CAMBRIDGE, Mass., January 9, 2024 /PRNewswire/ — MIT Loan Management Review reveals insights from more than 500 senior data and technology executives in “Five key AI and data science trends for 2024,” part of his AI in action series.
Artificial intelligence and data science made headlines in 2023 thanks to generative AI, state co-authors say Thomas H. DavenportPresident’s Distinguished Professor of Information Technology and Management at Babson College and member of the MIT Digital Economy Initiative, and Randy Beanan industry thought leader who is currently an Innovation Researcher, Data Strategy, for global consultancy Wavestone.
To find out what could keep it in the headlines in 2024, Davenport and Bean conducted three surveys over the past few months involving more than 500 executives closest to companies’ data science and AI strategies, to highlight what organizations think and do.
“Data science is increasingly essential for every organization. But it is not a static discipline, and organizations must continually adjust their data science skills and processes to get the most out of data, analytics and AI,” he said. Davenport said.
“Expect 2024 to be a year of transformation and change driven by AI adoption and a rethinking of the data, analytics and AI leadership role within large enterprises” , added Bean. “With 33% of medium and large organizations having appointed or looking for a chief AI officer, and with 83.2% of large enterprises now having a chief data and analytics officer in place, it “It is inevitable that we will see a consolidation of roles, a restructuring of responsibilities, the elimination of certain positions, and a critical rethinking of expectations for leadership in data and AI during the year 2024.”
“Five key AI and data science trends for 2024” selects surveys to identify developing issues that should be on every leader’s radar screen this year:
Generative AI shines but must deliver value. Survey responses suggest that while enthusiasm is high, the value of generative AI has not been fully realized. A high percentage of respondents believe technology has the potential to be transformational; 80% of respondents in one survey said they believed it would transform their organization, and 64% in another survey said it was the most transformational technology in a generation. A large majority of respondents are also increasing their investments in technology.
Data science is moving 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 deployment rates. Automation helps increase productivity and enable broader participation in data science.
Two versions of data products will dominate. Eighty percent of data and technology leaders in a survey said their organization was using or 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 concept of data products. About 30% view analytics and AI as distinct from data products and likely reserve that term for only reusable data assets. What matters is that an organization is consistent in how it defines and discusses data products.
Data scientists will become less sexy. The proliferation of roles such as data engineers who can solve aspects of the data science problem, as well as the rise of citizen data science, in which savvy business people create data themselves models or algorithms, are reducing the star power of data scientists.
Data, analytics and AI leaders are becoming less and less independent. In 2023, a growing number of organizations have reduced the proliferation of technology and data “heads,” including heads of data and analytics (and sometimes heads of AI). The functions performed by data and analytics managers have not disappeared; rather, they are increasingly subsumed into a broader set of technology, data and digital transformation functions managed by a “supertech leader” who typically reports to the CEO. In 2024, expect to see more of these prominent technology leaders who have all the capabilities necessary to create value from the data and technology professionals who report to them.
THE MIT Loan Management Review article “Five key AI and data science trends for 2024” publishes on 8 a.m. ET on January 9, 2024. This column is part of the series AI in action.
about the authors
Thomas H. Davenport is President’s Distinguished Professor of Information Technology and Management at Babson College, a member of the MIT Digital Economy Initiative and senior advisor to the Deloitte Chief Data and Analytics Officer program. He is co-author of All About AI: How Smart Businesses Win 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 thought leader, author, founder and CEO and is currently a Researcher in Innovation, Data Strategy, for global consultancy Wavestone. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in the Age of Disruption, Big Data, and AI (Wiley, 2021).
About MIT Loan Management Review
MIT Loan Management Review is an independent, research-based magazine and digital platform for business leaders, published in MIT Sloan School of Management. MIT SMR explores how leadership and management are transforming in a disruptive world. We help thoughtful leaders seize the exciting opportunities – and meet the challenges – created as technological, societal and environmental forces reshape the way organizations operate, compete and create value.
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