The article AI skills: 7 skills for more control and better results first appeared in the online magazine BASIC thinking. With our newsletter UPDATE you can start the day well informed every morning.
Many feel overwhelmed by the flood of new AI tools and updates. The engine revs, but you can’t make any progress. These seven ideas will show you how to use AI sensibly, structure your own workflow and finally escape AI overload.
The hunt for the latest AI tool and the latest AI development seems to provide a lot of acceleration, but somehow it doesn’t really make any progress.
If you take this seriously, the logical follow-up question for the new year is not: “Which AI tools do I need to know?”, but rather: “How do I develop a way of dealing with AI that really helps me move forward without getting too carried away?”
Seven ideas to combat AI overload in the new year
So that you and I are on the same level, I have to make one thing clear at the beginning: When it comes to meaningless acceleration, AI is not the cause, but rather the amplifier.
It shows how often we confuse speed with direction. And how easily “sticking with it” becomes a constant tension that ultimately even feels like a standstill.
Before we get into specific ideas, an important classification: The following suggestions are deliberately not classic “AI tips”. You will not find any tool recommendations here, no leaderboards and no “How to use AI correctly” instructions. Not because it’s all unimportant – but because precisely this perspective is often part of the problem.
If we only view AI as a collection of functions, we quickly lose sight of how we actually want to integrate it into our everyday lives. Then we optimize details without first clarifying where we want to go.
That’s exactly why the following ideas don’t focus on technique, but rather on your posture. They’re designed to help you get back behind the wheel and not just react when the next trend comes around the corner.
Think about it: This is how AI becomes a tool, not a stress factor
Some of the ideas and suggestions may seem unusual at first glance, perhaps even counterintuitive. Some people consciously brake when they would otherwise accelerate.
Others set clear boundaries where openness is usually advocated. This is no coincidence. Because if you want to get out of idle, you don’t need more speed, you need a gear change.
Therefore, see the following points less as a checklist and more as an invitation to experiment. You don’t have to implement everything. All it takes is one or two ideas to make you think or help you make your own use of AI clearer, calmer and more effective.
1. Only use AI if the desired result is clearly defined
Many uses of AI begin with “Let’s see what happens.” In my opinion, this is one of the main reasons for tool hopping and overload. Instead, try the following: You only start when you can clearly name the output format in one sentence.
Examples:
- “I want an email with three variants and a clear call to action.”
- “I want a decision template with pros/cons, risks, next step.”
- “I want a draft that I can finalize in 20 minutes.”
This idea seems banal, but it is a powerful filter. It separates “AI as a playground” from “AI as a tool”. And it prevents you from getting into the mode where you’re constantly optimizing but not completing anything.
2. A month without new prompts: Refine existing AI tools
You’ve probably noticed that the AI world loves new prompts. The problem is: New prompts create the feeling of progress, even though they are often just variations on what is already known.
So set a clear rule for January (or any other month): you won’t create new prompts. You only use existing ones and systematically improve them.
Real efficiency does not come from creativity in prompting, but from standardization: you build two to five “working prompts” that you really master.
You add examples, define quality criteria, set the tone and incorporate control questions. At the end of this month you will no longer have “tried AI” but will have created a small, stable prompt system for yourself.
3. Critically review AI results and ensure quality
AI, especially the common large language models such as ChatGPT and Gemini, have the peculiarity of giving you as a user a lot of confirmation. You probably know this: “That’s an important question.”, or “I can answer that for you precisely and in a structured manner.”.
But you don’t want approval, you want quality. Therefore, establish a kind of second instance before adopting AI results. In the future, use AI in two roles: Firstly, as a producer who creates a text, plan or draft. On the other hand, as a referee who evaluates the result according to a fixed catalog of criteria.
It is precisely the catalog of criteria that is important. Give it weights and penalties, for example:
- Unclear statements (-2),
- lack of concrete next steps (-3),
- too much buzz wording (-2),
- Contradictions (-4) or
- lack of connection to the target group (-3).
The referee should not be “nice”, but precise. In this way, AI does not become a yes-man, but rather a quality assurance system. And you get out of the feeling of constantly having to make improvements without knowing why.
4. Plan time windows for AI to be overwhelmed and reduce stress
That sounds absurd, but it’s psychologically smart. A large part of the unrest arises because excessive demands are experienced as an “exception” that needs to be eliminated quickly.
So turn it around: Plan small time windows in your calendar in which you explicitly don’t have to be productive, but just “play around” with AI.
Every second Wednesday, for example, you take 30 minutes for a kind of “AI radar”. You read two updates, test a feature, try a new tool.
Then you write down two insights: relevant or irrelevant. Not more. This means that the topic loses its constant pressure because it has a fixed place. The rest of the week is then free for implementation.
5. End old habits before introducing a new AI tool
AI tool collections are often the modern form of guilty conscience. You have ten tools, but no routine. So try out the following idea: Every new tool can only come if you consistently stop something else.
Examples:
- “If I use tool X, I stop doing Y manually.”
- “If I introduce tool X, I eliminate Z from my weekly routine.”
The “no” is expensive and that’s exactly why it works. It forces you to prioritize and prevents AI from becoming a collection of (unused) opportunities that burden you rather than relieve you due to the scope.
6. Plan annual goals backwards: Less is more
Many goals fail because they are thought of additively, according to the motto: “I want to do more.” In an already fast system, however, the subtractive is often the lever: “What do I NOT want to do anymore?”
Therefore, with an eye on the end of the year, write down three sentences: “In December I will no longer do …” (e.g. “… answer everything immediately”, “… start texts without structure”, “… make decisions without criteria”).
Then you use AI as a termination assistant: It should build processes for you, formulate text modules, create decision-making frameworks and generate templates so that you can actually end the things you mentioned.
7. Digital mindfulness: The offline session against AI overload
If you only want to establish one habit in 2026, try this: Write a fixed period of time in your calendar of around 60 to 90 minutes. This will be your “offline productivity session.”
For this session, have a notebook, pen and tea or coffee ready. What you don’t need is a screen of any kind.
Immediately before the session, you get exactly one input from KI, for example a list of ten blind spots about your current project or three alternative perspectives on a problem. Then you go into your session and think about the AI input.
This method is a very effective antidote to AI idleness. You use AI as a spark while you light the flame yourself. This will take you from idle to the right direction.
Directional Compass: The guiding question that you write in every AI window
In the end, it all comes down to one simple skill: being able to stay in direction. And that brings us to the bonus idea. Because in order to maintain direction, all you need is a guiding question that you use consistently, for example: “Will this make noticeable progress for me in 30 days or will it just keep me busy?”
Write it as the first line in every prompt. Let AI answer them too. You’ll be surprised how often the honest answer is, “That’s interesting, but not now.” This makes AI what it should be: an amplifier of your priorities and not an amplifier of your distractions.
Conclusion: get AI overload under control
The speed of development in the AI field will not slow down, that much is certain. What matters is how you deal with it. My recommendations are intended to help you keep control (or get it back) and thus decide or be able to decide on the direction.
This is exactly the development of my Christmas column: “taking your foot off the accelerator” becomes consciously shifting into the right gear and steering in the consciously chosen direction. The engine that is idling becomes an engine that has power but no longer revs pointlessly.
You don’t have to do everything. You don’t have to understand everything right away. Above all, you have to decide one thing: decide what matters to you. Then AI won’t become noise. Then it becomes a tailwind. I wish you all the best for 2026!
Also interesting:
- One-size-fits-all: ChatGPT makes social media posts all sound the same
- AI dialogues: Why artificial intelligence seems smarter than it is
- AI agents will change companies – but differently than expected
- Should we treat AI as a legal entity?
The post AI skills: 7 skills for more control and better results appeared first on BASIC thinking. Follow us too Google News and Flipboard or subscribe to our newsletter UPDATE.
As a Tech Industry expert, I believe that AI skills are crucial for professionals looking to stay competitive in today’s rapidly evolving tech landscape. The seven skills outlined for more control and better results provide a solid foundation for individuals to excel in the field of AI.
1. Data analysis and interpretation: Understanding how to collect, analyze, and interpret data is essential for building effective AI models. Professionals should be proficient in statistical analysis and have a strong grasp of machine learning algorithms.
2. Programming proficiency: Proficiency in programming languages such as Python, R, and Java is essential for developing AI applications. Professionals should also be familiar with frameworks like TensorFlow and PyTorch.
3. Problem-solving skills: AI professionals need to be able to identify and address complex problems in a systematic and efficient manner. Strong problem-solving skills are crucial for developing innovative AI solutions.
4. Communication skills: Effective communication is key for collaborating with team members, presenting findings to stakeholders, and explaining complex AI concepts to non-technical audiences. AI professionals should be able to communicate their ideas clearly and concisely.
5. Domain knowledge: Having a deep understanding of the industry or domain in which AI is being applied is crucial for developing relevant and effective solutions. Professionals should be knowledgeable about the specific challenges and opportunities within their field.
6. Ethical considerations: As AI becomes increasingly integrated into various aspects of society, professionals must be mindful of ethical considerations. Understanding the potential implications of AI technologies and ensuring they are deployed responsibly is essential.
7. Continuous learning: The field of AI is constantly evolving, so professionals must be committed to ongoing learning and skill development. Staying up-to-date with the latest advancements in AI technologies and techniques is essential for maintaining a competitive edge.
Overall, mastering these seven skills will empower professionals to have more control over their AI projects and achieve better results. By honing these skills, individuals can position themselves as valuable assets in the tech industry and drive innovation in the field of artificial intelligence.
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