Artificial intelligence as a business booster in the company - further practical tips for the introduction of AI in companies from eleven interim managers

💡 In addition to our article “ AI competence center for hybrid teams instead of an AI pope alone ”, here are further practical tips for introducing AI in companies.

🚀 The article offers practical tips for implementing AI projects based on the experiences of interim managers. Various topics are covered, including the profitable use of AI, shadow AI, data structuring, business intelligence, sustainability reporting, strategic planning, learning by doing, training, collaboration with external experts, ethical responsibility and future prospects.

💰 Profitable use of AI

Interim manager Eckhart Hilgenstock emphasizes that an AI project should pay for itself within 18 months. Otherwise, there is a risk that it will become obsolete within two to three years due to rapid technological developments. This assessment is based on practical experience and runs as a leitmotif throughout the entire book, in which the authors, all experts from the DACH region (Germany, Austria, Switzerland), give well-founded recommendations based on successful projects.

Eckhart Hilgenstock is one of the eleven authors of the book “Artificial Intelligence as a Business Booster in Companies” (ISBN 978-3-98674-110-5), which is imbued with practical advice. All authors are interim managers, i.e. temporary managers who have already successfully completed AI projects in Germany, Austria and Switzerland (DACH). That was the prerequisite for co-writing the work, which was jointly designed by United Interim, the leading community for interim managers in the DACH region, and the Diplomatic Council think tank with advisory status at the United Nations.

🛡️ Combat Shadow AI

A particularly pressing problem in many companies is so-called shadow AI. Dr. Harald Schönfeld, managing director of the personnel consultancy Butterflymanager, warns that employees often use AI tools such as ChatGPT or Gemini independently without management having control over this. This approach entails significant risks in the areas of compliance, data protection and security. Therefore, a controlled and transparent handling of AI is essential to avoid negative consequences for the company.

📊 Structuring mountains of data

Companies often have huge, unstructured amounts of data. The interim managers recommend organizing, analyzing and making this data usable. Even simple analysis tools can provide valuable insights: Who are the most important customers? Which products sell best? Which areas bring losses? Such questions can often be answered without complex AI, but structuring the data creates a basis for future AI applications.

🔄 Upgrade from Business Intelligence to Artificial Intelligence

Many companies already work with business intelligence (BI). The next logical step is augmentation with AI. Choosing the right tools is crucial here. These should support data analysis, financial planning and automation of repetitive tasks. Examples include automatic invoice verification and account reconciliation. According to the interim managers, this upgrade can usually pay off within twelve months.

🌍 Sustainability reporting with AI

The new Corporate Sustainability Reporting Directive (CSRD) brings with it additional reporting obligations for companies. AI can be a valuable support here by making it easier to collect and process non-financial information. This means that the return on investment (RoI) can also be realized in this area in the short term. Implementing AI in small, well-considered steps is crucial here. Small, manageable projects make it possible to gain experience, optimize processes and minimize risks.

🔍 Identifying potential and strategic planning

According to Eckhart Hilgenstock, companies should carefully analyze in which areas AI can bring the greatest benefit. Based on this analysis, clear timelines for the implementation of AI projects should be created. The initial focus should be on projects that offer short payback periods and direct benefits.

📚 Supplemented by a learning-by-doing approach

Comprehensive theoretical knowledge of AI is useful, but ultimately it is the practical implementation that counts. Companies should adopt a learning-by-doing approach to get the most out of their AI projects. Through continuous testing and adjustment, you can react flexibly to changes and adapt the technology optimally to your needs.

👩‍🏫 Training and further education

Successful integration of AI requires qualified employees. Training and further education are therefore essential. Companies should offer skills development programs to ensure their teams can effectively use new technologies. This not only affects IT specialists, but also employees in other areas such as marketing, sales and production.

🤝 Collaboration with external experts

AI projects often require special knowledge that is not always available within your own company. Collaborating with external specialists can therefore be helpful. Interim managers and other external experts can provide valuable input and know-how to accelerate and optimize implementation.

⚖️ Ethics and responsibility in AI use

In addition to the technical and economic aspects, the ethical dimension also plays an important role. Companies should ensure that their AI applications are used transparently, fairly and responsibly. This includes using data ethically and carefully considering the impact of AI on employees and customers.

🚀 Future prospects and innovation

The use of AI opens up numerous new possibilities. From personalized customer approach to efficient production processes to innovative business models – the potential is enormous. Companies should continually explore new application areas and be open to innovation. The willingness to constantly evolve and integrate new technologies will ultimately make the difference in competition.

🎯 With the right strategy

Introducing artificial intelligence into companies is a complex but worthwhile task. With the right strategy, pragmatic approaches and continuous training, AI can become a decisive business booster. Companies that use their data effectively, avoid shadow AI and invest specifically in small, manageable projects are well prepared for the challenges and opportunities of the future. Close collaboration with experts and an ethically responsible use of the technology round off the successful use of AI.

📣 Similar topics

• 📣 Practical tips for the successful introduction of AI in the company
• 🚀 Artificial intelligence as a business booster: strategies and approaches
• 🛡️ Shadow AI: Avoid compliance and data protection risks
• 🔍 Data structuring: The first step to effective AI use
• 💡 From Business Intelligence to Artificial Intelligence: A logical next step
• 🌍 Sustainability reporting: How AI supports companies
• 📊 Identifying potential: Strategic planning for the use of AI
• 🎓 Training and further education: Qualified employees are the key
• 🤝 Collaboration with experts: External support for successful AI projects
• ⚖️ Ethics and responsibility: Transparent and fair use of AI in the company

#️⃣ Hashtags: #AI #Data Protection #BusinessIntelligence #Sustainability #Training

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🔍 The ongoing development of artificial intelligence (AI) is changing the business world rapidly and sustainably. Companies that want to use these revolutionary technologies profitably should first systematically identify the potential in their different operational areas and create a comprehensive AI timeline based on this. This approach makes it possible to take targeted steps to secure competitive advantages and position yourself efficiently in the market.

🔍 Analysis of operational potential

The first step is a detailed analysis of existing operational areas to determine which functions and processes could benefit most from implementing AI. This requires a deep understanding of your own business processes and structures as well as a thorough knowledge of the various AI technologies available. Areas such as customer service, logistics, production, marketing and finance all have different needs and potential for using AI.

For example, in customer support, AI can be used in the form of chatbots to increase customer satisfaction and reduce service costs. In logistics, AI can optimize supply chains by using predictive models to efficiently manage inventory and reduce delivery times. Automated systems and Industry 4.0 technologies can be used in production to increase efficiency and minimize error rates. In marketing and finance, extensive data analysis and predictive tools can be used to develop and implement effective strategies.

📅 Creation of an AI timeline

After identifying the potential, it is important to translate these findings into a structured AI timeline. This timeline should not only include short-term projects, but also consider long-term visions and goals for deploying AI across the organization. A focused schedule helps prioritize the implementation of AI technologies and allocate resources efficiently.

To begin, consider AI projects that provide quick payback and immediate cost or competitive advantages. These could be projects that improve existing automation solutions or increase efficiency in areas where quick, measurable success can be expected. Another important aspect here is the training and further education of employees to ensure seamless integration of the new technologies.

🌟 Examples of success in practice

Many companies have already developed and implemented successful approaches to implementing AI. A prominent example is the use of machine learning at Amazon. The company uses algorithms to analyze customer behavior and generate personalized product recommendations, which has resulted in significant increases in sales. Another example is the automotive industry, where manufacturers like Tesla are using AI to develop self-driving vehicles that both increase safety and improve the driving experience.

🛠 Challenges and solutions

Despite the numerous benefits, implementing AI also comes with challenges. One of the biggest hurdles is the management and maintenance of large amounts of data necessary for training AI models. It is important to develop a robust data strategy and ensure that data quality is high and data protection guidelines are adhered to.

Another problem is the integration of new technologies into existing IT infrastructures. This often means that old systems need to be updated or even completely replaced, requiring significant investment and planning. Creating a flexible and scalable IT architecture can help here and take future developments into account.

Ethical aspects and the acceptance of employees also play an important role. AI technologies can trigger fears about replacing jobs and changing required skills. Transparent communication and comprehensive training programs are therefore crucial to building trust and engaging the workforce for change.

🌐 Future visions and long-term strategies

In the long term, the use of AI technologies opens up a variety of possibilities that go far beyond short-term profits. Examples of future-oriented AI projects include autonomous systems that could be used in various areas such as agriculture, transport and urban planning. Such systems have the potential to dramatically increase efficiency and sustainability.

Another pioneering area is the development of cognitive systems that are able to independently solve complex problems and develop creative solutions. Such applications could be used in scientific research or product development to drive innovation.

Creating a research and development department specifically dedicated to exploring new AI technologies and their applications can be of great benefit to companies. This department can analyze future trends, identify new opportunities and ensure that the company is always at the forefront of technological development.

🎯 Strategic approach

Implementing AI technologies in companies requires a strategic approach based on a thorough analysis of operational potential and a clearly structured AI timeline. It is important to start with projects that promise short-term success while having a vision for long-term developments. By overcoming challenges such as managing data and integrating it into existing systems, as well as taking ethical aspects into account and involving employees, companies can reap the diverse benefits of AI technologies. In the long term, AI innovations offer enormous potential for increased efficiency, new business areas and social benefits.

📣 Similar topics

• 🔍 Identifying potential in the company: First steps to AI success
• 🗓️ Creating a comprehensive AI timeline: plan, implement, benefit
• 🛠️ Analysis of operational potential: Where AI makes the biggest difference
• 🤖 Examples of success in practice: AI implementations that pay off
•  📈 Challenges in integrating AI and how to overcome them
• 💡 Solutions for AI challenges: data management, IT and more
• 📊 Future visions with AI: Long-term strategies and potential
• 🚀 Long-term strategies: AI innovations for sustainable corporate success
• 🧠 Cognitive systems and their revolutionary uses
• 📚 Strategic approach: Successful AI implementation through planning and training

#️⃣ Hashtags: #ArtificialIntelligence #Strategy #Innovation #Datamanagement #Futurevisions

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