Artificial intelligence is revolutionizing our world and the way we work: AI-based systems and tools enable a completely new level of process automation and efficiency. Their potential to increase business success in the long term is huge – but in practice, many companies struggle: according to the analyst firm Gartner, 30% of all AI projects are discontinued after the proof of concept, i.e. before they can even generate added value in the organization.
In the current issue of The Latest Think, we therefore address the question of what specific strategies are required for AI implementations to actually increase the business value of business processes. In this blog article, we take a look at three selected use cases in product content management – and the typical stumbling blocks that need to be overcome.
The most important things at a glance:
- AI is a central component of modern digitalization strategies and enables the automation and optimization of business processes.
- AI tools have already established themselves in various areas of the company, particularly in marketing and product content management.
- Poor data quality, inadequate structures and a lack of clarity about the business benefits can hamper the success of AI initiatives.
- A thoughtful approach and understanding of best practices around AI implementation is critical to realizing the full potential of the technology.
AI in product content management
Product content management encompasses all processes relating to the creation, management and distribution of product-related content and is one of the central tasks of marketing. When used correctly, AI can help to speed up content processes, deploy resources in a more targeted manner and ensure the quality of communication processes – whether internally or in a B2B, B2C or D2C context.
- AI in text creation: faster multilingual content
Automated text creation has already found its way into many companies. AI tools such as ChatGPT generate texts at the touch of a button – and in all required languages. In international companies in particular, this not only shortens time-to-market, but also significantly reduces the manual workload in the content team. However, in order for generative AI to deliver the desired texts, a clean database and clear editorial rules are required, which are also reflected in the prompts. If these requirements are not met, there is a risk that texts will be factually incorrect, inappropriately worded, inconsistent or off-brand. Translations also need to be checked editorially – despite AI support – in order to take cultural and linguistic nuances into account. The biggest challenge is often to master the balancing act between automation and editorial quality.
- AI in image processing: more efficiency in media production
Another field of application for AI is image processing. Repetitive tasks such as masking, retouching or creating channel-optimized derivatives can be handed over to the AI, leaving the creative teams more time for the really important tasks, such as setting up campaigns. However, as with copywriting, the same applies here: The quality of the source data is crucial for the result. Clear processing guidelines are also required. An approval process is essential, especially in sensitive areas of application or for brand or legally relevant adaptations.
- AI in playout: Relevant content at the right time
The targeted display of content is a central task in product content management – and an ideal area of application for AI. With AI, content can not only be played out automatically in the required formats, but can also be varied according to the situation and target group – for seasonal campaigns or different markets, for example. This enables cross-channel communication in real time while ensuring the relevance of brand messages. However, context is crucial in omnichannel marketing in particular – and cannot be tapped into by AI alone. It requires a deep understanding of the touchpoints and target group needs as well as a clearly defined rule base for the AI. If these guidelines are missing, it can happen that content is played out at the wrong time, in the wrong channel or in an unsuitable form.
Requirements for AI
In addition to the creation and playout of content, AI can also support the management of digital assets and the orchestration of workflows across the entire content ecosystem – for example through AI tagging in the digital asset management system or the automated assignment of tasks. For this to be possible, however, the basic data flows, processes and responsibilities must be clearly defined and documented.
In other words, if you want to use AI sensibly, you first need to tidy things up – both organizationally and systemically. Only then can the potential of intelligent automation be fully exploited in all areas. In the latest issue of The Latest Think, we show how companies can shape this path – and which factors determine success or failure.
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