Publicly traded for-profit organizations lead the way at 58%, well ahead of private for-profits (45%), nonprofits (38%), state and local governments (35%), and the federal government (19%). Since publicly traded organizations are held to defined profit-margin and revenue targets, their higher AI adoption suggests they see HR automation and innovation as critical enablers for meeting financial goals. Leveraging AI tools can then aid in accelerating talent acquisition, improving workforce planning, and streamlining administrative processes. While it’s unlikely AI adoption alone is the reason for stronger performance, this trend showcases the growing reliance on AI tools in HR, particularly among for-profit organizations seeking a competitive edge in their revenue growth. Across all sectors, HR and organizational leaders should consider how AI can help align people strategies with broader organizational objectives, driving efficiency and competitive advantage.
KEY FINDING NO. 1
Smart Hiring, Powered by AI
Recruiting is the HR practice area that organizations report using AI to support HR-related activities the most, with just over half of organizations (51%) using AI to support recruiting efforts. The most common applications are writing job descriptions (66%), screening resumes (44%), automating candidate searches (32%), customizing job postings (31%), and communicating with applicants (29%).
There are several reasons why HR professionals report that their organization is using AI to support recruiting activities, but the most common reason is the time savings and efficiency they are able to gain. Nearly 9 in 10 HR professionals whose organization uses AI to support recruiting activities (89%) say it saves them time or increases their efficiency. Over 1 in 3 (36%) say using AI to support recruiting activities helps them reduce their recruitment, interviewing, and/or hiring costs, and nearly 1 in 4 (24%) report that AI has improved their ability to identify top candidates.
What This Means for Your Organization
This data highlights the potential value that AI can bring to recruiting, in that it can allow HR to focus more of their attention on high-value tasks that matter most. HR leaders should view AI as a powerful enabler rather than a replacement in recruiting processes. By automating routine tasks (e.g., drafting job descriptions, screening resumes, sourcing candidates) AI frees up HR teams to focus on relationship building, candidate engagement, and strategic workforce planning. This not only drives time savings and cost efficiency but also enhances the ability to identify top talent through data-driven insights.
That said, maintaining the human element is critical to both candidate experience and sound decision-making. While AI can quickly surface qualified applicants, human intelligence remains indispensable for interpreting cultural fit, assessing soft skills, and mitigating bias.
Looking ahead, the rise of AI-enabled recruitment underscores the need for HR teams to cultivate new skills and mindsets. As routine tasks become automated, recruiters and hiring managers will need to sharpen their ability to interpret AI-driven insights. This means translating data-backed candidate recommendations into nuanced human judgments about culture add, long-term potential, and strategic fit. This places a premium on upskilling HR teams in areas such as data literacy, change management, and ethical AI governance. In turn, organizations can leverage AI not only to fill current job openings more quickly, but also to build talent pipelines that anticipate future skill needs. By blending AI’s speed with human intelligence, HR leaders will be better equipped to align recruitment strategies with business objectives to position their organizations to outpace competitors in both talent acquisition and retention.
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As found in SHRM’s 2024 Talent Trends: Artificial Intelligence in HR report, three-quarters of HR professionals agree that advancements in AI will heighten the value of human judgment in the workplace over the next five years, underscoring the need to balance algorithmic efficiency with empathetic interviewing, personalized feedback, and transparent communication. This balance helps preserve employer brand, foster inclusive hiring practices, and ensure candidates feel respected throughout the process.
KEY FINDING NO. 2
Across-the-Board AI Applications
42%
HR Technology
40%
Recruiting
39%
Learning & Development
Source: 2025 Talent Trends, SHRM, 2025.
N= 2,040
Currently, most organizations that use AI to support HR activities are doing so in straightforward ways. For example, on the L&D side, many organizations leveraging AI are using it to recommend or create personalized L&D opportunities for their employees (47%) or to track employees’ L&D progress (38%). Organizations using AI to support L&D activities report that it has made their L&D programs more effective (41%), has reduced costs associated with these activities (39%), and has increased employee engagement in these activities (38%). Beyond these core applications, a smaller segment is exploring more advanced AI use cases: applying AI to identify gaps in employee knowledge or skills, measuring program effectiveness, reporting other innovative L&D activities, piloting immersive learning experiences (e.g., virtual reality, augmented reality), and experimenting with game-based learning. While these emerging use cases are not yet mainstream, they signal important areas for growth and differentiation in AI-powered learning.
What This Means for Your Organization
AI’s growing role in workforce development and HR operations presents both an opportunity and a challenge for organizations eager to drive more strategic, talent-centric outcomes. While many companies have adopted AI for foundational L&D use cases, these early implementations only scratch the surface of what AI can achieve. In practice, intelligent algorithms can go beyond basic automation to model skills gaps across entire business units, predict which upskilling interventions will yield the greatest return on investment (ROI), and even forecast turnover risk based on learning engagement metrics. By leveraging AI-powered analytics to synthesize data from performance reviews, learning management systems, and career-pathing platforms, HR teams can surface high-potential talent earlier, design truly customized L&D curricula, and align learning investments more tightly to business objectives. The result is a more agile learning ecosystem that not only responds to immediate skill deficits but also anticipates future needs, helping organizations stay ahead of market disruptions and technological change.
However, moving from basic AI applications toward these more advanced capabilities requires deliberate planning and investment. Many HR departments have yet to integrate AI into strategic workforce planning in areas where predictive modeling can add outsized value, such as performance management and succession planning.
Finally, HR professionals themselves will need to build new competencies to take full advantage of AI’s potential. As AI automates manual tracking, the human role will shift toward interpreting advanced analytics, partnering with organizational leaders to translate insights into action, and designing learning experiences that leverage both human and machine intelligence. This means investing in upskilling HR teams in data literacy, model validation, and change management strategies so they can act as true “translators” between AI-driven outputs and on-the-ground people decisions. Organizations that succeed in fostering an AI-savvy HR function will not only deliver more effective, cost-efficient learning programs, but also create a culture of continuous improvement, where every employee’s development is guided by real-time insights and aligned to the organization’s strategic priorities. In doing so, organizations can flip the script from reactive, one-size-fits-all training to a proactive, data-driven workforce development model that drives engagement, agility, and long-term business growth.
Auditing Your Organization’s Existing Technology Ecosystem
Ensure that data pipelines are robust, standardized, and continuously updated in places such as learning management systems, performance platforms, and HRIS.
Without clean, interoperable data streams, AI models can produce misleading insights, jeopardizing both the credibility of HR analytics and the trust of front-line managers.
Establish clear governance frameworks around algorithmic fairness, data privacy, and ethical use.
Any AI model that recommends learning assignments or identifies flight-risk employees should be vetted routinely to detect unintended bias, ensure compliance with data protection regulations, and preserve transparency in how recommendations are generated.
KEY FINDING NO. 3
Upskilling for an AI Future
Although AI adoption in workplaces is growing, many organizations are falling behind when it comes to preparing their employees to work with these technologies. Two-thirds of respondents (67%) said they disagree or strongly disagree that their organization has been proactive in training and/or upskilling employees to work alongside AI technologies. When employees lack clear guidance on AI’s capabilities and limitations, organizations risk underutilizing the technology, fostering user frustration, making incorrect decisions, and introducing ethical or security vulnerabilities.
This data demonstrates that while organizations may be enthusiastic about AI, the broader workforce does not feel sufficiently equipped to work with it effectively. This skills gap is not just a technical deficiency; it is a strategic blind spot. Organizations that treat AI only as a technology investment risk missing the cultural and behavioral dimensions (e.g., leadership commitment, data literacy, transparency, collaboration, continuous learning, change management) required for successful implementation.
What This Means for Your Organization
Training employees to use AI safely, ethically, and securely is no longer an option, but a strategic imperative. Organizations should establish clear policies and guidelines that are available through quick reference guides or intranet portals, as well as reinforce continuous AI learning and compliance alongside formal training modules. Organizations should begin with establishing a shared foundation across job levels and departments so that everyone across the workforce shares a common vocabulary. From there, role-based upskilling paths ensure that teams gain hands-on experience most relevant to their functional roles. By aligning performance metrics and career paths to AI competencies, organizations can tie AI adoption to tangible incentives.
Without these concerted efforts, organizations risk wasted investments, unintended bias, and employee disengagement from AI use. Employers must move beyond pilot projects and basic AI awareness efforts to build a comprehensive, role‐specific upskilling framework, robust governance, and data‐driven infrastructure, all in support of an AI‐ready culture that positions the organization to thrive in an increasingly data‐centric future.
Cultivate an AI-Ready Mindset and Culture
Senior leaders and people managers must model AI fluency to signal that AI literacy is integral to performance.
Identify “champions” across the organization to deliver training sessions, fostering shared ownership and stimulating continuous learning.
Embed microlearning opportunities, such as short explainer videos and established “AI office hours,” to keep concepts fresh and prevent skills decay over time.
RECOMMENDATION
Leverage AI to Streamline Recruiting Processes Within Established Guidelines
AI can enhance recruiting by automating repetitive tasks, allowing HR professionals to focus on high-value, human-centered activities. By balancing AI’s efficiencies with ethical and human-centric practices, organizations can improve hiring outcomes and deliver exceptional candidate experiences.
- Audit current recruiting processes for AI integration opportunities.
- Establish guidelines to ensure AI complements human decision-making.
- Develop training sessions on ethical and effective AI use in recruitment.
Resources
TOOLKIT
Using AI for Employment Purposes
HOW-TO GUIDE
The AI Prompts Guide for HR (with Templates!)
WEBINAR
How to Choose the Best AI Tech for Your Hiring Team
CONCLUSION
The rapid acceleration of AI adoption in HR signals a fundamental shift in how work gets done and how talent is managed. With for-profit organizations at the forefront, AI is proving its value in driving efficiency and improving outcomes across HR operations. Recruiting remains the primary use case, with over half of companies leveraging AI for tasks including writing job descriptions, screening resumes, and automating candidate searches. Early adopters report significant gains in time savings, cost reductions, and improved candidate identification, yet they also underscore the indispensability of human judgment throughout these processes. Meanwhile, L&D applications are emerging steadily, laying the groundwork for more advanced use cases. Despite these promising capabilities, two-thirds of HR professionals believe their organizations have not done enough to upskill employees for an AI-powered future, revealing a critical disconnect between technology deployment and workforce readiness.
For workers, this transformation brings both opportunity and urgency. Routine, transactional tasks are increasingly automated, freeing HR teams to focus on strategic, human-centric responsibilities. At the same time, employees across all levels must develop a core understanding of AI’s capabilities, limitations, and ethical considerations. This entails building data literacy, embracing continuous microlearning opportunities, and cultivating an AI-ready mindset that values transparency and collaboration.
Organizations must evolve into learning ecosystems that balance technological innovation with robust governance and cultural readiness. Clear policies around data privacy, algorithmic fairness, and human oversight must be established to build trust and safeguard against unintended consequences. Moreover, embedding role-specific training modules, executive sponsorship, and “AI champions” across business units will help normalize AI fluency as a core competency. By integrating AI into strategic workforce planning, performance management, and succession pipelines, organizations can not only fill immediate talent gaps but also anticipate future skills needs.
Methodology
The survey was fielded to a sample of HR professionals via SHRM’s Voice of Work Research panel from Feb. 3 to Feb. 12, 2025. For the purposes of this study, participants were required to be employed full-time or part-time for an organization and employed in HR. In total, 2,040 HR professionals participated in the survey. Respondents represented organizations of all sizes in a wide variety of industries across the U.S. Data is unweighted.