Artificial intelligence is often discussed in collections as a productivity tool: automating workflows, improving segmentation, or enhancing decisioning. What is discussed far less is how quickly AI is becoming a governance and certification issue.
This shift recently surfaced in The Receivables Podcast with Sara Woggerman, Founder of ARM Compliance Business Solutions, where AI emerged not as a future risk, but as an active factor shaping certification expectations. The key insight was subtle but important: certification standards tend to evolve before formal regulation, particularly when state-level activity accelerates.
That pattern aligns with broader regulatory behavior. According to the National Conference of State Legislatures, a broad range of AI-related bills has been proposed across U.S. state legislatures, Puerto Rico, the U.S. Virgin Islands, and Washington, D.C. introducing AI legislation in recent sessions addressing automated decision-making, governance, and related AI policy issues. Certification frameworks, including RMAI’s, are beginning to reflect those pressures: well before federal enforcement becomes explicit.
Why Certification Standards Change Before Regulation
Certification standards serve a different function than regulation. They are not enforcement mechanisms; they are risk anticipation mechanisms.
When industry associations observe:
- Increased legislative proposals
- Rising complaint categories
- Expanding use of automated tools
they often begin embedding expectations into standards early. This allows the industry to demonstrate proactive self-governance rather than reactive compliance.
AI governance fits squarely into this model. While many organizations believe they can delay governance discussions until rules are finalized, certification standards rarely wait that long.
The Compliance Blind Spot Around AI
One of the most common assumptions in collections is that AI tools fall outside traditional compliance frameworks because:
- Vendors supply the models
- Decisions are “assisted,” not automated
- Human review still exists
From a governance perspective, none of those assumptions remove accountability. AI systems influence outcomes, whether by prioritizing accounts, shaping contact strategies, or recommending actions. Certification standards are beginning to ask the obvious follow-up question: how are those decisions governed, reviewed, and controlled?
Without governance, AI becomes a black box. Certification bodies are increasingly uncomfortable with black boxes.
The AI Governance Readiness Model
To address this emerging gap, organizations should begin assessing AI through an AI Governance Readiness Model, consisting of four core dimensions:
1. Visibility
Organizations must be able to identify where AI is used across operations, including vendor-provided tools embedded in platforms.
2. Accountability
Clear ownership must exist for AI-driven processes, including responsibility for outcomes, errors, and exceptions.
3. Controls
AI outputs should be subject to defined constraints, thresholds, or review mechanisms—especially where consumer impact is possible.
4. Documentation
Governance decisions, model purposes, and oversight processes must be documented in a way that can withstand audit scrutiny.
This framework does not require advanced technical expertise. It requires governance discipline.
Why State-Level Activity Matters More Than Federal Silence
A common misconception is that AI governance can wait because federal agencies have not issued sweeping enforcement actions. In reality, state-level activity is often the leading indicator.
According to the NCSL, states have focused heavily on transparency, explainability, and bias mitigation: principles that translate directly into governance expectations. Certification standards often absorb these principles long before enforcement occurs.
This explains why AI language is appearing incrementally in certification discussions rather than as a sudden overhaul. The shift is deliberate, not accidental.
Data Governance and AI Are Converging
AI governance cannot be separated from data governance. Models are only as compliant as the data they ingest.
IBM’s Cost of a Data Breach Report shows that faster detection and containment of breaches, often enabled by strong security practices, AI, and automation has contributed to a decline in global breach costs. The report also finds that organizations able to detect and contain breaches internally experience lower overall costs than those that cannot.
While the report focuses on security outcomes, the implication for certification is clear: weak data controls undermine AI oversight.
Certification standards increasingly expect organizations to understand:
- What data feeds automated systems
- How long that data is retained
- Who can access outputs and why
AI governance without data governance is incomplete.
Operational Risk for Agencies and Debt Buyers
For agencies, AI governance affects audit outcomes. For debt buyers, it affects vendor risk.
As buyers strengthen oversight requirements, certification becomes a proxy for governance maturity. Vendors unable to articulate how AI is governed may face increased scrutiny or exclusion: regardless of performance metrics.
This makes AI governance not just a compliance issue, but a commercial risk factor.
Preparing for Certification Expectations Before They Are Explicit
Organizations that wait for explicit certification language often find themselves retrofitting governance under pressure. Those that prepare early gain flexibility.
Preparation steps include:
- Inventorying AI-enabled processes
- Assigning governance ownership
- Defining review and escalation paths
- Documenting oversight decisions
These steps are far less disruptive when implemented proactively.
Conclusion: Governance Signals Come Before Rules
AI governance is not entering certification standards by accident. It reflects a predictable pattern: legislative pressure, industry risk, and early standard-setting.
Organizations that recognize these signals early are better positioned to manage compliance risk, vendor oversight, and certification outcomes. Those that delay may find governance expectations arriving faster than anticipated.
For leaders seeking deeper research and analysis on how certification standards evolve alongside technology risk, exploring additional insights at Receivables Info is a logical next step.
Author Attribution
About Adam Parks
Adam Parks has become a voice for the accounts receivables industry. With almost 20 years working in debt portfolio purchasing, debt sales, consulting, and technology systems, Adam now produces industry news hosting hundreds of Receivables Podcasts and manages branding, websites, and marketing for over 100 companies within the industry.