According to a recent report by Gartner, by 2025, artificial intelligence (AI) will be a top five investment priority for more than 30% of CIOs
globally. Some of the key trends that will shape the AI landscape in the next five years include:
AI engineering: A discipline that applies engineering best practices to AI development, deployment, and operation.
AI democratisation: The spread of AI tools and skills to a wider range of users, domains, and applications.
AI ethics: The study and practice of ensuring that AI systems are fair, accountable, transparent, and human-centric.
AI augmentation: The enhancement of human capabilities and performance by AI systems.
AI everywhere: The integration of AI into everyday devices, environments, and experiences.
Challenges in AI technology
Though there is a technology elevation in AI to machine learning, deep learning and generative AI, the rise of AI platforms and ecosystems also poses some risks and challenges, such as:
The concentration of power and data in a few large AI
platform providers, such as Google, Amazon, Microsoft, or Alibaba, which may stifle competition and innovation, and create dependencies and lock-ins for users and developers.
The lack of standards and governance for AI platforms and ecosystems, which may lead to inconsistencies, conflicts, and vulnerabilities in the quality, performance, and security of AI systems and services.
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