingly digital-savvy customers, so understanding the various emerging technology solutions is imperative to ensuring loyalty and improving customer satisfaction, while achieving operational efficiency in the post-pandemic era. Today's consumers, especially millennials and Gen Z, want to be self-sufficient.

Monitoring and examining sentiments have become increasingly popular with brands focused on automating their business processes. Mainly known as an innovative tool used by social media and marketing analysts, sentiment analysis, sometimes referred to as "social listening," has also proved helpful in other functional areas. We explain why companies should invest in sentiment analysis. Insight engines allow to use sentiment analysis across the enterprise and doesn't limit the tool to just one business need. Without machine learning (ML), methods like natural language processing (NLP) sentiment analysis would be unachievable.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The combination of Wi-Fi 6 and 5G mobility, combined with an increasingly wired and mobile world of internet of things (IoT) technology, promises to bring billions more devices onto networks in the coming years. This will have a profound impact on workplaces of the future, in ways that go far beyond the clear trends of remote employees and hybrid workforces. The world is entering a place where many people can seamlessly connect with fellow workers virtually from any location, with the workplace becoming more intelligent and hoteling becoming the norm. Examples include the ability to schedule a desk similar to seats at the movies or a flight, as well as the ability to crowdsource the temperature in the office.

Successful business operations are defined by intelligent decisions that come from actionable insights. Discovering how certain entities fit together to optimize profits and create efficient working environments is key to any thriving organization. This was the case before technology, and still is today. Before people even heard of the term "artificial intelligence", businesses still needed to distinguish correlations and draw conclusions from them. Many times, actions taken would not go as planned because these findings were not based on the entirety of one's data.

Information Technology Service Management (ITSM) helps in monitoring, analyzing, and improving the quality of IT services to enhance customer satisfaction. On the other hand, Artificial Intelligence for IT Operations (AIOps) aims at providing self-learning and intelligent solutions to run IT systems smoothly by optimizing the resources available. But what if these two could work together? What if they could help each other out? Well, they can, and they do!

It's human nature to choose the path of least resistance, as we're most comfortable with what we already know. Take the example of, "No one ever got fired for buying Cisco." But there comes a point in time when inaction becomes the greatest risk of all. Market transitions are real, and every product has a life cycle. Understanding these transitions and a vendor's product strategy is just as important--or sometimes more important--than selecting a specific company.

The study of natural language processing has been around for more than 50 years, but only recently has it reached the level of accuracy needed to provide real value. From interactive chatbots that can automatically respond to human requests to voice assistants used in our daily life, the power of AI-enabled natural language processing (NLP) is improving the interactions between humans and machines. NLP is broadly defined as the automatic manipulation of natural language, either in speech or text form, by software. NLP-enabled systems aim to understand human speech and typed language, interpret it in a form that machines can process, and respond back using human language forms rather than code. AI systems have greatly improved the accuracy and flexibility of NLP systems, enabling machines to communicate in hundreds of languages and across different application domains.

How does Text Analysis work?