ORGANIZATIONS with more mature real-time data practices are more likely to deploy artificial intelligence agents and report measurable results from AI projects, according to a study by International Data Corp. (IDC).
The IDC Real-Time Data Study, conducted in June 2026, surveyed 623 senior technology decision-makers from enterprises with at least $1 billion in revenue across eight countries. Respondents were concentrated in manufacturing, financial services and retail, with 78 percent identified as primary decision-makers for real-time data initiatives and 71 percent responsible for AI or agentic initiatives.
The study found that 80 percent of respondents were investing in or had already deployed agentic AI, while 90 percent had increased their focus on real-time data to support agentic AI. Eighty-five percent said real-time data was mission-critical and 84 percent considered it strategically important.
“Agentic AI has moved from experiment to operational dependency,” IDC said, noting that the shift has changed enterprise requirements for how quickly data must move.
IDC grouped respondents into four real-time data maturity tiers — Emerging, Developing, Advanced and Leaders — based on a composite score covering real-time data deployment, agentic AI adoption and measurable outcomes.
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AI MATURITY The hardest technical problems are about the data itself: Data quality and consistency lead the list, even among the most mature organizations. AI-GENERATED GRAPHICS
The share of organizations with several AI agents already in production rose from 20 percent among Emerging organizations to 59 percent among Leaders. The proportion reporting measurable outcomes on at least half of their AI projects increased from 26 percent among Emerging organizations to 89 percent among Leaders.
“The maturity climb is not academic,” IDC said, adding that Leaders convert real-time data investment into better AI results and business performance.
Advances in agentic AI capabilities were the leading driver of increased demand for real-time data, cited by 43 percent of respondents. Security and AI trust requirements followed at 40 percent, cost and scalability improvements at 38 percent, and regulatory or compliance pressures at 35 percent.
Connecting agents to real-time enterprise data was cited by 40 percent of respondents as a top barrier to scaling AI agents, tying with managing agents across different frameworks. Monitoring agent performance and accuracy followed at 37 percent.
“The challenge in building successful agents is connecting them to data,” IDC said.
Data quality and consistency were the leading technical challenge for real-time data initiatives over the next 12 to 18 months, cited by 46 percent of respondents. Latency and performance constraints followed at 40 percent, while governance, security and permissions were cited by 39 percent.
“Data quality is the top technical barrier to real-time data initiatives,” IDC said.
The study found that company size had little effect on real-time data maturity or AI success. Maturity scores ranged from 50 to 53 across companies with 500 to more than 10,000 employees, while the percentage of AI projects producing outcomes ranged from 46 percent to 51 percent.
Solace, which sponsored the InfoBrief, describes itself as an enterprise technology company providing a real-time data platform. Its platform includes event mesh, stream data processing, agentic processing and data access capabilities.
The study also found that organizations remain divided in their assessment of agentic AI. Eighty percent said it was changing the way they work, 79 percent said it was making the world a better place, while 71 percent said agentic AI was overhyped.