- According to McKinsey & Company, these AI applications have the potential to contribute between USD 2.6 trillion and 4.4 trillion annually to the global economy through various business scenarios.
- These trends in AI are more than technological forecasts; they represent a roadmap for innovation, growth, and societal progress. As AI evolves, it prompts a reevaluation of business practices and global interactions. Its potential is immense, and its impact is only beginning.
1. Customisation of enterprise AI
Enterprise AI customisation is increasingly popular, as businesses adopt tailored generative AI applications to address specific needs. By integrating proprietary data, these applications offer more accurate and relevant responses. This trend represents a shift towards more efficient and personalised AI-driven solutions. For instance, a global retail chain might use region-specific AI models trained on data like customer preferences and cultural nuances, leading to highly personalised customer interactions.
Businesses are increasingly turning to AI to enhance customer engagement, improve operational efficiency, and maintain market competitiveness. This shift is transforming the business landscape, where AI drives innovation and tackles specific market and operational challenges.
Also read: Interview with Xiao Yumin, CTO of TorchV AI: Harnessing unstructured data for business advantage
2. Open-source models close the gap
The AI landscape is undergoing a significant transformation as open source models from Mistral, MosaicML, and others rapidly advance, narrowing the gap with commercial counterparts like OpenAI. This shift is reshaping the AI ecosystem, making sophisticated tools more accessible and fostering a more competitive and diverse market. The acceleration in innovation reflects the intense competition in the AI sector, with both open source and commercial entities striving to lead in this burgeoning field.
Also read: Meta’s Llama AI models gain momentum with major businesses
3. API-driven AI and microservices
The rise of APIs simplifies creating complex AI applications, enhancing productivity. This led to improved customer service, data-driven marketing, and optimised inventory management, with API-driven solutions offering flexibility and scalability to adapt quickly to market changes.
4. AI as a national priority
Nations are increasingly treating AI development as a new space race, driving advances in research and economic growth. In 2023, the EU progressed towards finalising the European Union Artificial Intelligence Act, the first comprehensive global legal framework for AI. This legislation classifies AI systems by risk level—unacceptable, high, and limited—and sets specific obligations. It regulates a broad range of applications, from high-risk uses in welfare and education to low-risk chatbots, and bans certain high-risk applications like emotion recognition in the workplace and social scoring based on personal traits.
5. Multimodal generative AI
The shift from text-based to multimodal AI integrates text, speech, and images, enabling more contextually relevant responses and fostering innovation. For example, during a customer service call, AI can analyse spoken requests, interpret financial documents, and assess facial expressions in video consultations, combining these data points to deliver personalised financial advice and improve creditworthiness assessments with greater precision.
6. AI safety and ethics
As AI becomes more prevalent, ensuring its safety and ethics is increasingly important. Intel, Meta, and other industry leaders formed the AI Safety Alliance to develop standardised safety protocols and best practices. This alliance, which includes Oracle, AMD, Dell, and the Linux Foundation, aims to foster responsible AI innovation, support AI hardware growth, and establish global benchmarks for ethical AI. It promotes open AI development and ensures scientific rigor, trust, safety, and economic competitiveness, reflecting a strong commitment to AI safety and ethical practices.
