5 key trends in AI and data science for 2024

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We present to you the MIT Sloan Management Review that reveals the insights of more than 500 senior data and technology executives in “Five Key Trends in AI and Data Science for 2024,” a part of its AI in Action series.

“Five Key Trends in AI and Data Science for 2024” culls the surveys to identify developing issues that should be on every leader’s radar screen this year:

1.

Generative AI sparkles but needs to deliver value. Survey responses suggest that although excitement is high, the value of generative AI has not been delivered. Large percentages of respondents believe the technology has the potential to be transformational; 80% in one survey said they believe it will transform their organizations, and 64% in another survey said it is the most transformational technology in a generation. A large majority of survey takers are also increasing investment in the technology.

2.

Data science is shifting from artisanal to industrial. Companies are investing in platforms, processes and methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to increase productivity and deployment rates. Automation is helping to increase productivity and enable broader data science participation.

3.

Two versions of data products will dominate. Eighty percent of data and technology leaders in one survey said that their organizations were using or considering the use of data products and product management. But they mean two different things by “data products.” Just under half (48%) of respondents said that they include analytics and AI capabilities in the concept of data products. Some 30% view analytics and AI as separate from data products and presumably reserve that term for reusable data assets alone. What matters is that an organization is consistent in how it defines and discusses data products.

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4.

Data scientists will become less sexy. The proliferation of roles such as data engineers that can address pieces of the data science problem — along with the rise of citizen data science, where savvy business people create models or algorithms themselves — is causing the star power of data scientists to recede.

5.

Data, analytics, and AI leaders are becoming less independent. In 2023, increasing numbers of organizations cut back on the proliferation of technology and data “chiefs,” including chief data and analytics officers (and sometimes chief AI officers). The functions performed by data and analytics executives haven’t gone away; rather, they’re increasingly being subsumed within a broader set of technology, data, and digital transformation functions managed by a “supertech leader” who usually reports to the CEO. In 2024, expect to see more of these overarching tech leaders who have all the capabilities to create value from the data and technology professionals reporting to them.

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