The emergence of generative AI brought artificial intelligence and data science to the forefront in 2023. In 2024, three polls of data and technology leaders were undertaken, with over 500 top executives participating. AWS and Thoughtworks funded two polls of MIT’s Chief Data Officer and attendees of the Information Quality Symposium, while Wavestone, previously NewVantage Partners, conducted the third poll. These advancements are projected to have an impact on companies in the years that follow. Surveys give insights into the opinions and activities of people who are intimately involved with a company’s data science and AI plans and initiatives.
Below are new AI and data science trends in 2024
1. Generative AI is visually appealing, but it has to offer value
Gender-based AI has received a lot of attention, but its economic benefits have yet to be seen. Despite great excitement, most organizations continue to experiment on an individual or departmental level. Production deployments of generative AI will need additional investment and organizational transformation, such as rethinking business processes, reskilling personnel, and integrating new AI capabilities into current IT infrastructure. The most significant shift will be in data strategy, filtering unstructured material, enhancing data quality, and connecting several sources. 93% of respondents acknowledged that data strategy is crucial for generating value from generative AI, however, 57% had made no modifications to their data thus far.
2. Data science is moving from artisanal to industrial
Companies are investing in platforms, procedures, methods, feature stories, and machine learning operations (MLOps) solutions to boost data science model production and deployment. MLOps systems evaluate model accuracy and may require retraining with fresh data. While automation boosts productivity and expands data science involvement, the most significant benefit is the reuse of existing data sets, features, variables, and models.
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3. Two types of data products will come out on top
According to the Thoughtworks poll, 80% of data and technology executives say their organizations are using or thinking about data products and data product management. Data products are software products that use data, analytics, and AI for internal or external clients. Data product managers oversee these products. Examples include systems for recommendations and price optimization systems. However, 48% of respondents involve analytics and AI capacities in data products, 30% perceive them independently, and 16% do not consider them in a product context. A need for a definition that incorporates analytics and AI is expressed, although consistency in identifying and discussing data products is critical. A mix of “data products” and “analytics and AI products” can be effective, but a clear definition is necessary to avoid misunderstanding.
4. Data scientists are going to become less appealing
According to the Thoughtworks poll, 80% of data and technology leaders use or are thinking about using data products and managing them. Data products are software offerings that incorporate data, analytics, and AI for internal and external clients. However, 48% of respondents involve analytics and AI capabilities in data products, 30% perceive them independently, and 16% do not consider them from a product perspective. A consistent definition of data products is critical for maintaining the positive features of product management and avoiding confusion about what developers are expected to deliver.
5. Data, analytics, and artificial intelligence leaders are losing their authority
Organizations are lowering the quantity of technology and data “chiefs,” particularly chief data and analytics officers (and occasionally chief AI officers), due to short tenures and ambiguity regarding duties. These responsibilities are currently being consolidated into a larger set of technology, data, and digitization tasks overseen by a “Supertech leader” who often reports to the CEO. According to a Thoughtworks poll, 87% of respondents were either entirely or slightly unsure about where to turn for data- and technology-related services and concerns, and many C-level executives reported little interaction with other tech-oriented leaders.
Conclusion
In 2024, tech executives will place a greater emphasis on analytics and AI to generate value using data and technology workers. To make their goals a reality, these executives must be business-oriented, debating strategy with senior management, and translating it into systems and insights. They will additionally be required to be very business-oriented and capable of turning their ideas into practical processes.
