The way companies are managed is changing rapidly. In the past, decisions were often based on intuition and years of experience—today, data-driven analyses and AI play a central role. This transformation requires that leaders and entrepreneurs develop not only strategic thinking but also technological competence.

Three core points of this transformation are:

• Paradigm shift in leadership: AI and digitalization open up new possibilities for decision-making.
• Combining technological competence with leadership qualities: Modern managers and entrepreneurs need to master both areas.
• Transformation challenges in management: Leaders and entrepreneurs must learn to actively manage digital change.

A helpful framework for this can be found in the OECD Framework for AI Governance, opens an external URL in a new window.

Definition and Fundamentals

Artificial Intelligence (AI) is a branch of computer science that aims to mimic human cognitive abilities. AI systems can either be based on fixed, programmed processes or developed through machine learning. In machine learning, algorithms learn to perform tasks independently by repeatedly processing data. These algorithms are guided by predefined performance criteria and the informational content of the data to continuously improve their performance. The ability of AI systems to learn from data and adapt makes them a valuable tool in modern corporate management.

What does Data Literacy mean in the management context?

Data Literacy is the ability to understand, interpret, and convert data into well-founded strategic decisions. This competency is indispensable today because:

• Core competence for data-driven decision-making: Leaders must recognize data as a central strategic asset.
• Developing a data-driven leadership culture: Only those who understand the value of data can embed it in the corporate culture.

Quick Tip:
Consider incorporating a foundational Data Literacy course into your leadership development to systematically build the necessary technical understanding. For example, TU Wien offers practical courses in this area.

The strategic implementation of AI tools in management is a crucial step to master digital change. It is essential to strike a balance between automated systems and human decision-making. Organizations face the challenge of effectively integrating AI into their business strategy and ensuring that it is sustainably embedded in organizational processes.

Three key areas for AI technology integration are:

• Strategic implementation of AI tools: Identify areas where AI-driven solutions provide the greatest added value and develop pilot projects. These AI projects should be carefully planned and managed to achieve optimal results.
• Balance between automated and human decisions: Automate repetitive tasks while ensuring that critical decisions are still made by experienced managers.
• Change management in the context of AI transformation: Create acceptance and promote interdisciplinary exchange to sustainably embed change.

A comprehensive study on the impact of AI on leadership highlights how these approaches work in practice. (See: Study on the Impact of AI on Leadership, opens an external URL in a new window).

Success Factors and Challenges

Successfully implementing AI projects requires careful planning and organization. Effective AI management is crucial to steer the implementation of AI projects within companies. Key success factors include a clear definition of project goals, the careful selection and preparation of data, and continuous monitoring of project progress. Challenges in executing AI projects include integrating AI into existing systems, ensuring data quality, and addressing ethical and legal issues. Through a strategic approach and robust AI management, companies can overcome these challenges and fully leverage the benefits of AI technologies.

The digital age requires new skills from leaders—beyond traditional management.

Key competency areas include:

• Technological understanding: Leaders must develop a deep understanding of new technologies such as Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). This helps them make informed decisions and drive innovation.
• Regulation and certification: It is crucial for leaders to understand the importance of certifications in the field of AI. AI systems must undergo certification to ensure that they meet ethical and technical requirements, which is particularly important in sensitive areas such as autonomous driving or medical applications.
• Data competence: The ability to analyze and interpret data is indispensable. Leaders must be capable of making data-driven decisions and guiding their company’s data strategy.
• Agility and adaptability: In a rapidly changing digital landscape, leaders need to be agile and adaptable, capable of quickly responding to changes and adjusting their strategies accordingly.

Important competency fields:

• Technical understanding and AI literacy: Managers should grasp basic AI concepts to use the technology effectively.
• Ethical decision-making in the AI context: Develop guidelines to ensure that AI applications operate fairly, transparently, and traceably.
• Regulatory aspects and the AI Act: Understand EU regulations, such as the AI Act, which set specific guidelines for using AI systems in various areas, including critical infrastructure and high-risk systems.
• Emotional intelligence and team leadership: The ability to lead teams empathetically in a digitized environment remains indispensable.

For deeper insights into these topics, consider exploring the Leadership Skills in the Digital Age approach.

Neural Networks and Deep Learning

Neural networks are a branch of machine learning inspired by the connections of neurons in the human brain. These networks are trained by repeatedly presenting data, which allows them to classify the data more accurately over time. Deep learning is a specialized form of machine learning based on complex neural networks. Deep learning algorithms are capable of handling demanding tasks such as image and speech recognition. AI technologies like neural networks and deep learning are applied in various industries, including medicine, finance, and manufacturing. These technologies enable companies to develop innovative solutions and enhance their competitiveness.

Theory must translate into practice. Here are some concrete measures to help you integrate AI and Data Literacy into your company.

These practical examples demonstrate that targeted AI deployment is already delivering measurable successes across various industries.

Guide: Five Key Strategies for Successful AI Integration

• Status Analysis: Identify critical processes that can benefit from AI.
• Pilot Projects: Begin with a manageable area before scaling company-wide.
• Competence Building: Invest in training and internal competence centers.
• Long-Term Strategy: Develop a clear roadmap and define milestones.
• Partnerships: Leverage external expertise and collaborations.

The transformative power of Artificial Intelligence is indisputable—it is reshaping corporate leadership and creating new, exciting challenges. For executives and managers, this means actively shaping digital change while continuously upskilling. Building Data Literacy and strategically integrating AI are essential to making informed decisions and securing sustainable competitive advantages.

Your Next Steps:

• Implement Pilot Projects: Test how AI can optimize your processes in select areas.
• Foster Internal Competence Centers: Establish interdisciplinary teams that function as innovation hubs.
• Invest in Continuous Training: Enhance your and your team's Data Literacy for greater transparency and better decisions.

For those looking to dive deeper into these cutting-edge topics, TU Wien Academy offers the compact program "Data Literacy & AI Essentials". This program provides you with the know how about how to analyse data effectively, use it strategically and present it convincingly.