Tech Trends 2030: The next era of generative AI

Unpacking AI’s potential

AI has delivered tremendous value in industries over the past decades. Innovations in machine learning and neural networks enabled solutions like predictive maintenance or generative design. However, with the recent breakthrough in generative AI, new opportunities emerged, which – beyond all the hype and excitement – are delivering real value to industries. From Industrial Copilots to tackle skilled labor and accelerating AI-powered human-machine collaboration, to large language models (LLMs) as “translators” between APIs in industrial applications, generative AI’s potential in the industrial space is only expanding.

Key trends on our radar

Industrial foundation models

Industrial Foundation Models are pre-trained on industry-specific data, enabling faster and more accurate deployment of AI solutions.

Agentic AI

Agentic AI refers to the use of AI systems that possess a certain level of autonomy and decision-making capabilities in the industrial context.

Multimodal LLMs

Multimodal large language models (LLMs) combine language understanding with visual perception, processing data from text, images, and videos and industry specific data like time series.

Edge models

Industrial edge involves the deployment of AI algorithms and processing power at the edge of industrial networks, in closer proximity to the data source.

Specialized hardware

Specialized hardware — such as graphics processing units (GPUs) or language processing units (LPUs)-enabled edge devices — provide high-performance computing power at the edge, enabling real-time processing of AI algorithms.

Mastering the new era of generative AI: a holistic strategy

To ensure readiness for the advancements and challenges of industrial AI in 2030, it is essential that stakeholders adopt a comprehensive strategic approach.

• Innovation: Fostering a culture of innovation within the organization that embraces AI technology.
• Industrial environments: Ensuring requirements and standards of industrial environments: cybersecurity, harm reduction, legal compliance and the mitigation of bias in training data.
• Culture of AI: Enabling an industrial AI ecosystem-centric approach: Sharing data with partners, customers and experts in the best way will help organizations succeed in the emerging age of AI.