A sponsored post by the MetricStream Research Team
47% percent of risk professionals say they recognize the value of artificial intelligence in governance, risk, and compliance. Yet only 14% have integrated it into their GRC frameworks and processes.
These headline findings are from MetricStream’s recent GRC Practitioner Survey. It’s tempting to read the gap in the numbers as a confidence problem. However, the gap isn’t about belief. What most GRC leaders need is a practical way to move from a pilot that works in a demo to a program that works in production.
Why the Gap Persists
Three structural problems keep showing up when the 47% try to become the 14%.
The first is fragmented data.
In most organizations, risk data was never built to be machine-readable. It lives in narrative fields, inconsistent taxonomies, spreadsheets with varied column headers, and risk registers that mean something different in every business unit. An AI model built on such data fails quietly, producing plausible-looking outputs that are wrong in ways nobody catches until an examiner or an incident flags them.
The second is ownership.
AI governance in a typical risk organization is currently claimed by multiple owners: partially by IT, information security, data science, legal, and the risk function itself. In practice, this means no single team owns it end to end. When five functions each hold a slice of accountability, decisions are slowed to the pace of the most cautious stakeholder. Often, no single function can fully approve a use case, sign off on a control, or be held responsible when something goes wrong.
The third is a widening gap in perceptions between leadership levels.
Executives and boards often believe their organizations are further along in AI readiness than the practitioners running risk programs report. This mismatch shapes budget, timelines, and risk appetite in ways that don’t match operational reality, producing either overconfident rollouts or programs quietly stalled behind unrealistic expectations
The Cost of Standing Still
The cost of organizations staying in pilot mode isn’t hypothetical. It shows up in what happens when an organization moves past it.
One MetricStream customer, a compliance group spending more than €50M annually on people, technology, vendors, and data, offers a concrete before-and-after.
200 regulatory compliance staff previously consumed largely by manual oversight and regulatory and board reporting that took six weeks after period-end. After integrating AI into the workflow, the reporting cycle was compressed to under a week, the compliance program’s overall cost dropped 22%, decision speed improved by more than 60%, and roughly 30% of compliance professionals were redeployed from manual oversight to revenue-generating work.
While the above example is one organization’s result and not a universal case, it illustrates the underlying point. A pilot that never graduates into daily operational use can keep a compliance function with valuable resources anchored to longer reporting cycles and manual review.
A Practical Playbook
Closing the gap doesn’t have to be overcomplicated with a major platform overhaul or a multi-year transformation program. Organizations can start with a few basic steps.
- Start with one measurable use case. Pick something narrow enough to prove value in a quarter. This could be any simple use case, from third-party risk scoring to control testing prioritization or regulatory change triage. Be sure to define what success looks like before you start.
- Fix the data architecture before layering on AI. This is unglamorous but important work, as skipping this step can lead to costly mistakes downstream. Taxonomy alignment, deduplication, and establishing a single source of truth for risk and control data are essential before starting the AI in GRC journey.
- Name a single accountable owner. Every AI use case in the risk and compliance function should have one designated person or role with the authority to approve, pause, or discontinue its deployment. While cross-collaboration is essential, the ultimate decision-making is owned by a single accountable owner to ensure timely action.
- Give GRC teams sanctioned tools. Risk, compliance, audit, and cyber teams are far more likely to adopt AI when it is embedded directly into the workflows they use every day. By providing AI capabilities on a governed, AI-native GRC platform, organizations can deliver productivity without sacrificing oversight.
- Design human review into the workflow. The strongest AI-in-GRC programs don’t treat human oversight as a compliance afterthought bolted onto the end of a process. They build the checkpoint into the workflow itself at the point where a decision is actually made.
- Continue to train people for the job. The goal is to create GRC practitioners who can confidently partner with AI, not simply operate it. Organizations should invest in building capabilities that enable better risk decisions, such as evaluating AI-generated insights, recognizing when human judgment is required, validating recommendations against organizational context, and escalating exceptions when necessary.
- Measure readiness, not just adoption. Knowing how many users interact with AI says little about whether it is improving risk and compliance outcomes. Instead, organizations should track indicators that reflect the health of their AI-enabled GRC program, such as data quality, model performance and drift, human override rates, auditability, regulatory compliance, and the speed and quality of risk decisions.
The Real Task Ahead
The gap between recognizing AI’s value and realizing its impact will close only through deliberate execution. As the conversation around AI in GRC shifts from experimentation to execution, the opportunity for GRC leaders to move from a system of record to a system of intelligence, action, and decisions is immense. Success will come from combining AI GRC technology with strong governance, high-quality data, and empowered people.
