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New trends in AI technology
Artificial intelligence (AI) and data science have been making headlines in recent years, with the rise of generative AI capturing significant attention. As we enter 2024, leaders need to stay informed about the latest trends in AI and data science. To gain insights into these trends, we’ve analyzed multiple surveys conducted among data and technology executives. In this article, we’ll explore the five key and new trends in AI technology expected to shape the AI and data science landscape in 2024.
Trend 1: Generative AI’s Potential Value
Generative AI has generated tremendous excitement, but the critical question remains: is it delivering economic value to organizations? While many executives believe in the transformative potential of generative AI, the actual value delivered thus far has been limited. Although 80% of respondents in an AWS survey and 64% in a Wavestone survey believe that generative AI will transform their organizations, only a tiny percentage have implemented it at scale. Most companies are still in the experimental phase, with only 6% having a production application of generative AI. To harness the full potential of generative AI, organizations need to invest in reshaping their business processes, reskilling employees, and integrating AI capabilities into their existing infrastructure.
Trend 2: Industrialization of Data Science
Data science is transitioning from an artisanal activity to an industrialized process. Companies are increasingly investing in platforms, processes, and methodologies to accelerate the production of data science models. Machine learning operations (MLOps) systems, feature stories, and other tools are leveraged to enhance productivity and deployment rates. Automation, including automated machine learning tools, is crucial in boosting productivity. The reuse of existing data sets, features, and models is also contributing to increased productivity in data science.
Trend 3: Data Products in Focus
Data products are becoming a key area of focus for organizations. These products package data, analytics, and AI into software offerings for internal or external customers. In a ThoughtWorks survey, 80% of data and technology leaders stated that their organizations were using or considering data products and data product management. However, there is some ambiguity regarding the definition of data products, with different organizations viewing analytics and AI capabilities as part of data products or as separate entities. Clarity in defining and discussing data products is crucial to ensure effective development and delivery.
Trend 4: Evolving Role of Data Scientists
The role of data scientists is undergoing significant changes. While they were once considered the “unicorns” of data science, alternative roles are emerging to address different aspects of the data science process. Data engineers, machine learning engineers, translators, and data product managers are now playing important roles in data science initiatives. Citizen data science, where business professionals create models using automated machine learning tools, is also gaining prominence. While professional data scientists are still necessary for specific tasks, their demand is decreasing as other roles and technologies take on more responsibilities.
Trend 5: Integration of Data, Analytics, and AI Functions
Organizations are moving away from independent data, analytics, and AI leadership roles. Instead, these functions are integrated under a broader technology, data, and digital transformation leadership. A survey by ThoughtWorks revealed that 87% of respondents agreed that there was confusion within their organizations about where to turn for data- and technology-oriented services and issues. Collaboration among tech-oriented leaders is often lacking. In 2024, organizations are expected to have overarching tech leaders who can integrate data and technology functions, emphasize analytics and AI, and effectively translate strategy into actionable insights.
In conclusion, these five trends highlight the evolving landscape of AI and data science in 2024. Generative AI holds immense potential but requires further investment to deliver value. Data science is becoming more industrialized, focusing more on productivity and deployment. Data products are gaining prominence, but clarity in their definition is essential. The role of data scientists is changing, with alternative roles and technologies complementing their work. Finally, organizations integrate data, analytics, and AI functions under a unified leadership structure. By staying informed about these trends, leaders can navigate the evolving AI and data science landscape and make informed decisions for their organizations’ future.