Governance, Risk, and Compliance (GRC) teams are being asked to do more than ever. Achieving greater GRC efficiency means contending with more frameworks, vendors, and regulatory changes, with rarely any extra headcount to absorb them.
AI may look like the obvious answer to that pressure, at least on paper. But while leadership sees productivity gains within the quarter, your team sees a tool that has to be trusted with audit trails. Neither view is wrong, but the space between the benefits and the concerns is where many GRC programs are currently stuck.
Our 2026 GRC Benchmarking Report surveyed practitioners across cybersecurity, risk, compliance, finance, and legal to understand how teams are managing that balance. The findings point to a problem sitting beneath the entire AI conversation: how much of your week disappears before AI even steps in.
Key Takeaways
- GRC teams face an efficiency gap, spending much time on documentation instead of core tasks, while AI adoption raises concerns about data privacy and accuracy.
- Key activities consuming time include evidence collection, risk assessments, and third-party risk reviews, all relying on external responses.
- To close the GRC efficiency gap, organizations should prioritize data management and standardized processes before implementing AI solutions.
- Caution surrounding AI adoption stems from fears of inaccurate outputs affecting compliance, highlighting the need for integrated data management.
- GRC efficiency improves with connected processes, enabling better decision-making and greater time spent on analysis rather than documentation.
Table of Contents
- What Is the GRC Efficiency Gap?
- Where Does GRC Efficiency Get Lost Each Week?
- Why Are GRC Teams Cautious About AI Adoption?
- Why Does AI Fail on Fragmented GRC Workflows?
- 3 Things GRC Practitioners Expect AI to Do
- How Do You Close the GRC Efficiency Gap at Your Maturity Level?
- Narrow Your GRC Efficiency Gap With Onspring
What Is the GRC Efficiency Gap?
The GRC efficiency gap is the distance between the work your team was hired to do and the work that actually fills the calendar. You hired analysts to evaluate exposure, conduct risk identification, and recommend action. You use internal audits to test controls and question assumptions. Yet most of your team spends the bulk of their workweek chasing documents instead.
The gap shows up in familiar ways. Your team barely meets deadlines, and reports go out late. Nobody has time to ask whether your enterprise risk management program is working, only whether the paperwork is finished.
That is not a performance problem, and no amount of individual effort closes it. What makes the gap harder to ignore is that it widens on its own. Every new regulation comes with requirements and acquisitions that add systems not designed to talk to yours.
On top of this, headcount often doesn’t grow at the same pace as the work. So the same team absorbs more frameworks, assessments, and evidence requests each year, using roughly the same number of hours as before.
That is why efficiency has become a governance issue more than an operational one. When your team is stretched thin, the first thing to go is the judgment work that keeps your program credible.
Where Does GRC Efficiency Get Lost Each Week?
In our latest GRC Benchmarking Report, practitioners told us where their working hours go each week Three activities dominate the list:
- Evidence collection and documentation, named by 25.9% of respondents as their most time-consuming activity
- Risk assessments, at 19.8%
- Third-party risk reviews, at 16.5%
Each of these processes is essential, repetitive, and dependent on someone else responding to a request.
That dependency is the real cost. Your team is left waiting on a vendor to return a questionnaire, a system owner to produce a screenshot, or a business unit to confirm whether a control owner still works there.
GRC efficiency doesn’t vanish in one large block. It leaks out in small waits that never appear on anyone’s timesheet.
Meanwhile, the work that genuinely protects the organization, such as testing internal controls properly, conducting risk scoring, and questioning whether a risk rating still reflects reality, keeps sliding down the list.
Why Are GRC Teams Cautious About AI Adoption?
When GRC teams hesitate on AI, some may call them slow adopters. But the data suggests good reason for caution. Practitioners named data privacy (28.6%) and output accuracy (25.4%) as the top barriers limiting deployment.
Think about what your team handles daily: audit trails, vendor contracts, incident records, and control evidence. Any tool touching that material has to meet the same information security data protection standards regulations hold you to.
Accuracy carries equal weight. A consumer chatbot that invents an answer creates an annoyance. But artificial intelligence that invents a control mapping or cites a regulation incorrectly can create compliance violations. Caution here is professional judgment and deserves to be treated that way.
Why Does AI Fail on Fragmented GRC Workflows?
AI is not the answer to every challenge GRC teams face, at least not on its own. Part of this is due to information siloing. In the survey, only 15% of organizations describe their data as mostly integrated. Sixty-five percent call it moderately integrated, and 20% admit their information is scattered across teams and systems.
Imagine automation running on top of that. The tool pulls from three systems that each define a vendor differently, producing a confident summary that nobody can trace where the numbers originated. Instead of fixing weak data management and data protection concerns, AI’s faster processing can amplify the problems.
GRC efficiency comes from connected processes, not from faster tools bolted onto disconnected systems. That is why GRC platforms that unify frameworks, evidence, and vendor records make AI work. Comparatively, standalone tools stall in pilots.
3 Things GRC Practitioners Expect AI to Do
In our study, we asked GRC teams where they want help. Three answers stood out:
- 28.8% want faster audits and reviews.
- 24.8% want clearer visibility into their top risks.
- 22.4% want more time spent on analysis instead of documentation.
Each describes a bottleneck, not a task. Faster audits mean evidence that collects itself, and clearer visibility refers to information that lives in one place instead of six. More analysis time means removing the follow-up work instead of replacing the analyst doing it.
Start with these points. Scoping AI around a bottleneck your team already complains about gives you a defensible use case and a measurable outcome.
How Do You Close the GRC Efficiency Gap at Your Maturity Level?
There is no one next step to close the GRC efficiency gap because different teams stand in different places. Start with which state you find yourself in.
If You Are Not Using AI Yet
Companies that haven’t yet implemented AI in their GRC processes should start with data management foundations instead of tools. Consolidate evidence in one location using GRC platforms so requests no longer live in email threads and personal drives.
Standardize how risk assessments get requested, completed, and stored. TBuild risk registers that capture risk identification and baseline risk scoring across your organization. Then, map your frameworks against each other. This way, a single control satisfies several requirements instead of being tested four separate times.
None of this requires AI. All of it makes your risk management program faster right now. Moreover, your future AI deployment will be far more likely to succeed.
If You Are Stuck in Pilots
If you are stuck in AI pilots, you are part of the largest group. Roughly 44% of organizations are running experiments that haven’t scaled beyond a single team.
The way forward is governance through unified GRC platforms, not more experiments. Before you expand anything, answer three questions in writing:
- Who is accountable when an AI output turns out to be wrong?
- How does someone validate results before they enter a live workflow?
- Where is human review non-negotiable?
Teams that settle this early move faster. Clear rules turn AI into a governed part of your risk mitigation process instead of an unmanaged source of new exposure.
If You Are Already Scaling
Only 16.7% of organizations report demonstrable financial ROI from AI in GRC. This leaves most scaling teams defending their investment without hard numbers to back it up.
Make the operational case while the financial one catches up. Organizations seeing returns point to reduced cycle times (25%), higher throughput (20.7%), and improved decision-making (19%).
Connect those results to strategic goals that leadership already cares about. Look for faster audits, clearer visibility into your top risks, and more analyst hours spent on analysis instead of documentation.
Narrow Your GRC Efficiency Gap With Onspring
Improving GRC efficiency starts with knowing where you actually are. Onspring built an AI maturity assessment for that purpose. In about five minutes, it scores your current maturity, identifies your stage, benchmarks you against peer organizations, and returns practical next steps for your level.
The efficiency gap will not close on its own. Take the assessment and find out what is holding your GRC processes and risk mitigation strategies back.
