Why 44% of GRC Programs Are Still Stuck in AI Pilot Mode

There is no shortage of AI enthusiasm among GRC teams. As AI tools became available, organizations rushed to explore how it might simplify governance, risk and compliance. But nearly half quickly hit a wall when it came to implementation, according to our 2026 Benchmarking Report. Forty-four percent of the GRC professionals surveyed say their current stage of AI adoption is in the experimental or AI pilot phase. 

Why GRC Teams Are Stuck in AI Pilots 

Many GRC teams show high interest in AI features, task automation and agentic automation, but alignment failure and trust barriers limit mature adoption. In fact, only 13.5% of organizations have fully integrated AI into their core GRC processes, while16.7% say they don’t use the technology at all. 

But the stalled rollout is far from a technological failure. Here are the most common barriers that keep governance, risk and compliance teams stuck in AI pilot mode.

Data Privacy Concerns

The primary factor slowing broader AI adoption in GRC is data privacy. Among the 126 GRC professionals surveyed, 28.6% cited data privacy as the top barrier limiting deployment. And it’s no surprise, given the role GRC teams play and the data they handle.

Compliance data, vendor records, audit trails, internal policies and risk assessments are sensitive by nature. Most teams cannot confidently deploy AI systems on sensitive data without clear visibility into how providers store, process, retain and protect it. This is a security and governance imperative.

Regulatory frameworks, such as GDPR and HIPAA, already set strict expectations on how your organization handles sensitive data. Any AI tool touching compliance workflows has to meet the same requirements to maintain compliance.

Accuracy and Hallucination Risks

In GRC, accuracy is important for informed decision-making. The phenomenon of AI agents hallucinating (generating false or nonsensical information presented with conviction) is a huge concern. Over a quarter (25.4%) of IT and security professionals admit they are hesitant to adopt AI due to inaccurate outputs.

Unlike general AI such as a customer service chatbot, where you can easily flag inaccuracies, fabricated information from a specialized GRC AI could have significant consequences. For instance:

• Inaccurate risk assessments can make your team prioritize the wrong threat while overlooking high-risk issues
• Inaccurate control recommendations can leave compliance gaps that increase regulatory and operational risk
• False citations of regulations and industry standards can lead to noncompliant policies and procedures
• Fabricated audit documentation or unsupported conclusions can compromise audit integrity and create issues during regulatory reviews
• Inaccurate compliance reports can misrepresent your organization’s regulatory status, leading to potential enforcement actions
• Poor executive decisions based on inaccurate risk and compliance data can result in resource misallocation and increased exposure to risk

Unclear Return on Investment

The difficulty in showing direct financial proof of value from AI in GRC is a significant obstacle to broader adoption. Most GRC leaders continue to push for productivity gains, but the financial case hasn’t caught up yet. In fact, 83.3% of organizations admit they have yet to show a clear financial ROI from their AI investment in GRC. Only 16.7% report financial ROI.

The mismatch between leadership’s expectations and what GRC teams can prove keeps most teams under pressure to justify continued AI investment in GRC. The earliest benefits tend to be operational, with organizations reporting reduced business cycle time (25%) and higher task throughput (20.7%) rather than immediate cost-cutting. Until the financial case catches up, GRC leaders will likely need to make the operational case instead.

Manual Work Delays Responsible AI Rollout

GRC teams are spending too much time on manual work, which leaves little bandwidth for the work responsible AI rollout requires. The response from practitioners is telling of the most time-consuming manual tasks:

• 25.9% of respondents cited manual evidence collection and documentation
• 19.8% pointed to risk assessments
• 16.5% highlighted third-party reviews

Responsible use of artificial intelligence in GRC requires time for governance, validation, testing, policy development and ongoing oversight. Many teams lack that capacity because routine governance and compliance work already consumes much of their day, leaving little time to properly vet and deploy AI agents to perform the same work.

What Separates Programs Scaling AI From Those Still Piloting

Many organizations are investing in or experimenting with AI in GRC, but 43.7% say they have not yet seen returns. An additional 15.1% of GRC professionals are unsure whether they are receiving value from AI, while 24.6% say they have received limited ROI.

But among teams that scale AI, value shows up operationally before it shows up financially. Here are some practices common in GRC programs that move past AI pilot mode.

A Connected Data Foundation

Fragmented data is a common reason most organizations’ GRC programs are stuck in pilot mode. Yet, only 15% of organizations describe their data as mostly integrated. Sixty-five percent say their data is moderately integrated, and 20% admit fragmentation across teams and systems. 

Disparate data can limit your organization’s ability to scale. GRC programs that scale AI connect their frameworks, evidence repositories, vendor data and compliance data first, so AI has consistent, centralized information to work from.

Governance Built Before Deployment

More than a third of respondents (35.7%) say their organization has a clear governance framework for AI use in GRC, while another 34.1% have governance in progress. The remaining 30% rely on informal guidance or no governance at all.

GRC teams that move past AI experimentation treat governance as groundwork before handing off tasks to AI agents. This approach helps them define:

• Who’s accountable for AI outputs
• How the team validates results
• Where human review is non-negotiable

Artificial intelligence becomes a governed part of GRC operations rather than an unmanaged source of risk.

Operational Wins as the Early Proof Point

Among organizations that report AI ROI in GRC, the value shows up operationally first:

• 25% cite reduced business cycle time
• 20.7% report higher throughput
• 19% report improved decision-making

Financial returns often take longer to materialize, so organizations that scale AI demonstrate early value through operational gains before quantifying the financial impact. The same progression plays out in other AI deployments, such as IT support, where AI agents handling ticket triage deliver measurable throughput gains before anyone can prove hard cost savings.

Clear Priorities for Justifying Further Investment

GRC teams that secure continuous AI investment tend to align adoption with the operational value leadership already wants to see. Almost a third (28.8%) of respondents report wanting faster audits and reviews, 24.8% want clearer visibility into top risks, and 22.4% cite a desire for more time spent on data analysis instead of documentation. 

GRC professionals who move past AI experimentation frame their initiatives around specific operational outcomes to help justify greater AI investment to leadership.

Weigh Your Organization’s AI Maturity Against Industry Peers

Most GRC teams stuck in AI pilots aren’t behind because they lack interest in AI or have technological issues. For the most part, a stalled AI pilot is an alignment problem. 

At Onspring, we offer an AI maturity assessment to help you understand where your AI program stands. We’ll help you:

• Measure your organization’s current AI maturity score
• Identify your maturity stage
• Benchmark your implementation progress against peer organizations
• Receive customized recommendations and practical next steps based on your level

Start your assessment today and see how your organization’s AI maturity compares to industry peers.