AI is spreading relentlessly, implementation is advancing, and many executives are adopting an “AI First” stance. Technological development is moving at an incredible pace, and perhaps because of that, it raises more questions than ever. There is a lack of adequate strategies, experience, and ideas about where and how artificial intelligence can have the greatest impact.
For this reason, this article focuses on the human perspective. Practical approaches are needed to gain more control over AI usage and to achieve the best possible human-machine collaboration. In times when AI is taking over more and more tasks, the question becomes even more urgent: Which human skills matter most when it comes to working with and alongside AI?
Background: Why Teams Must Build “New” Skills
AI is clearly on the rise, but without a clear roadmap, it is of little use to many companies. In fact, without direction, many risk losing their way. It is advisable to stay up to date on the latest developments, the advantages and disadvantages of AI, and the relevant framework conditions. However, the real challenge lies in using artificial intelligence in an individual, goal-oriented, and task-specific way that creates value.
Cultivating Collaboration with AI
AI arrives, human skills disappear. That bleak scenario, like a zero-sum game but arguably worse, cannot be allowed to happen. How can it work differently? By deliberately developing and applying human skills in a focused way. When used well, they don’t compete with AI, they complement it. The goal is a collaboration that creates something new and stronger than either could achieve alone.
Recognizing and Leveraging Human Skills
To ensure that the whole becomes more than the sum of its parts, potentials must be clearly activated. Simply looking at the range of AI use cases is not enough. Functional strategies are also needed to further develop and deploy human skills. AI is not meant to replace people, but to take over certain tasks and empower them to achieve more.
The Leadership Role of IT Teams
In the workplace, there was initially a certain degree of skepticism regarding trust in AI. This has now largely subsided, driven primarily by IT teams that use, test, and recommend various AI applications as pioneers. Many employees trust that the IT department will provide high-quality AI solutions.
An important prerequisite for their implementation and goal-oriented use is the positive experience IT professionals gain with AI applications by saving time, increasing productivity, and automating routine tasks.
The time gained can, for example, be used to guide other employees and introduce them to a value-adding use of AI. On this basis, employees can gradually acquire the key skills needed to get the most out of their combined competencies with AI.
Together with IT, leadership is called upon to develop a strategy that promotes AI competencies both in a generalist sense and at an individual, task-based level.
AI Strategy: Taking a Step Back to Gain Momentum
The pressure to use AI is noticeably high. However, uncoordinated experiments, limited scalability, and barely measurable added value are the wrong approach. AI deployment only succeeds where processes are already well developed and utilized. AI builds on clean data and mature workflows.
This is how real added value is created through thoughtful deployment. With sufficient experience, learnings, and insight, AI ultimately promises to make genuine differences. Skillful handling of various AI applications in combination with human expertise, initially driven by the IT department, forms the essence to which many important skills in the AI era ultimately lead.
Overview: The Most In-Demand Skills
The idea that artificial intelligence will replace human workers on a large scale is misguided. However, it is profoundly changing the world of work. Some tasks are disappearing, many skills are no longer, hardly, or less in demand; others are gaining importance.
The rule of thumb is: What has always led to high quality and stood out becomes even more important. And what has always been more replaceable and routine clearly loses significance.
#1: Creativity and Innovative Power
Ideas, creativity, innovation, imagination, subtle connections: All of this is irreplaceable and an even more important benchmark for human work in the AI era. This also raises expectations, as original ideas are harder to develop and simple diligence is less in demand.
How to Achieve This
Creative approaches and innovations emerge in several ways:
- A wealth of experience, unconventional thinking, and imagination contribute to this.
- Looking into other departments, areas, disciplines, and industries leads to new, previously unseen approaches.
- Using AI as a sparring partner creates new perspectives. For example, one can deliberately search for supposedly bad ideas and experiment with unconventional prompts. On this basis, teams can think several steps ahead and develop something new.
#2: Emotional Intelligence
AI sentiment analyses are extremely helpful in quickly gaining an idea of the emotional undertone in conversations such as chat histories and responding accordingly. However, human depth, genuine empathy, and skillful handling of emotionally tense situations remain areas that require high human emotional intelligence. This is gaining importance and is particularly in demand and irreplaceable in areas such as customer service.
How to Achieve This
Many employees are now called upon to explicitly rely on and expand this strength. This can succeed in various ways and on different levels:
- In conversations, it should be practiced to “read” the other person’s mood, respond to it, and act empathetically. It is helpful to change perspectives as often as possible.
- Emotional intelligence also means being able to handle one’s own emotions well. It is advisable to observe oneself, name emotions, and analyze how one deals with them from time to time. Even brief reflection helps you remain emotionally balanced and in control over one’s feelings and actions.
- Role-playing with AI helps to be better prepared for specific emotionally charged situations. For example, users can ask AI to take on the role of an angry customer.
#3: Making (Data-Based) Decisions
Agentic AI already has the ability to make decisions within processes; it acts autonomously based on certain logics. However, making real, strategic decisions remains a human domain. When it comes to concrete resolutions, artificial intelligence can inform us, but it cannot take over the thinking and implementation for us.
How to Achieve This
The ability to make decisions – on both a small and large scale – can be trained, expanded, and refined:
- Especially people who do not regularly make (important) decisions want to be perfectly informed to avoid making any mistakes. But perfection is an illusion. Often, an information level of around 80 percent is sufficient to be clearly capable of making a decision.
- AI is also the perfect (sparring) partner in decision-making. With proper training, AI provides the important data, parameters, and contextual information. Humans review these and use them for decision-making.
- Learning and experience cannot be replaced: Those who want to consistently make good decisions often inevitably have to have made bad ones before. A healthy, trusting error culture within companies is important so that employees can truly learn from mistakes and apply their experience to the next decision.
#4: Critical and Unconventional Thinking
Expressing justified, productive, and goal-oriented criticism is an indispensable human skill. It often requires multiple approaches to thinking, deep subject-matter expertise, and tact.
Consciously breaking with conventional ways of thinking and approaches and questioning the familiar (and seemingly only good) also leads to new, promising solutions.
How to Achieve This
All of this requires a level of rationality and autonomy that is far removed from the typical capabilities of AI. This, too, can be specifically encouraged and invoked:
- We often make implicit assumptions that must first withstand a test of logic and evidence. Briefly and deliberately questioning your own assumptions, and consciously generating counterarguments, helps prevent groupthink and leads to better solutions. The good old justifications, Socratic questions, and a healthy error culture are also helpful.
- There are many ways to think and act unconventionally. To do so, one must break at least somewhat with familiar thought patterns. Changing perspectives, working with divergences, interdisciplinary approaches, or lowering inhibitions (having the courage to pursue seemingly crazy ideas) are suitable methods.
- For critical and unconventional approaches, the framework conditions are decisive. Psychological safety, time for genuine reflection, and, if possible, diversity within the team are needed. Leaders must deliberately seek the right questions and criticism instead of simply looking for confirming and affirming approaches.
#5: Understanding Technical Backgrounds
Only a few possess sound technical know-how. It is relatively difficult to acquire, and many do not necessarily need it. Nevertheless, or precisely because of that, it is an important differentiator that becomes particularly relevant in the AI era with its many technical possibilities and refinements.
How to Achieve This
There are several simple measures here:
- It is advisable to occasionally engage with the mechanisms behind AI and other technical applications in order to develop a deeper understanding and learn to handle the respective tools in a differentiated way.
- Teams can also regularly engage with new tools, prompts, and AI-supported workflows in order to test them and potentially explore new paths.
- A high level of self-confidence pays off: Many employees hold back in technical matters but have more learning opportunities than ever before. Those who continuously use these opportunities build better technical understanding over time and simultaneously gain confidence in dealing with new technologies and approaches.
Conclusion: Using AI Requires Leadership
Simply using AI because it exists falls far short. It is also not advantageous for a team to fail to clearly differentiate between the capabilities of the AI applications used and those of the employees. Ideally, a way of working should emerge that combines human and machine strengths and thus unlocks existing potential.
Despite all the advantages of various AI applications, a human counterbalance is needed, especially in those areas where machines are less likely to have success. This is about a symbiosis with AI that reflects the changes in the working environment.
Competent employees are no less in demand; the requirements are simply shifting. Those who see this as an opportunity can adapt quickly and benefit from the changes. Leaders must now take the lead and clearly bring out the important potentials of their employees, foster them, and enable their development.