OVERLAND PARK, Kans. — OVERLAND PARK, Kansas — July 8, 2026 — Onspring, a leading provider of integrated GRC software, today released its 2026 GRC Benchmarking Report. The study examines how cross-functional governance teams are balancing rapid AI adoption with data trust and compliance obligations in a highly complex risk landscape. The report provides a snapshot of where AI in GRC stands today and what is hindering organizations from moving from experimentation to trusted execution.

Based on insights from GRC practitioners across cybersecurity, risk, compliance, finance, and legal counsel, the report maps AI maturity against the backdrop of day-to-day administrative burdens. The findings reveal that while teams are highly eager to leverage AI to relieve operational friction, a lack of deep data integration, strict privacy boundaries, and output inaccuracies are stalling full-scale deployment across the enterprise.

"Over the past year, corporate leadership has demanded massive productivity gains through AI, but governance teams are discovering that you can’t scale automated tools on top of broken, siloed processes," said Michelle Randall, Chief Marketing Officer at Onspring. “GRC practitioners are naturally moving with extreme caution around AI. The bridge between experimentation and practical execution requires first solving the underlying workflow drag, unifying fragmented data and disconnected systems.”

Key Trends and Takeaways

• AI interest is exceptionally high, but maturity is behind: While AI dominates business conversations, it is isolated in practice. Nearly 45% of organizations report that AI is strictly in the experimental or pilot phase, while an additional 16.7% are not utilizing the technology at all. Only 13.5% have successfully embedded AI across their core workflows.
• Trust barriers directly dictate AI expansion limits: The primary friction points slowing broader adoption are fundamentally rooted in security and accuracy. Practitioners cite data privacy concerns (28.6%) and output accuracy or hallucinations (25.4%) as the top boundaries limiting AI deployment.
• Manual work leads as the clearest pain point: Repetitive operations are a severe drain on internal efficiency. Respondents identified evidence collection and documentation (25.9%), risk assessments (19.8%), and third-party reviews (16.5%) as the most time-consuming activities in their programs, pulling professionals away from higher-value data analysis.
• AI ROI is underdeveloped: Direct financial proof of value has yet to catch up to market investments, with only 16.7% of respondents reporting demonstrable financial ROI. However, early value is proving to be operational first, showing up clearly through reduced business cycle times (25%) and higher task throughput (20.7%) rather than immediate cost-cutting.

The findings point to an urgent need for organizations to transition toward connected GRC operations, where compliance frameworks, vendor ecosystems, and evidence repositories are centralized. As corporate risk profiles become more variable and dynamic, managing multiple frameworks across disconnected spreadsheets is no longer viable. Instead, companies must establish clear internal data preparation and automated safeguards to successfully unlock the promise of enterprise AI.