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Five Key Trends in AI and Data Science for 2024

CEO Tinh Phung
Artificial intelligence and data science have taken center stage in 2023, thanks to the emergence of generative AI. With its growing popularity, what can we expect from these fields in 2024? And how will these...

Carolyn Geason-Beissel/MIT SMR | Getty Images

Artificial intelligence and data science have taken center stage in 2023, thanks to the emergence of generative AI. With its growing popularity, what can we expect from these fields in 2024? And how will these trends impact businesses? Let's dive into the key insights from recent surveys conducted with data and technology executives.

1. Generative AI: Balancing Hype with Value Delivery

Generative AI has captured significant attention from businesses and consumers alike. However, its real economic value is yet to be fully realized. While excitement surrounding the technology is high, the majority of respondents feel that its potential is still untapped. According to the surveys, organizations believe generative AI could be transformational, with 80% of respondents from the AWS survey and 64% from the Wavestone survey stating that it is the most transformational technology of this generation. Despite this enthusiasm, only a small percentage of companies have implemented generative AI in actual production. For organizations to fully leverage the benefits of generative AI, investments in technology, organizational change, and data curation are necessary.

2. The Industrialization of Data Science

Data science is transitioning from an artisanal practice to an industrialized process. Companies now recognize the need to accelerate the production of data science models. To achieve this, organizations are investing in platforms, processes, methodologies, and tools such as machine learning operations (MLOps) systems and feature stores. These advancements increase productivity and deployment rates, allowing data science activities to be more efficient. While many capabilities come from external vendors, some organizations are developing their own platforms. Automation and the reuse of existing data sets, features, and models are contributing to the increased productivity of data science.

3. The Rise of Data Products

Data products and data product management have gained traction in organizations. These products package data, analytics, and AI into software offerings for internal or external customers. Survey results show that organizations perceive data products in two different ways. Approximately 48% of respondents consider analytics and AI as part of the concept, while 30% view them separately. The definition of data products can vary among organizations, but what matters most is consistency within an organization's own definition. Defining and discussing data products clearly is crucial to ensure that product development teams meet expectations.

4. The Changing Role of Data Scientists

The role of data scientists is evolving, and the allure of being a "unicorn" or having the "sexiest job of the 21st century" is fading. The increasing complexity of data science projects has given rise to various roles that specialize in different aspects of the work. Data engineers, machine learning engineers, translators, connectors, and data product managers are all emerging roles in the field. Citizen data science is also gaining prominence, enabling business professionals with quantitative skills to create models using automated machine learning tools. While professional data scientists are still essential for certain tasks, the demand for their expertise may decrease as other roles and tools become more prevalent.

5. The Evolution of Data, Analytics, and AI Leadership

Organizations are restructuring their leadership roles in technology, data, and digital transformation. The traditional positions of chief data and analytics officers are being absorbed into broader roles, often reporting directly to the CEO. These "supertech leaders" are responsible for driving value from data and technology professionals within organizations. Collaboration among tech-oriented leaders remains a challenge, with many organizations struggling to find a centralized source for data and technology services. In 2024, we can expect to see more of these overarching tech leaders who can bridge the gap between strategy and execution, translating business goals into insights and systems.

In conclusion, these five trends in AI and data science for 2024 demonstrate the evolving landscape of these fields. Organizations need to focus on delivering value through generative AI, industrializing data science, embracing data products, adapting to the changing role of data scientists, and reimagining leadership structures. By staying abreast of these trends, businesses can effectively leverage AI and data science to gain a competitive edge in the rapidly evolving digital landscape.

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