Business Law Guidance on AI Literacy and the Future of Hiring

Artificial intelligence is no longer a niche tool used by technical teams. It is quickly becoming a baseline expectation across roles, industries, and organizations. That shift is now visible in hiring trends, workforce planning, and executive decision-making, and it is raising legal questions many businesses have not yet paused to consider.

Recent workforce data and hiring discussions reflect a clear theme: employers increasingly expect some level of AI literacy from candidates, while many workers feel unprepared for how quickly those expectations are changing. For businesses, that gap is not just a talent issue. It is a compliance issue, a policy issue, and in some cases, a litigation risk.

As a business attorney licensed in Pennsylvania and New York, Todd Nurick of Nurick Law Group advises companies navigating how emerging technology expectations intersect with employment law, governance, and risk management. This type of business law guidance on AI literacy is becoming essential as hiring practices evolve.

Why Business Law Guidance on AI Literacy Matters Now

Hiring practices are shifting faster than many internal policies. Employers are updating job descriptions, screening criteria, and interview processes to reflect AI familiarity, often without fully assessing the legal implications.

When AI literacy becomes an implied requirement, businesses must consider how that expectation aligns with wage and hour rules, anti-discrimination laws, accommodation obligations, and documentation standards. What feels like a practical business decision can quietly create exposure if it is not approached thoughtfully.

The legal risk does not stem from using AI or valuing AI skills. It comes from inconsistency, poor documentation, and assumptions about what candidates or employees should already know.

AI Literacy and Hiring Criteria

Many employers are adding AI-related language to job postings, sometimes informally. Phrases like “AI-enabled workflows,” “automation familiarity,” or “data-driven decision tools” are increasingly common.

From a legal perspective, the question is not whether those skills are legitimate. It is whether they are clearly defined, consistently applied, and aligned with the actual requirements of the role.

Vague or shifting expectations can lead to claims that hiring criteria were applied unevenly or served as a proxy for excluding certain candidates. This is especially relevant where roles do not truly require advanced technical expertise but reference AI as a general concept.

Workforce Preparedness and Training Expectations

Another trend emerging on LinkedIn is the recognition that many workers feel unprepared for AI-driven changes. Employers are responding by offering internal training, upskilling programs, or informal learning expectations.

That response raises several legal considerations:

• Whether training time is compensable

• How training expectations affect employee classification

• Whether access to training is equitable

• How performance is evaluated during transitions

Clear policies and documentation matter. Without them, businesses risk disputes over pay, advancement, or disciplinary decisions tied to evolving skill expectations.

Automated Screening and Hiring Tools

AI-driven screening tools are also part of this conversation. Even when employers do not view themselves as “using AI,” many recruiting platforms rely on automated filtering, ranking, or scoring.

Businesses remain responsible for the outcomes of those tools. That includes understanding how they work at a high level, ensuring compliance with applicable employment laws, and being able to explain hiring decisions if challenged.

This is another area where business law guidance on AI literacy helps bridge the gap between operational convenience and legal responsibility.

Governance and Oversight Considerations

AI-related hiring decisions are increasingly viewed as a governance issue rather than a purely HR function. Boards and senior leadership are being asked how technology is used, what safeguards exist, and how decisions are reviewed.

That does not require technical mastery. It does require awareness, oversight, and a documented process for evaluating risk. Companies that cannot articulate how AI influences hiring may find themselves on unstable footing if issues arise.

Practical Steps for Employers

Businesses do not need to slow innovation to manage risk. They do need alignment.

Practical steps include:

• Reviewing job descriptions for clarity and consistency

• Assessing whether AI literacy requirements are role-specific

• Updating hiring and training policies

• Understanding how recruiting tools operate at a high level

• Coordinating legal, HR, and leadership discussions

• Documenting decision-making and oversight

These steps help ensure that evolving expectations are implemented thoughtfully rather than reactively.

Final Thoughts

AI literacy is becoming part of the modern workplace vocabulary. For employers, the challenge is not whether to adapt, but how to do so in a way that is fair, compliant, and defensible.

Todd Nurick and Nurick Law Group provide business law guidance to companies in Pennsylvania, New York, and nationally on how technology trends affect hiring practices, workforce planning, governance, and risk management.

This article is for informational purposes only and is not legal advice. Reading it does not create an attorney–client relationship. Todd Nurick and Nurick Law Group are not your attorneys unless and until there is a fully executed written fee agreement with Todd Nurick or Nurick Law Group.

Sources

LinkedIn workforce trend analysis and Jobs on the Rise reporting

U.S. Equal Employment Opportunity Commission guidance on hiring practices

U.S. Department of Labor guidance on training and wage considerations

Federal Trade Commission business guidance on automated decision tools

National Institute of Standards and Technology, AI Risk Management Framework