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Study Reveals Chief Data and Analytics Officers Lack Resources to Deliver AI/ML Innovation

Organizations not investing in hybrid and multicloud AI/ML capabilities are nearly five years behind their peers.

Note: TDWI’s editors carefully choose press releases related to the data and analytics industry. We have edited and/or condensed this release to highlight key information but make no claims as to its accuracy.

A new report from Domino Data Lab reveals that although data science is now a key revenue and innovation engine for the enterprise, most enterprise data and analytics leaders are inadequately resourced to deliver on what business leadership wants from AI and ML.

The survey of 100 chief data officers (CDOs) and chief data analytics officers (CDAOs) at companies with over $1B in annual revenue, "Build A Winning AI Offense: C-Level Strategies for an ML-Fueled Revenue Engine," was conducted by Wakefield Research. It paints a stunning picture of the mounting revenue expectations put on these leaders and their teams, the organizational imbalances data execs say their leadership must correct, and the toll that underfunded, understaffed, and under-governed data science practices take at many large organizations.

Data Science Teams Unprepared to Deliver on AI/ML Innovation Despite Corporate Revenue Expectations

Under pressure, the majority (67%) of CDOs and CDAOs are shifting their organization's data posture from defensive (i.e., data management, compliance, governance, and BI modernization) to offensive (i.e., driving new business value with analytics, ML, and AI applications). As such, it's no surprise that 95% say their company leadership expects investments in AI and ML applications will result in increased revenue.

Yet, though business leaders increasingly look to data science to be a key revenue engine and a driver of innovation, resources such as budget, people, and preparedness are not aligned with these corporate priorities. Indeed, data science is not funded to live up to leadership expectations -- less than a fifth (19%) say their data science teams have been provided sufficient AI and ML resources to meet leadership's expectations for a revenue increase.

"Data science executives need proper resources, empowerment, and support to achieve revenue and transformation goals," said Nick Elprin, co-founder and CEO of Domino Data Lab. "Boards and the full C-suite must invest in CDOs and CDAOs and put them in charge of people, process, and AI/ML technologies or risk existential competitive pressures."

CDOs and CDAOs Are Ready to Take the Reins and Budget

Many CDOs and CDAOs believe they play second fiddle to IT on a variety of AI/ML issues.

  • Nearly two-thirds (64%) say IT makes most data science platform decisions at their company.
  • Virtually all CDOs and CDAOs (99%) agreed that it is difficult to convince IT to focus their budget on data science, ML, and AI initiatives rather than traditional IT areas, such as security, governance, and interoperability.
  • More than three-quarters (76%) of CDOs and CDAOs see driving new business results with AI/ML as at least one of their top three priorities for 2023.

Unleashing the Full Potential of Data Science: Overcoming Pain Points Beyond Funding

People, process, and technology are critical pain points that data executives believe stand in the way of their outperforming competitors with data science. To build a winning data analytics offense, CDOs and CDAOs believe that their organization must not only modernize their internal team structures and elevate the roles of CDO and CDAO but also gain centralized support.

  • They are nearly unanimous (99%) in saying that centralized support was mission-critical for their organization's data science, ML, and AI initiatives, such as developing or expanding a center of excellence, or implementing common data science platforms.
  • Almost all (98%) said the speed at which companies can develop, operationalize, monitor, and continuously improve AI and ML solutions will determine who survives and thrives amid persistent economic challenges.
  • Though AI innovation is at a premium across industries, teams are flying blind and struggle to measure AI/ML impact; Four in five (81%) say their teams' current toolsets are less than fully capable of measuring the business impact of AI/ML.

Lagging Capabilities Result in AI Risks with Negative Impact Today

  • Respondents unanimously said their organizations have experienced negative consequences due to challenges developing and operationalizing their data science models and AI/ML applications; 43% have lost business opportunities and 41% admitted they have made poor decisions based on bad data or analysis.
  • Forty-four percent of CDOs and CDAOs believe failure to properly govern their AI/ML applications would mean losing $50 million or more.
  • Shockingly, despite high awareness of the risks, 46% of data execs say they do not have the governance tools needed to prevent their data scientists from creating risks to the organization.

The AI/ML Divide Is Real and Growing

In today's climate of rapidly rising data sovereignty regulations, hybrid and multicloud capabilities for training and deploying models wherever the data resides are more important than ever. The study revealed just how important those capabilities are, and how fast the divide between companies is growing. Companies without AI/ML platforms enabling hybrid and multicloud model training and deployment were found to lag behind those that have them by an average of five years.

To access the full study: (short registration required).

The Domino Data Lab survey was conducted by Wakefield Research ( among 100 U,S, chief data officers or chief data analytics officers at companies with over $1B annual revenue, between December 5th and December 18th, 2022, using an email invitation and an online survey. The margin of error for the study is +/- 9.8%.

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