In 2024, the initial cultural fascination with early generative AI has yielded tangible business results. This technology, which includes the ability to process and generate text, voice and video content, is revolutionizing how companies improve productivity, promote innovation and stimulate creativity. 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.
Using insights from extensive collaboration with customers and partners in more than 25 countries, we are excited to share well-informed predictions and emerging trends for 2024. This global perspective allowed us to observe how diverse industries both influence the evolving technology and are influenced by landscape. Here’s a look at what the future holds.
1. Adaptation of enterprise AI
Enterprise AI customization is on the rise, with businesses embracing custom generative AI applications. These applications are designed to meet specific business needs by integrating proprietary data and helping ensure more accurate and relevant answers. This trend indicates a move towards more efficient and personalized AI-driven business solutions. For example, a global retail chain could adopt region-specific AI models trained on data, such as customer preferences and cultural nuances. This approach results in highly personalized customer interactions.
In Japan, AI prioritizes efficiency and accuracy, while in Brazil it emphasizes warmth and engagement, reflecting each market’s cultural values. This trend is expected to expand to various industries, transforming AI from a generic tool into a key strategic asset. Businesses increasingly rely on AI for customer engagement, operational efficiency and market competitiveness, leading to a dynamic business landscape where AI fuels innovation and addresses specific market and industry challenges.
2. Open source AI models
In 2024, open source pre-trained AI models are gaining significant traction, empowering businesses to accelerate growth by combining these models with private or real-time data. This synergy increases productivity and cost efficiency. IBM actively contributes to open source AI models, exemplified by its collaboration with NASA.
A notable contribution is the geospatial AI foundation model, the largest of its kind on the Hugging Face platform and a pioneering open source AI foundation model created in partnership with NASA. This model expands access to NASA’s Earth science data, particularly for geospatial intelligence and advancing climate-related research. This model, derived from NASA satellite data, demonstrates the commitment by IBM to use open source technologies for critical areas such as climate change.
By making this model available on Hugging Face, a renowned repository for transformer models, IBM and NASA aim to democratize access and encourage its application in climate and earth science innovations. The model showed impressive results, achieving a 15% improvement over state-of-the-art techniques with only half the labeled data, making it a powerful tool for tasks such as deforestation tracking, crop yield prediction, and greenhouse gas detection and monitoring.
3. API-driven AI and microservices
The proliferation of application programming interfaces (APIs) will simplify the creation of complex AI-driven applications, boosting productivity across multiple sectors. IBM has developed a series of custom AI microservices for a retailer, accessible through APIs. These services included customer behavior analysis, inventory management and personalized marketing tools.
A key feature was the integration of IBM’s AI-powered intelligent assistants into the retailer’s customer service system. Able to handle numerous customer queries in real-time, these assistants provided customized responses based on individual customer data. In addition, the retailer used IBM’s AI-driven summarization tools to efficiently analyze customer feedback and sales data, enabling quick and informed decision-making. The impact was significant: customer service efficiency improved, marketing strategies became more data-driven and inventory management was optimized. The flexibility and scalability of these API-driven solutions enabled the retailer to quickly adapt to market changes.
4. AI as a national priority
Recognizing AI’s immense potential, nations worldwide are expected to prioritize its development in a manner reminiscent of a new space race. This heightened focus is driving significant advances in research, science and economic growth, firmly establishing AI as a strategic global asset.
In 2023, the European Union demonstrated its commitment to elevating AI as a national priority by progressing towards the finalization of the European Union Law on Artificial Intelligence (EU AI Law). This historic legislation represents the world’s first comprehensive legal framework for artificial intelligence, categorizing AI systems into different risk levels (unacceptable risk, high risk and limited risk) and imposing corresponding obligations.
The EU AI Act legislation represents an important step in the regulation of AI deployment within the EU, covering a wide range of AI applications, ranging from high-risk scenarios such as AI use in sensitive systems such as welfare, employment, education and transport, to low-risk applications such as chatbots. The EU AI law also prohibits certain AI applications that are considered to pose unacceptable risks, including the use of emotion recognition in the workplace and social scoring based on social behavior or personal characteristics.
5. Multimodal generative AI
The transition from text-based generative AI to multimodal language, the integration of text, speech and images promises contextually relevant responses and promotes innovation across diverse sectors. During a customer service call, for example, AI can analyze a customer’s spoken request, interpret their financial documents and assess their facial expressions in a video consultation. By synthesizing these data points (speech, text and visual cues), AI can provide more personalized financial advice and improve creditworthiness assessments with accuracy.
6. AI safety and ethics
As AI becomes more integrated into our lives, the focus on AI safety and ethics is intensifying. Leading AI organizations collaborate to develop robust AI systems with standardized safety protocols and best practices to help ensure ethical AI use. IBM and Meta launched the AI Safety Alliance to address AI issues, along with industry leaders such as Intel, Oracle, AMD, Dell and the Linux® Foundation. This alliance aims to create alternatives to closed AI systems, promote responsible AI innovation and help ensure scientific rigor, trust, safety, security, diversity and economic competitiveness. It also supports AI hardware growth, promotes open AI technology development, and sets global benchmarks, resources, and standards for responsible AI development. This initiative reflects IBM’s commitment to AI safety and ethics, promoting open innovation in AI development.
Embracing the Future: A Call to Action for the AI-Driven World of 2024
These trends and predictions are not simply technological predictions. They serve as a road map for innovation, growth and social progress. As AI evolves, it challenges us to rethink not only our business practices, but also our interactions with the world. The potential of AI is huge and its impact is just beginning. By staying informed, adaptive and proactive, we can harness the power of AI to build a more efficient, innovative and inclusive future.
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