What Franchise Leaders Should Know About Using AI in Hiring

5 Tips to Reduce Compliance Risk and Improve Talent Decisions

Artificial intelligence is rapidly reshaping how organizations attract, evaluate and onboard talent in franchise hiring. Recent industry data shows that approximately 87% of organizations now use AI-driven tools in hiring or onboarding processes, a percentage that has effectively doubled over the past two years.

At the same time, the use of AI in hiring is outpacing oversight and controls. Forbes reports that only 46% of organizations using AI have implemented formal risk management frameworks, creating a widening gap between innovation and compliance readiness/risk.

While AI offers speed and scale, it also introduces new and often underestimated risks, particularly in candidate vetting, background screening evaluation and onboarding decisions. Without proper controls, organizations may inadvertently expose themselves to legal, reputational and hiring-quality risks that can lead to lawsuits including class-action litigation. Employers remain responsible for all employment decisions made even when those decisions are partially automated with the assistance of AI tools.

Below are five practical tips to help employers reduce compliance exposure while improving the integrity of hiring decisions in an increasingly AI-enabled environment.

1. Ensure All Vetting Sources Meet Consumer Reporting Compliance Standards

One of the most overlooked risks in AI-assisted hiring is the use of non-compliant or unregulated data sources. Many organizations rely on online databases, scraped information or third-party tools that are not legitimate consumer reporting agencies under the Fair Credit Reporting Act (FCRA).

This creates significant risk exposure in the following ways:

• Inaccurate or unverifiable data being used in employment decisions.
• Machine decision making taking the place of true human vetting. 
• Lack of proper dispute processes for candidates. 
• Increased litigation risk for employers and affiliated franchises. 
• Violations tied to improper adverse action procedures involving candidate disputes.

Artificial intelligence does not eliminate traditional hiring risks, and, in some cases, can accelerate them by presenting incomplete or inaccurate information with a level of confidence that may cause users to overlook the need for human vetting or verification. 

States such as California, Colorado, Illinois and Texas, and municipalities including New York City have already passed restrictive AI laws pertaining to decision-making processes. Because regulations can vary by jurisdiction, employers should confirm that any AI-assisted screening sources comply with their applicable state and local requirements.

Tip: Only use properly vetted, FCRA-compliant background screening providers with longevity and stability in the industry, documented compliance workflows and audit trails.

2. Avoid “Quasi-Vetting” Using Social Media and AI Aggregation Tools

A growing trend involves the use of AI tools to scan social media, forums and publicly available data to create informal candidate risk profiles. While this may seem efficient, it introduces major compliance issues under:

• Equal Employment Opportunity Commission guidance (disparate impact and protected class inference). 
• Americans with Disabilities Act restrictions (medical or disability-related inference). 
• State-level privacy and labor laws. 
• Data accuracy and attribution concerns.

AI systems can also misclassify identity, context or sentiment leading to false conclusions that are difficult to defend in an employment decision if proper controls are not in place.

In addition, social media and other online content often reveal personal information about candidates relating to protected characteristics such as age, race, religion, disability and other classifications that hiring decision makers may not otherwise possess. This can increase the risk of discrimination claims and make it challenging for a company to demonstrate that such information played no role in the hiring process.

Tip: Social media and other AI screening methods should only be conducted through structured, compliant processes with documented relevance standards and filtered or vetted information – and not through ad hoc AI interpretation-only tools. Human vetting by trained staff should be the final step before anything is reported.

3. Address AI “Hallucinations” as a Real Hiring Risk

Unlike traditional databases, generative AI systems can produce outputs that appear factual but are not grounded in verified data. These “halucinations” can include:

• Incorrect employment history assumptions. 
• Misidentified criminal records. 
• Fabricated behavioral or personality assessments. 
• Erroneous educational verification details.

When these outputs are used in onboarding or hiring decisions, employers may unknowingly base decisions on non-existent or inaccurate information. In litigation, employers may be required to explain or defend the basis for a hiring decision. Reliance on unverified AI-generated information may create significant evidentiary and credibility concerns.

Tip: Treat AI-generated insights as unverified prompts to verify, not as decision-grade data, unless they are validated through compliant, primary-source verification methods and human review. Verification of all AI-generated material is a must.  

4. Implement Formal AI Governance and Risk Management Frameworks

Despite rapid adoption, less than half of organizations using AI in hiring have formal governance structures in place. This creates inconsistent decision making and increases exposure.

A strong framework should define:

• Which AI tools can and cannot be used for onboarding decisions. 
• Required human review checkpoints. 
• Documentation and audit standards. 
• Data privacy and retention rules. 
• Bias testing and monitoring protocols.

Without governance, AI becomes a decentralized decision engine often without accountability, thus increasing risk. Employers should work with their legal counsel to adopt formal AI policies and procedures.

Tip: Establish a cross-functional oversight model involving your onboarding, legal and key stakeholders to govern all AI-enabled hiring workflows.

5. Rebuild Hiring Discipline Around Transparent, Measurable Skills

One of the most important long-term shifts we see is the decline of “AI-assisted ambiguity” in hiring. As AI tools become more widespread, candidates are increasingly able to generate polished resumes, simulated assessments and automated narratives about themselves resulting in embellishment we call “resume inflation.”

This will eventually force organizations to return to a more disciplined approach focused on:

• Clearly defined role competencies. 
• Verifiable skill demonstration. 
• Structured, consistent evaluation criteria. 
• Transparent onboarding expectations. 
• In-person or video interviews and checking through identity verification software.

Over time, the “AI skills bubble” will likely deflate as employers become more adept at prioritizing what is demonstrable, teachable and measurable over what is simply well-presented.

Organizations that adapt early will reduce risk and build stronger, more honest talent pipelines and cultures where expectations and capabilities are clearly aligned, while staying in compliance.

Final Thoughts

AI is reshaping how onboarding judgment is formed, reviewed and executed, and AI in hiring will continue to evolve as organizations refine their approach to compliance and efficiency. The organizations that succeed will be those that adopt AI with the most discipline, compliance rigor and commitment to verifiable talent decisions.

As hiring becomes increasingly automated with AI, compliant, accurate and well-governed background screening, supported by qualified providers and trained professionals and staff, will remain essential to protecting organizational integrity and candidate quality.

Ultimately, employers – not robots – remain accountable for employment decisions. While AI can enhance efficiency and consistency, it cannot replace human judgment. Meaningful human involvement and detailed policies and procedures regarding AI usage are critical safeguards. This will allow organizations to be best positioned to utilize the benefits of AI while minimizing risk.

This article contains contributions from:

Alan Lasky of Reliable Background

Thomas M. O’Connell of Buchalter

Jennifer M. Misetich of Buchalter