AI in Product Content Management – Who Needs it Anyway

There is a lot of discussion today about the benefits of AI and new uses in product content management. Established PIM and DAM providers use AI to formulate new USPs and innovative new AI services are constantly making a name for themselves. For companies, the question is becoming more and more common: What is it really about the topic now and what do these services actually bring me?

Our new e-paper deals in detail with these questions, describes the current state of technologies for the areas of product information management and digital asset management and evaluates the benefits of the individual application options against the background of the conditions that must be met to implement the services.

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Scaling Creative Processes: AI in DAM

AI is already an integral part of DAM today – at least in organizations that process large amounts of image and video data every day. From automated tagging and derivative creation to image processing processes such as cropping, retouching or cropping product photos, AI services are already taking on a lot of work that not only takes an enormous amount of time when executed manually, but also tends to lead to more errors.

The use of AI in DAM can thus contribute to an enormous increase in the efficiency and quality of work processes and relieve creative teams accordingly. In this way, resources can be used more effectively and marketing activities can be optimized faster and more effectively. Most modern DAM solutions today have AI capabilities that make it easier to manage and edit digital content. This makes them particularly useful for companies in the fashion or FMCG sector. But DAM solutions also create significant added value in industry, where documents such as certificates, 3D images and user instructions must often be managed in addition to simple product images.

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Communicate More Effectively With AI for PIM

But even in the area of PIM, there are now numerous use cases where AI formerly takes over manual work processes and can thus lead to greater efficiency and a faster go-to-market. Using generative AI services from modern language models, entire product descriptions and marketing texts can be created automatically and based on old data. Employees no longer have to write texts themselves, but have the task of validating the texts created and, if necessary, optimizing them. The time gained as a result can be invested elsewhere, where more benefits can be generated.

Another possible use of AI in PIM is the automated creation of data models — a task that can traditionally take a lot of time when implementing PIM solutions, which is due in particular to the growing complexity of the task. Companies must supply more and more communication channels and data recipients with their product data and take the corresponding information requirements into account right from the start. As a result, data models are becoming increasingly complex, but at the same time they must remain flexible enough to adapt to new requirements. AI can help speed up data model development and, using predefined structures, which include industry-specific rules, for example, create a start that implementation teams can easily continue working on.

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The Biggest Aadded Value: Saving Time and Increasing Quality

The larger the amounts of product content that a company has to manage and the more complex the product communication processes, the more obvious are inefficiencies caused by manual work and process breaks. AI can make a significant contribution to eliminating these inefficiencies, automating workflows, and freeing employees from repetitive tasks. This not only saves money, but also enables organizations to use resources more effectively and pursue more ambitious communication goals.

AI services are also playing an increasingly important role in maintaining data quality. The frequency of errors made by AI when performing repetitive tasks is significantly lower than that of a human employee. Integrated validation mechanisms identify potential quality problems at an early stage and enable employees to only intervene when necessary. This in turn eliminates improvements and all other consequences of poor data quality.

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Added Value Yes – But Only With Perfect System Integration

The added value of AI cannot therefore be denied – but it should be noted that the technical infrastructure must create a number of prerequisites in order to exploit the potential of AI. Automation always requires optimal integration of individual solutions and data processes. This applies in particular to cross-system mechanisms, which should interlock as seamlessly as possible.

It is therefore advisable for companies that are considering using AI in product content management to find an experienced digitization partner who not only helps to optimally implement AI services, but also, thanks to its holistic overall view of system architecture and process chains, can advise on where the greatest potential lies and thus also which AI services actually make sense.

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Do you have any questions about AI in product content management? Then contact us directly or download our paper!

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