From Quiet Quitting to Quiet Thriving

How AI promotes a proactive work culture

Every movement has its counter-movement. This is also the case in HR. While “Quiet Quitting” describes a state in which employees only do the bare minimum, “Quiet Thriving” is a concept that encourages people to actively and creatively participate in their work sphere.

A proactive counter-design

The idea behind this approach is that people have a fundamental need for joy, development and fulfillment in their work. This creates motivation to not just strive for the minimum, even in a working environment that is perceived as rather negative, but to take on a proactive role. Such an attitude contributes to mental health and makes it possible to positively shape the environment within one’s own capabilities.

Exploring the possibilities

The “Circle of Control, Influence and Concern“, a model from positive psychology, shows the different levels of influence and the resulting options for action. It illustrates very well that some things are within our control and others are not. Recognizing and differentiating between them paves the way to more (inner) freedom and enjoyment of our own actions, even under suboptimal conditions.

In relation to the work context, this essentially raises three questions:

If I cannot control or influence my work environment, can I learn to accept it? (e.g. the behavior of my manager)
If a problem in my work environment is not within my control, can I influence it so that I experience at least some enjoyment at work? (e.g. work routine)
What is within my control? How can I gradually increase my scope of action in order to not only do the bare minimum, but to develop and contribute my own ideas out of an inner drive that pay into the company’s goals? Not out of obligation, but out of an authentic motivation that enhances me both personally and professionally?

In “Quiet Thriving”, employees consciously deal with their self-efficacy (however small it may be), proactively shape aspects that they can influence and adjust their attitudes and reactions with a look at the things that they cannot (initially) change. They also network with like-minded people and build a supportive community together in order to strengthen and utilize their collective self-efficacy.

Happier work thanks to AI

From an HR perspective, employees who perform monotonous tasks or tasks that are perceived as less meaningful are particularly prone to “quiet quitting”. Especially if they cannot see the immediate effect of their work and therefore do not feel valued. This is where HR can take targeted action and help shape the scope to promote “thriving”. One requirement for this is that HR employees recognize the emotional state of employees. Artificial intelligence (AI), which works with comprehensive language models (as we know them from ChatGPT), can help here. It determines how people in the organization are actually feeling by evaluating qualitative data (e.g. open text responses in a survey). Based on this data, HR can show demotivated and dissatisfied employees development and learning opportunities or enable a temporary transfer to another job.

For example, job rotations or further training can break established patterns, offer variety and provide new inspiration. Which development step is suitable for which employee can also be determined by the AI. It is important that these offers do not come as an additional burden “on top” and increase the employee’s workload, but represent a real alternative to the previous area of responsibility. A deep understanding of employees’ character traits, conditions and needs can also help to place the right people in the right roles within the organization at the right time and thus create optimal conditions for them to perform their tasks with motivation. Thanks to AI, HR has the opportunity for the first time to delve deep into the organization and unleash the full human potential that lies dormant within it.

Know your Privilege – and use it for the better.

It is important for me to emphasize that “Quiet Thriving” represents a privileged approach that is not applicable to all life and work situations. On the work side, it is particularly suitable for office jobs and industries in which skilled workers are scarce and employees have a certain amount of power. At the same time, the knowledge of the diverse nature of work experiences and environments should be an incentive for companies to make working life appreciative and fulfilling for as many people as possible. Emerging AI technology offers fascinating opportunities here. Let’s use them to leverage the creative potential that lies dormant in everyone.

Prof. Dr. Florian Feltes

Prof. Dr. Florian Feltes is co-founder and co-CEO of zortify and a forerunner in AI-supported HR innovation. Together with his team, he develops intelligent personality diagnostics and helps companies identify the perfect candidates—without expensive assessments and without bias. His vision: a world in which every company can effortlessly form high-performance teams and create work environments that allow human potential to flourish.

Empowered teams: doing the right thing (instead of doing everything right)

What is more important on the job – doing things right or doing the right things? – Most people will probably answer “both”. Nevertheless, in the context of changing corporate cultures, it is worth thinking about this question in its absolute either-or variant.

Good leadership: With self-reflection and unsympathetic filter

A good leader – is that something you are or is that something you become? – Let me put it this way: you are one if you are willing to become one. In other words, leadership requires a willingness to develop. Leading always means learning to lead. It is an ongoing process that is never finished, especially in the fast-paced world we live in.

The self-made skills shortage

Why should we analyze applicants in depth if we have no choice about who we hire anyway? We hear this question a lot. It reflects the frustration many companies feel about the lack of skilled workers. And rightly so? – We say: Yes and no. For one thing, we think that the shortage of skilled workers is not a “force of nature” that companies are helplessly exposed to.

AI vs. Human Intelligence

Understanding the Advantages and Shortcomings of Artificial Intelligence

Examining the differences between AI and Human Intelligence, and how they can work together to drive Innovation

Artificial intelligence (AI) has been a hot topic in recent years, with advancements in technology and machine learning leading to significant improvements in areas such as natural language processing and Machine vision. But while AI has many strengths, it also has its limitations. In this article, we will explore the capabilities and shortcomings of both AI and human intelligence and how they can complement each other to drive innovation.

Understanding AI and Human Intelligence

AI is a type of machine intelligence that can perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. It is based on algorithms that can learn from data, identify patterns, and make predictions. Human intelligence, on the other hand, is the cognitive ability of humans to learn, reason, and adapt to new situations.

Strengths and Limitations of AI

One of the major strengths of AI is its ability to process vast amounts of data quickly. Making it useful for tasks such as data analysis and predictive modeling. AI can also work around the clock without getting tired or making errors due to fatigue. However, AI is not perfect and has its limitations. For example, AI lacks the creativity and intuition of humans, making it less suitable for tasks that require a nuanced understanding of human emotions or complex social interactions.

Augmenting Human Intelligence with AI

One of the most promising applications of AI is in augmenting human intelligence. By leveraging the strengths of both AI and HI, we can create more effective solutions to complex problems.

Unleashing the power of AI: Enhancing Human Intelligence for breakthrough solutions.

One area where AI is already being used to augment human intelligence is in the field of recruiting. Traditional recruiting processes can be biased by human judgment, leading to unfair or discriminatory hiring practices. AI, on the other hand, can help to remove bias and make the recruitment process more objective.

Revolutionizing recruitment: How AI is reshaping the Hiring Landscape

AI-powered recruitment tools can analyze resumes and job applications to identify the most qualified candidates. Without being influenced by factors such as age, gender, or ethnicity. These tools can also analyze candidate behavior during interviews. Such as facial expressions and tone of voice, to provide insights into their suitability for the job.

Eliminating Bias, enhancing objectivity: the role of AI in recruiting.

By augmenting human intelligence with AI in the recruitment process, organizations can reduce bias and improve the quality of their hiring decisions. This can lead to a more diverse and inclusive workforce, as well as increased productivity and employee satisfaction. However, it’s important to note that AI is not a perfect solution, and there are still potential risks and limitations that must be addressed.

The Synergy of AI and HI

By combining the strengths of both AI and human intelligence, we can create powerful solutions that are capable of solving complex problems and driving innovation. For example, in the field of medicine, AI can analyze large amounts of medical data and identify patterns that humans may miss. Helping doctors to make more accurate diagnoses and develop more effective treatments.

Another example is in the field of finance, where AI can help to analyze large amounts of financial data and identify potential risks and opportunities, allowing investors to make more informed decisions. By working together, AI and human intelligence can drive innovation and create new opportunities in a wide range of industries.

Conclusion

In conclusion, AI and human intelligence have their strengths and limitations, but by working together, they can achieve more than they could on their own. As technology continues to advance, it is important to understand the capabilities and limitations of AI. And how it can be harnessed to augment human intelligence and drive innovation.

Prof. Dr. Florian Feltes

NLP 101: A Beginner’s Guide to Natural Language Processing

Building Trust with Explainable AI: Techniques & Approaches

As AI becomes more pervasive in our lives, it’s becoming increasingly important to understand how these systems work and to be able to trust their decisions. Explainable AI (XAI) is a growing field that aims to create more transparent and interpretable machine learning models.

Natural Language Processing in Business

As businesses become more customer-centric, understanding customer needs and wants is becoming increasingly important. Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that is revolutionizing the way businesses interact with their customers.

NLP 101 A Beginner’s Guide to Natural Language Processing

Natural Language Processing (NLP) is a field of study that focuses on analysis and synthesis of human language. NLP is a fascinating and rapidly evolving field with a wide range of applications, from chatbots and virtual assistants to sentiment analysis and machine translation. In this beginner’s guide to NLP, we’ll provide an overview of what NLP is, how it works, and some common applications of NLP.

What is NLP?

NLP is a branch of artificial intelligence that focuses on the processing of human language. The goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is natural and intuitive. NLP involves a wide range of techniques, including machine learning, deep learning, and statistical analysis.

At its core, NLP applies computational algorithms to language data to extract meaning and insights from the data. These algorithms are designed to simulate the way humans process language, by breaking down language into its parts and analyzing the relationships between those parts.

How Does NLP Work?

NLP algorithms use various techniques to analyze and understand human language. These techniques include natural language understanding, which involves the analysis of human language to extract meaning and context, and natural language generation, which involves the generation of human-like language by computers.

Natural Language Understanding

Natural language understanding involves the analysis of text data to extract meaning and context. This involves a wide range of techniques, including:

– Tokenization: Breaking text into individual words, phrases, or other meaningful units.
– Part-of-speech tagging: Assigning parts of speech to individual words (e.g., noun, verb, adjective).
– Named entity recognition: Identifying and classifying entities mentioned in the text (e.g., people, places, organizations).
– Dependency parsing: Analyzing the relationships between words in a sentence to identify the subject, object, and other components.
– Sentiment analysis: Analyzing the emotional tone of the text.
– Topic modeling: Identifying the underlying topics or themes present in a document or set of documents.

Natural Language Generation

Natural language generation involves the creation of human-like language by computers. This can be used for a wide range of applications, including chatbots, virtual assistants, and automated report generation. Natural language generation involves a wide range of techniques, including:

– Text planning: Determining the content and structure of the generated text.
– Sentence planning: Generating individual sentences based on the content and structure determined in the text planning stage.
– Surface realization: Converting the sentence plan into actual text.

Applications of NLP

NLP has a wide range of applications in various fields, including chatbots, virtual assistants, sentiment analysis, and machine translation.

Chatbots and Virtual Assistants

One of the most common applications of NLP is chatbots and virtual assistants. Chatbots and virtual assistants use NLP algorithms to understand and respond to user input naturally and intuitively. This makes them useful for a wide range of applications, including customer service, personal assistants, and more.

Sentiment Analysis

Sentiment analysis is another common application of NLP. It involves the analysis of text data to determine the emotional tone of the text. This can be useful for a wide range of applications, including market research, social media analysis, and customer feedback analysis.

Machine Translation

NLP is also used in machine translation, which involves the translation of text from one language to another. NLP algorithms are used to analyze and understand the text in one language, and then generate the equivalent text in another language.

Conclusion

Natural Language Processing (NLP) is a fascinating and rapidly evolving field with a wide range of applications. From chatbots and virtual assistants to sentiment analysis and machine translation, NLP is transforming the way we interact with computers and each other. By understanding the basics of NLP, you can gain a better understanding of this exciting field and the many ways in which it is changing the world.

Prof. Dr. Florian Feltes

KI vs. menschliche Intelligenz

Examining the differences between AI and Human Intelligence, and how they can work together to drive Innovation. Learn how AI can augment human abilities in decision-making and problem-solving.