3 AI Trends Identified by Gartner to Look Out for in 2024

Gartner is the world’s leading data research and advisory firm. At the Gartner Data & Analytics Summit 2023, the firm shared its vision of the key trends likely to impact and shape the future of Data Science and Machine Learning. Here’s a look back at the 3 AI trends to watch for your business in 2024.

En su Cumbre sobre Datos y Análisis celebrada en Sídney el pasado verano, Gartner esbozó las tendencias clave que influirán en el futuro de la ciencia de datos y el aprendizaje automático (DSML). En un momento en el que muchos sectores se ven afectados por el auge del uso de la IA en las empresas, la consultora destaca la creciente importancia de los datos en la inteligencia artificial, que está emprendiendo un camino más ético y responsable.

Trend #1: Edge AI as a Promise of Responsiveness

Una de las tendencias señaladas por Gartner para 2024 es la IA en el borde (Edge AI). Permite realizar cálculos cerca del lugar donde se recopilan los datos, lo que elimina la necesidad de un centro de computación en la nube centralizado o de un centro de datos externo. Esto favorece la toma de decisiones inteligentes con mayor rapidez, sin necesidad de conectarse a la nube ni a centros de datos remotos. Al permitir una ejecución más rápida de los algoritmos de IA, se reduce la latencia y los sistemas responden con mayor rapidez.

Edge AI applies to IoTs, taking advantage of available local computing power. This approach is crucial for applications requiring real-time decision-making, such as autonomous driving or smart medical devices. Edge AI also offers advantages in terms of data confidentiality and security. Indeed, because certain sensitive information can be processed locally without being transmitted to remote servers, this eliminates unnecessary data exposure to external threats.

This convergence of AI and edge computing paves the way for solutions that are not only more efficient but also more responsible, as they are potentially more energy-efficient. According to forecasts by the Gartner Institute, more than 55% of all data analysis performed by deep neural networks will take place at the point of capture in an Edge system by 2025, compared to less than 10% in 2021!

Trend #2: Responsible AI as an Ethical Promise

Gartner highlights the key role of Responsible AI in its AI trend forecast for 2024. This set of principles and practices aims to ensure that AI is used ethically and responsibly. It addresses the social, environmental, and economic impact of AI, and aims to minimize risks and maximize benefits.

In technological terms, Responsible AI translates into a series of measures aimed at improving the transparency, reliability, and safety of AI systems. The key focus is on data and algorithm transparency. This enables users to understand how AI systems work, and to detect any misappropriated biases so that data can be used in a virtuous and respectful way. The second major area is the reliability of AI systems, whose robustness must be guaranteed, even under complex conditions or in the event of computer attacks. Thus, AI systems must be secure to protect personal data and sensitive information.

According to the Gartner Institute, “Responsible AI makes AI a positive force rather than a threat to society and itself”. To achieve this, the advice is simple: adopt a risk-proportionate approach to bringing value to AI, while exercising extreme caution when applying solutions and models.

Trend #3: Data-Centric AI as a Promise of Relevance

Gartner’s third major AI trend for 2024 highlights the centrality of data in the mass adoption of AI. Artificial intelligence is based on algorithms, which determine its relevance and performance. But rather than focusing solely on algorithms, data-centric AI focuses more on the quality, diversity, and governance of data. The aim is to improve model accuracy by relying on rich, perfectly maintained data sets.

For companies, data-centric AI promises better customer understanding, more informed decision-making, and more robust innovations. By focusing on data quality, organizations can increase the effectiveness of their AI initiatives, reduce algorithmic biases, and boost user confidence. In doing so, data-centric AI offers a more reliable and sustainable way of harnessing the potential of artificial intelligence. According to Gartner forecasts, by 2024, 60% of AI data will be used to simulate reality, identify future scenarios, and reduce the risk of AI errors, compared with just 1% in 2021!

Between performance, ethics, compliance, safety, and responsibility, the AI 2024 roadmap is ambitious. Will you rise to the challenge?

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