Digitalization has developed rapidly in recent years, and one of the technologies that stands out in particular is artificial intelligence (AI). Especially in the area of product data management, the use of AI opens up numerous possibilities and opportunities, but also risks that need to be taken into account.
At events such as DMEXCO, it was surprisingly quiet when it came to AI and product data. Uncertainty and skepticism were still palpable. Risks such as loss of control, loss of quality, arbitrariness and a lack of standardization of processes and content are still present and we are still at the beginning. Our experience is very different and we are using AI-based tools with our customers, particularly in the area of validation and compliance.
Opportunities for the use of AI in product data management
Automation of routine tasks: AI can be used to automate repetitive and time-consuming tasks. This includes recording, categorizing and updating product data. By using machine learning, systems can learn how to perform these tasks more efficiently and with less human intervention.
Data quality and consistency: One of the biggest challenges in product data management is ensuring data quality. AI-supported systems can check data for inconsistencies and errors and automatically make correction suggestions or correct these errors. This leads to higher data quality and reduces the effort required for manual data cleansing.
Advanced analytics and forecasting: AI can analyze large amounts of data and identify patterns that would be difficult for humans to detect. This enables more precise market analyses and forecasts that help companies make informed decisions and optimize their product strategies.
Personalized customer experiences: By analyzing customer data and behavior, AI can develop personalized product recommendations and marketing strategies. This improves customer satisfaction and increases the likelihood of repeat purchases.
Image recognition and tagging of product images using AI
Automated image recognition is another area in which AI offers enormous advantages in product data management. Image AI systems use machine vision and deep learning to precisely analyze product images and extract relevant information, such as colors, shapes or specific product features. These AI image recognition technologies make it possible to automatically index product images and integrate them into the product information system. This not only saves time, but also ensures greater precision and consistency in the management of visual content. In addition, artificial intelligence can use image recognition to identify errors such as missing image attributes or poor image quality and offer suggestions for solutions. Companies benefit from optimized processes and an improved visual presentation of their products.
Opportunities of using AI in the management of product data
Increased efficiency: By automating and optimizing processes, companies can significantly increase their efficiency. This leads to cost savings and a faster time to market for new products.
Competitive advantage: Companies that successfully integrate AI into their product data management can gain a competitive advantage. Through more precise data analysis and better customer interactions, they can react more quickly to market changes and adapt their offerings accordingly.
Promoting innovation: AI opens up new opportunities for innovation. By analyzing trends and customer needs, companies can develop new products and services that are precisely tailored to market requirements.
Risks of using AI in product data management
Data protection and security: Handling large volumes of sensitive data poses risks in terms of data protection and data security. Companies must ensure that they comply with legal requirements and implement robust security measures to prevent data leaks and misuse.
Dependence on technology: Over-reliance on AI systems can lead to problems if these systems fail or malfunction. Companies need to ensure they have backup plans and human monitoring mechanisms in place to deal with such failures.
Ethics and transparency: The use of AI also raises ethical issues, particularly in terms of transparency and fairness. Companies need to ensure that their AI systems operate transparently and fairly and do not make discriminatory or unethical decisions.
Workplace changes: The automation of tasks through AI may lead to changes in the labor market. While some jobs will become redundant, new skills requirements for employees will arise. Companies will therefore need to invest in upskilling and reskilling their employees to ease the transition.
AI technologies for the generation, maintenance and procurement of product data
Various AI technologies are particularly suitable for generating, maintaining and procuring product data. Natural Language Processing (NLP) can be used to automatically generate and translate product descriptions. Image recognition technologies such as image AI and deep learning help to categorize product images and extract relevant metadata. Predictive analytics enables more accurate forecasts of demand and stock levels, while robotic process automation (RPA) takes over repetitive tasks such as data reconciliation and updating. These technologies help to improve data quality, speed up processes and increase efficiency in product data management.
Prominent examples of natural language processing (NLP) technologies
In the field of Natural Language Processing (NLP), there are many advanced technologies and services that companies can use to manage their product data efficiently. Here are some of the most prominent examples that we use at forbeyond:
- ChatGPT (OpenAI) An advanced language model used to generate human-like text, answer questions and conduct dialogs. It offers a wide range of applications in the field of text processing and conversation
- Gemini (Google) A powerful NLP system from Google that can handle advanced language processing tasks. It is used for various applications, including text analysis, machine learning and advanced data processing.
- Copilot (Microsoft) An AI-powered assistant that is integrated into Microsoft 365 applications. It uses NLP technologies to help users create content, analyze data and perform tasks by providing contextual suggestions and automation.
- Azure Text Analytics (Microsoft) A cloud-based text analytics service that provides features such as sentiment analysis, key phrase extraction, speech recognition and entity recognition.
Integration of AI services into PIM systems
The integration of AI services into product information management (PIM) systems is another important development. By combining AI and PIM, companies can manage and use their product data more efficiently. AI algorithms can help to automatically classify product information, generate product descriptions and carry out translations into different languages. In addition, better decisions can be made and marketing strategies optimized through the integration of AI-supported analyses and predictions. This intelligent combination increases data quality and enables a faster and more precise response to market requirements.
The introduction of artificial intelligence in product data management is a complex task that requires specialized expertise. Consulting companies bring in-depth technical knowledge and experience, help develop customized solutions and support integration into existing processes. They identify risks and offer training to promote the acceptance of new technologies, which sustainably increases the success of AI-supported systems in the company.
Rely on the consulting expertise of forbeyond
The use of artificial intelligence in product data management offers enormous possibilities and opportunities, but also entails certain risks. Companies that want to successfully integrate this technology need to be aware of both the benefits and the potential pitfalls. With careful planning, the support of consulting firms and a balanced approach, the benefits can be maximized and the risks minimized for long-term success.
Implementing Artificial Intelligence (AI) in product data management requires not only technical know-how, but also a strategic approach tailored to your company's individual requirements. At forbeyond, we have many years of experience and extensive expertise in successfully integrating AI technologies into your processes. Our team accompanies you from conception to implementation and helps you to make efficient use of the potential of AI - be it in automation, data analysis or image recognition. Rely on our expertise to future-proof your product data and gain a competitive edge.