Five Key AI and Data Science Trends for 2024 From MIT Sloan Management Review

CAMBRIDGE, MA., January 9, 2024 /PRNewswire/ — MIT Sloan Management Review reveals the insights of more than 500 senior data and technology executives in “Five Key AI and Data Science Trends for 2024,” part of its AI in action series.

Artificial intelligence and data science become front-page news in 2023 thanks to generative AI, state co-authors Thomas H. DavenportDistinguished Professor of Information Technology and President’s Management at Babson College and Fellow of the MIT Initiative on the Digital Economy and Randy Beanan industry leader who currently serves as Innovation Associate, Data Strategy, for global consultancy Wavestone.

To find out what might keep it on the front page in 2024, Davenport and Bean, over the past few months, conducted three surveys involving more than 500 executives closest to companies’ data science and AI strategies. to bring to light what organizations are thinking and doing.

“Data science is increasingly important to every organization. But it is not a static discipline, and organizations must continually adjust data science skills and processes to get the full value from data, analytics and AI,” said Davenport.

“Expect 2024 to be a year of transformation and change, driven by the adoption of AI and reshaping the leadership role of data, analytics and AI in leading companies,” Bean added. “With 33% of mid-to-large organizations having appointed or seeking a Chief AI Officer, and with 83.2% of leading companies having a Chief Data and Analytics Officer today, it is inevitable that we will see role consolidation, restructuring responsibilities, eliminating some positions, and some critical rethinking of data and expectations for AI leadership in 2024.”

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

Generative AI shines, but it must deliver value. Survey responses show that while excitement is high, the value of generative AI has not been delivered. Large percentages of respondents believe that technology has the potential to be transformative; 80% in one survey said they believed it would transform their organizations, and 64% in another survey said it was the most transformative technology in a generation. A large proportion of respondents are also increasing investments in technology.

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

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

Data scientists will become less sexy. The proliferation of roles like data engineers who can handle parts of the data science problem—along with the rise of citizen data science, where savvy businesspeople build models or algorithms themselves—is making the star of data scientists waning.

Leaders in data, analytics and artificial intelligence are becoming less independent. In 2023, an increasing number of organizations are reducing the prevalence of technology and data “chiefs,” including chief data and analytics officers (and sometimes chief AI officers). The functions performed by data and analytics managers have not disappeared; rather, they are increasingly involved in a broader set of technology, data and digital transformation functions managed by a “super tech leader” who typically reports to the CEO. In 2024, expect to see more of these all-encompassing technology leaders who have all the capabilities to create value from the data and technology professionals that report to them.

The MIT Sloan Management Review the article “Five Key Trends in AI and Data Science for 2024” published on 8 a.m. ET On January 9, 2024. This column is part of a series AI in action.

For the authors
Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management Babson College, a fellow at MIT’s Digital Economy Initiative and a senior advisor to Deloitte’s Chief Data and Analytics Officer Program. He is the co-author of All in AI: How Smart Companies Are Earning Big with Artificial Intelligence (HBR Press, 2023) and Working with AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022). Randy Bean is an industry leader, author, founder and CEO, and currently works as Innovation Associate, Data Strategy, for global consultancy Wavestone. He is the author of Fail Fast, Learn Faster: Lessons for Data-Driven Leadership in the Age of Disruption, Big Data and Artificial Intelligence (Wiley, 2021).

regarding MIT Sloan Management Review
MIT Sloan Management Review is an independent, research-based magazine and digital platform for business leaders, published in MIT Sloan School of Management. MY SMR explores how leadership and management are being transformed in a disruptive world. We help savvy leaders seize the exciting opportunities—and meet the challenges—created as technological, societal, and environmental forces change the way organizations operate, compete, and create value.

Get in touch with MIT Sloan Management Review On:

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SOURCE MIT Sloan Management Review

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