Successfully integrating AI in industry
Estimated reading time: 10 minutes
Increased efficiency and process optimization – the use of artificial intelligence (AI) in industry is revolutionizing production and business processes. More and more companies are recognizing that AI in industry not only automates workflows, but also contributes to more sustainable, data-driven business management. In this guide, we highlight how artificial intelligence can be used in industry, the advantages it offers, and how companies can make effective use of the technology and overcome the challenges it presents.
At a glanc
- What are the benefits of AI in industry? AI reduces downtime and waste, improves quality and safety, and automates routine tasks. This saves companies time and money and increases their productivity.
- Where is AI used in industry? Typical use cases include visual quality inspection, anomaly detection, energy optimization, demand and inventory forecasting, and knowledge-based search and assistance systems based on generative AI.
- How can AI be successfully introduced into industry? Check maturity level and evaluate data quality → prioritize relevant use cases → implement pilot project (PoC) → establish and scale MLOps → manage rollout and change management.
How does industry benefit from artificial intelligence?
The possibilities that artificial intelligence offers industry are diverse, far-reaching, and represent a key factor in the ongoing competitiveness of the German economy. These possibilities include:
- Development of innovative products and services
- Optimization of existing processes
- Prevention of failures
- Support for quality assurance
- Automation of repetitive tasks
Thanks to the use of AI, companies can not only conserve human resources, but also financial resources.
Predictive analytics through intelligent data recognition
In addition, artificial intelligence can play a decisive role in detecting anomalies or patterns in the data. This descriptive analysis of the data makes it possible to predict future conditions or events with a certain degree of accuracy. Based on these predictions, AI can make proactive decisions, such as ordering spare parts or consumables.
Scalable AI systems as the key to long-term success
Although some of these technologies are already being used in companies, this is usually only on a limited scale and without the possibility of scaling up. However, the full potential can only be realized through systematic implementation using precise machine learning operations and the application of AI platforms.
That is why our core business lies precisely there. We not only support you in setting up the necessary systems, but also in imparting the necessary expertise to benefit from AI in the long term.
Bring AI into your business!
Get a personalised AI consultation for your business now.
For which work processes is the use of AI worthwhile in industry?
The use of artificial intelligence can be an extraordinary asset for companies in the industrial sector. The following areas of AI application in particular lead to a noticeable increase in efficiency:
What does effective consulting on AI in industry look like?
The successful introduction of artificial intelligence in industry requires a strategic and holistic approach. Good AI consulting supports companies throughout the entire life cycle – from the initial idea to stable operation.
At MaibornWolff, we understand effective AI consulting to mean precisely this approach: comprehensive support that begins with a careful analysis of your specific needs and guides you through every phase of the project. And this is what that looks like for us:
Strategy development
Our Data Thinking Workshops help you develop customized data products. In these sessions, you will work with our digital designers, data scientists, and thought leaders to develop concrete next steps and roadmaps for your AI initiatives to effectively support your business goals. These workshops, which are individually tailored based on a preliminary discussion, last two to three days. They can take place in our facilitator rooms, remotely, or directly at your location to ensure optimal collaboration.
Use of cognitive services
In proofs of concept, i.e., practical preliminary tests, we work with you to determine whether specific intelligent services—known as cognitive services—are suited to your company's needs. Together, we define what constitutes a satisfactory result. We then conduct tests using real data to evaluate the performance of these services. This allows us to quickly and reliably determine whether the tested services are suitable for your use cases.
Conducting a maturity assessment
As part of the maturity assessment, we evaluate the maturity level of your organization in terms of processes, data, and tools. Together with you, we will design measures to improve your data infrastructure and develop a targeted data strategy. Of course, we will show you best practices and work with you to identify the top initiatives in your company.
Platform readiness assessment
Some AI methods are particularly well suited for building platforms that can be used across industries and in different business units. One example of this is knowledge management systems that enable voice-based access to internal company data. Learn how your company can benefit from building AI-driven platforms and how these platforms facilitate the integration and scaling of AI applications.
Implementation of MLOps
Understand the importance of machine learning operations (ML-Ops) for the success of your AI projects. We'll show you how ML-Ops can optimize the entire machine learning lifecycle, from data processing to model deployment.
Would you like advice on the topic of ‘AI in industry’?
We are happy to help. Arrange a free initial consultation with our AI experts here.
What are the challenges involved in implementing AI in industry?
Here we provide a brief overview of potential problems you may encounter when introducing AI into your company:
Lack of data
One of the biggest challenges for the successful use of AI in industry is ensuring a sufficient amount of high-quality data. AI systems need this data to work efficiently. Companies often face the difficulty of collecting enough relevant data or processing it appropriately, especially for specific use cases.
Need help unlocking the full potential of your data? We are happy to assist you! Find out more about our customised data analytics consulting services now.
Adaptation to specific conditions
The specific requirements and processes of each company necessitate a customized implementation of AI solutions. This involves technical adjustments and careful integration into existing system landscapes in order to achieve real efficiency and benefits.
Costs and resources
The introduction of AI often involves high initial investment. Companies must invest not only in the technology itself, but also in the necessary infrastructure and qualified personnel. Careful cost-benefit analysis is crucial to ensure that the company has the necessary resources to successfully implement the technology.
However, the early decision to invest in artificial intelligence will inevitably pay off for your company. It is advisable to start now rather than considering the introduction of AI later under time pressure, so as not to fall behind.
IT/AI maturity of the industry
Many industrial companies are breaking new ground with AI. They must learn to view AI systems not as ordinary components, but as complex systems that require specialized knowledge and new approaches.
Employee acceptance and participation
The introduction of AI can cause uncertainty and resistance among employees. It is therefore important to involve them in the process at an early stage, take their concerns seriously, and ensure a smooth transition to the new technologies.
Regulatory and ethical aspects
When using AI, companies must ensure that they comply with data protection regulations and observe ethical principles. This responsible use of AI is crucial for promoting trust and acceptance. However, given the complexity and multifaceted nature of these ethical issues in this area, it is important to carefully consider numerous dimensions:
- Accessibility and barrier-free access: This means that AI applications must be designed in such a way that everyone can use them, including people with physical or mental disabilities.
- Meaning and usefulness of AI use: AI should be used in a way that makes employees' working lives easier, protects their health, and contributes to their personal development.
- Non-discrimination and inclusion: The use of AI must not contribute to the exclusion or discrimination of individuals on the basis of their origin, gender, sexual orientation, religion, or other personal characteristics. Rather, it should help to reduce existing social inequalities and offer all people equal opportunities for participation and development.
- Sustainability: The energy consumption of AI systems should be kept as low as possible, and their development and application should always follow the principles of sustainability. This includes using the most resource-efficient AI options and aligning their logic with long-term positive and sustainable decisions.
White paper series (5 parts): Introducing GenAI in companies
The series highlights technology, organization, and governance—so you can securely and scalably embed AI in your company.
Should I implement AI in my company?
There is no simple answer to this question. However, one thing is clear: AI is here to stay! According to a survey by Statista from 2025, 20% of companies are already using AI in production. In other areas, such as marketing, the figure is as high as 57%. It is therefore advisable to start looking into potential applications now in order to maintain long-term competitiveness.
Whether an early start in AI integration is ultimately beneficial for your company depends, among other things, on the following aspects:
- Suitable use cases: Have you drawn a parallel between one of the use cases mentioned and your company? Then it might be time to consider artificial intelligence.
- Creating value from data: AI-based systems require large amounts of high-quality data to function properly. It is important to check the suitability of the available company data before implementing such systems.
- Resources: The introduction of AI requires a considerable investment of time and resources, which is why a thorough cost-benefit analysis in advance is crucial.
- Employee acceptance: However, the success of AI integration also depends largely on the openness and willingness of the workforce to embrace new technologies and integrate them into their everyday work.
- Good change management: Employee acceptance can also be promoted through targeted change management, which ensures that the transition to AI-supported processes runs smoothly and effectively involves employees.
Successful AI use cases in industry
Companies are increasingly using artificial intelligence to optimize processes and increase overall efficiency. Here we present examples of successful AI applications in various areas of business:
Acceleration of internal processes at GROB Werke
At GROB Werke, the focus was on optimizing internal search processes, as employees spent a lot of time searching for documents, data, or tools. The goal was to find a more efficient, faster, and user-friendly solution. In a four-week proof of concept, we used Azure Cognitive Search and GPT to develop a solution that significantly speeds up internal company searches.
The introduction of a user-centered, voice-controlled search allowed employees to make verbal requests and obtain the information they needed quickly and efficiently. The vision for the future is that employees will only need a headset to communicate with the machines on the production line, further simplifying the work process. The prompt, i.e., the command used to control an AI, should be able to be given verbally.
To better understand this future scenario, imagine a production employee who is looking for a very specific component. In the future, he should be able to simply voice this request through his headset. Immediately afterwards, they will receive precise feedback, such as: “The component you are looking for is currently in machine room B and will be ready for collection in 9 minutes.” This allows the employee to continue their work seamlessly and then collect the required component quickly and efficiently.
Knowledge management at Bayernwerk Netz GmbH
Bayernwerk Netz GmbH faced the challenge of preserving and making accessible the implicit knowledge and experience of long-standing employees. The risk was that valuable expertise would be lost when these employees retired.
To solve this problem, we developed an MS Teams app that was integrated into the Bayernwerk environment. This application serves as a platform for knowledge exchange and is designed to improve workflows without placing an additional burden on everyday work.
The close collaboration between the teams at Bayernwerk and MaibornWolff fostered the development of a system that not only makes implicit knowledge visible, but also creates the basis for continuous knowledge exchange and improved collaboration within the company.
Conclusion
Anyone considering using artificial intelligence in industry should not hesitate to test it. Implementing AI technologies can significantly increase efficiency within a company and improve processes in the long term. It is important to define clear goals and areas of application and to ensure the quality of the data used. Data protection and ethical aspects must not be ignored either.
Whether AI technologies offer real added value depends on the respective framework conditions, data quality, and willingness to change processes in the long term. Companies that start early benefit from faster learning curves and a sustainable competitive advantage.
FAQs
How is AI used in industry?
AI in industry is used for predictive maintenance, quality control, automated production processes, efficiency improvements and data analysis to minimise downtime and maximise productivity.
Is Industry 4.0 the same as artificial intelligence?
No. Industry 4.0 refers to the fourth industrial revolution, the digital transformation of the manufacturing industry. This includes the use of AI, but is not limited to it.
What types of AI are there?
A distinction is made between weak AI and strong AI. Weak AI focuses on specific tasks; ChatGPT is one example of this. Only weak, specialized AI systems are used in industry. Strong AI is currently purely fictional and does not yet exist.
How much does AI consulting cost?
The cost estimate for your individual AI consultation varies depending on the scope of the project. At the start of the project, we develop a tailor-made financing plan for you. Our principle that every project must offer real added value obliges us to explain the specific advantages and potential savings that your use case will bring right from the start.
Kyrill Schmid is Lead AI Engineer in the Data and AI division at MaibornWolff. The machine learning expert, who holds a doctorate, specialises in identifying, developing and harnessing the potential of artificial intelligence at the enterprise level. He guides and supports organisations in developing innovative AI solutions such as agent applications and RAG systems.