Agentic AI has moved from experiment to operational dependency, and it has quietly rewritten the enterprise’s requirements for how fast data must move, according to a report from Solace.
This is based on on a survey of 623 senior technology decision-makers at $1 billion-plus enterprises across the United States, United Kingdom, China, Germany, France, Japan, Australia and Canada.
IDC conducted a custom survey, commissioned by Solace, in June 2026.
Survey respondents deemed as leaders in real-time data maturity report roughly 23% average annual gains in areas like speed to market, risk reduction and the ability to sense and respond to change quickly.
“Every enterprise has the same AI models. The winners are the ones whose agents run on trusted, real-time data, and the numbers back it up. When data moves in real-time, AI delivers. When it doesn’t, AI fails to create sustainable impact,” said Paul Fitzpatrick, chief marketing and business development officer at Solace.
The findings make clear why real-time data has become the make-or-break factor for enterprise AI.
Real-time data and agentic AI work in lockstep as 80% of respondents stated they are investing in or already running AI agents, and 90% say they have increased their focus on real-time data to support their agentic AI plans.
When asked what is driving the growing need for real-time data, respondents named advances in agentic AI as the single biggest force re-shaping their data priorities, ahead of security, cost, and regulatory pressure
The hardest part of AI is connecting agents to trusted data as 40% of organizations reported that connecting AI agents in real-time to reliable enterprise data is among the top obstacles to putting them into production, not actually building the agents.
Data quality and consistency (46%) ranked as the number one technical challenge overall, cited even by the most advanced organizations
Success comes through operating practices, not budget or headcount. The study found that real-time readiness and AI success barely move with company size, and vary only modestly by industry.
What consistently separates leaders is how they work as 76% say they run real-time data across most or all of their operations, compared to only 7% among emerging organizations.
Leaders also standardize on a single unified platform rather than a patchwork of loosely connected tools, and put real-time talent directly into the teams that use it
The payoff compounds as agentic AI matures – the study reveals a clear roadmap: as organizations advance through the maturity tiers, agents in production and AI project success climb together.
Leaders roughly triple both, with agents deployed in production rising from 20% at emerging organizations to 59% among leaders, and the share of AI projects delivering measurable outcomes rising from 35% to 67%.
Satisfaction is high across the board, and investment is accelerating as 90% of organizations said their real-time data investments have met or exceeded expectations. Nearly all – 94% to 97% – plan to increase spending on both real-time data and agentic AI.
“The survey shows real-time data moving from a technical concern to a strategic one across the board,” said Carlos M. González, research manager at IDC.
“Organizations are citing agentic AI as the single biggest force driving their need for real-time data,” said Gonzalez. “That reordering of priorities tells you real-time data is becoming foundational infrastructure for AI, not an analytics feature.”
