Artificial intelligence is already influencing the employee lifecycle—from sourcing candidates and screening resumes to evaluating performance, recommending promotions, and informing workforce reductions. In our recent webinar, AI In Employment: Regulations, Risk, and Readiness, Berkshire Associates Managing Consultants Rachel Rubino and Nina Le examined where employers are using AI, the legal and practical risks those tools can create, and the governance practices organizations should put in place before—and after—deployment.
The central message: AI governance is not a one-time compliance exercise. It is a compliance lifecycle that requires thoughtful evaluation, human oversight, documentation, monitoring, and reassessment.
Employers are adopting AI across a wide spectrum of workplace activities. Lower-risk uses often focus on administrative efficiency, such as scheduling meetings, taking notes, answering routine questions, drafting policies, and preparing job descriptions. The risk rises as a tool begins to influence who receives an opportunity or how an employee is treated.
Recruiting and sourcing: Identifying active and passive candidates, personalizing outreach, and guiding applicants through basic qualification questions.
Screening and interviewing: Ranking résumés, conducting automated phone screens, transcribing interviews, and analyzing candidate responses or behavior.
Workforce analytics: Reviewing tenure, productivity, absenteeism, promotion history, and engagement data to identify trends or predict turnover.
Promotion and termination: Comparing employee metrics to recommend advancement, layoffs, or other consequential decisions
These tools may save time, improve consistency, and help teams process large volumes of information. But efficiency does not eliminate accountability. Employers remain responsible for employment outcomes, even when using a third-party AI-enabled tool.
A critical distinction is whether AI is merely assisting with a task, such as taking notes, or materially affecting an employment decision. A recruiter may technically make the final choice, but if an AI-enabled tool determines which applicants appear in the recruiter’s queue, the technology may have shaped the outcome.
Bias can enter through training data, system design, implementation, or day-to-day use. Historical data may reproduce historical disparities. Seemingly neutral factors may operate as proxies for protected characteristics. A model optimized for easily measured productivity may overlook mentoring, communication, judgment, or other qualitative contributions that are important to identify the best qualified candidate. Automated interviews may also create accessibility concerns or misinterpret speech patterns, accents, or other individual differences.
As AI moves from “copilot” to “agent”—from supporting a human to scoring, ranking, or conducting an assessment—the need for validation, transparency, and meaningful human review increases.
The regulatory landscape is evolving. Although no single federal employment statute comprehensively governs workplace AI, existing anti-discrimination laws still apply when technology is involved. Federal agencies have emphasized that automated systems do not receive an exemption from established civil rights, equal opportunity, and consumer protection requirements.
At the state and local level, requirements vary significantly. These laws address topics such as notice, consent, bias audits, adverse-impact standards, record retention, and data access, among other items. Multi-state employers should inventory where tools are used, where affected applicants and employees are located, and which obligations may apply.
Recent legal challenges illustrate the breadth of potential exposure, including alleged discrimination in screening algorithms, age bias in hiring and layoffs, inaccessible video-interview tools, privacy and data-use concerns, Fair Credit Reporting Act theories, and vendor claims that products are “bias-free.”
The practical lesson is broader than any single case: outsourcing a process does not outsource responsibility. Employers should be able to explain what a tool measures, why those factors are job-related, how the tool was validated, what data it uses, and how humans can question or override its output.
Responsible adoption starts before any vendor contract is signed and continues until the tool is retired. A repeatable lifecycle is recommended when using AI-enabled tools:
Define the need. Identify the business problem, what success looks like, and whether AI is more effective than the current process.
Vet the vendor. Ask how the model was developed, what training data was used, how it was tested, what validation evidence exists, and what information the vendor will share.
Establish governance. Create cross-functional ownership involving HR, legal, compliance, technology, procurement, and other relevant stakeholders.
Pilot and validate. Test the tool on the organization’s actual jobs and population, confirm that job descriptions reflect the work, and evaluate outcomes for adverse impact based on protected traits, such as race or sex.
Train users. Explain the tool’s purpose, limitations, appropriate uses, and when a person must review, challenge, or override an output.
Document and implement. Set boundaries, decision rights, escalation paths, accommodation procedures, and record-retention practices.
Monitor continuously. Monitor the validity of all AI-enabled tools on a regular basis, and especially when job requirements change.
Correct or retire thoughtfully. Investigate disparities and consider less discriminatory alternatives if issues arise. When retiring an AI-enabled tool, preserve or dispose of records in accordance with legal and business requirements.
Where is automated technology screening, ranking, scoring, recommending, or monitoring people—even if no one internally calls it “AI”?
Does the tool affect access to an interview, promotion, pay decision, accommodation, or continued employment?
What evidence shows that the factors measured are job-related, valid, and reliable?
Are any groups selected, screened out, or otherwise affected more significantly than other groups?
Can applicants and employees request an accommodation or correct inaccurate data?
Who owns the tools after launch, and how often will outcomes be reassessed?
What level of human review or oversight exists?
AI can support faster, more consistent employment processes, but responsible use requires more than purchasing a tool and accepting its recommendations. Organizations that inventory their technology, work closely with their vendors, validate specific uses, pay attention to federal and state requirements, and monitor outcomes will be better positioned to capture the benefits of AI while managing legal and operational risk.