What People Mean When They Say “AI”
Artificial intelligence has become a catch-all term that gets stretched to cover everything from spellcheck to large language models (LLMs). Strictly speaking, AI is technology that can learn. In practice, the label now gets applied to almost any automation, which makes it harder to separate what is genuinely new from what has been quietly working in the background for years.
The label matters less than the intent behind the tool. The most useful question for any organization is not whether a product uses AI, but what experience it creates for the people on the other end, whether those are patients, clinicians, job candidates, or recruiters.
Adoption is no longer the hurdle. According to Gallup, 52% of U.S. employees used AI in their role at least a few times a year as of May 2026. But using AI and transforming work are two different things. In organizations that have implemented AI, only 12% of employees strongly agree it has transformed how work gets done. Gallup also found that employees whose managers actively support AI use are 8.7 times as likely to say it has transformed their work, a reminder that culture and leadership shape results as much as the technology itself.
Where AI Helps Nurses Most, and Where It Cannot Go
Healthcare shows how uneven AI’s reach really is. In its 2025 AI at Work report, Indeed’s Hiring Lab found nursing among the roles most shielded from generative AI. Patient care depends on physical presence, empathy, and real-time clinical judgment, none of which AI can replace. What AI can change is the administrative work surrounding that core.
That administrative work is substantial. Nurses spend between 23% and 40% of a 12-hour shift on documentation, the equivalent of roughly 2.8 to 4.8 hours. KLAS Research found that 79% of acute care nurses lose time each week to unproductive charting, and nurses who lose three or more hours a week to it are more likely to say they plan to leave their organization (34%, compared with 20% of those who lose less than an hour).
Ambient AI documentation, which listens during care and drafts notes for nurses to review, is starting to give some of that time back. A 2026 study of one health system’s medical-surgical units found 20.6 minutes of recoverable time per 12-hour shift once ambient capture reached 60% or more, with a projected ceiling of 36.3 minutes if the best-performing unit’s results were achieved system-wide.
The ideal nurse is empathetic and calm under pressure, not someone who excels at entering data into a screen for half an hour at a time. Every minute returned to patient care matters, especially as the Health Resources and Services Administration (HRSA) projects an 8% national shortage of registered nurses in 2028, easing to 3% by 2038. Rural areas face a steeper path, with an 11% shortage projected in nonmetro areas in 2038, compared with 2% in metro areas.
How AI Is Reshaping Healthcare Hiring
Healthcare staffing faces its own version of the documentation problem. Recruiters spend hours on data entry and duplicated work that add nothing to their relationships with nurses or clients. Speed is a problem, too. When a nurse applies for a job at 1 a.m. on a Friday, that application has traditionally waited until a recruiter picked it up Monday morning, by which point the candidate may have moved on.
Supplemental Health Care (SHC) now uses voice AI screening to close that gap. When a nurse applies outside business hours, the system responds right away, explains the next steps, and asks whether the candidate is comfortable speaking with an AI agent. If they agree, a conversational screening captures the information a recruiter needs. The AI does not make hiring decisions. It gathers information so that people can decide on fit and talk with the candidate about the right opportunity.
Outside research points in a similar direction. In a Chicago Booth field experiment involving more than 70,000 applicants, candidates interviewed by an AI voice agent were 12% more likely to receive a job offer, 18% more likely to start the job, and 17% more likely to still be working after 30 days than those interviewed by human recruiters. When given the choice, 78% chose the AI interviewer. Humans reviewed every interview and made every hiring decision, and the roles studied were entry-level customer service positions, not clinical ones.
AI also raises a risk that is easy to miss. When technology removes friction, it also removes a natural check on bad ideas. If every staffing firm could call a candidate a thousand times a day at no cost, the result would be a terrible candidate experience. Organizations have to think through the unintended consequences of what AI makes possible.
Candidate attitudes show why the human role still matters. A Gartner survey found only 26% of job candidates trust AI to evaluate them fairly, and 25% trust an employer less when it uses AI to evaluate them. At the same time, 53% of job seekers were ghosted by an employer in the past year, up from 38% in 2024, according to Criteria’s 2026 Candidate Experience Report. Candidates welcome AI when it speeds up communication. When they need guidance, reassurance, or an answer to a hard question, they want a person.
The Compliance and Bias Questions Every Leader Should Ask
AI in hiring is no longer only a technology question. It is increasingly a legal one.
The case to watch is Mobley v. Workday, a class action in federal court in Northern California alleging that Workday’s AI screening tools disproportionately rejected Black, older, and disabled applicants. The claims have not been proven. But in 2024 the court allowed the case to proceed on the theory that an AI vendor can act as the employer’s agent, and on June 22, 2026, the judge allowed key discrimination claims to move forward. The takeaway for employers and staffing firms alike: using a vendor’s tool does not shift away responsibility for its results.
State and local rules are also taking shape:
- Illinois: Since January 1, 2026, HB 3773 has required employers to notify applicants and employees when AI is used in employment decisions, and it prohibits using AI in ways that discriminate or using zip codes as a proxy for protected classes.
- New York City: Local Law 144 requires employers and employment agencies to have automated hiring tools independently audited for bias each year, publish the results, and notify candidates.
- Colorado: SB 26-189, which replaced the state’s original AI Act, takes effect January 1, 2027, with notice, human review, and recordkeeping requirements for employers using AI in employment decisions.
Meanwhile, many organizations are moving faster than these safeguards. In a Resume.org survey, three in four companies that use AI in hiring said they let it reject candidates without human review, most often at the resume-screening stage. Only 26% required human oversight for every rejection.
How SHC Builds for Fairness: High Tech, High Touch
SHC’s approach to AI rests on its long-standing high-tech, high-touch philosophy, built on three commitments:
- Humans in the loop. AI supports the work, while people make the calls and give the advice.
- Technology that empowers people. Tools should make recruiters and clinicians better at what they already do.
- More time for connection. Removing repetitive, low-value tasks gives people more room for understanding, empathy, and strategy.
Fairness is part of the design, not an afterthought. SHC’s candidate and job matching tool was built with bias prevention as a central principle and tests itself daily to make sure it reviews all candidates equally. AI helps find a better fit between skills and roles, and when candidates have questions or want advice, people guide them.
The handoff between technology and people matters as much as the technology itself. A candidate who has a great experience with an AI screener but never hears from a recruiter ends up worse off than if AI had never been involved. That is why SHC designs its technology and its people processes together.
Editor’s note: Confirm the description of SHC’s matching tool and its daily bias testing with the Digital team before publishing. This claim came from the episode research brief and was not discussed on air.
What Healthcare and Education Leaders Should Do Now
For leaders evaluating AI tools or AI-assisted workforce partners, five steps stand out.
1. Ask where humans sit in the process. The Chicago Booth results came from a process where people reviewed every interview and made every hiring decision. Any vendor or partner should be able to describe its human-in-the-loop design in specific terms.
2. Know the rules where you hire. Illinois and New York City already require notice, and New York City requires annual bias audits. Colorado’s law takes effect January 1, 2027. Mobley v. Workday shows that relying on a vendor’s tool does not shift responsibility for its results.
3. Start with where time actually goes. Nurses can spend up to 4.8 hours of a 12-hour shift on documentation, and recruiters face similar administrative drains. AI aimed at a specific bottleneck delivers measurable value. AI deployed without that diagnosis often creates new friction.
4. Design the handoff. A smooth AI experience followed by a dropped follow-up is worse than no AI at all. Plan how and when a person takes over before you launch any candidate-facing tool.
5. Invest in managers, not just software. Gallup’s finding that manager support makes employees 8.7 times as likely to say AI transformed their work is a reminder that training and leadership determine whether tools deliver results.
Connect With SHC
AI tools will soon be available to every organization. What will set organizations apart is how thoughtfully they put those tools to work, and whether the result is a better experience for the people on both sides of the hiring relationship. If you are evaluating AI-assisted workforce solutions for your healthcare organization or school system, contact Supplemental Health Care to learn how our high-tech, high-touch approach can help.
FAQ
How is AI being used in healthcare hiring right now?
AI is being used to screen applications, respond to candidates outside business hours, and conduct voice-based screening conversations. At Supplemental Health Care, voice AI gathers information from applicants immediately, while people make every hiring decision.
Can AI replace nurses or other healthcare professionals?
No. Indeed’s Hiring Lab found nursing among the roles most shielded from generative AI, because patient care requires physical presence, empathy, and clinical judgment. AI is most useful for administrative work such as documentation, where a 2026 study found ambient AI could recover about 20 minutes per 12-hour shift.
What are the legal risks of using AI in hiring?
Mobley v. Workday suggests that both employers and AI vendors can face discrimination claims tied to AI screening tools. Illinois requires notice when AI is used in employment decisions, New York City requires annual bias audits of automated hiring tools, and Colorado’s law takes effect January 1, 2027.
Do job candidates accept AI in the hiring process?
Many do when it makes the process faster. In a Chicago Booth study, 78% of applicants chose an AI interviewer when given the option. But only 26% of candidates trust AI to evaluate them fairly, according to Gartner, which makes human oversight and clear communication essential.
What should leaders look for in an AI-assisted workforce partner?
Ask where humans sit in the process, how the partner tests its tools for bias, how it complies with state and local AI hiring laws, and how it hands candidates off from AI to a recruiter. A partner that cannot answer those questions clearly deserves a closer look.
search content