Putting AI into Action: Six Business Cases Across the Product Lifecycle

GenAI, in particular, holds significant potential for automating business processes. According to the new "GenAI Impact Report 2025" by adesso, half of the 400 executives surveyed in Germany indicated that they could automate more than 30% of their daily tasks with the help of GenAI across all areas of their businesses.

adesso illustrates how AI can be strategically implemented across the different phases of the product lifecycle to streamline processes, drive innovation, and enhance the overall customer experience.

In today’s advanced production environments, networks of sensors continuously collect data, which is analyzed in real-time using machine learning algorithms. These technologies help detect anomalies in manufacturing processes and enable predictive maintenance strategies.

GenAI further enhances innovation by generating and testing thousands of design variants through simulated load scenarios. Thanks to AI-driven process optimization, one automotive supplier successfully reduced the development time for new components by approximately 30% and gained greater flexibility to adapt to changing production conditions with adaptive engineering.

Modern marketing automation leverages various AI technologies to create and manage highly personalized campaigns across all customer touchpoints. Large Language Models (LLMs) process vast amounts of customer data, while Generative Adversarial Networks (GANs) dynamically produce tailored content for social media, email, and web platforms. NLP-powered chatbots deliver personalized experiences in customer interactions, significantly boosting conversion rates. One e-commerce company, for example, increased its click-through rates by over 25% by applying these advanced technologies.

Additionally, more accurate targeting has led to substantial reductions in advertising costs. Continuous monitoring of customer sentiment now enables real-time hyper-personalization. However, companies must carefully balance the benefits of personalization with the need to protect customer data and privacy.

In sales, GenAI-enhanced CRM systems make it possible to predict the most effective customer offers accurately. Technologies like Graph Neural Networks (GNNs) analyze complex relationships between products, customer interactions, and sales activities to uncover hidden patterns. Bayesian Inference calculates the likelihood of customer responses to specific offers, while Reinforcement Learning (RL) continuously tests and refines sales strategies to optimize the Next Best Actions (NBA) in real-time.

By leveraging these advanced AI methods, a B2B company increased its deal closure rates by 40%. However, for successful adoption, companies need more than just technical solutions. They also require effective change management and continuous training programs for their sales teams.

AI-supported analysis systems enhance the ability to detect irregularities in transactions. Unsupervised learning-based anomaly detection identifies unusual patterns within transaction data, while computer vision technologies automatically validate submitted documents.

A financial services provider detected fraudulent activities 70% faster and optimized the prioritization of support requests using automated clustering models. Continuously evolving these models is crucial to adapting to emerging fraud scenarios and ensuring human oversight when making critical decisions.

Natural Language Processing (NLP) technologies and knowledge graphs are utilized in the insurance and finance sectors to analyze contract documents and loss reports. Transformer models accurately classify suspicious transactions, while federated learning allows companies to collaboratively train AI models without sharing sensitive customer data.

In practice, this AI-driven risk analysis has cut manual review efforts by approximately 50%, accelerating the processing of legitimate claims. However, human expertise is still essential for handling critical cases.

The integration of conversational AI into customer service enhances natural interactions and enables the handling of complex queries. Real-time speech-to-text models analyze calls to identify customer needs, while sentiment analysis algorithms gauge the customer's mood and tailor responses accordingly.

After implementing such systems, a telecommunications provider was able to automate over 75% of routine inquiries, significantly boosting customer satisfaction through personalized, context-aware communication. However, companies must maintain transparency in AI-driven communication and ensure a seamless transition to human agents when needed.

Integrating AI-driven technologies into customer service processes is transforming how businesses engage with their clients. By automating routine inquiries and personalizing interactions, companies can improve operational efficiency and enhance customer satisfaction.

As businesses continue to leverage AI for smarter automation, it is essential to balance technology with human oversight, ensuring a seamless and transparent customer experience. With the right tools and approach, organizations can unlock significant value, driving growth and fostering stronger relationships with their customers.