In summary, artificial intelligence (AI) has now taken on a central role in many classic IT areas such as data technology, digital sustainability, change management and cybersecurity. According to a study by ISG Research, 85 percent of companies worldwide plan to invest in AI technologies in the next two years. The biggest challenge is to change the fundamental way of thinking about IT, as AI transformations are disruptive and exponential.
The role of experts in product development has fundamentally changed, with data-driven innovations and dialogue with AI-supported systems becoming increasingly important. Companies must focus on knowledge management, process optimization and the redesign of business models in order to achieve widespread automation. CIOs face the balancing act of balancing innovation and regulation to maintain competitiveness without neglecting the risks of AI.
The introduction of AI poses challenges in terms of workflows, creating new jobs and addressing issues such as confidentiality, security, sovereignty and environmental impact in the context of ESG requirements. The use of AI requires careful governance to maximize positive impacts and address ethical concerns.
CIOs must also address ethical and copyright issues when acquiring base AI models and ensure responsible and legal use. Effective AI governance is essential to manage exponential change and identify and evaluate new opportunities and applications.
The market for IT and AI service providers is rated better by users if they can also demonstrate comprehensive consulting capabilities. Generative AI architecture is playing an increasingly important role, although there is still room for improvement. AI infrastructure is powered by "AI accelerators" dominates that advance machine learning modeling and training.
Generative AI is used in various industries, particularly in financial services. Knowledge management, predictive analytics, customer support and application development are central areas of application. The availability of high-quality data is crucial for the success of AI applications. In the next five to eight years, specialized units for AI ethics, so-called “AI Ethics Boards,” are expected to emerge. They will have to deal with stricter regulations and ethical standards for large AI models.