Five Key Trends in AI and Data Science for 2024

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Illustration Credit: Carolyn Geason-Beissel/MIT SMR | Getty Images

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These developing issues should be on every leader’s radar screen, data executives say.

This column series looks at the biggest data and analytics challenges facing modern companies and dives deep into successful use cases that can help other organizations accelerate their AI progress.

Artificial intelligence and data science became front-page news in 2023. The rise of generative AI, of course, drove this dramatic surge in visibility. So, what might happen in the field in 2024 that will keep it on the front page? And how will these trends really affect businesses?

During the past several months, we’ve conducted three surveys of data and technology executives. Two involved MIT’s Chief Data Officer and Information Quality Symposium attendees — one sponsored by Amazon Web Services (AWS) and another by Thoughtworks (not yet published). The third survey was conducted by Wavestone, formerly NewVantage Partners, whose annual surveys we’ve written about in the past. In total, the new surveys involved more than 500 senior executives, perhaps with some overlap in participation.

Surveys don’t predict the future, but they do suggest what those people closest to companies’ data science and AI strategies and projects are thinking and doing. According to those data executives, here are [two of] the top five developing issues that deserve your close attention:

1. Generative AI sparkles but needs to deliver value.

As we noted, generative AI has captured a massive amount of business and consumer attention. But is it really delivering economic value to the organizations that adopt it? The survey results suggest that although excitement about the technology is very high, value has largely not yet been delivered. Large percentages of respondents believe that generative AI has the potential to be transformational; 80% of respondents to the AWS survey said they believe it will transform their organizations, and 64% in the Wavestone survey said it is the most transformational technology in a generation. A large majority of survey takers are also increasing investment in the technology. However, most companies are still just experimenting, either at the individual or departmental level. Only 6% of companies in the AWS survey had any production application of generative AI, and only 5% in the Wavestone survey had any production deployment at scale.

Surveys suggest that though excitement about generative AI is very high, value has largely not yet been delivered.

Production deployments of generative AI will, of course, require more investment and organizational change, not just experiments. Business processes will need to be redesigned, and employees will need to be reskilled (or, probably in only a few cases, replaced by generative AI systems). The new AI capabilities will need to be integrated into the existing technology infrastructure.

Perhaps the most important change will involve data — curating unstructured content, improving data quality, and integrating diverse sources. In the AWS survey, 93% of respondents agreed that data strategy is critical to getting value from generative AI, but 57% had made no changes to their data thus far.

2. Data science is shifting from artisanal to industrial.

Companies feel the need to accelerate the production of data science models. What was once an artisanal activity is becoming more industrialized. Companies are investing in platforms, processes and methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to increase productivity and deployment rates. MLOps systems monitor the status of machine learning models and detect whether they are still predicting accurately. If they’re not, the models might need to be retrained with new data.

Producing data models — once an artisanal activity — is becoming more industrialized.

Most of these capabilities come from external vendors, but some organizations are now developing their own platforms. Although automation (including automated machine learning tools, which we discuss below) is helping to increase productivity and enable broader data science participation, the greatest boon to data science productivity is probably the reuse of existing data sets, features or variables, and even entire models.

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Here is a direct link to the complete article.

Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a fellow of the MIT Initiative on the Digital Economy, and senior adviser to the Deloitte Chief Data and Analytics Officer Program. He is coauthor of All in on AI: How Smart Companies Win Big With Artificial Intelligence (HBR Press, 2023) and Working With AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022). Randy Bean (@randybeannvp) is an industry thought leader, author, founder, and CEO and currently serves as innovation fellow, data strategy, for global consultancy Wavestone. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).



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