Only 16.7% of Organizations See ROI from GRC AI. Here’s Why.

AI adoption is accelerating across GRC programs. According to Onspring’s 2026 GRC Benchmarking Report, 44.4% of GRC teams are experimenting with AI, 25.4% are using it for specific GRC tasks and 13.5% have already embedded it throughout their workflows. 

However, widespread use of GRC AI hasn’t translated into widespread success. The same report revealed that only 16.7% of organizations achieve measurable AI ROI. This gap between adoption and return on investment comes from how GRC teams use artificial intelligence, not from AI limitations.

Why Most GRC AI Investments Fail To Deliver ROI

Our report found that 43.7% of organizations don’t see returns from their GRC AI investments, while another 15.1% aren’t sure whether they’ve achieved measurable ROI. When organizations fail to measure AI ROI effectively, they miss critical insights into their technology’s true impact. Here’s why most AI deployments in GRC fail.

Tech-First Implementation

Organizations that adopt GRC AI because it’s the latest technology are less likely to achieve meaningful results. They often deploy it without assessing their current AI maturity or understanding the problem it solves or the outcomes it should achieve. Measuring ROI becomes difficult because there’s no clear benchmark for success. 

Poorly Defined Success Metrics

Most governance teams still struggle to define KPIs that measure AI’s value beyond cost savings and revenue growth. As a result, they may conclude that GRC AI isn’t delivering ROI when it’s improving operational efficiency, such as reducing manual work.

When measuring AI ROI, some organizations also track vanity metrics. For example, AI adoption rate among your GRC team might seem like a good KPI. But it doesn’t show business outcomes that justifies investing in the technology.

Fragmented Systems and Data

When GRC data is fragmented across departmental systems, it’s difficult to scale GRC AI throughout your enterprise. With AI-driven solutions limited to just a few use cases, it’s harder for technology to enable broad operational improvements that drive business value. 

The Operational Foundations Required Before AI Can Generate Returns

Adopting powerful technology isn’t enough to achieve measurable AI ROI in GRC. You need to build a foundation that enables artificial intelligence to work effectively. Here are the key elements to consider.

Standardized Processes

About 70% of GRC professionals agree that AI has the greatest impact when it handles recurring, administrative GRC operations. Organizations at higher AI maturity levels have standardized GRC processes that allow them to create repeatable workflows that AI can automate. 

High-Quality Data

GRC AI is only as good as the data it accesses and analyzes. For example, outdated records, inconsistent naming conventions or missing evidence make AI-generated recommendations unreliable in GRC programs. To enhance the quality of your GRC data, it should be:

• Accurate: Eliminate errors before feeding data into your GRC software so that AI can generate reliable insights. 
• Complete: Fill any information gaps in your GRC records to give AI the full context it needs to inform decisions or perform tasks. 
• Up to date: Keep GRC records current so AI bases decisions or recommendations on the latest information. 

Defined Ownership and Accountability

AI simplifies and speeds up GRC work, but it shouldn’t replace human accountability. Organizations still need to determine who handles each GRC process, decision or outcome, even when AI automations are involved.

Clear ownership ensures AI-generated outputs are reviewed and validated before teams can act upon them. Human oversight is especially important for high-impact decisions involving regulatory requirements. 

Connected GRC Systems

When audit, compliance and risk management departments store GRC data independently, they create silos that make it difficult for AI to access all the relevant details it needs to generate reliable insights. Through system integration, you can connect monitoring  tools to a centralized platform like Onspring. 

In a unified GRC system, AI has a comprehensive view of risks, controls, policies, audit findings and compliance activities. This improves the usefulness of AI recommendations while enabling automation across multiple workflows instead of isolated tasks, which is essential for achieving enterprise-wide AI ROI.. 

How To Use AI in GRC for Measurable Business Outcomes

You don’t need to overhaul your GRC workflows to achieve measurable AI ROI. Instead, use AI in areas where it simplifies work, speeds up processes and helps teams make data-driven decisions. The following steps can help you use GRC AI and get measurable returns. 

1. Create Governance Rules Regarding AI Usage

Before deploying GRC AI, establish guidelines for its use. This may include: 

• Defining accountability structures so you always know who is responsible when GRC AI influences decisions
• Building oversight mechanisms into AI workflows to ensure key decisions undergo thorough review and approval

With AI governance policies, it’s easy to scale adoption throughout your organization. Widespread AI use in a business increases the likelihood that the technology will generate meaningful results and demonstrable AI ROI. 

2. Automate Recurring, Time-Consuming GRC Tasks With AI

GRC teams still spend a lot of time on repetitive tasks, such as evidence collection and risk assessments. AI systems can handle these tasks on your behalf, reducing manual work and increasing employee productivity.

They can automatically gather information from multiple sources, validate records to prepare them for review, assess risks and summarize documents. Instead of spending time on operations you can automate with AI, your team focuses on other important work such as improving internal controls. This shift in focus often results in significant cost savings as teams redirect effort toward strategic initiatives.

3. Train Your GRC Teams to Interpret and Use AI Output

GRC AI delivers value only when your team understands its insights and knows how to act on them. To make that happen, train employees to interpret AI outputs and recognize the technology’s limitations. Training gives your staff confidence to use AI-supported workflows, which enables GRC leaders to assess how artificial intelligence affects daily GRC operations. 

4. Measure AI ROI in GRC

According to Onspring’s report, organizations that see ROI from GRC AI measure it through operational improvements. Understanding the right ROI calculations helps teams track what matters most.

• Reduced cycle time (25%): Tracks the percentage decrease in the time required to complete a process from start to finish
• Higher throughput (20.7%): Measures the ability to do more with less, such as completing more GRC compliance reviews or risk assessments quickly and efficiently without increasing headcount
• Better decision-making (19%): Shows up, for example, as more accurate insights that help GRC teams prioritize risks and respond to incidents faster

Improving GRC With Trustworthy AI

Most organizations fail to achieve meaningful ROI from their GRC AI due to implementation and data quality issues. Instead of pursuing enterprise-wide AI adoption all at once, identify specific use cases that solve GRC problems and then measure results using operational KPIs. 

Additionally, use GRC software with AI features that can drive ROI. For example, Onspring’s AI saves time and reduces manual GRC work by automating tasks such as evidence collection, third-party review, risk assessments and cross-departmental compliance verification. 

Because agentic AI is available throughout Onspring’s GRC software, you can use it anywhere on the platform. Additionally, it automates workflows based on the rules you define, so you decide when and how AI operates in your GRC program. This makes it easy to maintain control and human oversight.  

Find out if using GRC AI at scale is your next step. Take our 5-minute AI maturity assessment to see how your business compares against its peers and identify the next steps for expanding GRC AI across your organization.