AI Is Creating New Roles and Skills in Data & Analytics

Sixty-seven percent of mature organizations are creating new roles for generative artificial intelligence (GenAI) and 87% of these organizations have a dedicated AI team, according to a recent Gartner survey of 703 respondents in the U.S., U.K. and Germany. The creation of must-have roles for AI is important as it ensures that the organization has the necessary expertise to effectively leverage AI technologies and drive successful outcomes.

During the Gartner Data & Analytics Summit, taking place this week in London, we sat down with Jorg Heizenberg, VP Analyst at Gartner, to discuss the new AI roles being added to D&A teams to align with business objectives. 

Journalists who would like to speak with Jorgen regarding this topic can contact Laurence.Goasduff@Gartner.com. Members of the media can reference this material in articles with proper attribution to Gartner.

A: Gartner’s 2024 survey of 479 chief data and analytics officers (CDAOs) found that a lack of skills and staff was a major roadblock prohibiting their D&A team from being successful. Thissituation will only accelerate as the use of AI and GenAI applications and tools continues to grow. Fifty-three percent of the CDAOs surveyed said they’ve already deployed GenAI or committed to pilot it in the next six months.

The deployment of AI and GenAI within organizations is demanding specific capabilities, meaning that for the D&A team, the investment in new talent is real, urgent and required. There is no benchmark that determines the size of the D&A team as there are too many dependencies. The question CDAOs should ask themselves is how many roles they need to ensure their teams are successful and effective. 

While data engineers, data scientists, and machine learning (ML) engineers are already must-have positions, there are some nascent roles CDAOs should consider if their organizations are increasing AI adoption (see Figure 1).

These new roles are emerging because advances in AI are creating more different and complex skills such as real-time analytics, in-context learning, or training, versioning and deployment of the ML model.

In the context of GenAI, CDAOs should consider hiring a knowledge engineer who focuses on developing ontologies, knowledge graphs, rules or other symbolic models to represent the collective intelligence of the organization, or insights into a specific business process or domain.

A model manager should also be on their radar. Model managers ensure the ML model is set up correctly and the processes around it behave as expected through all the steps of its life cycle, including drift monitoring and the selection of existing (foundation) models for (re)use. The addition of an AI ethicist to the D&A team should also be considered. An AI ethicist thinks through the unintended consequences of the use of data and AI and determines how to manage risks and opportunities. Overall, the D&A team should appoint a Head of AI who is accountable for the development and implementation of strategies within the organization.

A: First and foremost, D&A leaders must develop data literacy and AI literacy programs for their teams and the enterprise.

Some organizations are already putting in place either or both of those programs. Thirty-nine percent of CDAOs surveyed said that they’re already running a GenAI literacy program for the broader organization, and 26% are committed to deploying it within six months. However, Gartner analysts said that by 2028 one out of four regretted staff attrition will be attributable to managers’ lack of data literacy.

AI literacy is the ability to effectively and responsibly utilize AI in context (business and societal) with competency to identify relevant use cases, as well as implement and operate corresponding AI applications. Data literacy consists of enabling the workforce to consume, analyze and make smart decisions with data.

Business leaders should not ignore data and AI literacies. They are interrelated as half of AI techniques are fueled by data, and data literacy is essential to AI literacy and vice versa. Both literacies are essential to educate business executives, upskill staff and hire roles that enable D&A and AI.

A: It is necessary to precisely determine the places for humans and machines. If there are AI-led machines that can help the D&A team in their tasks, leaders need to give them a place in the organization with clear governance to avoid causing issues for humanity. Humans are needed for giving AI context and to drive the adoption of data, analytics and AI in the organization, and change employee behaviors. For example, AI-led machines can identify bottlenecks in the supply-chain and give recommendations to a supply chain manager on how to improve the delivery of the goods on-time and in-full.

Furthermore, AI’s ability to unpredictably generate false information means that its conclusions can only be used where the results are evaluated and controlled by a human, and where humans can correct errors. So data governance becomes a core aspect.

By retaining the human dimension, leaders can give D&A users the power to influence or even control the way analysis is created and increase the likelihood of its use in their decision-making. Ultimately, efforts to drive decision automation without considering the human role in decisions will result in a data-driven organization without conscience or consistent purpose. Humans remain the key decision makers.

Gartner clients can learn more in “How Should CDAOs Organize for AI and What Roles Are Required?” and “10 Steps to Reskill and Upskill Your Data and Analytics Talent.”