Agentic AI adoption seen reaching consumer scale in 2026
Agentic AI is moving beyond enterprise pilots and could reach consumer mass-market adoption as early as 2026, according to a global survey of technology leaders conducted by IEEE. “The Impact of Technology in 2026 and Beyond: an IEEE Global Study” surveyed 400 CIOs, CTOs, IT directors, and other technology leaders in Brazil, China, India, Japan, the U.K. and the U.S. at organizations with more than 1,000 employees across multiple industries.
For eeNews Europe readers, the findings provide insight into how agentic AI may influence system design, data infrastructure, and skills demand across industries that rely on embedded intelligence, automation, and large-scale data processing.
Agentic AI use extends from enterprises to consumers
The IEEE survey suggests that 96 percent of respondents expect agentic AI innovation and adoption to continue at a rapid pace in 2026. While much of the current focus remains on enterprise use cases, respondents also anticipate widespread consumer-facing applications.
According to the study, agentic AI is expected to be adopted by consumers for tasks such as personal scheduling, data privacy management, health monitoring, and automated errands. These systems are described as operating independently once assigned a task, while still requiring human validation, a distinction that may influence how such tools are integrated into safety-critical or regulated environments.
At the same time, 91 percent of respondents believe increased reliance on agentic AI will drive higher demand for data analysts, as organizations seek to verify outputs, manage transparency, and identify potential vulnerabilities in AI-generated results.
Skills demand and technology spillover effects
The survey indicates that ethical AI practices and data analysis are among the most sought-after skills for AI-related roles in 2026, alongside machine learning and data modelling. This shift may reflect growing concern around governance, accountability, and explainability as agentic AI systems take on more responsibility.
Beyond workforce impacts, respondents expect AI to influence a range of adjacent technology areas. Robotics is identified as the most affected field, followed by extended reality and autonomous vehicles. In this context, humanoid robots are increasingly viewed as a longer-term workplace presence rather than a novelty, potentially shaping requirements for sensing, control electronics, and embedded compute.
Industry sectors expected to see significant AI-driven change include software, financial services, healthcare, and automotive and transportation, all areas with strong relevance to European system designers and integrators.
Infrastructure and governance challenges remain
Despite optimism around adoption, respondents remain cautious about infrastructure readiness. Nearly half of those surveyed believe it could take five to seven years to build sufficient global data centre capacity to support growing AI workloads, with others expecting even longer timelines.
Governance also remains a differentiator. Survey participants reported stricter controls when deploying third-party AI systems compared with internally developed tools, suggesting that provenance and system ownership continue to shape AI deployment strategies.
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