Regional HR's AI Reality Check: Most Firms Still in Low Gear
PHILADELPHIA, PA - A new State of AI & Automation for HR: 2026 Benchmarks Report from Phenom shows a clear pattern: companies say they want AI, but most are stuck at basic levels that don't move the needle on hiring, development, or retention.
The report scored nearly 500 organizations on a five-level maturity model across two dimensions: intelligence (from no AI to fully integrated) and automation (from manual to fully automated). The gap between ambition and execution is wide - and fixable with a focused plan.
Where most HR teams stand
- Fewer than 1% reached high intelligence (Level 4).
- Only 5% achieved high automation.
- 86% operate at assisted or semi-automated intelligence levels.
- 83% are limited to task-level or partial process automation.
Translation: many teams have tools, few have systems. Manual workflows and disconnected platforms are still slowing down hiring and talent management - right when speed and precision matter most.
Industry snapshot
- Healthcare: 90% adopted automated candidate campaigns and nurturing.
- Financial services: highest use of AI for candidate matching and fit at 68%.
- Transportation: strongest adoption of automated interview scheduling at 59%.
- Retail: 88% lack advanced automated screening despite high-volume hiring.
What HR leaders are trying to solve
In a companion survey, 76% said their top goals were automating manual tasks and improving recruiter productivity. Yet 66% reported low or no AI adoption in talent management, and nearly one-third said they have limited knowledge of how to apply AI effectively.
The message is clear: intent is there, know-how and integration are not.
Proof it works (when done right)
Franciscan Health reported gains across its hiring funnel after deploying AI-driven tools for its career site, application screening, and a 24/7 chatbot. "These innovations help streamline the hiring process, improve our efficiency and enhance our experiences," said Ellen Page, director of talent acquisition at Franciscan Health.
Elara Caring uses AI voice agents to manage large-scale hiring across multiple states. "Candidates interviewed by AI accepted their first assignment faster and logged an average of three hours more per week than those interviewed by human recruiters," said Anne Strickroot, vice president of talent acquisition. Time-to-hire dropped by about 1.3 days - a small number that compounds into real capacity.
Phenom's take
The report offers a practical roadmap: automate screening and scheduling, deploy AI matching, and use real-time intelligence to improve interview quality and reduce fraud risk. "The question every CHRO should be asking their team is: How fast can they get AI to work for their business?" said Mahe Bayireddi, CEO and co-founder of Phenom.
A 90-day plan to move up the maturity curve
- 0-30 days: Automate high-volume tasks (screening, scheduling). Standardize req data. Map integrations between ATS, CRM, and assessments.
- 30-60 days: Turn on AI matching for priority roles. Launch automated candidate campaigns and nurturing. Pilot a recruiter co-pilot for notes, summaries, and outreach drafts.
- 60-90 days: Apply real-time interview intelligence (quality scoring, fraud flags). Add automated re-discovery of silver-medalist candidates. Roll out dashboards for time-to-hire, candidate response time, and interview-to-offer ratio.
Metrics that prove impact
- Speed: time-to-apply, time-to-first-contact, time-to-interview, time-to-offer.
- Quality: qualified applicant rate, interview-to-offer, 90-day retention.
- Productivity: reqs per recruiter, outreach-to-response, automated vs. manual touchpoints.
- Cost: cost-per-hire, agency spend, drop-off rate by funnel stage.
Risk, compliance, and change management
- Stand up a cross-functional AI review group (TA, HRBP, Legal, DEI, IT) and document use cases, data sources, and intended outcomes.
- Adopt a common risk framework and bias checks. See the NIST AI Risk Management Framework and EEOC guidance on AI in employment selection.
- Train recruiters and hiring managers on prompts, review workflows, and when to override AI recommendations.
Applying the findings to your context
- High-volume roles: Automate screening, scheduling, and campaign nurturing first.
- Specialized roles: Use AI matching plus skills inference; add structured interview intelligence to improve signal quality.
- Multi-location hiring: Use voice or chat agents for 24/7 coverage and dynamic load balancing across markets.
- Retail and frontline: Prioritize advanced screening to reduce drop-off and increase same-day interview rates.
Bottom line
Most teams have AI somewhere; few have it working end-to-end. Focus on one or two outcomes, automate the bottlenecks, measure weekly, and expand only when the data moves. That's how you turn intent into results.
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