The current state of AI use in companies: the challenges in the productive implementation of AI

Between theory and practice: the hidden weaknesses of modern AI technology

Artificial intelligence (AI) has undergone impressive development in recent years and inspires their skills in numerous areas of application. Nevertheless, many companies are faced with the paradoxical situation that AI systems can master complex tasks, but often fail because of supposedly simple challenges. This discrepancy between the theoretical potential and the practical implementation raises important questions that we will illuminate in more detail in this article.

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The current state of AI use in companies

In today's working world, it is becoming normal for more and more employees to integrate AI tools such as Chatgpt into their everyday work. This selective use typically includes tasks such as internet research, text translations or the writing of smaller software code sections. In large companies in particular, in-house AI portals have become established that enable legal and data protection-compliant access to external voice models or facilitate access to internal business knowledge.

Current studies show that 35% of large German companies are already using AI technologies, while for small and medium-sized companies the adoption rate is significantly lower at around 12%. These figures make it clear that AI is increasingly moving into the corporate world, but is still far from being implemented across the board. It is particularly striking that despite the growing spread of AI tools, the number of examples in which AI actually led to fundamental improvements in business processes remains surprisingly low.

Typical areas of application of AI in companies

The current use of AI in companies mainly focuses on the following areas:

1. Customer service: Automated feedback analyzes and AI chat bots for faster and more efficient fulfillment of customer needs.
2. Text and image position: AI tools for the faster and cheaper creation of texts, images and videos for marketing, newsletter and other content.
3. Meetings: Programs that record, write and summarize video calls and support them in finding an appointment.
4. Recruiting: Increasing efficiency and saving time in recruiting processes through AI-based pre-selection and analysis of applications.
5. Monitoring: Monitoring processes, early detection of sources of error and upcoming trends as well as support in the evaluation of campaigns.

Despite these diverse possible uses, the transformative effect of AI on corporate processes often remains behind the expectations. The discrepancy between the theoretical potential and the practical implementation indicates fundamental challenges that go beyond the usual introductory difficulties of new technologies.

The productivity paradox of AI

Interestingly, studies show that AI tools such as Chatgpt can increase the productivity of office workers by up to 40%, in particular when creating texts and other creative tasks. Independent ratings confirm an average of 18%. These numbers are in an apparent contradiction to the small number of successful company-wide AI transformations.

This paradox can be partially explained by the fact that the selective use of AI tools by individual employees can increase their individual productivity, but does not automatically lead to a comprehensive transformation of business processes. The successful integration of AI in corporate processes requires more than just the provision of tools - it requires a fundamental rethink in the way how work is organized and executed.

The difference between selective use and real transformation

The selective use of AI tools by individual employees can lead to local efficiency increases, but often remains isolated and does not lead to a systemic transformation of the company processes. A real AI transformation, on the other hand, includes the strategic integration of AI in core processes of the company and leads to fundamental changes in the way of working and business models.

According to a study by the IBM Institute for Business Value, companies that integrate AI into their transformation process are often more successful than their competitors. However, such a transformation requires more than just implementing new technologies -it requires a change in corporate strategies and cultures. These profound changes present many companies with considerable challenges that go beyond purely technical aspects.

Central obstacles to AI implementation

The reasons for failure or the delayed introduction of AI projects in companies are diverse and complex. The most important obstacles are examined below:

1. Data quality and availability

One of the greatest challenges in implementing AI is the quality and availability of the data. AI systems are just as good as the data on which they are trained. Many companies struggle with unstructured or incorrect data, which can significantly impair the effectiveness of AI applications.

A current study shows that 42% of companies indicate that more than half of their AI projects have been delayed due to problems with data provision or have not brought the hoped-for results. For companies in which fewer than half of their data are centralized, 68% of sales due to failed or delayed AI projects report.

The challenges in the area of ​​data quality include:

• Data in silos across different departments
• Inconsistent data formats
• Lack of historical data for AI training
• Data protection and security concerns that restrict data access

2nd lack of qualified specialists

The establishment of a competent data science team is a significant hurdle for many companies. The market for AI technology is still at an early stage, and the demand for AI experts has risen sharply in recent years, while the number of specialists available has not been able to keep up with this growth.

According to a LinkedIn report, the demand for AI experts has increased by 74% in the past four years. Small and medium -sized companies in particular have difficulty finding and financing the necessary experts. Only 25% of managers in Germany feel well prepared for AI, while the global average is only 8%.

To counter this shortage of skilled workers, companies must:

• Invest in the training of their existing employees
• To consult external experts
• Create a culture of knowledge exchange

3. Integration with existing systems

The integration of AI solutions into existing IT infrastructures poses major challenges for many companies. Older systems in particular that have not been designed for the integration of AI can lead to significant problems. The challenges include:

• Outdated infrastructure that cannot meet the requirements of modern AI
• Lack of standardized interfaces for seamless connections
• Incompatible data storage systems
• High costs in connection with the modernization of the infrastructure

According to a survey, 67% of companies that manage their data centrally apply over 80% of their technical resources to maintain data pipelines. This high resource binding for maintenance tasks hinders the development and implementation of innovative AI solutions.

4. Unclear goals and expectations

A frequent mistake in AI projects is the lack of clear and measurable goals. Companies often start AI initiatives without a precise definition of what they want to achieve. This leads to unrealistic expectations and ultimately disappointments if the AI ​​does not provide the desired results.

The definition of clear, realistic and measurable goals is crucial for the success of AI projects. Companies should ask themselves:

• What specific problem should the AI ​​solve?
• How can success be measured?
• Which resources are required for implementation?
• Which time frame is realistic?

5. Acceptance and cultural change

The introduction of AI technologies can trigger fears of job losses or an increased workload for employees. Good change management is therefore crucial to create acceptance and successfully design the transformation.

The support from top management plays a central role in this. Without the commitment of the management level, it becomes difficult to provide the necessary resources and implement the necessary organizational changes. Training and further training of the employees are also crucial to ensure the success of the AI ​​transformation.

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Success examples: When AI transforms business processes

Despite the numerous challenges, there are companies that successfully use AI to transform their business processes. These examples show that with the right strategy and implementation of AI can actually lead to fundamental improvements.

Siemens: Predictive maintenance in production

Siemens uses Ki to implement predictive maintenance (forward -looking maintenance) in his manufacturing processes. By analyzing large amounts of data from machines and systems, Siemens can recognize potential failures at an early stage and proactively plan maintenance measures. This minimizes downtime and increases productivity. Siemens's AI systems continuously learn to what further improve the accuracy of the predictions over time.

JP Morgan: fraud recognition in the financial sector

JP Morgan uses AI to recognize fraud patterns in financial transactions. The AI ​​analyzes huge amounts of transaction data in real time and identifies suspicious activities that could indicate fraud. JP Morgan helped this technology to increase the security of your financial services and reduce financial losses. The AI-based systems are able to adapt to new fraud patterns, which continuously improves the efficiency and accuracy of fraud recognition.

Beiersdorf: AI innovations in the skin care area

The innovation management of the skin care company Beiersdorf promotes the use of trend-setting AI tools. The company has taken a pilot function between IT and specialist departments to effectively implement AI technologies. In 2019, the Hamburg -based company introduced an intelligent chat bot, which was later supplemented by an internal instance of Chatgpt. The aim of these generative AI systems is to expand and not replace the strengths of the employees.

These examples show that AI actually has the potential to fundamentally improve business processes. However, such successes require a well thought-out strategy, sufficient resources and a deep understanding of both technological and organizational aspects of AI implementation.

Solution approaches for successful AI transformation

In order to overcome the challenges of implementing AI and achieve successful transformation, companies can pursue various strategies:

1. Solid planning and clear objective

Solid planning is the foundation of successful AI projects. At the beginning there is the clear definition of the goals: what exactly should be achieved with the AI ​​solution? This requires a comprehensive actual analysis of the current technological conditions and processes in the company. The selection of the suitable data sources and ensuring data quality is also crucial.

The planning process should be iterative, with regular checks and adjustments in order to be able to react flexibly to changes. Companies should first focus on smaller, well -defined projects that enable quick successes and serve as the basis for more comprehensive transformations.

2. Agile methods for AI implementation

Agile methods, known from software development, also have their advantages when implementing AI projects. Through iterative development processes and regular feedback, project teams can quickly react to new requirements and findings. Scrum and Kanban are examples of agile approaches that enable a focused and flexible way of working through short development cycles and sprints.

This approach is particularly important for AI projects, since these are often associated with uncertainties and changing requirements. With regular checks and adjustments, companies can ensure that their AI projects stay on course and provide the desired results.

3. Effective change management

The introduction of AI brings in profound changes in work processes and corporate structures. Solid change management is therefore indispensable to reduce resistance and increase the acceptance of employees. It is important to include all stakeholders at an early stage and to communicate transparently over the goals and advantages of the AI ​​projects.

Training and further training play a central role in preparing employees for working with AI and reducing fears. Thanks to the active involvement of employees in the transformation process, companies can not only reduce resistance, but also gain valuable feedback and ideas for optimizing AI solutions.

4. Building AI competencies

In order to counter the lack of qualified specialists, companies should invest in the establishment of internal AI competencies. This can be achieved through various measures:

• Training of existing employees in AI-relevant skills
• Setting of AI experts for key positions
• Cooperation with external consultants and service providers
• Partnerships with universities and research institutions

The establishment of an interdisciplinary team that combines both technical know-how and industry knowledge is crucial for the success of AI projects. By combining different perspectives, companies can ensure that their AI solutions are both technically solid and business relevant.

5. Improvement of the data infrastructure

Since the data quality and availability is a central challenge in AI implementation, companies should invest in improving their data infrastructure. This includes:

• Consolidation of data silos and creation of a central database
• Implementation of data quality management processes
• Building a scalable and flexible data architecture
• Ensuring data protection and security

A solid data infrastructure forms the basis for successful AI projects and enables companies to exploit the full potential of their data. By investing in data management and government, companies can ensure that their AI systems are based on high -quality and relevant data.

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The future of AI in companies

The AI ​​transformation will continue to accelerate in the coming years and develop into an integral part of everyday life and work. New technologies will make the boundaries between the digital and the physical world blur and offer innovative opportunities to network, create things or to work better together.

Personalized AI assistant

What started with simple tools like Chatgpt is now becoming much more powerful: Personalized AI agents become game changers. These AI assistants will increasingly change to individual needs and the way in which people manage their everyday life and working life will change seriously.

From personal assistants who help employees manage their time to tailor-made AI analyzes-these personalized agents will give users the opportunity to bring their own data and offer them insights and functions that were previously only reserved for large companies with considerable financial resources.

Integration of AI in business processes

The integration of AI in business processes will become even more seamless and comprehensive in the future. By combining AI with existing business process models, the introduction of AI technologies into companies makes it easier than ever. The integration of AI technologies is directly via a graphic BPMN modeling, which means that business data can be intelligently linked to business processes.

This integration enables the automation of routine tasks and the optimization of business processes, which leads to an increase in efficiency and productivity. Companies that invest early in this integration will gain a strategic advantage over their competitors.

Competition advantage through AI

With the increasing spread of AI, companies will in future be able to be divided into two categories: those who use AI effectively and those who remain. Companies that invest early in training and the appropriate infrastructure get a strategic advantage and can test what works and what is not in practice.

The integration of chatt and other AI tools in companies will sooner or later decide on competitiveness. Anyone who closes new technologies will not be able to prevail against competing companies at least in the long term - an experience that has already been made in digitization.

A new thinking for AI solutions

The challenges in the productive implementation of AI in companies are diverse and complex. They range from technical hurdles such as data quality and integration with existing systems to the lack of qualified specialists to organizational aspects such as unclear goals and opposition in the workforce.

The uniformity with which companies fail with real transformation through AI indicates a profound problem. It's not just about introducing new technologies, but about a basic rethink in the way we design and implement IT solutions.

Successful AI transformations require a holistic approach that takes into account technological, organizational and cultural aspects alike. Companies have to think again and do not consider AI as an isolated tool, but as an integral part of their strategy.

The future belongs to the companies that manage to seamlessly integrate AI into their business processes and to establish a culture of continuous innovation and adaptation. Through clear objectives, agile methods, effective change management, building AI competencies and solid data infrastructure, companies can overcome the challenges of AI implementation and exploit the full potential of this transformative technology.

The productive implementation of AI requires a new thinking - away from isolated technology projects to a holistic transformation that takes into account people, process and technology equally. This is the only way to overcome the gap between the theoretical potential and the practical implementation of AI and achieve real competitive advantages.

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