#391 The role of artificial intelligence (AI) in the future of work
The role of artificial intelligence (AI) in the future of work
Scientific evidence on the omnipresent topic
Artificial intelligence is a ubiquitous topic.
AI, artificial intelligence, the topic can no longer be kept out of context. Unfortunately, many people without a background often feel called upon to make statements with little or nothing in common with AI. Even an explanation of ChatGPT's prompt line only scratches the surface of the issue. Bad investments concerning AI are often the result. Tools are introduced that only fulfil expectations partially or not at all. Anyone who then did not like AI quickly showed up to practise full-time criticism. AI is an important topic that can positively impact the business world and every business model.
How can the situation be addressed sustainably with AI?
Studies
First, the facts presented here come from a comprehensive training programme at the University of Pennsylvania, a top-10 university worldwide, also conducted by Wharton Business School, the world's leading business school according to current rankings. Therefore, these are scientifically sound facts and study results.
The discussion about AI is mainly centred around ChatGPT, a large language model (LLM) that only represents a small part of AI. Vision AI, the spatial representation, is already used in companies for education and training scenarios. Voice AI, the linguistic variant, has already found its way into feedback and telephone scenarios. Many people are currently worried: Will an AI replace my job?
Evidence
The short answer is: most likely not. Studies have shown that neither humans nor AI alone have achieved the best results in a task. The best results were achieved when humans and AI worked together. In addition, studies by the University of Pennsylvania have shown that there is still a need for more highly qualified and less or low-skilled employees. It is only in the middle-skill range that fewer people are needed. The reason is simple: tasks such as data entry, supervision or noting attendances and absences can be done better and, above all, more emotionally neutralised by an AI. If you find yourself in this area, it's time for further training and qualification. Companies often help with this, as there is great interest in retaining talent these days. Many people in the so-called 'low-skilled worker' sector are surprised at the demand. Technically skilled people, on the other hand, are not. To date, machines with AI can help with harvesting, for example, but cannot replace harvest workers, as even the best AI-controlled machines to date cause too much damage to the goods for retailers to buy them. So, if someone tells you that AI will lead to the threat of mass unemployment, you can counter this statement with the current study results.
Implementation
In addition to the issue of data protection, the question of bias, the unconscious judgement, must be considered during implementation. If you train your own AI in recruiting with data from the last 50 years, you will mainly hire white and male people, even though there were better applicants. Inequities in data are thus reproduced and exacerbated. Amazon had to withdraw a racist HR algorithm, Microsoft had to take a fascist and racist chatbot offline. However, there are numerous examples of AI that have delivered significant benefits. It is, therefore, important for projects to generate quick wins in the organisation, especially at the beginning. People must quickly see that AI is helpful and does not cause any damage, e.g. by cutting jobs. A retail chain has provided its own GPT for all employees, including an app, which means that questions can now be answered in all languages via an app and store managers are relieved. A chatbot was set up for simple IT questions. Simple IT questions can now be answered from anywhere, without a phone call or queue, and around the clock. The service desk can finally handle the complex cases that make the IT landscape positive and sustainable. In HR, an airline introduced first-round screening using AI. The algorithm checks whether people can speak German and English fluently. With over 500 applications in most cases, this is a massive relief for HR, which can now focus on key aspects of personnel development. There were huge benefits in all cases, and no job was lost. On the contrary, the jobs were relieved and upgraded, as they can now take care of the more critical aspects. An AI Excellence steering committee ensures that everyone involved has a voice. Therefore, never put three groups of people at the forefront of AI projects: people who abuse data protection as protection against progress, technically completely clueless people or unreflective AI rejecters as well as unreflective AI supporters.
Conclusion: AI is always worthwhile if the context and implementation are right.
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More on this topic in this week's podcast: Apple Podcasts / Spotify
See below for the Podcast transcript.
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Niels Brabandt is an expert in sustainable leadership with more than 20 years of experience in practice and science.
Niels Brabandt: Professional Training, Speaking, Coaching, Consulting, Mentoring, Project & Interim Management. Event host, MC, moderator.
Podcast Transcript
Niels Brabandt
It's time to talk about AI, and you probably think when you listen to this podcast for a while that this topic is overdue. And, yes, I can tell you why I waited for a bit. You probably realize that AI now is omnipresent. It's an extremely important topic in today's working world and the world of work. However, you also have seen that some people became AI expert pretty much overnight, and no one knows where this comes from. And usually their advice is as useless as their nonexisting evidence. I waited a bit more for a very simple reason.
I, during the last month, did a professional training at the University of Pennsylvania as a top 10 university in the US, and especially at the Wharton Business School, which is the number one business school worldwide. They specialize in AI. It was very hard training I got there for month month month. However, now I can tell you substantial evidence and what science really says about AI instead of just giving you another opinion. And this opinion usually ends with chat gpt can write nicer emails than you do, which is a bit of a duh moment, and we want to have more than just another person stating, I clicked on chat gpt, and here's what I have learned by just writing something. The new year now begins and AI is omnipresent. Suddenly, anyone's an AI expert, so be careful who you talk to.
I will give you advice on that as well here. Some people now think I I has to be implemented immediately. Some people think we're a bit behind, so we have to invest quickly. Often FOMO, fear of missing out quickly kicks in and that becomes one part of the big big problem. Sometimes money is wasted and then people say, well, we tried AI and it didn't really work for us. And then, of course, anyone anyone who was critical about AI said, I told you so, and then the topic is dead in your organization. The question is, how can we face the situation with AI sustainably?
And that is what we're going to do today, but that's what we're going to talk about today. So first, what's the situation? AI is omnipresent. However, most people only know AI from check GPT, which is an LLM, a large language model. GPT is a generative or generate something. P for pretrained, and t is for transformer. So it is pretrained with data and you probably heard that this sometimes causes certain issues.
And transformer, it transforms language into whatever you ask for. So then, of course, might end with pretty okay results or it ends with something where you said that's a bit disappointing. And, of course, large language models, these LLMs are only one part of AI. AI, by the way, is already available in real world practice. Often you see this at the moment with larger organization when they go outside with their marketing. I give you a very simple example because oh, I I I give you very, very well known example because some of these examples show you how much of a benefit AI can be. So when you reach, for example, look at Vision AI, vision means you have something visible.
You probably saw the advertising pictures of people having either the Apple, the the Apple AI glasses on their head or the Samsung AI glass putting their mobile phone in front of it, and you think, yeah, I think I heard of that. Sometimes, let's say when you, for example, build chips and you want to train people on any situation possible, it is hardly possible to create every single situation physically. And in AI, this is a lot easier to create any situation where you want to train people on. And when you then say, hey, here are 500 different situation we're going to train you on virtually, it's way better than saying, hey, we have to wait in reality, and here are 10 situations now, and let's wait for the others because it might be too late or can take too long. So AI is extremely helpful. Usually, when you have these kind of virtual trainings, you have more than just the Apple or Samsung glasses. You have a lot more complex tools.
There are different, different versions available. However, I'm not gonna advertise for anything here. Very important is that's not all. When you go, for example, for voice AI, and voice AI is often combined with Vision AI. I give you a very simple example when I work with the company called Lipaya. We deliver training there. I'm a freelance trainer there.
And we often have training. 1st, you have a session with a trainer, then they go for the virtual, situation, especially when you, for example, have critical discussions. So, for example, you have to fire someone, you train someone on how to lay off people, or you have a critical talk, and you don't really dare to do something in front of a group. You probably know these kind of situations. When you do it with AI, you can do absolutely anything you ever wanted to try and see the situation, and you also can see the reaction of that AI. And voice AI talks back to you. It also gives you feedback.
And voice AI always helps you that you can actually train what you learned. Of course, you can also do role play. That's the third part we often do with the training at Lipaya. We have professional actors using that kind of approach. So you have the real world trainer, then the virtual, version, and then you have the actor altogether delivering the best benefit.
That is just one example of it. That is just one example. Very important here is that of course, you might now say, well, this is only available for large corporation and no, it is not. And the other option often people go for is, well, AI is going to replace our jobs. Isn't it? So first, don't be worried about that. And I will tell you in a minute proven by science why you don't have to be worried except when you're in one situation.
So first, AI is already in place with when when you think AI is new, it's not. So in finance, it's around for decades already. In marketing, it's around for years. In medicine, I can tell you halicin, which is an own penicillin was discovered by an AI because this AI could just wrap together and and and then just summarize papers from all over the world that scientists found out to find a penicillin via AI. Of course, not when you upload the papers and then say, 30 minutes later, here's the penicillin. It took about 6 weeks to get the new penicillin out of the AI. However, with humans, it would have taken at least 6 months and only if those humans had known which papers are the suitable ones and finding that out could take years if not decades.
So AI saves lives. So you see that AI is already in use. And when you now think about what about replacing jobs? So there is scientific evidence that I quote the scientific studies that were conducted by the University of Pennsylvania and the Wharton Business School here. We need more high skilled workers because AI needs to be fed, needs to be designed, needs to be trained. We need more high skilled workers and also we need more, and please don't consider this a disrespectful statement, just a scientific term. We need more low skilled workers.
Low skilled workers means high school degree or less. And low skilled workers, I just give you a very, very simple example. When you, for example, think that we have automatic harvest, we do not have that. Many approaches were taken, 1,000,000,000 were spent, and there are now machines that can help with the harvest. So there is still no machine that can do fully automatic harvest because the damage to the product taken out of the ground or off the tree is still too bad. That simply retail wouldn't say we're going to sell this. So you still need a lot of high skill and low skill workers.
However, there is one aspect where AI can replace jobs, and that's the only place, and that is the middle skill worker tier. The middle skill worker tier where basically someone says, look, I do supervising that everyone shows up in the call center and does their work, and I manually feed Excel sheets. That is something which an AI can do way better, and I can tell you why. Let's say you are someone who's super you you you you you're a supervisor in the call center, and one of your people is late.
1, 2, 3, 4, 5, 6, 7 times. And, of course, you don't wanna lay them off because it's hard to find new people today, and training them takes even longer. So you have to deal with the people who are simply there, but you will get annoyed at that person. And being annoyed and angry with that person will have massive impact most likely because most likely in that job, you were not professionally trained on leadership issues or aspects. So you will deliver bad leadership and then more even more people leave due to your bad leadership. And AI doesn't have these sentiments. And AI doesn't get annoyed, doesn't get bored with you, doesn't get annoyed with you.
And when you are laid 1, 2, 3 times and the data says after 3 times, there need to be some sort of action taken, the AI will do so as it was trained without any sentiment included. This typical supervisor management with very simple decisions connected can be done by an AI, and then AI can do it better, more consistently, and without any emotions involved, which is way better than humans can do. So when you by the way, often people also ask, what about temp workers? Temp workers, by the way, stay the same. So there's the same amount of need for temp workers that can be either there temporarily, for certain projects, can be consult, or can be people who just work for you for for a couple of months or couple of years on limited contracts. So there's the same amount needed for temp So this kind of just supervisory management, taking care that people are there, showing up on time, that can be done way better by systems powered by AI than humans could ever do. So when you now say that you are in this mid tier, you better qualify yourself very quickly.
And by the way, that's your task, no one else's task. When you have an employer, you might ask them as well to help you or they give you certain qualifications because the way from middle tier either goes up or goes down or goes out when they say we don't meet you anymore. So be sure that you do not end up in this middle tier that can be easily replaced by robots. So why do people now think that AI doesn't replace all of the jobs? Very simple. Maybe you have the following situation. Maybe you just saw thought it's a coincidence.
Maybe you thought, can Chegg GPT write me an email? And then Chegg GPT asked what kind of email, and you give you give a bit of data, and Chegg GPT is going to write the email and you say, that's pretty good.
I just take this result. I optimize it a bit more. It's not perfect, but it's good. But when I optimize, it's actually perfect.
And that's exactly what science says as well. When you when we have a certain task and we ask 3 different options, option 1, we only ask the human.
Option 2, we only ask an AI. Option 3, we ask the human to work together with the AI. The best result by far by far is done humans with AI, not AI replacing humans. Because especially when you have repetitive tasks, AI is very quick. However, especially when you have interconnected tasks with a bit of creativity, AI can be creative, throwing on lots of versions. However, these versions might not align with your core values, with what you've done before, doesn't connect to your audience, or or or or simply misses a certain point that only you know because it's informal knowledge that is not in the training data that you gave to the AI in the first place. So you see that the best version will always come out when humans work with the AI and not just and not just fear that AI is going to replace you because most likely it will not.
However, it will have an impact on your industry, and you better take care that you know something about AI reasonably soon. Because when when it gets to the implementation, there are always the same issues and of course we need to be aware of risks. First one is data protection laws. Data protection laws, there are basically 2 different worlds. The US world where basically people have to need some sort of when you look at CCPA, that's the that's the the data protection they they have in California. In many states, they don't have anything. Then people can opt out from something.
But before that, basically, it means data available can be used. The European Union approaches, you need explicit agreement from the person, and also they have the right to a meaningful explanation when you, for example, use AI to get to certain conclusions. You have to give a meaningful explanation why you got to that conclusion. And that, of course, can be difficult when you don't already know how an AI works. So data protection is an issue here. However, there's also the aspect of bias. And the aspect of bias is that when you, for example, now think, well, I think we use, our recruiting data.
We are we are an organization. We are on the market for 80 years. So I'm gonna feed my AI with all the recruiting data I have, absolutely anything I have because the more the better, and now they will hire the best people. When you teach an AI system your recruiting data of the last 80 years, I can tell you what happens afterwards. You hire men, you hire white men, and you hire white men from certain ZIP codes. Because suddenly, when any data is available, suddenly, it will look at absolutely anything from pictures to skin color to ZIP codes to first names to last and making up the weirdest rules just because AI sees, oh, there are a lot more of these names, a lot less of that name, so maybe this name is more suitable for the job. So training the AI itself is a heavily complex task.
And believe me, when I sat there with these mathematical models at this university course, I was swearing back and forth because I am not a math person. Absolutely not. However, it was extremely important for me to understand what was going on. Training an AI system is incredibly complex, which is why you shouldn't think that AI is going to replace humans because it is simply the the the amount of data you would need in real time to comply with the laws, the situation, making everything fit in your organization, your team, your situation, your project, your moment right now. That is such an enormous amount of data that then needs to be basically cut down to the exact amount of data that you can use to not put any kind of bias into the system. That will lead to huge challenges. And that is why humans working with AI will always come to the better conclusion compared to just humans or just AI.
And now I give you very importantly, often people ask, are there any kind of quick, any any kind of practical examples? And, yes, there are. So what you need when you want to implement AI in your organization, the first thing you set up is AI excellence team and AI excellence team. The AI excellence teams consist of people who are highly qualified in AI, people who will be affected by AI, meaning users, and, of course, also someone from the legal department, also someone from compliance department. So anyone who can contribute in a meaningful way or will be affected in a meaningful way by AI. And you, of course, need to vote for certain people because you can't have 100 people on a board because then you will you won't get anything done. So you have the AI excellence team.
Then please have decentralized projects. Nothing is more frustrating as if anyone sees like, oh, look. The sales team gets AI as the only department. I told you so. Right? It's all about sales, and no one values anything we do here.
It's only about sales. Only always the same. Sales gets first, then years later marketing, and that's basically or maybe a bit of bookkeeping. But for us for us here in the HR, nothing. Absolutely nothing. So you see, decentralized project means anyone has skin in the game. Anyone holds a stake in this, and that keeps people engaged.
However, when you choose the projects, the very first projects you need to have are always the projects that deliver quick wins. Do not start with the project that changes the whole business model to then only find out that you did something wrong. And especially the critical people will say, I told you so and everything's dead in the water from there, including probably your career. You go and you start with quick wins because then people see, oh, look. Look. This is actually useful. This actually helps us.
This this actually makes my life better. You start with quick wins, and I give you 3 real world examples where in Germany with the strictest and tightest data protection laws worldwide by far, this was implemented. So the first one was what I call retail GPT. Instead of the name retail, they took the name of their retail chain. A certain retail chain found out that lots of time from leaders in their local retail stores was, I don't want to say wasted, but was taken by people when they were onboarded, they simply still had questions. So for example, I give you a very simple question here. Why do we have a blue knife and a yellow knife, for just at the at our meat station?
What's that for? I knew I had it in the onboarding training, but, you know, I I listened to 500 PowerPoints.
I I I just forgot. The very simple answer here is one color is for, for example, veal, another color is, for example, for chicken. When you cut the wrong meal the wrong meal with the wrong knife, you have cross contamination, you can discard everything you have heavily expensive, immediately cuts into your profit margin. And all of that comes on top of the fact that many people in retail do not have German as their first language when they start training in that area, and you might have that situation in your country as well. So what they did was they fed their GPT, the generative pretrained transformer, just as Chegg GPT, including a voice model where people could then ask the question, why do we have a blue and a yellow knife behind the the meet desk? They could ask that question in their native language. For example, Bulgarian or Romanian or Ukrainian or Spanish or French or English or whatever else.
They they they could simply talk to the app, and the app gave the answer either in speaking or in writing in their native language. And very important here was it was given either in speaking or writing people could choose because not everyone, and you might not see this problem when you listen to this podcast right now, not everyone had the same level of alphabetic school education as most likely we had when we listen to this podcast right now because getting to this podcast means you needed to go through a certain level of of research to get to this podcast. So and that simply made available 3 to 4 hours per week for every single market leader, for every single retail location leader, 3 to 4 hours per week. The return on investment was 10% of working time now available for more important tasks.
Not a single job was lost. Because I can tell you before that, they didn't say, oh, you know, as a leader in your retail store, it's quite stressful. We give you dedicated time for a q and a.
They simply said, make it work. You have to do that on the side beside or be besides all the other tasks. And then they simply did crazy over hours, many of them unpaid and undocumented, which is, by the way, in Germany, illegal. So that is just the retail GPT here. 2nd second real world example is a chatbot for simple IT issues. Maybe you remember when you said in front of an Excel team, you wonder, what is this lookup thing like, hlookup or or or vlookup? Something with lookup and then it searches.
How did that work again? And you type it into a chatbot, and the chatbot either talks to you and guides you through the process or simply gives you an explanation in writing depending on what you prefer. And that simply took a massive amount of calls off the service desk. Again, not a single job was lost because many of the complex issues at the service desk were simply unsolved because the service desk said we don't have time for this. They simply said, when should I do this? People calling me left, right, and center to change the the color of their screensaver. So suddenly, the service desk could actually look into complex problems, changing the IT landscape in a meaningful positive way, winners on all sides.
And, again, not a single job lost. Humans working with AI enhancing everything. 3rd one, recruiting in HR. And very important here, there is no software that can replace the whole HR recruiting process. Not possible. With the data today, when when you look into data that we have, humans are still so heavily biased that that the data we create try to get human bias out of data. It'll take years, and you need millions of datasets.
And when you wanna, for example, say, hey. Could the AI help us with re recruiting? Yes. It can. I give you very simple example. And that example is the airline industry. In the airline industry, people say, well, we need flight attendants. And you cannot think of which kind of people apply because they think they belong on a plane.
Very quickly, when they open up a single position for flight attendants, 300, 400, 500 people, or more apply. And I give you the example that we have right here. A German airline has very simple rules in the beginning. However, these rules aren't something you can simply check on a PDF because the rules were, you need to be so first of course, you you need to be able to work in Germany. You need to be fluent in German. You need to be you need to be able to speak fluently. So you should not, and, you know, when when, not like that.
You need to be able to form structured phrases and express yourself accordingly in the German language. Besides that, you need to be fluent in English on at least a very good level as well. And again, here, fluently. So you have to talk to every single applicant, at least what that's what they did back in the days. Today, they have an HR for that. They they they have an AI for that. And the AI simply has a chat with the applicant in the first round, simply selecting out the people who are unable to express themselves at least on the minimum level they they they expect people to speak.
And that simply takes a massive burden of the HR. And by the way, the software I'm talking about here is Vittorio. Vittorio is a software that was part of a PhD that was published, and the PhD person then founded the company. It is also founded by the TUM, the Technical University in Munich, a top fifty university. It is it is used by huge corporations set with millions and millions of datasets, and it took years to get the bias out of the software. So when you think, oh, we're gonna write our own piece of software with the 400 applicants with the last 2 years, that's gonna be massively biased. Don't have any hopes that that that dataset is going to get you anywhere.
So Retoreo is the software you can use to, for example, check for simple things, English language skills, German language skills, are people able to to to make a structured point, not get angry, and all of that without any kind of bias because there was check for bias and it went through optimization processes for years years years. And also believe me, press already jumped on it and try and try, for example, to have people with darker skin color or very traditional clothes or very modern clothing, and they hope that the AI is going to give any kind of response in a negative way due to outer appearance and no chance. It all worked. So here you have Vittorio as one example of AI that actually takes a massive burn out of the HR department. By the way, again, not a single job lost. Don't think that anyone in management said, oh, HR. It's very stressful for you.
So we're going to give you 2 weeks off to have 500 chats with people checking their language skills. No. That was not the case. Either they had external companies which was tremendously expensive, and you don't even know what they do so you're not sure about the results, or simply people sat down for weeks checking through all of these people, but besides doing their main job as well. So, again, humans working with AI and everything gets better. Here you see three examples of very quick wins. These are projects you do first.
Sometimes the software is already available on the market. Sometimes there's a generic software, for example, a chatbot that you have to feed with your data. For example, when you have a ticketing system on your help desk, so your your hotline, your service desk, you you can feed them the problems of the last 50 years all into one chatbot to make people help themselves quicker. Especially, for example, and these are, by the way, the people who were raving the most. People who had worked from home and they were working during the hours when the service desk was unavailable. So for example, they were working either before 8:8 AM or after 5 PM because the day they prefer to do so. And certainly, you had someone who could help you with your IT issues outside these hours, outside the usual hours.
It's a very, very quick win. However, there are typical mistakes we have to talk about as well. There are certain people you do not put in the front line. And I know this will now not cause fascination with everyone, and not everyone will will be happy with what I say right now. Certain people do not below 1st row center when it comes to implementing AI. Number 1, you cannot go with data protection experts first because data protection experts are often so averse to anything. And by the way, I unfortunately had to lift way too many examples where data protection experts say, oh, it's not compliant, and we found out it was.
They simply wanted to block off the risk, and they always say it. When I say it's not data protection compliant, anyone will say, oh, we walk away from here. I don't want to know how many AI projects already unrightfully died and were not put in practice due to someone shouting data protection. Data protection cannot protect you from actions. When you use data protection to be protected against actions, you become part of the problem. Of course, data protection is tremendously important. I'm not saying don't use these people at all, but they are not in the first row.
AI is not approached with the first approach on data protection. The first is always the idea, and then you take it from there. So people not to put in 1st row, number 1, data protection experts. Number 2, people who are technically clueless. And I know this is not not nice, but you have these pseudo AI experts out there who suddenly on LinkedIn overnight became AI experts, check for academic qualifications. And I do not mean necessarily that people need to have a degree. However, people need to have knowledge on the academic level.
You can have a degree, you can have an executive education or courses or whatnot. But simply say, I know AI because I watch YouTube. No. No. No. It is way too complex, way too complicated, and way too important. The harm you can do is massive and harm happened, by the way.
It's a couple of things went wrong. For example, Microsoft put a chatbot out there that suddenly got into racist and fascist discussions with people chatting to it. Amazon had to openly admit that Amazon Amazon, a huge corporation had to openly admit that their algorithm was racist, and they had to put it down. And then we develop it, optimize it, then put it back up again, and ever since it's running. So and by the way, these these recruiting algorithms also are usually something that people who apply somewhere like. Because maybe, you know, when you apply for a job and you don't hear back for 6 weeks, maybe you realize that is not great. With AI, you have an answer within 48 hours when it works through all the data overnight.
So it is a benefit for everyone when the data is set up well. So technically, kudos people, no chance. And the third aspect is people who are non AI qualified. And I tell you who are the people who are non AI qualified. Non AI qualified are the people who are either totally against it because they are they are always ravaging against it no matter what kind of piece of evidence you put in front of them. On the other side, non AI qualified people are also the ones who tell you AI is the solution to everything because it's not. These people will put everything in place, and when the damage appears, they say, oh, this is just part of the game. No. It's not.
At least not on that extent. It's not. So AI qualified people have a reasonable knowledge, expertise, and experience on risk factors. You have to consider to have a reasonable approach how to put AI into real world practice. And these are the people you need to put. These are the people you you need to put in 1st row center to the frontier of the AI development and implementation in your organization. Conclusion. AI pays off when you put it in the right context with the right people, in your organization.
Because when you work with the organization, with the expertise, with the science, and especially with the people in your organization together for quick wins and then sustainable growth, then you will have the best benefits from AI you can ever think of. And as I mentioned before, hardly any jobs ever will be lost. Everything gets better from here, and I wish you all the best implementing it from here. And, of course, you might now say, woah. What a topic for for the 2nd week of the year. And I agree. It's not easy.
And by the way, I really held back with the topic to give you profile and state. But, of course, this is not even 1% of the knowledge or the technical expert, probably 0.1% of what you need to say about AI. It's a starting point here. Very important is when you now say I need to discuss something, just drop me an email. Nbnbhyphennetworks. com, and then we have an email, take it from there, and chat. I also put this email in the show notes of this podcast.
I put my LinkedIn in the show notes of this podcast, and I also put my website nbhyphen networks.biz in the show notes of this podcast. By the way, on the website, you also find an article on the matter, and you also find the transcript of this podcast at the bottom of this article. In addition to that, when you now say, hey. I want to have live sessions. Feel free to join our next live session when you go to expert.nbhyphennetworks.com. From there, and I also put that link in the show notes of this podcast, you can put your email address in there, and you receive only one email every Wednesday morning. It's a 100% content at free guarantee.
It's, full access to all articles, full access to all podcasts, and most importantly, free and immediate access with one link which is always there, including the date and the time when our next live session is going to happen. And I'm looking forward to seeing you there. However, the most important step is always the third one.
Apply, apply, apply what you heard in this podcast. Because only when you apply what you heard, you will see the positive changes that you obviously want to see in your organization. I wish you all the best doing so. If you have any kind of questions, feel free to contact me anytime.
I'm available 247. Looking forward to hearing from you. And at the end of this podcast, there's only one thing left for me to say. Thank you very much for your time.