How Compliance Teams Can Tap Into the AI Budget: Get Your Slice of the Pie

Why Compliance Must Compete for AI Budget

AI investment is no longer optional for most enterprises; it is now a strategic line item that finance leaders expect to move the needle on efficiency, control, and growth. At the same time, GAN Integrity’s recent research shows that AI adoption is already outpacing AI governance in many organizations, creating real exposure that compliance is best positioned to manage and offer up opportunities to scale AI use cases.​​

Yet AI ownership and budgets are still skewed toward IT and data teams: just 25% assign it to Compliance. When compliance is not at the table, AI tools get deployed without sufficient guardrails, leading to:​

• Unmanaged regulatory risk in high‑stakes use cases

• Shadow AI in tools and third parties the organization doesn’t fully understand

• Underinvestment in AI for core compliance processes like due diligence, investigations, and monitoring

If compliance leaders want AI tools that truly support their mandate, they need to show not only that AI reduces risk, but that it delivers the kind of hard ROI and operational leverage CFOs expect.​​

What Finance Leaders Need to Hear About AI

Finance teams are under their own pressure to make AI investments pay off, and they’re developing a consistent playbook. Several clear expectations emerge that can help shape how compliance teams approach CFOs when it comes to AI investment:

• Start with specific, high-ROI use cases. CFOs prefer targeted pilots that prove value before larger rollouts.​

• Demonstrate payback quickly (often within 12–18 months) and in recognizable financial metrics, such as cost avoidance, process savings, and reduced external spend.​

• Ensure data quality and governance are in place; no CFO wants to fund tools built on incomplete or messy data.​​

• Show cross-functional benefits, not narrow departmental wins, especially where AI strengthens financial control, auditability, and risk visibility.​

For compliance teams, that means reframing AI from “innovative tech for compliance” to “a control and efficiency engine that helps the business reduce spend, unlock capacity, and avoid costly surprises.”​​

Where AI Delivers Measurable Value in Compliance

To secure budget, you need more than theoretical benefits. You need practical AI use cases that can be translated into numbers. Below is a strategic use case for TPRM.

Use Case: Third‑Party Due Diligence at Scale

GAN Integrity’s due diligence research shows that more than half of organizations manage over 500 third parties, and a significant share manage thousands, often with lean compliance teams of ten FTEs or fewer. At the same time, only about 35% of third parties undergo enhanced due diligence, leaving large blind spots.​

AI can drive returns by:

• Automating first-level screening (sanctions, adverse media, corporate registries) and document review to reduce manual research time.​

• Reducing false positives in sanctions and media screening, which today represent one of the biggest drags on reviewer capacity.​

• Enabling continuous monitoring for risk changes rather than relying solely on periodic refreshes.​

The ROI case: If AI reduces manual review time per third party by even 30–50% and cuts false positives substantially, teams can expand coverage to more counterparties without adding headcount, converting a fixed cost constraint into scalable coverage.​

Translating AI Benefits into ROI and Budget Language

To unlock AI budget, compliance leaders need to tie use cases to metrics that resonate with finance. Below is a simple structure you can reuse in your next business case.

1. Cost Savings and Cost Avoidance

CFO‑oriented materials on AI in legal and spend management illustrate that technology can deliver up to 10% reductions in external legal spend by enforcing billing rules and eliminating leakage, often achieving payback within the first year. Compliance can take a similar approach:

• Quantify current spend on external investigations, due diligence providers, and manual review work that AI could reduce.​

• Estimate cost avoidance from catching issues earlier (for example, blocking relationships with sanctioned or high‑risk third parties before contracts are signed).​

• Show how AI‑enabled early detection lowers the probability or impact of regulatory actions and fines.

A simple model could look like:

• Baseline: average external due diligence cost per high‑risk third party, multiplied by annual volume.​

• AI scenario: percentage of those checks that can be automated or handled in‑house at much lower marginal cost.​

2. Productivity and Capacity Gains

GAN Integrity’s due diligence data shows that enhanced reviews often take weeks, sometimes months, and it is this long tail of timelines that creates friction for the business. AI offers an attractive narrative: same or better quality, but faster.​

You can express this as:

• Hours saved per case or third‑party review × number of cases per year = total hours reclaimed.​

• Convert hours to FTE capacity by dividing by annual working hours, then compare that to the headcount you would otherwise need to hire to keep pace.​

For finance, this is not about “nice to have efficiency” but about avoiding future hiring while still meeting rising regulatory and business demands.

3. Risk Reduction and Program Defensibility

While harder to quantify, risk reduction still has budget value if you frame it consistently:

• Highlight the growing focus on third‑party ecosystems and deeper‑tier risks, where AI‑supported visibility is increasingly necessary to meet expectations like those under the CSDDD and similar regimes.​

• Describe how continuous AI‑enabled monitoring creates a defensible record that the organization is actively managing risk, which can influence enforcement outcomes and negotiation positions with regulators.​

This shifts AI from a pure cost center to a strategic control that protects enterprise value.

A Five‑Step Playbook to Get Your Slice of the AI Budget

To move from theory to funding, compliance teams need a disciplined, finance‑ready approach. Here is a practical playbook you can apply.

Step 1: Map Your Highest‑Value AI Use Cases

Start by mapping your compliance processes against three questions:

• Where do we have scale problems (many third parties, many cases, many data sources)?​

• Where do we have persistent bottlenecks (weeks‑long due diligence timelines, backlog in investigations, slow policy updates)?​

• Where would better visibility meaningfully reduce risk?

Select 2–3 use cases that are:

• High‑volume or high‑value

• Easy to measure (time, cost, or risk impact)

• Aligned with enterprise priorities

Anchor your initial business case around those, rather than a broad, abstract “AI for compliance” vision.

Step 2: Establish Baselines with Data

Finance will push back on any ROI claim that lacks a baseline. Use your existing systems to quantify:

• Current volumes (number of third parties, cases, etc) and their typical cycle times.

• Current human effort

• Current external spend

Where you don’t have perfect data, define reasonable estimates and mark them as such. The point is to demonstrate that you understand your own numbers and can update them once pilots are live.

Step 3: Design a Pilot That Looks Like a Finance Experiment

CFOs are increasingly recommending a “start small, test, and scale” approach to AI investments. Structure your pilot accordingly:​

• Narrow scope: for example, use AI to triage and summarize low‑ to medium‑risk third‑party files, or to automate first‑level case intake for hotline reports.​

• Clear success metrics: cycle‑time reduction, hours saved, expanded coverage, or reduced external spend.​

• Time‑bound: 3–6 months with a defined before/after comparison window.

This lets finance treat compliance’s AI spend the same way they evaluate their own AI initiatives: a measured bet with clear performance indicators.​

Step 4: Speak in CFO‑Friendly Terms

When you present your case, frame it in the language finance teams already use for AI:

• “We’re not asking for a moonshot. We’re asking for a contained pilot targeting X use case, with a projected Y% cycle‑time reduction and a realistic path to Z% cost avoidance or capacity gain within 12–18 months.”

• “This investment helps close a known AI adoption‑governance gap that X% of organizations rate as medium or high risk, while bringing compliance in line with best practices we already see in finance and legal (X% invoice coverage, data‑driven controls).”​​

• “Without this investment, we will reach a point where we can’t expand due diligence coverage or onboarding speed without adding headcount, given our current third‑party volumes and team size.”​

Make it clear that you view AI not as a technology project, but as an operating‑model upgrade that aligns with enterprise efficiency and risk‑management priorities.

Step 5: Tie AI Investment to Governance and Roadmaps

Decision makers are wary of AI projects that run ahead of governance or separate from company-wide innovation plans. Regulatory expectations now favor cross‑functional, board‑level oversight with clear policies, risk‑assessment frameworks, and training.​

Position your AI request as:

• Embedded within a broader AI innovation roadmap that includes policies, training, third‑party AI assessments, and incident response.​

• Consistent with the regulatory frameworks your organization is already tracking, such as the DOJ’s Evaluation of Corporate Compliance Programs and AI‑specific regimes like the EU AI Act.​

• Supported by compliance’s role as a catalyst: helping establish processes now so that AI can safely scale across the enterprise later.​

This reassures finance and IT that you are not only asking for tools, but also taking responsibility for the guardrails.

How GAN Integrity Can Help Compliance Teams Win AI Investment Decisions

GAN Integrity’s platform is built to help compliance teams see everything, adapt to anything, and get the help they need, especially as AI reshapes how risk is managed and programs are scaled. Here are several realities that directly strengthen your AI business case:

• Third‑party ecosystems are expanding faster than teams can keep up, with deep‑tier visibility emerging as the next frontier for both regulators and stakeholders.​

• AI‑supported due diligence, when paired with strong governance, offers a path to expand coverage, reduce false positives, and enhance early‑warning detection without proportional cost increases.​

By unifying compliance, ethics, and third‑party risk management on a flexible platform, and layering in AI‑driven capabilities responsibly, GAN helps compliance teams move from manual, time consuming processes on a shoestring budget to a scalable, data‑driven operating model. That’s exactly the kind of transformation finance leaders are looking to fund.

Interested in learning more? Speak with one of our experts today.