Alteryx survey: 96% of data and IT pros use AI, but frequent use and real-time decisioning lag

An Alteryx survey of 700 data analysts and 700 IT leaders, reported May 21, 2026, found that 96% of respondents say they use AI in their work, yet only 49% do so always or most of the time-underlining a gap between broad adoption and frequent, integrated use. That disparity matters because it highlights where organizations are still dependent on human — led processes and where investments must focus to move AI from occasional assistance to everyday decision support. Validating AI outputs is emerging as a critical, career — defining skill in this transition.

Agentic AI is on the near-term agenda: 59% of respondents predict they will actively employ AI agents within the next 12 months. In production today, the most common agentic uses are drafting standardized communications or summaries (59%), scheduling or routing workflow tasks such as alert triage and process automation (54%), and generating reports or dashboards without manual intervention (48%).

Survey participants also report agentic systems performing monitoring and alerting (45%), cleaning, preprocessing or validating routine data sets (45%), running routine statistical analyses or basic predictive models (34%), and automatically generating insights or recommendations from data (23%). Roughly half of respondents indicated a willingness to grant agents broad data access, while 44% said that human oversight is critical before such access is allowed. Current tooling and established workflows continue to shape how AI is deployed: 61% of respondents rely on spreadsheets for foundational data work, 56% use business intelligence tools, and 51% use dedicated data preparation platforms. Those entrenched tools help explain why many organizations struggle to shorten decision cycles.

Decision — speed capabilities remain limited: only 20% of organizations can move from analysis to a business decision within a few hours, and just 5% report true real-time decision — making ability. Foundational data preparation is time-consuming as well-respondents reported spending close to six hours per week on cleaning and prepping data, with 48% spending six to 10 hours weekly. Additionally, 45% said AI-related data preparation and validation can amount to about 10 hours a week.

For builders and platform teams, the survey highlights priorities that go beyond raw model capability: better explainability and provenance, stronger data-cleaning and integration tooling, clearer ownership and governance pathways, and mechanisms for agent access controls and human — in-the-loop oversight. Addressing latency and improving observability will be essential for organizations seeking to move from periodic, labor — intensive decisioning to near-real-time, agent — enabled operations.