CEOs Forecast AI-Driven Operational Capability Overhauls

Gartner's CEO and Senior Business Executive Survey of 469 CEOs and senior executives, conducted across three quarters and ending in the fourth quarter of 2025, found that 80% expect AI to drive a medium to high degree of change in operational capabilities, shifting emphasis from "digital business" to what Gartner calls autonomous business. The survey reports that 54% of respondents said their current automation is limited to specific tasks; by the end of 2028 only 13% expect to remain at that level, 32% expect to use self-learning and adaptable AI to support human decision-making, and 27% expect organisations to operate primarily without human intervention. The report also states that 28% of CEOs see transactional revenue as most at risk from AI and that 17% expect significant change to their customer base. Don Scheibenreif is quoted saying, "Autonomous business is a strategy where self-learning software agents and machine customers make decisions, take action and create new types of value for organizations." David Furlonger is quoted saying, "CEOs are realizing that AI is not simply another layer of automation. It is a catalyst for rebuilding the enterprise itself."

Industry-pattern observations: organisations moving toward autonomy typically increase investment in data infrastructure, model lifecycle tooling, and runtime governance. These capabilities are necessary to support continuous learning systems and to manage drift, reliability, and safety at scale. Observers note that a shift from task-level automation to self-learning agents usually raises integration complexity across legacy systems, monitoring requirements, and the need for explainability controls.

Editorial analysis: the survey's mix of expectations-substantial numbers anticipating human-in-the-loop augmentation alongside a sizable minority expecting primarily autonomous operation-matches prior market transitions where heterogeneous adoption paths coexist. Historically, such mixed adoption creates a multi-track transformation: some units adopt adaptive AI to augment decision-making while others pilot fully autonomous workflows in narrow domains. The reported risk to transactional revenue aligns with public examples where automated procurement, pricing engines, and agentic negotiation reduce intermediary roles.

• •CEO and board-level capability statements and budgets for data platforms, model ops, and runtime governance, because these areas enable the autonomous features cited in the survey.
• •Pilot scope and metrics, to see whether organisations are pursuing narrow autonomous agents or broader cross-functional autonomy.
• •Vendor partnerships and procurement trends for prebuilt agent frameworks versus in-house model development, which will shape operational dependencies.

Editorial analysis: teams preparing for broader autonomy should prioritise stable data pipelines, reproducible model training, and production monitoring; organisations taking a capabilities-first approach will likely need to formalise interfaces between human workflows and automated agents. Practitioners should also watch how revenue-model experiments and machine-customer interactions are instrumented, since the survey highlights transactional revenue exposure.