At the 2026 DataIQ Boston Peer Exchange, 40 senior data and AI leaders gathered for a discussion about turning AI investment into measurable business outcomes. Randy Bean opened with findings from his 2026 AI and Data Leadership Executive Survey, setting up a theme that ran through the sessions that followed: data and AI leadership is moving from providing insight towards taking greater responsibility for decisions and outcomes.
Randy told the room that when his survey began tracking the role in 2012, just over 10% of organisations had appointed a Chief Data Officer. Today, he said, that figure is 90%; but becoming established does not mean becoming secure.
Randy described senior data leaders being displaced as organisations decide where AI should sit. CIOs, CDOs, CAIOs, and business functions all potentially have a claim, while AI gives individual functions greater opportunity to build capabilities for themselves.
Asked what those executives are really fighting over, Randy stated: “Power and survival.” The lesson is that owning data alone may no longer provide sufficient strategic authority when the organisation is reorganising around AI.
Business value is the CDO’s licence to operate
What separates a successful data and AI leader in this environment? For Randy, the starting point is the ability to connect data and AI directly to the business. “If you don’t have a path to measurable business value from your data and AI investments, maybe you should go back this afternoon and shut them down,” he said.
That means being able to articulate the purpose of investments without retreating into the vocabulary of the data function.
Randy recalled a CDO asking for $15 million for MDM. The business response was that they did not know what it was and that the CDO should not return until they could explain it in business terms. The example is a reminder to communicate clearly. If data and AI leaders want responsibility for AI outcomes, they need to demonstrate that they understand the commercial problem before advocating the technical solution.
Redesign the work before inserting AI
Operationalising AI means resisting the temptation to place new technology on top of an unchanged process. That requires data and AI leaders to determine which elements of a workflow genuinely benefit from AI and then redraw decision rights between humans and machines accordingly.
Employees need general AI literacy in addition to understanding how AI changes the job they are doing: when to use it, when to question it, and how it fits into the wider workflow.
Reskilling and incentives go together because organisations cannot expect widespread adoption simply because an AI capability has been made available, reinforcing Randy’s observation that technology is not the principal obstacle. More than 90% of respondents to Randy’s binary survey question identified people, process, culture, and change, rather than technology limitations, as the primary impediment to data and AI adoption.
The CDO mandate is moving closer to the decision
The discussion Randy opened in Boston does not lead to a neat answer about whether the CDO, CIO, or CAIO should control AI. While some leaders argued that corporate culture and the way the roadmap is developed matter more than the reporting line, Randy showed just how contested those reporting lines remain.
CDOs have spent years establishing governance, platforms, products, and foundations. AI gives them an opportunity to connect those capabilities directly to how their organisations make decisions. That requires authority earned through measurable outcomes rather than ownership: fewer pilots, reusable platforms, explicit business owners, clear baselines, redesigned workflows, defined decision boundaries, and named accountability.
It was the challenge at the heart of Randy’s opening contribution: AI has made data more strategically important. The harder task for data leaders is turning that importance into business impact.
