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Step by Step to Success with AI-Powered Solutions

Step by Step to Success with AI-Powered Solutions

by Marina Begic | Oct 31, 2024 | Best Practice, Digital Transformation, Leadership and AI | 0 comments

Step by Step to Success with AI-supported Solutions

Do you prefer to listen to this article? Click below to access our AI-generated audio version!

Step by Step to Success with AI-supported Solutions

A successful AI transformation in human resources development does not happen overnight. It is a process that requires a strategic approach and an agile implementation. How do you get employees on board? Combining both poles.

Sound familiar? Employees and colleagues approach you and tell you about the latest AI apps they are trying out. Many of them mention, some do not, that they do not even know whether they are allowed to use AI tools in the company. Your company may be working on a policy right now. There may not yet be a company-wide regulation or it may not yet be known. Don’t worry, you’re not alone in this attitude; 80% of companies are also waiting.

The importance of AI integration for employee motivation and competitive advantage

This could be fatal, on the one hand, because the initial motivation of employees, which is so often desired from the point of view of personnel development and leadership, could be curbed as a result. On the other hand, it could put them at a competitive disadvantage. A possible first-mover advantage shrinks with every day that passes without working on optimization using AI.

In addition to the motivation, some have no interest in the topic of AI and think they can “sit it out” or are unable to exert any influence anyway. So how can we get everyone on board? After all, as we know, we move faster when we’re all rowing in the same direction.

AI Readiness Check: assess the starting position.

Before companies embark on the AI transformation of their HR development, it makes sense to assess their current AI readiness. An AI readiness check helps to evaluate the initial situation and discuss it together. This AI readiness check provides, for example, results in the categories of strategic orientation, know-how, culture, resources, and data after a 10-minute self-assessment.

With a common starting point, it is easier to identify opportunities for integrating AI into the L&D strategy. Such a readiness check can be used in any area of the company.

AI Transformation (1000 x 600 mm)

Agile approach – AI Autonomy Day

Organize an AI Autonomy Day: Provide everyone with a day a year to work on any topic in the field of AI. Provide as little guidance as possible. Also, provide a small budget for any licenses. It is important to note that, by the GDPR, no personal or company data should be transferred to unprotected LLMs.

Trying it out can happen individually, in a team, across departments/hierarchies/teams. Provide a communication platform and make it transparent which topics are being worked on. Set a date for presenting the results. Invite relevant stakeholders and decide together what will be done with the results. Repeat the process every six months or even every quarter.

To take it to a strategic level, we recommend setting up an AI transformation roadmap. This roadmap will track the progress and plans for each area. You can decide whether to share the roadmap with everyone individually or by team/area. The results and the maturity level of the project should be recorded by the company management.

This tool offers you the following advantages:

  • Clear vision and strategic direction
  • Improved communication and alignment with stakeholders
  • Controlled resource and time management
  • Risk reduction and adaptability
  • Focused innovation and development of use cases
  • Structured approach – a clear process gets the ball rolling

To find out what leverage AI can have in your company, you need to have a basic knowledge of AI and the (technical) possibilities of AI (weak AI vs reactive AI vs AI with limited storage capacity) . On the other hand, you need to know your processes very well. Those who already have a detailed process map have a clear advantage here.

As a first step, evaluate the AI potential based on your processes and divide your processes into three process types. Then evaluate the savings potential of the activity to determine your starting point with the greatest potential.



Weak AI vs. Reactive AI vs. AI With Limited Storage Capacity

Weak AI: performs tasks, does not learn new skills (voice assistants, generative AI: image generation, text generation, research & diagnosis)

Reactive AI: does not build up memory, reacts to stimuli (spam filters, recommendation systems based on historical data such as Netflix, Amazon)

AI with limited storage capacity: stores knowledge, learns from it and makes forecasts (weather, finance, self-driving cars, chatbots)

(Source: https://ki-campus.org/videos/ki-in-unserem-alltag?locale=de)

Further training & communication

To use the topic of AI profitably and responsibly for everyone in personal development, in the team, and the company, you cannot avoid acquiring a basic knowledge of AI yourself and motivating others to do the same.

Join communities, exchange ideas with peers, and take small steps. But take them consistently and lead by example. Support your colleagues and managers with training, workshops, exchanges, and networking opportunities. Create personalized wikis and AI assistants (e.g. using GPT Builder) to make knowledge more accessible.

 

Marina Begic

Marina Begic

Head of Business Development – Digital Transformation Driver

Marina has been working on new, effective learning methods and the future of corporate learning for over 15 years. In her current role, she is responsible for Digital Business Development at MDI, where her focus is not driven by the current buzzwords, but primarily on the feasibility of digital transformation for clients such as Erste Group, Lenzing, Semperit, Deutsche Bahn, Andritz AG, Uniqa, Mayr-Melnhof, Frequentis, RHIM. Her greatest strength is bringing loose ends together, which she impressively demonstrates time and time again with her big picture view and multi-dimensional approach. Her greatest passion is to provide learners not only with an experience, but also with real, lasting value for their real challenges.

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by Gunther Fürstberger | 14. November 2023 | International leadership development, Leadership and AI, Leadership in the digital transformation | 0 Comments

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Navigating the Ethical Challenges of Algorithmic Decision-Making

Navigating the Ethical Challenges of Algorithmic Decision-Making

by Raphaela Pouzar | Aug 27, 2024 | Leadership and AI, Leadership in the digital transformation, Short Knowledge Bits | 0 comments

 

Navigating the Ethical Challenges of Algorithmic Decision-Making

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Navigating the Ethical Challenges of Algorithmic Decision-Making

by KI generated

https://blog.mdi-training.com/wp-content/uploads/2024/08/Navigating-the-Ethical-Challenges-of-Algorithmic-Decision-Making1.mp3

Navigating the Ethical Challenges of Algorithmic Decision-Making

Artificial Intelligence (AI) is transforming our world, from personalized recommendations to advanced medical diagnostics. However, as AI becomes more integrated into our lives, a critical issue has emerged: algorithmic bias. This bias occurs when AI systems are trained on flawed or incomplete data that lacks diversity, leading to unfair or discriminatory outcomes and often perpetuating existing inequalities.

What is AI Bias?

AI bias arises when algorithms reflect and amplify societal prejudices embedded in their training data. For example, suppose an AI system is trained predominantly on data from one demographic group. In that case, it may perform poorly for others, leading to unfair decisions in areas like hiring, healthcare, and law enforcement.

The Dual Nature of AI

AI offers immense benefits, but these are accompanied by significant risks. In healthcare, for instance, AI can assist in diagnosing diseases, but if the training data lacks diversity, the AI may misdiagnose conditions in underrepresented groups, exacerbating health disparities.

Global Implications: Data Colonialism

AI bias isn’t just a local issue—it has global ramifications. In many cases, data from the Global South is used to train AI systems that benefit the Global North, a practice known as data colonialism. This exploitation deepens global inequalities, as those in the Global South often do not share in the benefits of AI technologies.

 

Global Implications: Data Colonialism

Addressing AI Bias

To mitigate AI bias, it is essential to adopt a responsible approach to AI development. This involves recognizing the duality of AI—its potential for both benefit and harm—and taking proactive steps to minimize risks. Here are some key strategies:

  1. Diverse and Representative Data: Ensuring that AI systems are trained on diverse and representative datasets is crucial. This includes data from different demographic groups, geographic regions, and socio-economic backgrounds to minimize bias.
  2. Transparency and Accountability: AI systems should be transparent, with clear explanations of how decisions are made. Additionally, there should be mechanisms for accountability, allowing users to challenge and understand decisions that negatively impact them.
  3. Ethics by Design: Integrating ethical considerations into the design and development of AI systems is vital. This includes ongoing assessments of how AI systems impact different groups and the potential risks involved.
  4. Global Collaboration: Addressing AI bias requires global collaboration, with input from diverse stakeholders, including those from marginalized communities. Policymakers, industry leaders, and civil society must work together to create regulations and standards that ensure AI development is fair and inclusive.

Conclusion: A Path Forward

As we move forward into an increasingly AI-driven world, it is crucial to remain vigilant about the biases embedded in these technologies. Only by acknowledging and addressing these issues can companies ensure that AI serves as a tool for positive change, rather than perpetuating existing inequalities.

Murikah, W., Nthenge, J. K., & Musyoka, F. M. (2024). Bias and ethics of AI systems applied in auditing – A systematic review. Scientific African, 16, e02281. https://doi.org/10.1016/j.sciaf.2024.e02281

Ukanwa, K. (2024). Algorithmic bias: Social science research integration through the 3-D Dependable AI Framework. Current Opinion in Psychology, 58, 101836. https://doi.org/10.1016/j.copsyc.2024.101836

Caro-Burnett, J., & Kaneko, S. (2022). Is society ready for AI ethical decision-making? Lessons from a study on autonomous cars. Journal of Behavioral and Experimental Economics, 98, 101881. https://doi.org/10.1016/j.socec.2022.101881

Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization, 33(3), 100478. https://doi.org/10.1016/j.infoandorg.2023.100478

Raphaela Pouzar

Raphaela Pouzar

Marketing Team Assistant

Raphaela Pouzar, a Bachelor of Business Administration student at IMC Krems, works at MDI as a marketing assistant alongside her studies. Additionally, she is currently pursuing an AI certificate from Harvard Business School.

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The ARS Seminar: Lateral Leadership and AI Role-Playing

The ARS Seminar: Lateral Leadership and AI Role-Playing

by Gunther Fürstberger | Aug 23, 2024 | Digital Transformation, Leadership in the digital transformation, MDI Inside | 0 comments

The ARS Seminar: Lateral Leadership and AI Role-Playing

Immerse yourself in the world of lateral leadership with us and experience realistic role-playing with state-of-the-art AI technology! These methods bring a breath of fresh air to the world of leadership development and show how modern approaches can be successfully applied in practice.

Role-playing in Training

Role-plays have been used in communication training for decades to try out real-life challenges in a safe environment and develop skills. These usually take place either with voluntary participants in front of the group or several groups in parallel.

In the first case, the trainer can give direct feedback, in the second case more participants have the opportunity to practice. Not everyone loves role-playing, but overall it is an effective method for developing social skills.

However, the method also has disadvantages that can now be overcome by artificial intelligence: Trainers or even professional actresses don’t work for free. Training and application dates may be far apart, and by then much will have been forgotten.

Role-playing in training

Large Language Models

With the help of LLMs (Large Language Models), on the other hand, it is possible to train as often as required at a fraction of the cost shortly before real-life use. The ChatGPT 4.0 app offers well-functioning language input and output and can access extensive knowledge, such as the DISG model or the Harvard Principles of Negotiation.

Some trainers at MDI have been experimenting with it for a year now, and the participants are enthusiastic. We demonstrate a role play in front of the group, involve several participants, and then pass on the prompt used for self-practice.

AI in Various Management Systems

We have further developed the prompts so that the feedback from the AI is precisely aligned with the models taught in the seminar. In addition, the resistance intensity of the AI role-play partner has been optimized.

Anyone who would like to experience this in reality will have the opportunity to do so in the ARS seminar “Lateral Leadership” from 22.10. to 23.10. Further exciting role-playing functions are currently being developed.

Mag. Gunther Fürstberger

Mag. Gunther Fürstberger

CEO | MDI Management Development International

Gunther Fürstberger is a management trainer, author and CEO of Metaforum and MDI – a global consulting company providing solutions for leadership development. His main interest is to make the world a better place through excellent leadership. He has worked for clients including ABB, Abbvie, Boehringer Ingelheim, DHL, Hornbach, PWC and Swarovski. His core competence is leadership in digital transformation. He gained his own leadership experience as HR Manager of McDonald’s Central Europe/Central Asia.  At the age of 20 he already started working as a trainer.

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Weak vs. Strong AI: Unraveling the Myths

Weak vs. Strong AI: Unraveling the Myths

by Raphaela Pouzar | Jul 22, 2024 | eLearning, Leadership and AI, Short Knowledge Bits | 0 comments

 

Weak vs Strong AI – Unraveling the Myths

You prefer to listen to this article? Click here to access our AI-generated audio version:

Weak vs Strong AI – Unraveling the Myths

Artificial Intelligence has become an integral part of our daily lives, from virtual assistants like Siri and Alexa to recommendation systems on Netflix and Amazon. However, there remains a significant misconception that AI can do more than humans, including possessing human-like understanding and reasoning abilities. To clarify these misconceptions, it’s essential to distinguish between “weak AI” and “strong AI,” the two paradigms within AI research.

Weak AI vs. Strong AI

Weak AI, also known as narrow AI, is designed to perform specific tasks such as image classification, game playing, or speech recognition. These systems excel at their designated functions but cannot understand or infer the underlying causes behind the data they process.

They operate based on pre-defined rules and large datasets, showcasing behavioristic characteristics rather than true cognitive processes. For example, deep learning, a subset of weak AI, uses artificial neural networks to identify patterns and make predictions but does not possess true understanding or consciousness.

In contrast, strong AI, or artificial general intelligence (AGI), aims to replicate human cognitive abilities. This includes reasoning, understanding, and the ability to learn from experiences like humans. Strong AI is not just a tool for problem-solving but is envisioned to have actual mental states, consciousness, and the capacity for imagination.

Despite its theoretical appeal, strong AI remains an elusive goal, as current AI systems lack common sense and the ability to comprehend hidden processes behind the data they analyze.

Misconceptions about AI Capabilities

One major misconception is that AI can surpass human capabilities in all aspects. While AI systems can outperform humans in specific tasks, such as playing chess or Go, they do not possess the holistic understanding and cognitive flexibility inherent to human intelligence. Current AI, intense learning models, are adept at processing vast amounts of data to make predictions but do so without understanding the context or causality behind the data.

Moreover, the myth that AI is “all algorithms” oversimplifies its complexity. AI systems rely not only on algorithms but also on high-quality data, effective user interfaces, and interdisciplinary research. Reducing AI to just a set of algorithms ignores the broader context of its development and application.

 

Weak vs strong AI-2

The Practical Implications of Weak AI

Despite the limitations of weak AI, its practical applications are profound. Weak AI systems provide scalable, efficient solutions for handling big data and have become ubiquitous in various industries. From spam filters and recommendation systems to voice assistants and navigation tools, weak AI enhances our daily lives by automating and optimizing routine tasks.

The Elusive Goal of Strong AI

Strong AI, with its promise of human-like cognition and understanding, remains a topic of extensive research and debate. Some argue that to achieve strong AI, future research should focus on developing compositional generative predictive models (CGPMs) that mimic the human brain’s ability to infer and understand the causes behind sensory inputs.

However, the realization of strong AI poses ethical and practical challenges, including the risk of manipulation and the need for these systems to align with long-term, homeostasis-oriented purposes. However, the development of strong AI is still a distant goal.

Conclusion

Understanding the distinction between weak AI and strong AI helps dispel common myths about AI capabilities. While weak AI systems provide valuable tools for specific tasks, strong AI remains a theoretical concept, far from realization.

Acknowledging these differences is crucial for setting realistic expectations and guiding future AI research and development. As AI technology continues to evolve, it is imperative to focus on ethical considerations and ensure that these advancements serve to enhance human potential rather than overshadow it.

Butz, M. V. (2021). Towards Strong AI. Künstl Intell, 35(1), 91–101. https://doi.org/10.1007/s13218-021-00705-x

Flowers, J. C. (2021). Strong and weak AI: Deweyan considerations. Künstl Intell. Retrieved from Worcester State University. jflowers@worcester.edu

Liu, B. (2021). “Weak AI” is likely to never become “Strong AI”, so what is its greatest value for us? arXiv preprint arXiv:2103.15294. https://doi.org/10.48550/arXiv.2103.15294

Nussbaum, F. G. (2023). A comprehensive review of AI myths and misconceptions. Review: AI Myths and Misconceptions (Version: October 31, 2023). Retrieved from frank@fgnussbaum.com

Raphaela Pouzar

Raphaela Pouzar

Marketing Team Assistant

Raphaela Pouzar, a Bachelor of Business Administration student at IMC Krems, works at MDI as a marketing assistant alongside her studies. Additionally, she is currently pursuing an AI certificate from Harvard Business School.

  • LinkedIn

Download our International Whitepaper:

 

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Leadership in Transition: Insights from Leadership Horizon 2024

Leadership in Transition: Insights from Leadership Horizon 2024

by Christoph Wirl | Jul 4, 2024 | Leadership and AI, Leadership Tips, MDI Inside | 0 comments

Photo credits: Jenny Theolin

 

Leadership in Transition: Insights from Leadership Horizon 2024

This was our Leadership Horizon Conference of 2024! Click here and immerse yourself in the Highlights.

Leadership in transition: Insights from Leadership Horizon 2024

Here you can find the original report by magazine training editor Christoph Wirl: 

A guide to the most effective strategies for integrating artificial intelligence – We report on the Leadership Horizon conference.

MDI’s Leadership Horizon Conference retook place this year on June 4 at SO/Vienna and was dedicated to the increasing fusion of leadership practices and Artificial Intelligence (AI). It served as a platform for the exchange of practical findings and strategies for integrating AI into management processes.

Around 160 HR experts and managers from various industries came together to share their experiences and visions. The event was English-language and hybrid, allowing international participants to attend both online and in person.

The impressive line-up of speakers was consistently compelling. The event offered a successful mix of inspiring keynotes, practice-oriented workshops, and insightful business cases.

The two sides of the AI coin

CEO Gunther Fürstberger opened the conference with a virtual avatar, created with the HeyGen program’s help, and looked very similar to the real speaker. He spoke about the importance of artificial intelligence for both companies and managers.

He showed both sides of the coin: For example, many people have already been killed through the use of AI-controlled drones in war zones. At the same time, however, the use of artificial intelligence in medicine has already saved the lives of millions of people or significantly improved their health.

AI therefore has a clear impact on our society, which is why we need to develop and use this technology responsibly. We need to harness the potential of AI for the benefit of humanity while carefully weighing up the potential risks and ethical challenges.

Photocredits: Jenny Theolin

AI is not magic

When the conference moderators asked the audience who was not yet sufficiently familiar with the topic of artificial intelligence, more than half of the attendees responded. This just goes to show that we need to engage more with this forward-looking topic.

Afterward, magician Tobias Grünfelder from the Abracademy fascinated the participants and drew parallels between the art of magic and artificial intelligence:

“AI is not magic – we just need to look behind the scenes. Just as professional magicians do not rely on a single trick, we should also take a holistic approach to the application of artificial intelligence. It is not enough to see AI as some kind of magic bullet that solves all problems by itself. Instead, it is important to understand the underlying mechanisms and functioning of AI systems to be able to use them effectively and responsibly.”

AI-powered leadership

Johannes von Mikulicz-Radecki also emphasized the complexity of using AI in leadership in his presentation “AI-Powered Leadership”:

“The time when you could experiment with AI and try out different use cases is over. Now it’s about putting what you’ve learned into practice.”

This requires managers to clearly understand the possibilities and limitations of artificial intelligence and the ability to strategically integrate this technology into their business processes. In addition, von Mikulicz-Radecki emphasized the general relevance of AI tools in companies:

“Artificial intelligence is too important to be left only to management.”

Photocredits: Jenny Theolin

An insight into UNIQA’s AI strategies

Later, keynote speaker Cristina Anculescu showed how UNIQA has optimized the employee experience with the help of AI tools. She reported that 47% of Gen Z employees surveyed said they received better career advice from AI than from their managers. For example, some upload their LinkedIn profile to ChatGPT and ask for suggestions for improvement.

In response, UNIQA has used AI to develop and promote individual career paths for its employees. Intelligent algorithms are used to create individual development plans. These are based on the strengths, interests, and goals of individual employees.

The downside of flexible home office arrangements

After the lunch break, renowned expert and speaker Ryan Jenkins was connected live from the USA. The technical implementation went smoothly so that both the participants on-site and the online participants were able to follow his presentation without any problems. He addressed the importance of interpersonal connections within a company:

“When employees feel isolated, they are up to seven times more likely to work inefficiently.”

He noted that due to excessive use of our smartphones and online media, we are often unable to build and maintain relationships with our colleagues. In this day and age, people are more independent of each other than ever before, making it harder to make and maintain connections. Jenkins therefore warned that the often overly flexible home office regulations should be critically scrutinized.

Photo credits: Jenny Theolin

Dangers and fears around AI

Rebecca Johnson presented the possibility of learning using AI avatars and introduced a specific tool that enables particularly effective practice of negotiation skills.

In her final presentation, Bailey Parnell tried to allay the audience’s fears of artificial intelligence. She explained basic concepts such as machine learning and deep learning and introduced various AI tools, including Canva, Suno, Copilot, and image generation software.

She also addressed the potential dangers of AI and discussed possible job losses due to automation, the loss of privacy and the problem of bias in AI systems.

Conclusion on Leadership in Transition: Insights from Leadership Horizon 2024

The Leadership Horizon Conference 2024 once again proved to be a great event for executives who want to integrate AI into their corporate strategies. It provided valuable insights into current trends and inspired thinking about the future role of technology in leadership.

Download our International Whitepaper:

 

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